diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..e6fea7cb2ffa7bd6303bfcf69b56f693b7025f53
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,34 @@
+# .dockerignore for the Hugging Face Space repo.
+#
+# Copy this file to the ROOT of your HF Space repo alongside Dockerfile.
+#
+# CRITICAL: models/ is NOT excluded here, because the HF Space image MUST
+# contain the runtime model artifacts. (Contrast with the project's root
+# .dockerignore, which DOES exclude models/ since local docker-compose
+# mounts them as a read-only volume.)
+
+.git/
+.gitattributes
+__pycache__/
+*.pyc
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+
+# Editor / OS
+.DS_Store
+Thumbs.db
+.idea/
+.vscode/
+
+# Frontend / Node — the Space serves the backend only.
+node_modules/
+.next/
+
+# Static photos and team logos ARE included in the deployed image.
+# They live at backend/app/static/players/*.jpg and teams/*.png and need
+# to be served by FastAPI's static mount. The deploying user should
+# commit them to the Space repo via Git LFS so they:
+#   1) push reliably without bloating non-LFS git history, and
+#   2) get COPY'd into the Docker image during build.
+# (No exclusion rules here — letting Docker include them on purpose.)
diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..d354da6c482519a0b95e85b77edea97f9d1e1448 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/** filter=lfs diff=lfs merge=lfs -text
+*.jsonl filter=lfs diff=lfs merge=lfs -text
+backend/app/static/players/*.jpg filter=lfs diff=lfs merge=lfs -text
+backend/app/static/teams/*.png filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..6db20ab0e19f97322b2c604931cf0dea7b7f895b
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,36 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+.pytest_cache/
+.ruff_cache/
+.mypy_cache/
+
+# Virtual environments
+.venv/
+venv/
+env/
+backend/.venv/
+
+# Local environment files
+.env
+.env.*
+!.env.example
+!.env.local.example
+
+# Frontend build/dependencies
+node_modules/
+frontend/node_modules/
+frontend/.next/
+frontend/out/
+frontend/dist/
+
+# Non-serving research/training artifacts kept locally only
+models/gp1/
+models/gp2/action_sentences.jsonl
+models/gp2/action2vec.model
+
+# OS/editor noise
+.DS_Store
+Thumbs.db
+*.log
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..661cbf15b27c08d5773ab033301689a35cc27248
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,54 @@
+# Hugging Face Space Dockerfile.
+#
+# IMPORTANT: this file is committed in this repo at hf-space/Dockerfile,
+# but is meant to be COPIED to the ROOT of your HF Space repo (not this
+# repo). The HF Space repo layout should be:
+#
+#   <space-repo>/
+#   ├── Dockerfile             ← copy this file here
+#   ├── .dockerignore          ← copy hf-space/.dockerignore here
+#   ├── README.md              ← HF requires this at root
+#   ├── backend/               ← copy from this repo
+#   ├── src/                   ← copy from this repo
+#   └── models/gp2/            ← copy from this repo (tracked via Git LFS)
+#
+# Unlike backend/Dockerfile (which omits models because the local Docker
+# stack mounts them as a volume), this Dockerfile COPIES models/ into the
+# image so the Space is self-contained.
+
+FROM python:3.11-slim
+
+RUN apt-get update \
+ && apt-get install -y --no-install-recommends gcc g++ curl \
+ && rm -rf /var/lib/apt/lists/*
+
+# HF Spaces require the running user to have UID 1000 and HOME=/home/user.
+RUN useradd -m -u 1000 user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR /app
+
+COPY --chown=user backend/requirements.txt /app/backend/requirements.txt
+USER user
+RUN pip install --no-cache-dir --user -r /app/backend/requirements.txt
+USER root
+
+COPY --chown=user backend/ /app/backend/
+COPY --chown=user src/ /app/src/
+COPY --chown=user models/ /app/models/
+
+RUN chown -R user:user /app
+USER user
+WORKDIR /app/backend
+
+ENV PORT=7860 \
+    PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
+
+EXPOSE 7860
+
+HEALTHCHECK --interval=30s --timeout=5s --retries=3 --start-period=120s \
+    CMD curl -fsS http://localhost:${PORT}/api/health || exit 1
+
+CMD ["sh", "-c", "uvicorn app.main:app --host 0.0.0.0 --port ${PORT}"]
diff --git a/backend/.dockerignore b/backend/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..a2e3391e619b8405b3e7c27950e027319d8c4873
--- /dev/null
+++ b/backend/.dockerignore
@@ -0,0 +1,12 @@
+# Note: the effective ignore for backend builds is ROOT .dockerignore, since
+# the build context is the project root. This file is kept for clarity and
+# in case someone builds with the backend/ directory as the context.
+__pycache__/
+*.pyc
+.venv/
+.pytest_cache/
+tests/
+app/static/players/*
+app/static/teams/*
+!app/static/players/.gitkeep
+!app/static/teams/.gitkeep
diff --git a/backend/Dockerfile b/backend/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..2d2bcd4b178c9c56b0a05d5d79499bffa68b4946
--- /dev/null
+++ b/backend/Dockerfile
@@ -0,0 +1,43 @@
+# Backend Dockerfile for LOCAL docker-compose use.
+# Build context is the PROJECT ROOT (see docker-compose.yml), so all COPY
+# paths here are relative to the project root, not to backend/.
+#
+# models/ is NOT copied into this image. Local development mounts it via a
+# docker-compose volume (./models:/app/models:ro). For Hugging Face Spaces,
+# use hf-space/Dockerfile, which copies models/ in.
+
+FROM python:3.11-slim
+
+# Build tools required for some scientific-Python wheels on slim images,
+# plus curl for the HEALTHCHECK.
+RUN apt-get update \
+ && apt-get install -y --no-install-recommends gcc g++ curl \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN useradd -m -u 1000 appuser
+
+WORKDIR /app
+
+# Install Python deps first (cacheable layer).
+COPY backend/requirements.txt /app/backend/requirements.txt
+RUN pip install --no-cache-dir -r /app/backend/requirements.txt
+
+# Application code.
+COPY backend/ /app/backend/
+COPY src/ /app/src/
+
+RUN chown -R appuser:appuser /app
+
+USER appuser
+WORKDIR /app/backend
+
+ENV PORT=7860 \
+    PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
+
+EXPOSE 7860
+
+HEALTHCHECK --interval=30s --timeout=5s --retries=3 \
+    CMD curl -fsS http://localhost:${PORT}/api/health || exit 1
+
+CMD ["sh", "-c", "uvicorn app.main:app --host 0.0.0.0 --port ${PORT}"]
diff --git a/backend/app/__init__.py b/backend/app/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/backend/app/config.py b/backend/app/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f039e5d0a585487c85516930940875119d1a8d0
--- /dev/null
+++ b/backend/app/config.py
@@ -0,0 +1,29 @@
+"""Backend configuration — env vars and derived paths."""
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+# Path layout: this file lives at /app/backend/app/config.py in the container,
+# so parents[2] is /app — the project root that contains src/ and models/.
+# Same math applies locally.
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+BACKEND_ROOT = Path(__file__).resolve().parents[1]
+APP_DIR = Path(__file__).resolve().parent           # /app/backend/app
+STATIC_DIR = APP_DIR / "static"                     # /app/backend/app/static
+PLAYERS_STATIC_DIR = STATIC_DIR / "players"
+TEAMS_STATIC_DIR = STATIC_DIR / "teams"
+
+PORT = int(os.getenv("PORT", "7860"))
+
+_BASE_CORS_ORIGINS = ["http://localhost:3000"]
+# Permissive by design for v1: any vercel.app subdomain may call the backend.
+# Tradeoff accepted because this is a portfolio/demo backend with no auth and
+# no per-tenant data; locking it down requires the Vercel deployment URL to
+# be known at backend deploy time, which would couple the two deploys.
+CORS_ORIGIN_REGEX = r"https://.*\.vercel\.app"
+
+def cors_origins() -> list[str]:
+    extra = os.getenv("CORS_EXTRA_ORIGINS", "")
+    extras = [o.strip() for o in extra.split(",") if o.strip()]
+    return _BASE_CORS_ORIGINS + extras
diff --git a/backend/app/deps.py b/backend/app/deps.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb5e4787d30a5425b40b897a07793e773be36c0b
--- /dev/null
+++ b/backend/app/deps.py
@@ -0,0 +1,13 @@
+"""Shared FastAPI dependencies."""
+from __future__ import annotations
+
+from fastapi import HTTPException, status
+
+from app.services import engine
+
+
+def require_engine() -> None:
+    """Raise 503 if the engine is not in the 'ready' state."""
+    if engine.engine_state != "ready":
+        detail = f"Engine unavailable: {engine.engine_message or 'not loaded'}"
+        raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail=detail)
diff --git a/backend/app/main.py b/backend/app/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..97b3e4f969292a2bf2dd855d6c3e4e996bb82da2
--- /dev/null
+++ b/backend/app/main.py
@@ -0,0 +1,82 @@
+"""FastAPI application entry point."""
+from __future__ import annotations
+
+import logging
+import time
+import traceback
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+
+from app.config import CORS_ORIGIN_REGEX, STATIC_DIR, cors_origins
+from app.routes import catalog, health, players, search, validations
+from app.services import engine
+from app.services.jobs import store
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s %(name)s %(message)s",
+)
+logger = logging.getLogger("app")
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    start = time.monotonic()
+    health._set_start_time(start)
+    logger.info("Starting up. Project root: %s", engine.PROJECT_ROOT if hasattr(engine, "PROJECT_ROOT") else "(unknown)")
+
+    # If the engine module itself failed to import, leave state as "unavailable"
+    # and skip warm-up. App still serves /api/health and /api/upgrades, /api/filters.
+    if engine.scouting_engine is None:
+        logger.warning("Engine module did not import; serving in degraded mode.")
+    else:
+        try:
+            await engine.run_engine(engine.scouting_engine.list_available_players, "", 1)
+            engine.set_state("ready")
+            elapsed = time.monotonic() - start
+            logger.info("Engine ready (warm-up %.2fs)", elapsed)
+        except Exception as e:
+            logger.error("Engine warm-up failed:\n%s", traceback.format_exc())
+            engine.set_state("unavailable", str(e))
+
+    store.start()
+
+    try:
+        yield
+    finally:
+        logger.info("Shutting down.")
+        await store.stop()
+        engine.shutdown_executor()
+
+
+app = FastAPI(
+    title="Replacement Scout API",
+    version="0.1.0",
+    lifespan=lifespan,
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=cors_origins(),
+    allow_origin_regex=CORS_ORIGIN_REGEX,
+    allow_credentials=False,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Static images (player photos, team logos). Directory is auto-created on
+# startup; missing individual files are handled by the URL helpers
+# (services/images.py) which return None when a file is absent.
+STATIC_DIR.mkdir(parents=True, exist_ok=True)
+(STATIC_DIR / "players").mkdir(exist_ok=True)
+(STATIC_DIR / "teams").mkdir(exist_ok=True)
+app.mount("/static", StaticFiles(directory=str(STATIC_DIR)), name="static")
+
+app.include_router(health.router, prefix="/api")
+app.include_router(catalog.router, prefix="/api")
+app.include_router(players.router, prefix="/api")
+app.include_router(search.router, prefix="/api")
+app.include_router(validations.router, prefix="/api")
diff --git a/backend/app/routes/__init__.py b/backend/app/routes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/backend/app/routes/catalog.py b/backend/app/routes/catalog.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef449bf538ec0bfec8ab45fd58753153a94dbd7d
--- /dev/null
+++ b/backend/app/routes/catalog.py
@@ -0,0 +1,47 @@
+"""
+/api/upgrades and /api/filters.
+
+Both are model-INDEPENDENT — the underlying engine functions are pure
+constant returns and do NOT call _ensure_loaded(). Verified against
+src/gp2/evaluation/scouting_engine.py:145 (list_available_filters) and :291
+(list_available_upgrades). Safe to call even when engine_state is
+'unavailable'.
+"""
+from __future__ import annotations
+
+from fastapi import APIRouter, HTTPException
+
+from app.services import engine
+
+router = APIRouter()
+
+
+@router.get("/upgrades")
+def get_upgrades() -> dict:
+    if engine.scouting_engine is None:
+        raise HTTPException(503, detail="Engine module failed to import")
+    flat = engine.scouting_engine.list_available_upgrades()
+    grouped: dict[str, list[dict]] = {"onball": [], "offball": []}
+    for u in flat:
+        bucket = u.get("applies_to")
+        if bucket in grouped:
+            grouped[bucket].append(u)
+    return grouped
+
+
+_COMING_SOON_FILTERS = [
+    "min_age",
+    "max_age",
+    "leagues",
+    "min_market_value",
+    "max_market_value",
+]
+
+
+@router.get("/filters")
+def get_filters() -> dict:
+    if engine.scouting_engine is None:
+        raise HTTPException(503, detail="Engine module failed to import")
+    result = dict(engine.scouting_engine.list_available_filters())
+    result["coming_soon"] = _COMING_SOON_FILTERS
+    return result
diff --git a/backend/app/routes/health.py b/backend/app/routes/health.py
new file mode 100644
index 0000000000000000000000000000000000000000..23dff7a6281cf458c2c9e76e4de47d73bbe81b2a
--- /dev/null
+++ b/backend/app/routes/health.py
@@ -0,0 +1,30 @@
+"""Health endpoint. Model-independent — always 200 as long as the app imports."""
+from __future__ import annotations
+
+import time
+
+from fastapi import APIRouter
+
+from app.schemas.common import HealthResponse
+from app.services import engine
+
+router = APIRouter()
+
+# Module-level start time. Set in main.py via _set_start_time on lifespan startup.
+_start_time: float = time.monotonic()
+
+
+def _set_start_time(t: float) -> None:
+    global _start_time
+    _start_time = t
+
+
+@router.get("/health", response_model=HealthResponse)
+def health() -> HealthResponse:
+    return HealthResponse(
+        status="ok",
+        engine_loaded=engine.engine_loaded(),
+        engine_state=engine.engine_state,  # type: ignore[arg-type]
+        engine_message=engine.engine_message,
+        uptime_seconds=round(time.monotonic() - _start_time, 3),
+    )
diff --git a/backend/app/routes/players.py b/backend/app/routes/players.py
new file mode 100644
index 0000000000000000000000000000000000000000..608e344d17701e48ddd658dd8645ec157cd589de
--- /dev/null
+++ b/backend/app/routes/players.py
@@ -0,0 +1,111 @@
+"""Player lookup and similar-players endpoints."""
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from fastapi import APIRouter, Depends, HTTPException, Query, Request
+
+from app.deps import require_engine
+from app.services import engine, player_index
+from app.services.heatmap import NUM_ZONES, player_heatmap
+from app.services.images import logo_url_for, photo_url_for
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter()
+
+
+def _attach_images(p: dict[str, Any], request: Request) -> dict[str, Any]:
+    p = dict(p)  # don't mutate engine's cache
+    p["photo_url"] = photo_url_for(p.get("player_id", ""), request)
+    p["team_logo_url"] = logo_url_for(p.get("team"), request)
+    return p
+
+
+@router.get("/players")
+async def list_players(
+    request: Request,
+    q: str = Query("", description="Accent- and case-insensitive substring match on player name."),
+    limit: int = Query(20, ge=1, le=50),
+    _: None = Depends(require_engine),
+) -> list[dict[str, Any]]:
+    # Use the local accent-insensitive index instead of calling the engine
+    # directly: the engine's matcher does plain Unicode substring, so
+    # `q=mane` would miss "Sadio Mané". The index pulls the full player
+    # list once via the engine (cheap, no inference), pre-strips accents,
+    # then matches in pure Python.
+    try:
+        await player_index.ensure_built()
+        results = player_index.search(q, limit)
+    except Exception as e:
+        logger.exception("/api/players failed")
+        raise HTTPException(500, detail=str(e))
+    return [_attach_images(p, request) for p in results]
+
+
+@router.get("/players/{player_id}")
+async def get_player(
+    player_id: str,
+    request: Request,
+    _: None = Depends(require_engine),
+) -> dict[str, Any]:
+    try:
+        summary = await engine.run_engine(engine.scouting_engine.get_player_summary, player_id)
+    except Exception as e:
+        logger.exception("get_player_summary failed")
+        raise HTTPException(500, detail=str(e))
+    if summary is None:
+        raise HTTPException(404, detail=f"Player not found: {player_id}")
+    return _attach_images(summary, request)
+
+
+@router.get("/players/{player_id}/heatmap")
+async def player_heatmap_endpoint(
+    player_id: str,
+    _: None = Depends(require_engine),
+) -> dict[str, Any]:
+    """
+    Return a 6×3 pitch-zone activity heatmap for a player.
+
+    Aggregates the start-zone of every structured token (on-ball + off-ball)
+    across all of the player's matches in the dataset. Pure modifier tokens
+    (e.g. `progressive_pass`, `cut_inside_right`) have no zone and are skipped.
+
+    Response: {counts: int[18], total: int, num_x: 6, num_y: 3} — counts are
+    indexed row-major from the top-left of the StatsBomb pitch (120×80,
+    attacking left-to-right).
+    """
+    try:
+        counts = await player_heatmap(player_id)
+    except FileNotFoundError as e:
+        raise HTTPException(503, detail=f"Heatmap data unavailable: {e}")
+    except Exception as e:
+        logger.exception("heatmap failed")
+        raise HTTPException(500, detail=str(e))
+    if counts is None:
+        raise HTTPException(404, detail=f"Player not found: {player_id}")
+    return {"counts": counts, "total": sum(counts), "num_x": 6, "num_y": 3, "num_zones": NUM_ZONES}
+
+
+@router.get("/players/{player_id}/similar")
+async def similar_players(
+    player_id: str,
+    request: Request,
+    top_k: int = Query(10, ge=1, le=100),
+    _: None = Depends(require_engine),
+) -> list[dict[str, Any]]:
+    try:
+        result = await engine.run_engine(
+            engine.scouting_engine.search_replacements,
+            sources=[player_id],
+            upgrades=[],
+            top_k=top_k,
+        )
+    except ValueError as e:
+        # Engine raises ValueError for unknown sources / no sources resolved.
+        raise HTTPException(404, detail=str(e))
+    except Exception as e:
+        logger.exception("similar search failed")
+        raise HTTPException(500, detail=str(e))
+    return [_attach_images(c, request) for c in result.get("candidates", [])]
diff --git a/backend/app/routes/search.py b/backend/app/routes/search.py
new file mode 100644
index 0000000000000000000000000000000000000000..f489087287215f03ebbae925350e46830d1eb8c2
--- /dev/null
+++ b/backend/app/routes/search.py
@@ -0,0 +1,92 @@
+"""
+POST /api/search — synchronous if no upgrades, otherwise enqueued as an async job.
+GET  /api/search/{job_id} — model-independent job-state lookup.
+"""
+from __future__ import annotations
+
+import logging
+
+from fastapi import APIRouter, Depends, HTTPException, Request, Response, status
+
+from app.deps import require_engine
+from app.schemas.search import (
+    JobAcceptedResponse,
+    JobRecord,
+    SearchRequest,
+    SyncSearchResponse,
+)
+from app.services import engine
+from app.services.images import enrich_search_result
+from app.services.jobs import RETRY_AFTER_SECONDS, store
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter()
+
+
+def _has_upgrades(req: SearchRequest) -> bool:
+    if req.upgrades is None:
+        return False
+    if isinstance(req.upgrades, (list, dict)) and len(req.upgrades) == 0:
+        return False
+    return True
+
+
+@router.post("/search")
+async def post_search(
+    req: SearchRequest,
+    response: Response,
+    request: Request,
+    _: None = Depends(require_engine),
+):
+    kwargs = dict(
+        sources=req.sources,
+        upgrades=req.upgrades if req.upgrades is not None else [],
+        upgrade_intensity=req.upgrade_intensity,
+        top_k=req.top_k,
+        filters=req.filters,
+        exclude_sources=req.exclude_sources,
+        seed=req.seed,
+    )
+
+    if not _has_upgrades(req):
+        # Fast path: similarity-only search runs in well under a second.
+        try:
+            result = await engine.run_engine(
+                engine.scouting_engine.search_replacements, **kwargs
+            )
+        except ValueError as e:
+            raise HTTPException(400, detail=str(e))
+        except Exception as e:
+            logger.exception("sync search failed")
+            raise HTTPException(500, detail=str(e))
+        # Attach photo_url + team_logo_url to sources and candidates.
+        return SyncSearchResponse(**enrich_search_result(result, request))
+
+    # Upgrade path: enqueue as a background job.
+    job_id = store.enqueue(kwargs)
+    if job_id is None:
+        response.headers["Retry-After"] = str(RETRY_AFTER_SECONDS)
+        raise HTTPException(
+            status_code=status.HTTP_429_TOO_MANY_REQUESTS,
+            detail="Search queue full, retry shortly",
+            headers={
+                "Retry-After": str(RETRY_AFTER_SECONDS),
+            },
+        )
+    return JobAcceptedResponse(job_id=job_id, status="pending")
+
+
+@router.get("/search/{job_id}", response_model=JobRecord)
+def get_job(job_id: str, request: Request) -> JobRecord:
+    # Model-independent: only reads the in-memory job store.
+    record = store.get(job_id)
+    if record is None:
+        raise HTTPException(404, detail=f"Job not found: {job_id}")
+    # If the job is done, enrich its result with per-request image URLs.
+    if record.get("status") == "done" and record.get("result"):
+        record = {
+            **record,
+            "result": enrich_search_result(record["result"], request),
+        }
+    return JobRecord(**record)
diff --git a/backend/app/routes/validations.py b/backend/app/routes/validations.py
new file mode 100644
index 0000000000000000000000000000000000000000..40087360b953eb35846b8821ce83ac70b7326a04
--- /dev/null
+++ b/backend/app/routes/validations.py
@@ -0,0 +1,251 @@
+"""
+/api/validations/mane — the Mané regression check, surfaced as an API endpoint.
+
+Reproduces the config from src/gp2/evaluation/mane_case_validation.py without
+importing its `validate()` function. Post-filters defenders, re-ranks
+attackers, finds Mané's attacker_rank, returns a verdict.
+
+Cache: deterministic (single variant, seed-fixed), so we cache the response
+in module state after the first successful run. Double-checked-lock pattern
+prevents two simultaneous cold-cache callers from each running the ~30–60 s
+inference.
+"""
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Any
+
+from fastapi import APIRouter, Depends, HTTPException, Request
+
+from app.deps import require_engine
+from app.services import engine
+from app.services.heatmap import NUM_ZONES, aggregate_heatmap, player_heatmap
+from app.services.images import logo_url_for, photo_url_for
+from app.services.mane_preset import (
+    DEFENDER_POSITIONS,
+    KLOPP_UPGRADES_VALIDATED,
+    LIVERPOOL_2015_16_ATTACKERS,
+    MANE_FINAL_TOP_N,
+    MANE_SEED,
+    MANE_TOP_K,
+)
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+_mane_cache: dict[str, Any] | None = None
+_mane_lock = asyncio.Lock()
+
+
+def _verdict_for(rank: int | None) -> tuple[str, str]:
+    if rank is None:
+        return (
+            "FAIL",
+            "Mané did not appear in the post-filtered attacker candidate pool.",
+        )
+    if rank <= 5:
+        return ("EXCELLENT", f"Mané ranked #{rank} among attackers — methodology strongly recovers Liverpool's actual 2016 signing.")
+    if rank <= 10:
+        return ("STRONG", f"Mané ranked #{rank} among attackers — methodology cleanly recovers Liverpool's actual 2016 signing.")
+    if rank <= 20:
+        return ("ACCEPTABLE", f"Mané ranked #{rank} among attackers — methodology recovers Liverpool's actual 2016 signing, with noise.")
+    if rank <= MANE_FINAL_TOP_N:
+        return ("MARGINAL", f"Mané ranked #{rank} among attackers — within the top {MANE_FINAL_TOP_N} but on the edge.")
+    return (
+        "FAIL",
+        f"Mané ranked #{rank} — outside the top {MANE_FINAL_TOP_N} attackers.",
+    )
+
+
+def _build_response(engine_result: dict[str, Any]) -> dict[str, Any]:
+    candidates: list[dict[str, Any]] = engine_result.get("candidates", []) or []
+    warnings: list[str] = engine_result.get("warnings", []) or []
+
+    attackers = [c for c in candidates if c.get("primary_position") not in DEFENDER_POSITIONS]
+    filtered_defender_count = len(candidates) - len(attackers)
+
+    mane_rank: int | None = None
+    for i, c in enumerate(attackers, start=1):
+        c["attacker_rank"] = i
+        if mane_rank is None and "Mané" in (c.get("name") or ""):
+            mane_rank = i
+
+    top_attackers = attackers[:MANE_FINAL_TOP_N]
+    verdict, description = _verdict_for(mane_rank)
+
+    return {
+        "query": engine_result.get("query", {}),
+        "candidates": top_attackers,
+        "mane_rank": mane_rank,
+        "verdict": verdict,
+        "verdict_description": description,
+        "filtered_defender_count": filtered_defender_count,
+        "warnings": warnings,
+    }
+
+
+async def _compute() -> dict[str, Any]:
+    result = await engine.run_engine(
+        engine.scouting_engine.search_replacements,
+        sources=LIVERPOOL_2015_16_ATTACKERS,
+        upgrades=KLOPP_UPGRADES_VALIDATED,
+        top_k=MANE_TOP_K,
+        seed=MANE_SEED,
+    )
+    return _build_response(result)
+
+
+@router.get("/validations/mane")
+async def mane_validation(_: None = Depends(require_engine)) -> dict[str, Any]:
+    global _mane_cache
+    if _mane_cache is not None:
+        return _mane_cache
+    async with _mane_lock:
+        # Double-checked: another coroutine may have populated the cache while we were
+        # waiting on the lock.
+        if _mane_cache is not None:
+            return _mane_cache
+        try:
+            response = await _compute()
+        except ValueError as e:
+            raise HTTPException(400, detail=str(e))
+        except Exception as e:
+            logger.exception("Mané validation failed")
+            raise HTTPException(500, detail=str(e))
+        _mane_cache = response
+        return _mane_cache
+
+
+_mane_heatmap_cache: dict[str, Any] | None = None
+_mane_heatmap_lock = asyncio.Lock()
+
+
+@router.get("/validations/mane/heatmap")
+async def mane_heatmap(
+    request: Request,
+    _: None = Depends(require_engine),
+) -> dict[str, Any]:
+    """
+    Heatmaps for the Mané validation explainer:
+      - `pool`: aggregated zone counts across the 6 Liverpool 2015-16 attackers
+      - `mane`: Mané's own zone counts
+      - `top_candidates`: heatmaps for the top 5 attacking candidates the
+        methodology surfaced (defenders filtered out). Lets the user see
+        that all 5 share a similar spatial profile — visual proof of the
+        ranking.
+
+    Depends on the main /api/validations/mane having been computed (or
+    computes it on demand) so we know who the top-5 attackers are.
+    """
+    global _mane_heatmap_cache
+    if _mane_heatmap_cache is not None:
+        return _enrich_heatmap_response(_mane_heatmap_cache, request)
+    async with _mane_heatmap_lock:
+        if _mane_heatmap_cache is not None:
+            return _enrich_heatmap_response(_mane_heatmap_cache, request)
+
+        # Resolve the 6 source player names → player_ids via the engine's
+        # name lookup (this is cheap — no model inference required, just a
+        # dict scan).
+        try:
+            source_ids: list[str] = []
+            source_names: list[str] = []
+            for name in LIVERPOOL_2015_16_ATTACKERS:
+                pid = await engine.run_engine(engine.scouting_engine.find_player_id, name)
+                if pid is None:
+                    continue
+                summary = await engine.run_engine(
+                    engine.scouting_engine.get_player_summary, pid
+                )
+                source_ids.append(pid)
+                if summary and summary.get("name"):
+                    source_names.append(summary["name"])
+
+            # Find Mané by name. The engine's matcher is plain Unicode substring;
+            # the accented form is required.
+            mane_id = await engine.run_engine(engine.scouting_engine.find_player_id, "Mané")
+
+            pool_counts = await aggregate_heatmap(source_ids)
+            mane_counts = await player_heatmap(mane_id) if mane_id else None
+
+            # Top-5 attacking candidates: reuse the main validation result.
+            # If it hasn't been computed yet, compute it now (single inference,
+            # then cached for both endpoints).
+            global _mane_cache
+            validation = _mane_cache
+            if validation is None:
+                validation = await _compute()
+                _mane_cache = validation
+            top_candidates = []
+            for c in (validation.get("candidates") or [])[:5]:
+                cid = c.get("player_id")
+                if not cid:
+                    continue
+                counts = await player_heatmap(str(cid))
+                if counts is None:
+                    continue
+                # Photo URLs are NOT cached here — they're attached at response
+                # time below because the base URL depends on the current
+                # request (different deploy host, different scheme, etc.).
+                top_candidates.append({
+                    "player_id": cid,
+                    "name": c.get("name"),
+                    "primary_position": c.get("primary_position"),
+                    "team": c.get("team"),
+                    "similarity": c.get("similarity"),
+                    "attacker_rank": c.get("attacker_rank"),
+                    "counts": counts,
+                    "total": sum(counts),
+                    "is_mane": "Mané" in (c.get("name") or ""),
+                })
+
+        except FileNotFoundError as e:
+            raise HTTPException(503, detail=f"Heatmap data unavailable: {e}")
+        except Exception as e:
+            logger.exception("Mané heatmap failed")
+            raise HTTPException(500, detail=str(e))
+
+        _mane_heatmap_cache = {
+            "pool": {
+                "counts": pool_counts,
+                "total": sum(pool_counts),
+                "source_names": source_names,
+                "label": "Liverpool 2015-16 attacking pool",
+            },
+            "mane": {
+                "counts": mane_counts,
+                "total": sum(mane_counts) if mane_counts else 0,
+                "label": "Sadio Mané",
+                "player_id": mane_id,
+            },
+            "top_candidates": top_candidates,
+            "num_x": 6,
+            "num_y": 3,
+            "num_zones": NUM_ZONES,
+        }
+
+    return _enrich_heatmap_response(_mane_heatmap_cache, request)
+
+
+def _enrich_heatmap_response(cached: dict[str, Any], request: Request) -> dict[str, Any]:
+    """Attach per-request image URLs to the cached heatmap response.
+
+    Image URLs include the request's base URL, so they MUST be computed per
+    request (different deploy hosts, schemes, ports). We never mutate the
+    cache itself.
+    """
+    enriched_candidates = []
+    for c in cached.get("top_candidates", []):
+        c2 = dict(c)
+        c2["photo_url"] = photo_url_for(str(c.get("player_id") or ""), request)
+        c2["team_logo_url"] = logo_url_for(c.get("team"), request)
+        enriched_candidates.append(c2)
+    return {
+        **cached,
+        "top_candidates": enriched_candidates,
+        "mane": {
+            **cached.get("mane", {}),
+            "photo_url": photo_url_for(str(cached.get("mane", {}).get("player_id") or ""), request),
+        },
+    }
diff --git a/backend/app/schemas/__init__.py b/backend/app/schemas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/backend/app/schemas/common.py b/backend/app/schemas/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..a476a32e87cfb31fde5192c8852ed0338a423c1c
--- /dev/null
+++ b/backend/app/schemas/common.py
@@ -0,0 +1,18 @@
+"""Schemas shared across routes."""
+from __future__ import annotations
+
+from typing import Literal
+
+from pydantic import BaseModel
+
+
+class HealthResponse(BaseModel):
+    status: Literal["ok"] = "ok"
+    engine_loaded: bool
+    engine_state: Literal["warming", "ready", "unavailable"]
+    engine_message: str | None = None
+    uptime_seconds: float
+
+
+class ErrorResponse(BaseModel):
+    detail: str
diff --git a/backend/app/schemas/player.py b/backend/app/schemas/player.py
new file mode 100644
index 0000000000000000000000000000000000000000..b49be21942246728c0d04db3c18466ac7b17923d
--- /dev/null
+++ b/backend/app/schemas/player.py
@@ -0,0 +1,36 @@
+"""Player-related schemas. Loose typing on engine-returned fields because
+the engine's dicts include some Optional values we surface as-is."""
+from __future__ import annotations
+
+from typing import Any
+
+from pydantic import BaseModel
+
+
+class PlayerSummary(BaseModel):
+    player_id: str
+    name: str
+    primary_position: str
+    team: str
+    versatility_score: float
+    num_matches: int
+    photo_url: str | None = None
+    team_logo_url: str | None = None
+
+
+class PlayerDetail(PlayerSummary):
+    num_distinct_positions: int
+    position_distribution: dict[str, Any]
+
+
+class Candidate(BaseModel):
+    rank: int
+    player_id: str
+    name: str
+    primary_position: str
+    team: str
+    similarity: float
+    versatility_score: float
+    num_matches: int
+    photo_url: str | None = None
+    team_logo_url: str | None = None
diff --git a/backend/app/schemas/search.py b/backend/app/schemas/search.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b16f60fa706887652cc801f483005ce24a388d0
--- /dev/null
+++ b/backend/app/schemas/search.py
@@ -0,0 +1,47 @@
+"""Search request/response schemas."""
+from __future__ import annotations
+
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field, field_validator
+
+
+class SearchRequest(BaseModel):
+    sources: list[str] = Field(..., min_length=1)
+    upgrades: list[str] | dict[str, float] | None = None
+    upgrade_intensity: float = Field(0.5, ge=0.0, le=1.0)
+    top_k: int = Field(30, ge=1, le=100)
+    filters: dict[str, Any] | None = None
+    exclude_sources: bool = True
+    seed: int = 42
+
+    @field_validator("upgrades")
+    @classmethod
+    def _validate_upgrade_probs(cls, v):
+        if isinstance(v, dict):
+            for key, prob in v.items():
+                if not isinstance(prob, (int, float)):
+                    raise ValueError(f"upgrade probability for '{key}' must be numeric")
+                if not (0.0 <= float(prob) <= 1.0):
+                    raise ValueError(f"upgrade probability for '{key}' must be in [0.0, 1.0]")
+        return v
+
+
+class SyncSearchResponse(BaseModel):
+    query: dict[str, Any]
+    candidates: list[dict[str, Any]]
+    warnings: list[str]
+
+
+class JobAcceptedResponse(BaseModel):
+    job_id: str
+    status: Literal["pending"]
+
+
+class JobRecord(BaseModel):
+    job_id: str
+    status: Literal["pending", "running", "done", "error"]
+    created_at: str
+    updated_at: str
+    result: dict[str, Any] | None = None
+    error: str | None = None
diff --git a/backend/app/services/__init__.py b/backend/app/services/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/backend/app/services/engine.py b/backend/app/services/engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..15b939428dc191877053a51a510e87b0c8f51ca3
--- /dev/null
+++ b/backend/app/services/engine.py
@@ -0,0 +1,84 @@
+"""
+Engine wrapper.
+
+Imports the locked GP2 scouting engine via sys.path and exposes a single
+async entry point `run_engine(fn, *args, **kwargs)` that routes every
+CPU-bound engine call through a shared single-worker ThreadPoolExecutor.
+
+The single-worker constraint is structural concurrency control: it means we
+can never run two model inferences in parallel, regardless of which endpoint
+triggered them. The /api/search job queue's "1 running + 3 pending" policy
+is layered on top for upgrade jobs specifically.
+"""
+from __future__ import annotations
+
+import asyncio
+import logging
+import sys
+import traceback
+from concurrent.futures import ThreadPoolExecutor
+from typing import Any, Callable
+
+from app.config import PROJECT_ROOT
+
+logger = logging.getLogger(__name__)
+
+# Make `from src.gp2.evaluation import scouting_engine` resolvable.
+_project_root_str = str(PROJECT_ROOT)
+if _project_root_str not in sys.path:
+    sys.path.insert(0, _project_root_str)
+
+# State exposed to the rest of the app.
+engine_state: str = "warming"      # "warming" | "ready" | "unavailable"
+engine_message: str | None = None
+
+# Import the engine lazily-friendly: we still import at module load so the
+# rest of the app can reference `scouting_engine.*` directly, but if the
+# import itself fails we mark the engine unavailable and re-raise so the
+# caller sees a clean error.
+try:
+    from src.gp2.evaluation import scouting_engine  # type: ignore
+except Exception as e:  # pragma: no cover — only fires if src/ is missing
+    scouting_engine = None  # type: ignore[assignment]
+    engine_state = "unavailable"
+    engine_message = f"Engine import failed: {e}"
+    logger.error("Engine import failed:\n%s", traceback.format_exc())
+
+
+# Shared single-worker executor. Lazily (re)created so that a shutdown
+# during one app lifespan doesn't permanently kill the executor for a
+# subsequent lifespan in the same process — important for pytest's
+# TestClient pattern, where each test may open a fresh TestClient.
+_executor: ThreadPoolExecutor | None = None
+
+
+def _ensure_executor() -> ThreadPoolExecutor:
+    global _executor
+    if _executor is None:
+        _executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="engine")
+    return _executor
+
+
+def engine_loaded() -> bool:
+    return engine_state == "ready"
+
+
+def set_state(state: str, message: str | None = None) -> None:
+    global engine_state, engine_message
+    engine_state = state
+    engine_message = message
+
+
+async def run_engine(fn: Callable[..., Any], *args: Any, **kwargs: Any) -> Any:
+    """Run a CPU-bound engine function on the shared single-worker executor."""
+    loop = asyncio.get_running_loop()
+    return await loop.run_in_executor(_ensure_executor(), lambda: fn(*args, **kwargs))
+
+
+def shutdown_executor() -> None:
+    """Stop the executor. Safe to call multiple times; a later run_engine
+    call will lazily create a fresh executor."""
+    global _executor
+    if _executor is not None:
+        _executor.shutdown(wait=False, cancel_futures=False)
+        _executor = None
diff --git a/backend/app/services/heatmap.py b/backend/app/services/heatmap.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5b7a8bf312f3cf060f21ab67d96d585dd6bae7a
--- /dev/null
+++ b/backend/app/services/heatmap.py
@@ -0,0 +1,141 @@
+"""
+Heatmap aggregation service.
+
+Streams `models/gp2/player_match_docs_split.jsonl` and counts pitch-zone
+occurrences per player. The engine's tokens encode locations as zone IDs
+on a 6×3 grid (NUM_X_BINS=6, NUM_Y_BINS=3, row-major from top-left of a
+120×80 StatsBomb pitch — see src/gp2/preprocess/zones.py).
+
+Token shapes we extract zones from:
+  pass|z17_z11|f|r|l|med    -> 17 (start zone)
+  carry|z16_z17|fwd         -> 16
+  shot|z11|on|r|open|xg_l|mid -> 11
+  pressure|z16|reg          -> 16
+  duel|z14|ground|lost      -> 14
+  ... etc.
+
+Tokens that are pure modifiers (e.g. `progressive_pass`, `cut_inside_right`,
+`under_pressure`) have no zone field and are skipped.
+
+We use *start zone only*, which represents "where this player was active."
+This is the right primitive for "show me their pitch footprint." We also
+keep the totals so the heatmap can be normalized to percentages downstream.
+
+The data file is ~132 MB and contains ~49k player-match documents — a full
+scan takes a few seconds. The result for each player is cached in
+process memory; the index `_zone_index` (built lazily on first call) maps
+player_id -> 18-element count array.
+"""
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import re
+from pathlib import Path
+from typing import Iterable, Optional
+
+from app.config import PROJECT_ROOT
+
+logger = logging.getLogger(__name__)
+
+# Matches `z<digits>` at the start of the second pipe-field, possibly followed
+# by `_z<digits>` (start_end pair) or other field separators. We only care
+# about the first (start) zone.
+_ZONE_RE = re.compile(r"^z(\d+)")
+
+NUM_ZONES = 18  # 6×3 grid
+DOCS_PATH = PROJECT_ROOT / "models" / "gp2" / "player_match_docs_split.jsonl"
+
+# player_id -> [count_z0, count_z1, ..., count_z17]
+_zone_index: Optional[dict[str, list[int]]] = None
+_index_lock = asyncio.Lock()
+
+
+def _extract_zone(token: str) -> Optional[int]:
+    """Return the start-zone id (0-17) for a structured token, or None for modifier-only tokens."""
+    # Tokens look like "<verb>|<zone-or-pair>|<...>" — split once, look at field[1].
+    parts = token.split("|", 2)
+    if len(parts) < 2:
+        return None
+    m = _ZONE_RE.match(parts[1])
+    if not m:
+        return None
+    try:
+        zid = int(m.group(1))
+    except ValueError:
+        return None
+    if 0 <= zid < NUM_ZONES:
+        return zid
+    return None
+
+
+def _build_index_sync() -> dict[str, list[int]]:
+    """Stream the docs jsonl once and tally zones per player. Blocking I/O — caller must offload."""
+    if not DOCS_PATH.exists():
+        raise FileNotFoundError(f"{DOCS_PATH} not found")
+
+    index: dict[str, list[int]] = {}
+    n_docs = 0
+    with open(DOCS_PATH, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                doc = json.loads(line)
+            except json.JSONDecodeError:
+                continue
+            pid = doc.get("player_id")
+            if pid is None:
+                continue
+            pid = str(pid)
+            counts = index.get(pid)
+            if counts is None:
+                counts = [0] * NUM_ZONES
+                index[pid] = counts
+            for tok in doc.get("onball_tokens", []) or []:
+                z = _extract_zone(tok)
+                if z is not None:
+                    counts[z] += 1
+            for tok in doc.get("offball_tokens", []) or []:
+                z = _extract_zone(tok)
+                if z is not None:
+                    counts[z] += 1
+            n_docs += 1
+    logger.info("Heatmap index built: %d players from %d documents", len(index), n_docs)
+    return index
+
+
+async def _ensure_index() -> dict[str, list[int]]:
+    global _zone_index
+    if _zone_index is not None:
+        return _zone_index
+    async with _index_lock:
+        if _zone_index is not None:
+            return _zone_index
+        loop = asyncio.get_running_loop()
+        _zone_index = await loop.run_in_executor(None, _build_index_sync)
+        return _zone_index
+
+
+async def player_heatmap(player_id: str) -> Optional[list[int]]:
+    """Return the 18-element zone-count array for a player, or None if unknown."""
+    idx = await _ensure_index()
+    counts = idx.get(str(player_id))
+    if counts is None:
+        return None
+    return list(counts)
+
+
+async def aggregate_heatmap(player_ids: Iterable[str]) -> list[int]:
+    """Sum zone counts across a set of player_ids. Missing IDs contribute zeros."""
+    idx = await _ensure_index()
+    out = [0] * NUM_ZONES
+    for pid in player_ids:
+        counts = idx.get(str(pid))
+        if counts is None:
+            continue
+        for i, c in enumerate(counts):
+            out[i] += c
+    return out
diff --git a/backend/app/services/images.py b/backend/app/services/images.py
new file mode 100644
index 0000000000000000000000000000000000000000..a38a79f8393bf5b17f3804a9aebba8e89fb743d1
--- /dev/null
+++ b/backend/app/services/images.py
@@ -0,0 +1,60 @@
+"""Static image URL resolution. Missing files → None (frontend uses initials fallback)."""
+from __future__ import annotations
+
+import re
+from typing import Any
+
+from fastapi import Request
+
+from app.config import PLAYERS_STATIC_DIR, TEAMS_STATIC_DIR
+
+_SLUG_NON_ALNUM = re.compile(r"[^a-z0-9]+")
+
+
+def team_slug(team_name: str) -> str:
+    s = _SLUG_NON_ALNUM.sub("-", team_name.lower()).strip("-")
+    return s or "unknown"
+
+
+def photo_url_for(player_id: str, request: Request) -> str | None:
+    if not player_id:
+        return None
+    if not (PLAYERS_STATIC_DIR / f"{player_id}.jpg").is_file():
+        return None
+    return f"{str(request.base_url).rstrip('/')}/static/players/{player_id}.jpg"
+
+
+def logo_url_for(team_name: str | None, request: Request) -> str | None:
+    if not team_name:
+        return None
+    slug = team_slug(team_name)
+    if not (TEAMS_STATIC_DIR / f"{slug}.png").is_file():
+        return None
+    return f"{str(request.base_url).rstrip('/')}/static/teams/{slug}.png"
+
+
+def attach_images(p: dict[str, Any], request: Request) -> dict[str, Any]:
+    """Return a copy of a player/candidate dict with photo_url + team_logo_url
+    populated using the current request's base URL. Safe to call on engine
+    output (does not mutate the engine's cache)."""
+    out = dict(p)
+    out["photo_url"] = photo_url_for(str(p.get("player_id") or ""), request)
+    out["team_logo_url"] = logo_url_for(p.get("team"), request)
+    return out
+
+
+def enrich_search_result(result: dict[str, Any], request: Request) -> dict[str, Any]:
+    """Add photo_url + team_logo_url to every player in a search-result dict.
+
+    Applies to both `query.sources` (the resolved source players) and the
+    `candidates` array. Returns a new dict — does not mutate the input.
+    """
+    query = dict(result.get("query") or {})
+    sources = [attach_images(s, request) for s in (query.get("sources") or [])]
+    query["sources"] = sources
+    candidates = [attach_images(c, request) for c in (result.get("candidates") or [])]
+    return {
+        **result,
+        "query": query,
+        "candidates": candidates,
+    }
diff --git a/backend/app/services/jobs.py b/backend/app/services/jobs.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ec2718671e5827e835ba79fbcad92ca63aa14a9
--- /dev/null
+++ b/backend/app/services/jobs.py
@@ -0,0 +1,153 @@
+"""
+In-memory async job store for upgrade searches.
+
+Design:
+  - One asyncio.Queue (maxsize=3) of pending job_ids.
+  - One long-running worker task drains it; each job runs through
+    services.engine.run_engine(...), which uses the shared single-worker
+    executor — so heavy work is automatically serialized with any other
+    engine call site (validation endpoint, similar-players, etc.).
+  - Records live in a dict keyed by job_id. Records older than 1 hour are
+    swept on every enqueue and every status lookup.
+
+Lost on restart by design. Acceptable for v1; a Redis/SQLite store is the
+follow-up if production reliability is needed.
+"""
+from __future__ import annotations
+
+import asyncio
+import logging
+import traceback
+import uuid
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta, timezone
+from typing import Any, Optional
+
+from app.services import engine
+
+logger = logging.getLogger(__name__)
+
+JOB_TTL = timedelta(hours=1)
+QUEUE_MAXSIZE = 3
+RETRY_AFTER_SECONDS = 30
+
+
+def _now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+@dataclass
+class Job:
+    job_id: str
+    status: str  # "pending" | "running" | "done" | "error"
+    created_at: str
+    updated_at: str
+    kwargs: dict[str, Any]
+    result: Optional[dict[str, Any]] = None
+    error: Optional[str] = None
+
+    def to_dict(self) -> dict[str, Any]:
+        # kwargs is internal — never expose it
+        return {
+            "job_id": self.job_id,
+            "status": self.status,
+            "created_at": self.created_at,
+            "updated_at": self.updated_at,
+            "result": self.result,
+            "error": self.error,
+        }
+
+
+class JobStore:
+    def __init__(self) -> None:
+        self._jobs: dict[str, Job] = {}
+        self._queue: asyncio.Queue[str] = asyncio.Queue(maxsize=QUEUE_MAXSIZE)
+        self._worker_task: asyncio.Task | None = None
+        self._running_job_id: str | None = None
+
+    def _sweep(self) -> None:
+        cutoff = datetime.now(timezone.utc) - JOB_TTL
+        stale = [
+            jid for jid, j in self._jobs.items()
+            if datetime.fromisoformat(j.created_at) < cutoff
+        ]
+        for jid in stale:
+            del self._jobs[jid]
+
+    def is_full(self) -> bool:
+        # "Full" = there is currently a running job AND the pending queue is at capacity.
+        return self._running_job_id is not None and self._queue.full()
+
+    def enqueue(self, kwargs: dict[str, Any]) -> str | None:
+        """Enqueue a job. Returns job_id, or None if capacity is exceeded."""
+        self._sweep()
+        if self.is_full():
+            return None
+        job_id = uuid.uuid4().hex
+        now = _now_iso()
+        self._jobs[job_id] = Job(
+            job_id=job_id,
+            status="pending",
+            created_at=now,
+            updated_at=now,
+            kwargs=kwargs,
+        )
+        try:
+            self._queue.put_nowait(job_id)
+        except asyncio.QueueFull:
+            # Shouldn't happen given the is_full() guard, but be defensive.
+            del self._jobs[job_id]
+            return None
+        return job_id
+
+    def get(self, job_id: str) -> dict[str, Any] | None:
+        self._sweep()
+        job = self._jobs.get(job_id)
+        return job.to_dict() if job else None
+
+    async def _worker(self) -> None:
+        from app.services.engine import scouting_engine  # late import; may be None
+
+        while True:
+            try:
+                job_id = await self._queue.get()
+            except asyncio.CancelledError:
+                return
+            job = self._jobs.get(job_id)
+            if job is None:
+                continue
+            self._running_job_id = job_id
+            job.status = "running"
+            job.updated_at = _now_iso()
+            try:
+                if scouting_engine is None:
+                    raise RuntimeError("Engine unavailable")
+                result = await engine.run_engine(
+                    scouting_engine.search_replacements, **job.kwargs
+                )
+                job.result = result
+                job.status = "done"
+            except Exception as e:
+                logger.error("Job %s failed:\n%s", job_id, traceback.format_exc())
+                job.error = f"{type(e).__name__}: {e}"
+                job.status = "error"
+            finally:
+                job.updated_at = _now_iso()
+                self._running_job_id = None
+
+    def start(self, loop: asyncio.AbstractEventLoop | None = None) -> None:
+        if self._worker_task is None or self._worker_task.done():
+            self._worker_task = asyncio.create_task(self._worker(), name="job-worker")
+
+    async def stop(self) -> None:
+        if self._worker_task is not None:
+            self._worker_task.cancel()
+            try:
+                await self._worker_task
+            except (asyncio.CancelledError, Exception):
+                pass
+            self._worker_task = None
+
+
+# Module-level singleton; the FastAPI app touches this via `from app.services.jobs import store`.
+store = JobStore()
diff --git a/backend/app/services/mane_preset.py b/backend/app/services/mane_preset.py
new file mode 100644
index 0000000000000000000000000000000000000000..533b303360fc01301da40986c016091eac6c17b3
--- /dev/null
+++ b/backend/app/services/mane_preset.py
@@ -0,0 +1,45 @@
+"""
+Mané-validation preset, reproduced from `src/gp2/evaluation/mane_case_validation.py`.
+
+Reproduced verbatim (not imported) because we don't want to call the
+script's `validate()` function — it has print-heavy reporting side effects
+and a different return contract. The constants here are the regression
+checkpoint; if they drift from the engine's expectations, that's intentional
+breakage to be reviewed.
+"""
+from __future__ import annotations
+
+# Six sources — Coutinho was removed in the engine's regression file
+# (mane_case_validation.py:37, comment "##coutinho removed").
+LIVERPOOL_2015_16_ATTACKERS: list[str] = [
+    "Lallana",
+    "Firmino",
+    "Sturridge",
+    "Origi",
+    "Ibe",
+    "Benteke",
+]
+
+KLOPP_UPGRADES_VALIDATED: dict[str, float] = {
+    "cut_inside": 0.7,
+    "finishing": 0.5,
+    "progression": 0.4,
+    "chance_creation": 0.4,
+    "dribbling": 0.4,
+    "pressing": 0.7,
+}
+
+DEFENDER_POSITIONS: set[str] = {
+    "Goalkeeper",
+    "Center Back",
+    "Left Center Back",
+    "Right Center Back",
+    "Left Back",
+    "Right Back",
+    "Left Wing Back",
+    "Right Wing Back",
+}
+
+MANE_TOP_K: int = 60
+MANE_SEED: int = 42
+MANE_FINAL_TOP_N: int = 30
diff --git a/backend/app/services/player_index.py b/backend/app/services/player_index.py
new file mode 100644
index 0000000000000000000000000000000000000000..c650bb4b869cbd7970fccba8c21b1e71b02b64b4
--- /dev/null
+++ b/backend/app/services/player_index.py
@@ -0,0 +1,97 @@
+"""
+In-memory player index with accent-insensitive substring matching.
+
+The locked engine's `list_available_players(query, limit)` does plain
+lower()+substring matching, so `q=mane` does NOT match "Sadio Mané" because
+'e' != 'é'. We can't fix that in the engine (src/gp2/ is locked), so we
+wrap it here:
+
+  1. On first use, fetch the full player list once via
+     `engine.list_available_players("", BIG)` (cheap — just dict
+     construction over cached metadata; no model inference).
+  2. Pre-compute an accent-stripped lowercased name for each player.
+  3. Per request, strip accents from the user's query and substring-match
+     against the pre-computed names. Return canonical (accented) entries.
+
+Cache invalidation: none — the player set is fixed for a given model
+build. If models are reloaded, the engine warmup would discard our cache
+indirectly (we'd need to restart the process to rebuild it). Acceptable.
+"""
+from __future__ import annotations
+
+import asyncio
+import threading
+import unicodedata
+from typing import Any
+
+from app.services import engine
+
+# A bigger limit than any plausible player count; the engine returns at
+# most metadata.size items regardless.
+_FULL_LIST_LIMIT = 100_000
+
+_lock = threading.Lock()
+_full_list: list[dict[str, Any]] | None = None
+_normalized: list[str] | None = None
+
+
+def _strip_accents(s: str) -> str:
+    """NFD decomposition + drop combining marks. 'Mané'→'Mane', 'Bruyne'→'Bruyne'."""
+    return "".join(
+        c for c in unicodedata.normalize("NFD", s)
+        if unicodedata.category(c) != "Mn"
+    ).lower()
+
+
+def _build_sync() -> None:
+    """Synchronous (engine call) — must run on the engine executor."""
+    global _full_list, _normalized
+    if engine.scouting_engine is None:
+        raise RuntimeError("Engine not available")
+    rows = engine.scouting_engine.list_available_players("", _FULL_LIST_LIMIT)
+    _full_list = rows
+    _normalized = [_strip_accents(r.get("name", "")) for r in rows]
+
+
+async def ensure_built() -> None:
+    """Build the index if needed. Safe to call from any request handler."""
+    if _full_list is not None:
+        return
+    # Engine call goes through the shared single-worker executor so we don't
+    # block the event loop and we serialize with other engine work.
+    await engine.run_engine(_acquire_and_build)
+
+
+def _acquire_and_build() -> None:
+    # Runs on the engine thread; the lock guards against the (unlikely)
+    # case where two requests both call ensure_built() and both make it
+    # past the early-return check before either has populated the cache.
+    with _lock:
+        if _full_list is None:
+            _build_sync()
+
+
+def search(query: str, limit: int) -> list[dict[str, Any]]:
+    """Substring-match `query` against accent-stripped names. Caller is
+    expected to have awaited `ensure_built()` first."""
+    if _full_list is None or _normalized is None:
+        return []
+    q = _strip_accents(query).strip()
+    if not q:
+        return list(_full_list[:limit])
+    out: list[dict[str, Any]] = []
+    for row, norm in zip(_full_list, _normalized):
+        if q in norm:
+            out.append(row)
+            if len(out) >= limit:
+                break
+    return out
+
+
+# Test hook — lets tests reset the cache between runs without restarting
+# the process.
+def _reset() -> None:
+    global _full_list, _normalized
+    with _lock:
+        _full_list = None
+        _normalized = None
diff --git a/backend/app/static/players/.gitkeep b/backend/app/static/players/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/backend/app/static/players/10007.jpg b/backend/app/static/players/10007.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8b8098fe5947afb522a30f2776d65caf25b03722
--- /dev/null
+++ b/backend/app/static/players/10007.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c00e984e4c150cb9b3b21f442bdbf75cefe6c373afebcd1fa6c0404febfc8922
+size 7470
diff --git a/backend/app/static/players/10030.jpg b/backend/app/static/players/10030.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e2d56f399562d278517685fcc46c289a1df158ac
--- /dev/null
+++ b/backend/app/static/players/10030.jpg
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+version https://git-lfs.github.com/spec/v1
+oid sha256:e659b682ef7efbec3e2b32ec3d875484998ef0fe248411313a7937b7e2f33585
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new file mode 100644
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--- /dev/null
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+oid sha256:c2de27a201b359d9ed34a4d388a921570577ddfff84063851de61ed0806dc2f1
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diff --git a/backend/app/static/players/10032.jpg b/backend/app/static/players/10032.jpg
new file mode 100644
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--- /dev/null
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+oid sha256:5252131a0658c0a790befe9397d57a341569b01f0fb1fc65cbbeea108db1c1d1
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new file mode 100644
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new file mode 100644
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--- /dev/null
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new file mode 100644
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+version https://git-lfs.github.com/spec/v1
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diff --git a/backend/pyproject.toml b/backend/pyproject.toml
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+[build-system]
+requires = ["setuptools>=68", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "replacement-scout-backend"
+version = "0.1.0"
+description = "FastAPI wrapper around the locked GP2 scouting engine"
+requires-python = ">=3.11,<3.12"
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+addopts = "-q"
diff --git a/backend/requirements-scripts.txt b/backend/requirements-scripts.txt
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--- /dev/null
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+# Dependencies for optional helper scripts under backend/scripts/.
+# Not required to run the API; only install if you plan to use the scripts.
+#
+# Usage:
+#   pip install -r requirements-scripts.txt
+Pillow>=10.0
+requests>=2.31
+tqdm>=4.66
diff --git a/backend/requirements.txt b/backend/requirements.txt
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--- /dev/null
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+fastapi>=0.110,<1.0
+uvicorn[standard]>=0.27,<1.0
+pydantic>=2.5,<3.0
+
+# ML stack — pinned to the project's working .venv (Python 3.11.9).
+# Verified via `D:/Documents/player-replacement/.venv/Scripts/python.exe -c "import numpy, scipy, gensim; ..."`
+# gensim 4.4 supports NumPy 2.x, so no numpy<2.0 ceiling is needed.
+numpy==2.4.4
+scipy==1.17.1
+gensim==4.4.0
+
+# Test deps.
+# httpx is required by FastAPI's TestClient (Starlette TestClient has used
+# httpx as its transport since FastAPI 0.87); without it,
+# `from fastapi.testclient import TestClient` raises at import time.
+pytest>=8.0
+httpx>=0.27
diff --git a/backend/scripts/fetch_wikidata_images.py b/backend/scripts/fetch_wikidata_images.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b8d0f5fe438e2f8be1ec0c67f918f1dca6b9698
--- /dev/null
+++ b/backend/scripts/fetch_wikidata_images.py
@@ -0,0 +1,379 @@
+"""
+fetch_wikidata_images.py — OPTIONAL polish.
+
+Fetches player photos and team logos from Wikidata / Wikimedia Commons and
+writes them under backend/app/static/{players,teams}/. The web app works
+fully WITHOUT this — PlayerAvatar falls back to deterministic initials when
+files are missing.
+
+Expected runtime: ~45–90 minutes for the full ~2,200-player dataset (longer
+because of the three-source fallback chain on misses).
+Image sources tried in order:
+  1) Wikidata (strict label + altLabel) — preferred, freely licensed
+  2) Wikipedia search (full-text "{name} footballer", returns page image) —
+     lenient matching, catches players whose Wikidata label is shorter
+  3) TheSportsDB — final fallback
+Coverage seen in practice: ~95%+ players for Big-5 men's footballers,
+~95% teams. Resumable — existing files are skipped, so re-running fills
+gaps without re-downloading.
+
+Usage:
+    pip install -r backend/requirements-scripts.txt
+    python backend/scripts/fetch_wikidata_images.py [--limit N] [--teams-only] [--players-only]
+
+Rate limit: 1 request/second to Wikidata (be a good citizen — the SPARQL
+endpoint is free).
+"""
+from __future__ import annotations
+
+import argparse
+import io
+import json
+import logging
+import re
+import sys
+import time
+from pathlib import Path
+from typing import Iterable
+
+import requests
+from PIL import Image
+from tqdm import tqdm
+
+# Resolve project paths the same way the engine does.
+SCRIPT_DIR = Path(__file__).resolve().parent
+BACKEND_DIR = SCRIPT_DIR.parent
+PROJECT_ROOT = BACKEND_DIR.parent
+METADATA_PATH = PROJECT_ROOT / "models" / "gp2" / "player_metadata_v2.json"
+PLAYERS_OUT = BACKEND_DIR / "app" / "static" / "players"
+TEAMS_OUT = BACKEND_DIR / "app" / "static" / "teams"
+
+WIKIDATA_SPARQL = "https://query.wikidata.org/sparql"
+WIKIMEDIA_FILE = "https://commons.wikimedia.org/wiki/Special:FilePath/{name}"
+# TheSportsDB is a free community-maintained sports DB with good coverage of
+# Big-5-league male footballers from the 2010s. We use its free `searchplayers`
+# endpoint as a fallback when Wikidata returns no P18 image. The "/3/" key is
+# the documented public/test key.
+THESPORTSDB_SEARCH = "https://www.thesportsdb.com/api/v1/json/3/searchplayers.php"
+# Wikipedia search API with `pageimages` is a third fallback. Full-text
+# search is far more lenient with name variants than Wikidata's strict
+# label matching, so this catches players whose Wikipedia article is the
+# short form ("José Callejón") even when our metadata has the formal full
+# name ("José María Callejón Bueno"). Returns the page's main image
+# (usually the infobox portrait) when one exists.
+WIKIPEDIA_API = "https://en.wikipedia.org/w/api.php"
+USER_AGENT = (
+    "ReplacementScoutBot/0.1 (https://github.com/mugalal/football-player-replacement; "
+    "academic/portfolio use)"
+)
+RATE_LIMIT_SECONDS = 1.0
+# 400×400 source images stay crisp at the new xl/2xl avatar sizes (up to 176px display).
+# Existing 200×200 fetches on disk are not re-downloaded; only newly-resolved players use this.
+PLAYER_SIZE = (400, 400)
+LOGO_MAX_SIZE = (256, 256)
+JPEG_QUALITY = 85
+IMAGE_DOWNLOAD_DEADLINE_SECONDS = 45
+
+# Q937857 = association football player.
+PLAYER_SPARQL = """
+SELECT ?item ?image WHERE {{
+  ?item wdt:P106 wd:Q937857 .
+  ?item wdt:P18 ?image .
+  {{ ?item rdfs:label "{name}"@en . }}
+  UNION
+  {{ ?item skos:altLabel "{name}"@en . }}
+}}
+LIMIT 10
+""".strip()
+
+# Q476028 = association football club.
+CLUB_SPARQL = """
+SELECT ?item ?logo WHERE {{
+  ?item wdt:P31/wdt:P279* wd:Q476028 .
+  ?item wdt:P154 ?logo .
+  {{ ?item rdfs:label "{name}"@en . }}
+  UNION
+  {{ ?item skos:altLabel "{name}"@en . }}
+  UNION
+  {{
+    ?item rdfs:label ?label .
+    FILTER(LANG(?label) = "en")
+    FILTER(CONTAINS(LCASE(STR(?label)), LCASE("{name}")))
+  }}
+}}
+LIMIT 10
+""".strip()
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s %(message)s",
+)
+log = logging.getLogger("fetch-images")
+
+_SLUG_NON_ALNUM = re.compile(r"[^a-z0-9]+")
+def team_slug(name: str) -> str:
+    return _SLUG_NON_ALNUM.sub("-", name.lower()).strip("-") or "unknown"
+
+
+def sparql(query: str, attempt: int = 0) -> list[dict]:
+    """Call Wikidata SPARQL endpoint with exponential backoff on 429/5xx."""
+    try:
+        r = requests.get(
+            WIKIDATA_SPARQL,
+            params={"query": query, "format": "json"},
+            headers={"User-Agent": USER_AGENT, "Accept": "application/sparql-results+json"},
+            timeout=30,
+        )
+    except requests.RequestException as e:
+        log.warning("SPARQL network error: %s", e)
+        if attempt < 4:
+            time.sleep(2 ** attempt)
+            return sparql(query, attempt + 1)
+        return []
+
+    if r.status_code in (429, 500, 502, 503, 504):
+        if attempt < 4:
+            wait = 2 ** attempt
+            log.warning("SPARQL %d, backing off %ss", r.status_code, wait)
+            time.sleep(wait)
+            return sparql(query, attempt + 1)
+        return []
+    if not r.ok:
+        return []
+    return r.json().get("results", {}).get("bindings", [])
+
+
+def download_image(image_url: str) -> bytes | None:
+    """Wikidata P18 values look like
+    'http://commons.wikimedia.org/wiki/Special:FilePath/Foo.jpg' — fetch directly."""
+    image_url = image_url.replace("http://", "https://", 1)
+    try:
+        with requests.get(
+            image_url,
+            headers={"User-Agent": USER_AGENT},
+            stream=True,
+            timeout=(10, 15),
+        ) as r:
+            if not r.ok:
+                return None
+            chunks: list[bytes] = []
+            started = time.monotonic()
+            for chunk in r.iter_content(chunk_size=64 * 1024):
+                if time.monotonic() - started > IMAGE_DOWNLOAD_DEADLINE_SECONDS:
+                    log.warning("Image download timed out (%s)", image_url)
+                    return None
+                if chunk:
+                    chunks.append(chunk)
+            return b"".join(chunks)
+    except requests.RequestException as e:
+        log.warning("Image download failed (%s): %s", image_url, e)
+        return None
+
+
+def wikipedia_player_image(name: str) -> str | None:
+    """Search Wikipedia for a footballer page; return its main image URL or None."""
+    try:
+        r = requests.get(
+            WIKIPEDIA_API,
+            params={
+                "action": "query",
+                "format": "json",
+                "prop": "pageimages",
+                "piprop": "original",
+                "generator": "search",
+                "gsrsearch": f"{name} footballer",
+                "gsrlimit": 3,
+            },
+            headers={"User-Agent": USER_AGENT},
+            timeout=15,
+        )
+    except requests.RequestException as e:
+        log.warning("Wikipedia network error: %s", e)
+        return None
+    if not r.ok:
+        return None
+    try:
+        payload = r.json()
+    except ValueError:
+        return None
+    pages = (payload.get("query") or {}).get("pages") or {}
+    # API returns pages keyed by pageid. Iterate and take the first one with
+    # an `original` (full-resolution) image.
+    for page in pages.values():
+        original = page.get("original")
+        if isinstance(original, dict) and original.get("source"):
+            return original["source"]
+    return None
+
+
+def thesportsdb_player_image(name: str) -> str | None:
+    """Search TheSportsDB for a footballer image URL. Returns None on miss."""
+    try:
+        r = requests.get(
+            THESPORTSDB_SEARCH,
+            params={"p": name},
+            headers={"User-Agent": USER_AGENT},
+            timeout=15,
+        )
+    except requests.RequestException as e:
+        log.warning("TheSportsDB network error: %s", e)
+        return None
+    if not r.ok:
+        return None
+    try:
+        payload = r.json()
+    except ValueError:
+        return None
+    players = payload.get("player") or []
+    # Filter to soccer/football entries, then prefer entries with a thumb.
+    for p in players:
+        sport = (p.get("strSport") or "").lower()
+        if sport not in ("soccer", "football"):
+            continue
+        for key in ("strCutout", "strThumb"):
+            url = p.get(key)
+            if url:
+                return url
+    # Last resort: any player entry with a thumb, even if sport tag is empty.
+    for p in players:
+        for key in ("strCutout", "strThumb"):
+            url = p.get(key)
+            if url:
+                return url
+    return None
+
+
+def fetch_player_photo(name: str, out_path: Path) -> bool:
+    if out_path.exists():
+        return True
+    # 1) Wikidata first (preferred — better metadata, freely licensed).
+    rows = sparql(PLAYER_SPARQL.format(name=_escape(name)))
+    time.sleep(RATE_LIMIT_SECONDS)
+    image_url = next(
+        (row["image"]["value"] for row in rows if "image" in row),
+        None,
+    )
+    # 2) Fallback to Wikipedia search (lenient name matching — typically
+    # the biggest coverage win for players whose formal name doesn't match
+    # the Wikidata label).
+    if not image_url:
+        image_url = wikipedia_player_image(name)
+        time.sleep(RATE_LIMIT_SECONDS)
+    # 3) Final fallback: TheSportsDB.
+    if not image_url:
+        image_url = thesportsdb_player_image(name)
+        time.sleep(RATE_LIMIT_SECONDS)
+    if not image_url:
+        return False
+    data = download_image(image_url)
+    if not data:
+        return False
+    try:
+        with Image.open(io.BytesIO(data)) as img:
+            img = img.convert("RGB")
+            img.thumbnail(PLAYER_SIZE, Image.LANCZOS)
+            # Center-crop to a square for a clean avatar.
+            w, h = img.size
+            side = min(w, h)
+            left, top = (w - side) // 2, (h - side) // 2
+            img = img.crop((left, top, left + side, top + side))
+            img = img.resize(PLAYER_SIZE, Image.LANCZOS)
+            out_path.parent.mkdir(parents=True, exist_ok=True)
+            img.save(out_path, "JPEG", quality=JPEG_QUALITY, optimize=True)
+        return True
+    except Exception as e:
+        log.warning("Image processing failed (%s): %s", name, e)
+        return False
+
+
+def fetch_team_logo(name: str, out_path: Path) -> bool:
+    if out_path.exists():
+        return True
+    rows = sparql(CLUB_SPARQL.format(name=_escape(name)))
+    time.sleep(RATE_LIMIT_SECONDS)
+    image_url = next(
+        (row["logo"]["value"] for row in rows if "logo" in row),
+        None,
+    )
+    if not image_url:
+        return False
+    data = download_image(_commons_thumbnail_url(image_url, LOGO_MAX_SIZE[0]))
+    if not data:
+        return False
+    try:
+        with Image.open(io.BytesIO(data)) as img:
+            img.thumbnail(LOGO_MAX_SIZE, Image.LANCZOS)
+            out_path.parent.mkdir(parents=True, exist_ok=True)
+            # Preserve transparency for logos — save as PNG.
+            if img.mode not in ("RGBA", "LA"):
+                img = img.convert("RGBA")
+            img.save(out_path, "PNG", optimize=True)
+        return True
+    except Exception as e:
+        log.warning("Logo processing failed (%s): %s", name, e)
+        return False
+
+
+def _escape(s: str) -> str:
+    # SPARQL string-literal escape — double quotes and backslashes only.
+    return s.replace("\\", "\\\\").replace('"', '\\"')
+
+
+def _commons_thumbnail_url(image_url: str, width: int) -> str:
+    # Special:FilePath?width=N asks Wikimedia to rasterize SVG logos to PNG.
+    sep = "&" if "?" in image_url else "?"
+    return f"{image_url}{sep}width={width}"
+
+
+def load_metadata() -> dict[str, dict]:
+    if not METADATA_PATH.exists():
+        log.error("Missing %s — cannot run.", METADATA_PATH)
+        sys.exit(1)
+    with open(METADATA_PATH, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("--limit", type=int, default=None, help="Cap how many players/teams to process.")
+    ap.add_argument("--start-index", type=int, default=0, help="Skip the first N players/teams before processing.")
+    ap.add_argument("--players-only", action="store_true")
+    ap.add_argument("--teams-only", action="store_true")
+    args = ap.parse_args()
+
+    metadata = load_metadata()
+    do_players = not args.teams_only
+    do_teams = not args.players_only
+
+    if do_players:
+        players: Iterable[tuple[str, str]] = (
+            (pid, m.get("name", "")) for pid, m in metadata.items() if m.get("name")
+        )
+        players = list(players)
+        if args.start_index:
+            players = players[args.start_index :]
+        if args.limit is not None:
+            players = players[: args.limit]
+        log.info("Fetching photos for %d players → %s", len(players), PLAYERS_OUT)
+        ok = 0
+        for pid, name in tqdm(players, desc="players"):
+            if fetch_player_photo(name, PLAYERS_OUT / f"{pid}.jpg"):
+                ok += 1
+        log.info("Players: %d/%d (%.0f%%)", ok, len(players), 100 * ok / max(1, len(players)))
+
+    if do_teams:
+        team_names = sorted({m.get("team", "") for m in metadata.values() if m.get("team")})
+        if args.start_index:
+            team_names = team_names[args.start_index :]
+        if args.limit is not None:
+            team_names = team_names[: args.limit]
+        log.info("Fetching logos for %d teams → %s", len(team_names), TEAMS_OUT)
+        ok = 0
+        for name in tqdm(team_names, desc="teams"):
+            if fetch_team_logo(name, TEAMS_OUT / f"{team_slug(name)}.png"):
+                ok += 1
+        log.info("Teams: %d/%d (%.0f%%)", ok, len(team_names), 100 * ok / max(1, len(team_names)))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/backend/tests/__init__.py b/backend/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/backend/tests/test_smoke.py b/backend/tests/test_smoke.py
new file mode 100644
index 0000000000000000000000000000000000000000..364527f03b0918e01efca68e4873d0ff5d19dd51
--- /dev/null
+++ b/backend/tests/test_smoke.py
@@ -0,0 +1,70 @@
+"""
+Smoke tests — must pass with or without models/gp2/ on disk.
+
+We use FastAPI's TestClient (synchronous, simpler than httpx.AsyncClient for
+basic HTTP checks). TestClient triggers the app's lifespan when used as a
+context manager, so warm-up runs but is allowed to fail without breaking
+these tests — the endpoints under test are all model-independent.
+
+The accent-insensitive test is conditional on engine readiness so the suite
+still passes in CI / fresh clones without model artifacts.
+"""
+from __future__ import annotations
+
+import pytest
+from fastapi.testclient import TestClient
+
+from app.main import app
+from app.services import engine
+
+
+def test_health_returns_200():
+    with TestClient(app) as client:
+        r = client.get("/api/health")
+        assert r.status_code == 200
+        body = r.json()
+        assert body["status"] == "ok"
+        assert body["engine_state"] in {"warming", "ready", "unavailable"}
+        assert isinstance(body["uptime_seconds"], (int, float))
+
+
+def test_upgrades_returns_grouped():
+    with TestClient(app) as client:
+        r = client.get("/api/upgrades")
+        # Model-independent: should be 200 as long as the engine module imported.
+        # If src/gp2 itself failed to import (extreme case), 503 is acceptable.
+        assert r.status_code in (200, 503)
+        if r.status_code == 200:
+            body = r.json()
+            assert "onball" in body
+            assert "offball" in body
+            assert isinstance(body["onball"], list)
+            assert isinstance(body["offball"], list)
+
+
+def test_filters_has_coming_soon():
+    with TestClient(app) as client:
+        r = client.get("/api/filters")
+        assert r.status_code in (200, 503)
+        if r.status_code == 200:
+            body = r.json()
+            assert "coming_soon" in body
+            assert isinstance(body["coming_soon"], list)
+            # Specific keys we promised in the spec:
+            for k in ("min_age", "max_age", "leagues", "min_market_value", "max_market_value"):
+                assert k in body["coming_soon"]
+
+
+def test_players_search_is_accent_insensitive():
+    """`q=mane` must match `Sadio Mané`. Conditional on engine readiness
+    so the suite still passes when models are absent (e.g. fresh clones)."""
+    with TestClient(app) as client:
+        if not engine.engine_loaded():
+            pytest.skip("engine not loaded — models/gp2/ absent")
+        r = client.get("/api/players", params={"q": "mane", "limit": 10})
+        assert r.status_code == 200, r.text
+        rows = r.json()
+        names = [row["name"] for row in rows]
+        assert any("Mané" in n for n in names), (
+            f"Expected 'Mané' in results for q=mane, got: {names}"
+        )
diff --git a/models/gp2/liverpool_formation_profile_1516.json b/models/gp2/liverpool_formation_profile_1516.json
new file mode 100644
index 0000000000000000000000000000000000000000..c9896ca7b303629e5a37bae2d32da0a6694ed376
--- /dev/null
+++ b/models/gp2/liverpool_formation_profile_1516.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c44bf2469e32dd3ef0d9aa7b9ee89dde4cfbb4411ab72c6ae0dcd01487d336f
+size 17680
diff --git a/models/gp2/liverpool_gaps_1516.json b/models/gp2/liverpool_gaps_1516.json
new file mode 100644
index 0000000000000000000000000000000000000000..b6ed1032b615fb426e0f0ac5e3c803b6af46a068
--- /dev/null
+++ b/models/gp2/liverpool_gaps_1516.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dafa63b0a098a536c155bf70787071ec802fa756ffb911dfa61c4df86e7b8aea
+size 11644
diff --git a/models/gp2/outlier_validation_results.json b/models/gp2/outlier_validation_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..67eb60da6647f97add287085b74efd7b9abd0a2e
--- /dev/null
+++ b/models/gp2/outlier_validation_results.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:997352de9323636aef5d151bb4442fef25d42c400840df3d554f3c4d47c2c38f
+size 3013
diff --git a/models/gp2/player2vec_64d.npz b/models/gp2/player2vec_64d.npz
new file mode 100644
index 0000000000000000000000000000000000000000..c865304b44d58fe3cdf60b15d8912b02b243ec5e
--- /dev/null
+++ b/models/gp2/player2vec_64d.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34ce3ef3eb5761afb482885190ac3654da59ada44f9d5cc4dc6e9ea792cf1451
+size 1185616
diff --git a/models/gp2/player_info.json b/models/gp2/player_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..296b168e16a479391d1c147ab6a2828ed50fa4a6
--- /dev/null
+++ b/models/gp2/player_info.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f75881f21641a6ed328c2e62d25d1d7003c7b8ee084141ec95978715138c5da3
+size 308757
diff --git a/models/gp2/player_match_docs_split.jsonl b/models/gp2/player_match_docs_split.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4cf4da97182fec58e97d453b381a9f3c0259d6ac
--- /dev/null
+++ b/models/gp2/player_match_docs_split.jsonl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5eea7bbe195835ee0920b39d185a9e01395c68b84f1f1f77ba1a9c3c00237b91
+size 138190525
diff --git a/models/gp2/player_metadata_v2.json b/models/gp2/player_metadata_v2.json
new file mode 100644
index 0000000000000000000000000000000000000000..067eb31dd14e3e3385a996358595e11152b51069
--- /dev/null
+++ b/models/gp2/player_metadata_v2.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5feafee95e2c71adfee3f71f0689ecb4995746fadbb7586aeace78346420037e
+size 916496
diff --git a/models/gp2/playermatch2vec_offball.model b/models/gp2/playermatch2vec_offball.model
new file mode 100644
index 0000000000000000000000000000000000000000..8543ba07a1f3796e89bc595166fde7aea8ccd59a
--- /dev/null
+++ b/models/gp2/playermatch2vec_offball.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:649fa081de6276165381f72933318cc1cb8180d071ca0f3a386948248fbc9f31
+size 4785900
diff --git a/models/gp2/playermatch2vec_onball.model b/models/gp2/playermatch2vec_onball.model
new file mode 100644
index 0000000000000000000000000000000000000000..25ea29226ee8fdff81f7ce6feef90eea5c943d0b
--- /dev/null
+++ b/models/gp2/playermatch2vec_onball.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e508b12931daa99c27d2e9d598d1fd9ef0642d7d9a76043ea8cce96df3b1ed61
+size 14383240
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/gp1/__init__.py b/src/gp1/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/gp1/build_play2vec.py b/src/gp1/build_play2vec.py
new file mode 100644
index 0000000000000000000000000000000000000000..12c7aa7be72276f04f02d72514cc7fc6d1067f08
--- /dev/null
+++ b/src/gp1/build_play2vec.py
@@ -0,0 +1,85 @@
+# src/build_play2vec.py
+
+import json
+import numpy as np
+from pathlib import Path
+from gensim.models import Word2Vec
+
+
+# =====================
+# CONFIG
+# =====================
+
+ACTION2VEC_PATH = "models/gp1/multi_token/action2vec.model"
+PLAY_SENTENCES_PATH = "models/gp1/multi_token/sentences_play2vec.jsonl"
+
+OUT_EMB_PATH = "models/gp1/multi_token/play2vec.npy"
+OUT_INDEX_PATH = "models/gp1/multi_token/play2vec_index.json"
+
+
+# =====================
+# LOADERS
+# =====================
+
+def load_play_sentences(path):
+    plays = []
+    with open(path, "r", encoding="utf-8") as f:
+        for line in f:
+            obj = json.loads(line)
+            plays.append((tuple(obj["play_id"]), obj["tokens"]))
+    return plays
+
+
+# =====================
+# PLAY2VEC BUILDER
+# =====================
+
+def build_play2vec(action_model, plays):
+    vectors = []
+    index = []
+
+    for play_id, tokens in plays:
+        token_vecs = []
+
+        for t in tokens:
+            if t in action_model.wv:
+                token_vecs.append(action_model.wv[t])
+
+        if not token_vecs:
+            continue  # skip empty / invalid plays
+
+        play_vec = np.mean(token_vecs, axis=0)
+        vectors.append(play_vec)
+        index.append(play_id)
+
+    return np.vstack(vectors), index
+
+
+# =====================
+# MAIN
+# =====================
+
+def main():
+    print("Loading Action2Vec model...")
+    action_model = Word2Vec.load(ACTION2VEC_PATH)
+
+    print("Loading play sentences...")
+    plays = load_play_sentences(PLAY_SENTENCES_PATH)
+    print(f"Loaded {len(plays)} plays")
+
+    print("Building Play2Vec embeddings...")
+    play_vectors, play_index = build_play2vec(action_model, plays)
+
+    print(f"Final Play2Vec shape: {play_vectors.shape}")
+
+    print("Saving outputs...")
+    np.save(OUT_EMB_PATH, play_vectors)
+
+    with open(OUT_INDEX_PATH, "w", encoding="utf-8") as f:
+        json.dump(play_index, f)
+
+    print("Play2Vec saved successfully")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp1/build_play2vec_single.py b/src/gp1/build_play2vec_single.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddbe37849d802e05f9054b46551c61948a44677a
--- /dev/null
+++ b/src/gp1/build_play2vec_single.py
@@ -0,0 +1,59 @@
+import json
+import numpy as np
+from gensim.models import Word2Vec
+
+ACTION2VEC_MODEL = "models/gp1/single_token/action2vec_single.model"
+PLAY_SENTENCES = "models/gp1/single_token/sentences_play2vec_single.jsonl"
+
+OUT_EMB = "models/gp1/single_token/play2vec.npy"
+OUT_INDEX = "models/gp1/single_token/play2vec_index.json"
+
+def load_sentences(path):
+    plays = []
+    with open(path, "r", encoding="utf-8") as f:
+        for line in f:
+            obj = json.loads(line)
+            plays.append((tuple(obj["play_id"]), obj["tokens"]))
+    return plays
+
+
+def build_play2vec(model, plays):
+    vectors = []
+    index = []
+
+    for pid, tokens in plays:
+        token_vecs = [model.wv[t] for t in tokens if t in model.wv]
+
+        if not token_vecs:
+            continue
+
+        # mean aggregation
+        vec = np.mean(token_vecs, axis=0)
+
+        vectors.append(vec)
+        index.append(pid)
+
+    return np.vstack(vectors), index
+
+
+def main():
+    print("Loading Action2Vec...")
+    model = Word2Vec.load(ACTION2VEC_MODEL)
+
+    print("Loading plays...")
+    plays = load_sentences(PLAY_SENTENCES)
+
+    print("Building Play2Vec...")
+    X, idx = build_play2vec(model, plays)
+
+    print("Saving...")
+    np.save(OUT_EMB, X)
+
+    with open(OUT_INDEX, "w", encoding="utf-8") as f:
+        json.dump([list(x) for x in idx], f)
+
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp1/build_player2vec.py b/src/gp1/build_player2vec.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a2a25b431b4112fea9e342c5d22e108e5f5412a
--- /dev/null
+++ b/src/gp1/build_player2vec.py
@@ -0,0 +1,63 @@
+import json
+import numpy as np
+from collections import defaultdict
+
+# =====================
+# PATHS
+# =====================
+
+PM2VEC_PATH = "models/gp1/multi_token/player_match2vec.npy"
+PM_INDEX_PATH = "models/gp1/multi_token/player_match2vec_index.json"
+OUT_DIR = "models/gp1/multi_token/"
+
+# =====================
+# LOAD PLAYER-MATCH VECTORS
+# =====================
+
+print("Loading PlayerMatch2Vec...")
+
+player_match_vectors = np.load(PM2VEC_PATH)
+
+with open(PM_INDEX_PATH, "r") as f:
+    player_match_index = json.load(f)
+
+assert len(player_match_vectors) == len(player_match_index)
+
+print(f"Loaded {len(player_match_index)} player-match vectors")
+
+# =====================
+# GROUP BY PLAYER
+# =====================
+
+player_vectors = defaultdict(list)
+
+for vec, (player_id, _match_id) in zip(player_match_vectors, player_match_index):
+    player_vectors[player_id].append(vec)
+
+print(f"Found {len(player_vectors)} unique players")
+
+# =====================
+# AGGREGATE (MEAN)
+# =====================
+
+player2vec = []
+player2vec_index = []
+
+for player_id, vecs in player_vectors.items():
+    mean_vec = np.mean(vecs, axis=0)
+    player2vec.append(mean_vec)
+    player2vec_index.append(player_id)
+
+player2vec = np.vstack(player2vec)
+
+# =====================
+# SAVE
+# =====================
+
+np.save(f"{OUT_DIR}/player2vec.npy", player2vec)
+
+with open(f"{OUT_DIR}/player2vec_index.json", "w") as f:
+    json.dump(player2vec_index, f)
+
+print("Player2Vec saved")
+print("Shape:", player2vec.shape)
diff --git a/src/gp1/build_player2vec_single.py b/src/gp1/build_player2vec_single.py
new file mode 100644
index 0000000000000000000000000000000000000000..d05e55f09f3a0c012b1a2d7a82da4a5a2b3bfe02
--- /dev/null
+++ b/src/gp1/build_player2vec_single.py
@@ -0,0 +1,43 @@
+import json
+import numpy as np
+from collections import defaultdict
+
+PM2VEC_PATH = "models/gp1/single_token/player_match2vec_single.npy"
+PM_INDEX_PATH = "models/gp1/single_token/player_match2vec_index_single.json"
+OUT_DIR = "models/gp1/single_token"
+
+print("Loading PlayerMatch2Vec (single token)...")
+
+player_match_vectors = np.load(PM2VEC_PATH)
+
+with open(PM_INDEX_PATH, "r") as f:
+    player_match_index = json.load(f)
+
+assert len(player_match_vectors) == len(player_match_index)
+
+print(f"Loaded {len(player_match_index)} player-match vectors")
+
+player_vectors = defaultdict(list)
+
+for vec, (player_id, _match_id) in zip(player_match_vectors, player_match_index):
+    player_vectors[player_id].append(vec)
+
+print(f"Found {len(player_vectors)} unique players")
+
+player2vec = []
+player2vec_index = []
+
+for player_id, vecs in player_vectors.items():
+    mean_vec = np.mean(vecs, axis=0)
+    player2vec.append(mean_vec)
+    player2vec_index.append(player_id)
+
+player2vec = np.vstack(player2vec)
+
+np.save(f"{OUT_DIR}/player2vec_single.npy", player2vec)
+
+with open(f"{OUT_DIR}/player2vec_index_single.json", "w") as f:
+    json.dump(player2vec_index, f)
+
+print("Player2Vec (single token) saved")
+print("Shape:", player2vec.shape)
diff --git a/src/gp1/build_player_id_to_name.py b/src/gp1/build_player_id_to_name.py
new file mode 100644
index 0000000000000000000000000000000000000000..79ff5a4b0a11cbbe85297169f4877a471d0e21c8
--- /dev/null
+++ b/src/gp1/build_player_id_to_name.py
@@ -0,0 +1,23 @@
+import json
+from pathlib import Path
+
+DATA_DIR = "data"
+OUT_PATH = "models/gp1/shared/player_id_to_name.json"
+
+player_id_to_name = {}
+
+for fname in Path(DATA_DIR).glob("*.json"):
+    with open(fname, "r", encoding="utf-8") as f:
+        events = json.load(f)
+
+    for ev in events:
+        if "player" in ev:
+            pid = ev["player"]["id"]
+            name = ev["player"]["name"]
+            if pid not in player_id_to_name:
+                player_id_to_name[pid] = name
+
+with open(OUT_PATH, "w", encoding="utf-8") as f:
+    json.dump(player_id_to_name, f, ensure_ascii=False, indent=2)
+
+print(f"Saved {len(player_id_to_name)} player names → {OUT_PATH}")
diff --git a/src/gp1/build_player_primary_position.py b/src/gp1/build_player_primary_position.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffa0a21899ae6e1182396c5c854bcfc6515b2a50
--- /dev/null
+++ b/src/gp1/build_player_primary_position.py
@@ -0,0 +1,40 @@
+import json
+import glob
+from collections import Counter, defaultdict
+
+DATA_DIR = "data"
+OUT_PATH = "models/gp1/shared/player_id_to_primary_position.json"
+
+def main():
+    player_positions = defaultdict(Counter)
+
+    files = glob.glob(f"{DATA_DIR}/*.json")
+    print(f"Scanning {len(files)} match files...")
+
+    for path in files:
+        with open(path, encoding="utf-8") as f:
+            events = json.load(f)
+
+        for ev in events:
+            player = ev.get("player")
+            position = ev.get("position")
+
+            if player and position:
+                pid = str(player["id"])
+                pos_name = position["name"]
+                player_positions[pid][pos_name] += 1
+
+    # majority vote
+    player_primary_pos = {
+        pid: counter.most_common(1)[0][0]
+        for pid, counter in player_positions.items()
+    }
+
+    with open(OUT_PATH, "w", encoding="utf-8") as f:
+        json.dump(player_primary_pos, f, indent=2, ensure_ascii=False)
+
+    print(f"Saved primary positions for {len(player_primary_pos)} players")
+    print(f"Output -> {OUT_PATH}")
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp1/build_playermatch2vec.py b/src/gp1/build_playermatch2vec.py
new file mode 100644
index 0000000000000000000000000000000000000000..6561e686d96344a092c34a4efeac7f5fd29ab293
--- /dev/null
+++ b/src/gp1/build_playermatch2vec.py
@@ -0,0 +1,75 @@
+import json
+import numpy as np
+from pathlib import Path
+from collections import defaultdict
+
+PLAY2VEC_PATH = "models/gp1/multi_token/play2vec.npy"
+PLAY_INDEX_PATH = "models/gp1/multi_token/play2vec_index.json"
+EVENTS_DIR = "data"
+OUT_DIR = "models/gp1/multi_token"
+
+print("Loading Play2Vec...")
+play_vectors = np.load(PLAY2VEC_PATH)
+
+# play2vec_index.json is a LIST
+with open(PLAY_INDEX_PATH, "r") as f:
+    play_index_list = json.load(f)
+
+# Build: (match_id, period, possession) -> row index
+play_index = {}
+for idx, play in enumerate(play_index_list):
+    match_id, period, possession, _team_id = play
+    play_index[(match_id, period, possession)] = idx
+
+print(f"Loaded {len(play_index)} plays")
+
+player_match_plays = defaultdict(lambda: defaultdict(set))
+
+print("Scanning raw events...")
+
+for fname in Path(EVENTS_DIR).glob("*.json"):
+    match_id = int(fname.stem)
+
+    with open(fname, "r", encoding="utf-8") as f:
+        events = json.load(f)
+
+    for ev in events:
+        if "player" not in ev:
+            continue
+
+        player_id = ev["player"]["id"]
+        period = ev.get("period")
+        possession = ev.get("possession")
+
+        if period is None or possession is None:
+            continue
+
+        key = (match_id, period, possession)
+        if key not in play_index:
+            continue
+
+        play_idx = play_index[key]
+        player_match_plays[player_id][match_id].add(play_idx)
+
+print(f"Found {len(player_match_plays)} players")
+
+player_match_vectors = []
+player_match_index = []
+
+for player_id, matches in player_match_plays.items():
+    for match_id, play_ids in matches.items():
+        vecs = play_vectors[list(play_ids)]
+        mean_vec = vecs.mean(axis=0)
+
+        player_match_vectors.append(mean_vec)
+        player_match_index.append((player_id, match_id))
+
+player_match_vectors = np.vstack(player_match_vectors)
+
+np.save(f"{OUT_DIR}/player_match2vec.npy", player_match_vectors)
+
+with open(f"{OUT_DIR}/player_match2vec_index.json", "w") as f:
+    json.dump(player_match_index, f)
+
+print("PlayerMatch2Vec saved")
+print("Shape:", player_match_vectors.shape)
diff --git a/src/gp1/build_playermatch2vec_single.py b/src/gp1/build_playermatch2vec_single.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f67edd382a0937fae3a4e6bef02293147242dee
--- /dev/null
+++ b/src/gp1/build_playermatch2vec_single.py
@@ -0,0 +1,79 @@
+import json
+import numpy as np
+from pathlib import Path
+from collections import defaultdict
+
+# =====================
+# PATHS (single token)
+# =====================
+
+PLAY2VEC_PATH = "models/gp1/single_token/play2vec.npy"
+PLAY_INDEX_PATH = "models/gp1/single_token/play2vec_index.json"
+EVENTS_DIR = "data"
+OUT_DIR = "models/gp1/single_token"
+
+print("Loading Play2Vec (single token)...")
+play_vectors = np.load(PLAY2VEC_PATH)
+
+with open(PLAY_INDEX_PATH, "r") as f:
+    play_index_list = json.load(f)
+
+# Build (match, period, possession) -> embedding row id
+play_index = {}
+for idx, play in enumerate(play_index_list):
+    match_id, period, possession, _team_id = play
+    play_index[(match_id, period, possession)] = idx
+
+print(f"Loaded {len(play_index)} plays")
+
+player_match_plays = defaultdict(lambda: defaultdict(set))
+
+print("Scanning raw events...")
+
+for fname in Path(EVENTS_DIR).glob("*.json"):
+    match_id = int(fname.stem)
+
+    with open(fname, "r", encoding="utf-8") as f:
+        events = json.load(f)
+
+    for ev in events:
+        if "player" not in ev:
+            continue
+
+        player_id = ev["player"]["id"]
+        period = ev.get("period")
+        possession = ev.get("possession")
+
+        if period is None or possession is None:
+            continue
+
+        key = (match_id, period, possession)
+
+        if key not in play_index:
+            continue
+
+        play_idx = play_index[key]
+        player_match_plays[player_id][match_id].add(play_idx)
+
+print(f"Found {len(player_match_plays)} players")
+
+player_match_vectors = []
+player_match_index = []
+
+for player_id, matches in player_match_plays.items():
+    for match_id, play_ids in matches.items():
+        vecs = play_vectors[list(play_ids)]
+        mean_vec = vecs.mean(axis=0)
+
+        player_match_vectors.append(mean_vec)
+        player_match_index.append((player_id, match_id))
+
+player_match_vectors = np.vstack(player_match_vectors)
+
+np.save(f"{OUT_DIR}/player_match2vec_single.npy", player_match_vectors)
+
+with open(f"{OUT_DIR}/player_match2vec_index_single.json", "w") as f:
+    json.dump(player_match_index, f)
+
+print("PlayerMatch2Vec (single token) saved")
+print("Shape:", player_match_vectors.shape)
diff --git a/src/gp1/data_processing.py b/src/gp1/data_processing.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae65d28320fefd45e64eb0f4f37484bf16001488
--- /dev/null
+++ b/src/gp1/data_processing.py
@@ -0,0 +1,297 @@
+"""
+StatsBomb → football2vec-style preprocessing (v1.0)
+---------------------------------------------------
+
+- Reads StatsBomb event JSON files from ./data/
+- Converts each event into MULTIPLE tokens (Action2Vec idea)
+- Groups events by (match_id, period, possession)
+- Each possession becomes ONE sentence (Play2Vec idea)
+- Saves sentences to models/sentences.jsonl
+
+Author: Omar Sameh (GP prototype)
+Reference: Ofir Magdaci – football2vec
+"""
+
+import json
+import os
+from collections import defaultdict, Counter
+
+# =====================
+# CONFIG
+# =====================
+
+DATA_DIR = "data"
+OUT_ACTION2VEC = "models/gp1/multi_token/sentences_action2vec.jsonl"
+OUT_PLAY2VEC   = "models/gp1/multi_token/sentences_play2vec.jsonl"
+
+
+NUM_X_BINS = 6
+NUM_Y_BINS = 3
+
+PRUNE_THRESHOLD = 3          # keep tokens with freq >= threshold
+PASS_SHORT_THRESHOLD = 20.0  # meters
+
+VALID_EVENT_TYPES = {
+    "pass",
+    "carry",
+    "dribble",
+    "shot",
+    "duel",
+    "pressure",
+    "ball receipt*",
+    "interception",
+    "clearance",
+    "ball recovery",
+    "miscontrol",
+    "foul won",
+    "foul committed"
+}
+
+
+# =====================
+# HELPERS
+# =====================
+
+def xy_to_zone(x, y, num_x=NUM_X_BINS, num_y=NUM_Y_BINS):
+    """
+    Convert StatsBomb (x,y) to zone token.
+    Pitch: x ∈ [0,120], y ∈ [0,80]
+    """
+    if x is None or y is None:
+        return "zone_unknown"
+
+    x = max(0.0, min(120.0, float(x)))
+    y = max(0.0, min(80.0, float(y)))
+
+    bx = min(int((x / 120.0) * num_x), num_x - 1)
+    by = min(int((y / 80.0) * num_y), num_y - 1)
+
+    return f"zone_{by * num_x + bx}"
+
+
+def load_all_events(data_dir):
+    """
+    Load all StatsBomb event files and inject match_id from filename.
+    """
+    events = []
+
+    for fname in sorted(os.listdir(data_dir)):
+        if not fname.endswith(".json"):
+            continue
+
+        match_id = int(fname.replace(".json", ""))
+        path = os.path.join(data_dir, fname)
+
+        with open(path, "r", encoding="utf-8") as f:
+            try:
+                match_events = json.load(f)
+                for ev in match_events:
+                    ev["match_id"] = match_id
+                    events.append(ev)
+            except Exception as e:
+                print(f"[WARN] Failed to read {fname}: {e}")
+
+    return events
+
+
+# =====================
+# EVENT → TOKENS
+# =====================
+
+def event_to_tokens(ev):
+    """
+    Convert ONE StatsBomb event into MULTIPLE tokens.
+    """
+    tokens = []
+
+    ev_type = ev.get("type", {}).get("name", "").lower()
+    if ev_type not in VALID_EVENT_TYPES:
+        return tokens
+    loc = ev.get("location", [None, None])
+    zone = xy_to_zone(loc[0], loc[1])
+
+    # Base action tokens
+    if ev_type:
+        tokens.append(ev_type)
+        tokens.append(f"{ev_type}_{zone}")
+
+    # ---- PASS ----
+    if ev_type == "pass":
+        p = ev.get("pass", {})
+
+        length = p.get("length")
+        height = p.get("height", {}).get("name", "").lower()
+        outcome = p.get("outcome")
+
+        if length is not None:
+            if length <= PASS_SHORT_THRESHOLD:
+                tokens.append("pass_short")
+            else:
+                tokens.append("pass_long")
+
+        if height:
+            tokens.append(f"pass_height_{height}")
+
+        if outcome is None:
+            tokens.append("pass_success")
+        else:
+            tokens.append("pass_fail")
+
+    # ---- SHOT ----
+    if ev_type == "shot":
+        s = ev.get("shot", {})
+        body = s.get("body_part", {}).get("name", "").lower()
+        outcome = s.get("outcome", {}).get("name", "").lower()
+
+        if body:
+            tokens.append(f"shot_body_{body}")
+        if outcome:
+            tokens.append(f"shot_outcome_{outcome}")
+
+    return tokens
+
+
+# =====================
+# BUILD SENTENCES
+# =====================
+
+def build_possession_sentences(events):
+    """
+    Group events into possession-level sentences.
+    Key = (match_id, period, possession, possession_team_id)
+    """
+    sentences = defaultdict(list)
+
+    for ev in events:
+        match_id = ev.get("match_id")
+        period = ev.get("period")
+        possession = ev.get("possession")
+        possession_team = ev.get("possession_team", {}).get("id")
+
+        if (
+            match_id is None
+            or period is None
+            or possession is None
+            or possession_team is None
+        ):
+            continue
+
+        key = (match_id, period, possession, possession_team)
+        tokens = event_to_tokens(ev)
+
+        if tokens:
+            sentences[key].extend(tokens)
+
+    # Remove very short possessions
+    return [
+        (key, tokens)
+        for key, tokens in sentences.items()
+        if len(tokens) >= 3
+    ]
+
+
+
+# =====================
+# PRUNING
+# =====================
+
+def prune_sentences(sentences, threshold=PRUNE_THRESHOLD):
+    """
+    sentences: [ (play_id, [tokens]) ]
+    """
+    counter = Counter()
+
+    # Count token frequencies
+    for _, tokens in sentences:
+        counter.update(tokens)
+
+    pruned = []
+    for play_id, tokens in sentences:
+        filtered = [t for t in tokens if counter[t] > threshold]
+        if len(filtered) >= 3:
+            pruned.append((play_id, filtered))
+
+    return pruned, counter
+
+
+
+# =====================
+# SAVE
+# =====================
+
+def save_action2vec_sentences(sentences, out_path):
+    """
+    Saves ONLY token lists (used for Action2Vec training)
+    Format:
+        ["pass", "pass_zone_3", ...]
+    """
+    os.makedirs(os.path.dirname(out_path), exist_ok=True)
+    with open(out_path, "w", encoding="utf-8") as f:
+        for _, tokens in sentences:
+            f.write(json.dumps(tokens) + "\n")
+
+
+def save_play2vec_sentences(sentences, out_path):
+    """
+    Saves play_id + tokens (used for Play2Vec)
+    Format:
+        {
+          "play_id": [match_id, period, possession, possession_team_id],
+          "tokens": [...]
+        }
+    """
+    os.makedirs(os.path.dirname(out_path), exist_ok=True)
+    with open(out_path, "w", encoding="utf-8") as f:
+        for play_id, tokens in sentences:
+            record = {
+                "play_id": list(play_id),
+                "tokens": tokens
+            }
+            f.write(json.dumps(record) + "\n")
+            
+
+
+# =====================
+# MAIN
+# =====================
+
+def main():
+    print("Loading events...")
+    events = load_all_events(DATA_DIR)
+    print(f"Loaded {len(events):,} events")
+
+    print("\nBuilding possession-level sentences...")
+    sentences = build_possession_sentences(events)
+    print(f"Built {len(sentences):,} possession sentences")
+
+    print("\nPruning rare tokens...")
+    sentences, vocab = prune_sentences(sentences)
+
+    print(f"Final sentences: {len(sentences):,}")
+    print(f"Vocabulary size: {len(vocab):,}")
+
+    # =====================
+    # SANITY CHECKS
+    # =====================
+    lengths = [len(s) for s in sentences]
+    print("\nPossession length stats:")
+    print(f"  Min  : {min(lengths)}")
+    print(f"  Mean : {sum(lengths)/len(lengths):.2f}")
+    print(f"  Max  : {max(lengths)}")
+
+    print("\nTop 20 most frequent tokens:")
+    for tok, cnt in vocab.most_common(20):
+        print(f"  {tok:<25} {cnt}")
+
+    print("\nSaving sentences...")
+    save_action2vec_sentences(sentences, OUT_ACTION2VEC)
+    print(f"Saved to {OUT_ACTION2VEC}")
+
+    print("Saving Play2Vec sentences...")
+    save_play2vec_sentences(sentences, OUT_PLAY2VEC)
+    print(f"Saved to {OUT_PLAY2VEC}")
+    
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp1/demo_player_similarity_single.py b/src/gp1/demo_player_similarity_single.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f3d71f321ebaaa6573d570c0f0b5815f2325831
--- /dev/null
+++ b/src/gp1/demo_player_similarity_single.py
@@ -0,0 +1,75 @@
+import json
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+from embedding_postprocessing import remove_common_components
+
+# =====================
+# PATHS (single token)
+# =====================
+
+P2VEC_PATH = "models/gp1/single_token/player2vec_single.npy"
+P2VEC_INDEX_PATH = "models/gp1/single_token/player2vec_index_single.json"
+PLAYER_NAMES_PATH = "models/gp1/shared/player_id_to_name.json"
+
+# =====================
+# LOAD
+# =====================
+
+player_vectors = np.load(P2VEC_PATH)
+
+# ---- OPTIONAL BUT RECOMMENDED ----
+# Remove dominant context
+player_vectors = remove_common_components(player_vectors, n_components=1)
+
+# Normalize
+player_vectors /= np.linalg.norm(player_vectors, axis=1, keepdims=True)
+
+with open(P2VEC_INDEX_PATH, "r") as f:
+    player_index = json.load(f)
+
+with open(PLAYER_NAMES_PATH, "r", encoding="utf-8") as f:
+    player_id_to_name = json.load(f)
+
+player_id_to_row = {int(pid): i for i, pid in enumerate(player_index)}
+
+
+# =====================
+# QUERY FUNCTION
+# =====================
+
+def get_similar_players_single(player_id, top_k=5):
+    if player_id not in player_id_to_row:
+        raise ValueError("Player ID not found in Player2Vec (single token)")
+
+    idx = player_id_to_row[player_id]
+    query_vec = player_vectors[idx:idx+1]
+
+    sims = cosine_similarity(query_vec, player_vectors)[0]
+    ranked = np.argsort(-sims)
+
+    neighbors = []
+    for i in ranked[1: top_k + 1]:
+        pid = int(player_index[i])
+        neighbors.append({
+            "player_id": pid,
+            "name": player_id_to_name.get(str(pid), "Unknown"),
+            "similarity": float(sims[i])
+        })
+
+    return neighbors
+
+
+# =====================
+# DEMO
+# =====================
+
+if __name__ == "__main__":
+    query_player_id = 5216  # example
+
+    print("\nSingle-token Player2Vec similarity search")
+    print("-----------------------------------------")
+    print(f"Query Player: {player_id_to_name.get(str(query_player_id), 'Unknown')}")
+
+    for n in get_similar_players_single(query_player_id, top_k=5):
+        print(f"{n['name']} (ID: {n['player_id']}), similarity={n['similarity']:.3f}")
diff --git a/src/gp1/embedding_postprocessing.py b/src/gp1/embedding_postprocessing.py
new file mode 100644
index 0000000000000000000000000000000000000000..e445f798fbab2e3b9a5e20f2efd1b4cf65dc88cf
--- /dev/null
+++ b/src/gp1/embedding_postprocessing.py
@@ -0,0 +1,30 @@
+import numpy as np
+from sklearn.decomposition import PCA
+
+def remove_common_components(X, n_components=1, center=True):
+    """
+    Remove top principal components from embedding matrix.
+
+    Args:
+        X: np.ndarray of shape (N, D)
+        n_components: number of dominant components to remove
+        center: whether to mean-center before PCA
+
+    Returns:
+        X_clean: np.ndarray of shape (N, D)
+    """
+    X_proc = X.copy()
+
+    if center:
+        mean = X_proc.mean(axis=0, keepdims=True)
+        X_proc = X_proc - mean
+
+    pca = PCA(n_components=n_components)
+    pca.fit(X_proc)
+
+    components = pca.components_
+
+    for comp in components:
+        X_proc -= (X_proc @ comp[:, None]) * comp[None, :]
+
+    return X_proc
diff --git a/src/gp1/explore_events_schema.py b/src/gp1/explore_events_schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e258c92f8688f1fd2bc2d9f0c30b1b3ab0584da
--- /dev/null
+++ b/src/gp1/explore_events_schema.py
@@ -0,0 +1,131 @@
+import os
+import json
+from collections import defaultdict, Counter
+from tqdm import tqdm
+
+DATA_DIR = "data"
+OUT_DIR = "models/gp1/shared/event_analysis"
+
+os.makedirs(OUT_DIR, exist_ok=True)
+
+
+def load_match(path):
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def main():
+    print("🔍 Scanning StatsBomb events...")
+
+    event_types = Counter()
+    positions = Counter()
+
+    # nested attributes
+    attribute_keys = defaultdict(set)
+    attribute_values = defaultdict(set)
+
+    # specific tracking
+    pass_attrs = defaultdict(set)
+    shot_attrs = defaultdict(set)
+    carry_attrs = defaultdict(set)
+    dribble_attrs = defaultdict(set)
+    duel_attrs = defaultdict(set)
+
+    files = [f for f in os.listdir(DATA_DIR) if f.endswith(".json")]
+
+    for file in tqdm(files):
+        match = load_match(os.path.join(DATA_DIR, file))
+
+        for ev in match:
+            # =========================
+            # Event Type
+            # =========================
+            if "type" in ev and ev["type"]:
+                etype = ev["type"]["name"]
+                event_types[etype] += 1
+
+            # =========================
+            # Player Position
+            # =========================
+            if "position" in ev and ev["position"]:
+                pos = ev["position"]["name"]
+                positions[pos] += 1
+
+            # =========================
+            # Generic attributes scan
+            # =========================
+            for key, val in ev.items():
+                if isinstance(val, dict):
+                    attribute_keys[key].update(val.keys())
+
+                    for sub_k, sub_v in val.items():
+                        if isinstance(sub_v, dict) and "name" in sub_v:
+                            attribute_values[f"{key}.{sub_k}"].add(sub_v["name"])
+
+                        elif isinstance(sub_v, list):
+                            continue
+
+                        else:
+                            attribute_values[f"{key}.{sub_k}"].add(str(sub_v))
+
+            # =========================
+            # Specific event breakdown
+            # =========================
+            if "pass" in ev:
+                for k, v in ev["pass"].items():
+                    if isinstance(v, dict) and "name" in v:
+                        pass_attrs[k].add(v["name"])
+                    else:
+                        pass_attrs[k].add(str(v))
+
+            if "shot" in ev:
+                for k, v in ev["shot"].items():
+                    if isinstance(v, dict) and "name" in v:
+                        shot_attrs[k].add(v["name"])
+                    else:
+                        shot_attrs[k].add(str(v))
+
+            if "carry" in ev:
+                for k, v in ev["carry"].items():
+                    carry_attrs[k].add(str(v))
+
+            if "dribble" in ev:
+                for k, v in ev["dribble"].items():
+                    if isinstance(v, dict) and "name" in v:
+                        dribble_attrs[k].add(v["name"])
+                    else:
+                        dribble_attrs[k].add(str(v))
+
+            if "duel" in ev:
+                for k, v in ev["duel"].items():
+                    if isinstance(v, dict) and "name" in v:
+                        duel_attrs[k].add(v["name"])
+                    else:
+                        duel_attrs[k].add(str(v))
+
+    print("✅ Done scanning")
+
+    # =========================
+    # Save results
+    # =========================
+
+    def save(obj, name):
+        with open(os.path.join(OUT_DIR, name), "w", encoding="utf-8") as f:
+            json.dump(obj, f, indent=2)
+
+    save(dict(event_types), "event_types.json")
+    save(dict(positions), "positions.json")
+    save({k: list(v) for k, v in attribute_keys.items()}, "attribute_keys.json")
+    save({k: list(v) for k, v in attribute_values.items()}, "attribute_values.json")
+
+    save({k: list(v) for k, v in pass_attrs.items()}, "pass_attributes.json")
+    save({k: list(v) for k, v in shot_attrs.items()}, "shot_attributes.json")
+    save({k: list(v) for k, v in carry_attrs.items()}, "carry_attributes.json")
+    save({k: list(v) for k, v in dribble_attrs.items()}, "dribble_attributes.json")
+    save({k: list(v) for k, v in duel_attrs.items()}, "duel_attributes.json")
+
+    print(f"📁 Results saved to: {OUT_DIR}")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/src/gp1/inspect_action2vec.py b/src/gp1/inspect_action2vec.py
new file mode 100644
index 0000000000000000000000000000000000000000..a94cc9c9518c856ba25efb7a987dceeb1b6e0304
--- /dev/null
+++ b/src/gp1/inspect_action2vec.py
@@ -0,0 +1,63 @@
+"""
+Action2Vec Sanity Checks
+-----------------------
+
+- Loads trained Action2Vec model
+- Inspects nearest neighbors for key football tokens
+- Used to validate semantic quality of embeddings
+
+Author: Omar Sameh
+"""
+
+from gensim.models import Word2Vec
+
+# =====================
+# CONFIG
+# =====================
+
+MODEL_PATH = "models/gp1/multi_token/action2vec.model"
+
+TOKENS_TO_CHECK = [
+    "pass",
+    "pass_short",
+    "pass_long",
+    "carry",
+    "shot",
+    "duel",
+    "pressure",
+    "pass_zone_10",
+    "pass_zone_14"
+]
+
+TOP_N = 10
+
+
+# =====================
+# HELPERS
+# =====================
+
+def inspect_neighbors(model, token, topn=10):
+    if token not in model.wv:
+        print(f"\nToken '{token}' not in vocabulary.")
+        return
+
+    print(f"\nNearest neighbors for '{token}':")
+    for word, score in model.wv.most_similar(token, topn=topn):
+        print(f"  {word:<30} {score:.3f}")
+
+
+# =====================
+# MAIN
+# =====================
+
+def main():
+    print("Loading Action2Vec model...")
+    model = Word2Vec.load(MODEL_PATH)
+    print(f"Model loaded. Vocabulary size: {len(model.wv)}")
+
+    for token in TOKENS_TO_CHECK:
+        inspect_neighbors(model, token, TOP_N)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp1/macro_position_build.py b/src/gp1/macro_position_build.py
new file mode 100644
index 0000000000000000000000000000000000000000..d78281176988472222b500f151b9b54093821c91
--- /dev/null
+++ b/src/gp1/macro_position_build.py
@@ -0,0 +1,47 @@
+import json
+
+POS_MAP = {}
+
+# Load extracted positions
+with open("models/gp1/shared/unique_positions.json", encoding="utf-8") as f:
+    positions = json.load(f)
+
+for p in positions:
+    name = p.lower()
+
+    if "goalkeeper" in name:
+        macro = "GK"
+
+    elif "back" in name:
+        macro = "DEF"
+
+    elif "wing back" in name:
+        macro = "DEF"
+
+    elif "center back" in name:
+        macro = "DEF"
+
+    elif "defensive midfield" in name:
+        macro = "MID"
+
+    elif "attacking midfield" in name:
+        macro = "MID"
+
+    elif "midfield" in name:
+        macro = "MID"
+
+    elif "forward" in name or "wing" in name:
+        macro = "ATT"
+
+    elif "substitute" in name:
+        macro = "UNKNOWN"
+
+    else:
+        macro = "UNKNOWN"
+
+    POS_MAP[p] = macro
+
+with open("models/shared/position_macro_map.json", "w", encoding="utf-8") as f:
+    json.dump(POS_MAP, f, ensure_ascii=False, indent=2)
+
+print("Saved to models/shared/position_macro_map.json")
diff --git a/src/gp1/player_similarity_demo.py b/src/gp1/player_similarity_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7823400c7b4dc5c910a7b53b0ecfc692222d652
--- /dev/null
+++ b/src/gp1/player_similarity_demo.py
@@ -0,0 +1,77 @@
+import json
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+from src.gp1.embedding_postprocessing import remove_common_components
+
+# =====================
+# PATHS
+# =====================
+
+P2VEC_PATH = "models/gp1/multi_token/player2vec.npy"
+P2VEC_INDEX_PATH = "models/gp1/multi_token/player2vec_index.json"
+PLAYER_NAMES_PATH = "models/gp1/shared/player_id_to_name.json"
+
+# =====================
+# LOAD DATA
+# =====================
+
+
+player_vectors = np.load(P2VEC_PATH)
+
+# Remove dominant football context
+player_vectors = remove_common_components(player_vectors, n_components=1)
+
+# L2 normalize AFTER cleaning
+player_vectors /= np.linalg.norm(player_vectors, axis=1, keepdims=True)
+
+
+with open(P2VEC_INDEX_PATH, "r") as f:
+    player_index = json.load(f)
+
+with open(PLAYER_NAMES_PATH, "r", encoding="utf-8") as f:
+    player_id_to_name = json.load(f)
+
+player_id_to_row = {
+    int(pid): i for i, pid in enumerate(player_index)
+}
+
+# =====================
+# QUERY FUNCTION
+# =====================
+
+def get_similar_players(player_id, top_k=5):
+    if player_id not in player_id_to_row:
+        raise ValueError("Player ID not found in Player2Vec")
+
+    idx = player_id_to_row[player_id]
+    query_vec = player_vectors[idx].reshape(1, -1)
+
+    sims = cosine_similarity(query_vec, player_vectors)[0]
+    ranked = np.argsort(-sims)
+
+    results = []
+    for i in ranked[1 : top_k + 1]:  # skip self
+        pid = int(player_index[i])
+        results.append({
+            "player_id": pid,
+            "name": player_id_to_name.get(str(pid), "Unknown"),
+            "similarity": float(sims[i])
+        })
+
+    return results
+
+# =====================
+# DEMO
+# =====================
+
+if __name__ == "__main__":
+    query_player_id = 5203  # Player ID to query
+    print(f"\nQuery Player: {player_id_to_name[str(query_player_id)]}\n")
+
+    neighbors = get_similar_players(query_player_id, top_k=5)
+
+    for n in neighbors:
+        print(
+            f"{n['name']} (ID: {n['player_id']}), similarity={n['similarity']:.3f}"
+        )
diff --git a/src/gp1/position_extractor b/src/gp1/position_extractor
new file mode 100644
index 0000000000000000000000000000000000000000..589696e7bd9ccd118b3870dd278252eca0b0a3b7
--- /dev/null
+++ b/src/gp1/position_extractor
@@ -0,0 +1,55 @@
+"""
+Extract every unique StatsBomb positional label
+-----------------------------------------------
+
+• Scans all event JSON files in ./data
+• Collects every distinct "position.name"
+• Saves:
+    - models/gp1/shared/unique_positions.json
+    - models/gp1/shared/position_frequencies.json
+"""
+
+import json
+import glob
+import os
+from collections import Counter
+
+DATA_DIR = "data"
+OUT_UNIQUE = "models/gp1/shared/unique_positions.json"
+OUT_FREQS = "models/gp1/shared/position_frequencies.json"
+
+
+def main():
+    positions = Counter()
+
+    files = glob.glob(f"{DATA_DIR}/*.json")
+    print(f"Scanning {len(files)} match files...")
+
+    for path in files:
+        with open(path, encoding="utf-8") as f:
+            events = json.load(f)
+
+        for ev in events:
+            pos = ev.get("position", {}).get("name")
+            if pos:
+                positions[pos] += 1
+
+    unique_positions = sorted(list(positions.keys()))
+
+    os.makedirs("models/shared", exist_ok=True)
+
+    # ---- save unique list ----
+    with open(OUT_UNIQUE, "w", encoding="utf-8") as f:
+        json.dump(unique_positions, f, ensure_ascii=False, indent=2)
+
+    # ---- save full frequency table ----
+    with open(OUT_FREQS, "w", encoding="utf-8") as f:
+        json.dump(positions.most_common(), f, ensure_ascii=False, indent=2)
+
+    print("\nDone.")
+    print(f"Unique positions saved to: {OUT_UNIQUE}")
+    print(f"Frequencies saved to:      {OUT_FREQS}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp1/tokenize_single_action2vec.py b/src/gp1/tokenize_single_action2vec.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b9b96f3a8b280605555ff73eef7680c4a86dcc0
--- /dev/null
+++ b/src/gp1/tokenize_single_action2vec.py
@@ -0,0 +1,151 @@
+"""
+Single-token Action2Vec preprocessing
+-------------------------------------
+
+Exactly same pipeline structure as multi-token version BUT:
+
+Event -> ONE fused token such as:
+
+  pass_zone5_success
+  pass_zone14_fail
+
+  shot_zone12_right_foot
+  shot_zone2_head
+
+  carry_zone7
+  pressure_zone3
+  ball_receipt_zone10
+
+Notes:
+  • No artificial '_na' tokens are used
+  • Outcomes are only included when StatsBomb defines them
+  • Shot tokens include body part to match multi-token richness
+"""
+
+import json
+import os
+from collections import defaultdict
+
+from data_processing import (
+    load_all_events,
+    xy_to_zone,
+    NUM_X_BINS,
+    NUM_Y_BINS
+)
+
+DATA_DIR = "data"
+
+OUT_PLAY2VEC = "models/gp1/single_token/sentences_play2vec_single.jsonl"
+OUT_ACTION2VEC = "models/gp1/single_token/sentences_action2vec_single.jsonl"
+
+
+def event_to_single_token(ev):
+    """
+    Convert ONE StatsBomb event into ONE fused token
+    """
+
+    ev_type = ev.get("type", {}).get("name", "").lower()
+
+    # field zone
+    loc = ev.get("location", [None, None])
+    zone = xy_to_zone(loc[0], loc[1], NUM_X_BINS, NUM_Y_BINS)
+
+    # default token base
+    base = f"{ev_type}_zone_{zone}"
+
+    # -------- PASS --------
+    if ev_type == "pass":
+        p = ev.get("pass", {})
+        # StatsBomb marks failure explicitly, success = lack of failure
+        if p.get("outcome") is None:
+            return f"{base}_success"
+        else:
+            return f"{base}_fail"
+
+    # -------- SHOT --------
+    elif ev_type == "shot":
+        s = ev.get("shot", {})
+
+        # body part (Right Foot, Head...)
+        body = s.get("body_part", {}).get("name")
+        if body:
+            body = body.lower().replace(" ", "_")
+            return f"{base}_{body}"
+
+        # fallback if somehow body missing
+        return base
+
+    # -------- DRIBBLE --------
+    elif ev_type == "dribble":
+        d = ev.get("dribble", {})
+        outcome = d.get("outcome", {}).get("name")
+
+        if outcome:
+            outcome = outcome.lower().replace(" ", "_")
+            return f"{base}_{outcome}"
+
+        return base
+
+    # -------- OTHER EVENTS (carry, pressure, receipt, etc.) --------
+    # They do NOT have explicit outcome in StatsBomb
+    return base
+
+
+def build_possession_sentences_single(events):
+    """
+    Aggregate tokens by (match, period, possession, team)
+    """
+
+    sentences = defaultdict(list)
+
+    for ev in events:
+        match_id = ev["match_id"]
+        period = ev.get("period")
+        possession = ev.get("possession")
+        team = ev.get("possession_team", {}).get("id")
+
+        key = (match_id, period, possession, team)
+
+        token = event_to_single_token(ev)
+        sentences[key].append(token)
+
+    # keep possessions with >= 3 tokens
+    return [
+        (key, toks)
+        for key, toks in sentences.items()
+        if len(toks) >= 3
+    ]
+
+
+def save_jsonl(sentences, path, include_tokens_only=False):
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+
+    with open(path, "w", encoding="utf-8") as f:
+        for key, toks in sentences:
+            if include_tokens_only:
+                f.write(json.dumps(toks) + "\n")
+            else:
+                f.write(json.dumps({
+                    "play_id": list(key),
+                    "tokens": toks
+                }) + "\n")
+
+
+def main():
+    print("Loading events...")
+    events = load_all_events(DATA_DIR)
+
+    print("Building single-token possession sentences...")
+    sentences = build_possession_sentences_single(events)
+
+    print(f"Final possessions: {len(sentences)}")
+
+    print("Saving...")
+    save_jsonl(sentences, OUT_PLAY2VEC)
+    save_jsonl(sentences, OUT_ACTION2VEC, include_tokens_only=True)
+
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp1/train_action2vec.py b/src/gp1/train_action2vec.py
new file mode 100644
index 0000000000000000000000000000000000000000..9471dd7fec9621143922dcb331f1e3ca52a19412
--- /dev/null
+++ b/src/gp1/train_action2vec.py
@@ -0,0 +1,72 @@
+"""
+Action2Vec Training
+-------------------
+
+- Trains Word2Vec on possession-level action token sequences
+- Input: models/sentences.jsonl
+- Output: models/action2vec.model
+
+Author: Omar Sameh
+Reference: Ofir Magdaci – football2vec
+"""
+
+import json
+from gensim.models import Word2Vec
+from pathlib import Path
+
+# =====================
+# CONFIG
+# =====================
+
+SENTENCES_PATH = "models/gp1/multi_token/sentences.jsonl"
+MODEL_OUT = "models/gp1/multi_token/action2vec.model"
+
+VECTOR_SIZE = 100
+WINDOW_SIZE = 5
+MIN_COUNT = 3
+EPOCHS = 15
+SG = 1  # Skip-gram
+
+# =====================
+# LOAD SENTENCES
+# =====================
+
+def load_sentences(path):
+    sentences = []
+    with open(path, "r", encoding="utf-8") as f:
+        for line in f:
+            sentences.append(json.loads(line))
+    return sentences
+
+
+# =====================
+# MAIN
+# =====================
+
+def main():
+    print("Loading possession sentences...")
+    sentences = load_sentences(SENTENCES_PATH)
+    print(f"Loaded {len(sentences):,} sentences")
+
+    print("\nTraining Action2Vec (Word2Vec)...")
+    model = Word2Vec(
+        sentences=sentences,
+        vector_size=VECTOR_SIZE,
+        window=WINDOW_SIZE,
+        min_count=MIN_COUNT,
+        sg=SG,
+        negative=5,
+        workers=4,
+        epochs=EPOCHS
+    )
+
+    print("\nSaving model...")
+    Path(MODEL_OUT).parent.mkdir(parents=True, exist_ok=True)
+    model.save(MODEL_OUT)
+
+    print(f"Model saved to {MODEL_OUT}")
+    print("\nVocabulary size:", len(model.wv))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp1/train_action2vec_single.py b/src/gp1/train_action2vec_single.py
new file mode 100644
index 0000000000000000000000000000000000000000..d92182e8562a87c856c8d6d236213919c15cc369
--- /dev/null
+++ b/src/gp1/train_action2vec_single.py
@@ -0,0 +1,42 @@
+import json
+from gensim.models import Word2Vec
+
+INPUT_SENTENCES = "models/gp1/single_token/sentences_action2vec_single.jsonl"
+OUT_MODEL = "models/gp1/single_token/action2vec_single.model"
+
+EMBEDDING_DIM = 100
+WINDOW = 10
+MIN_COUNT = 5
+WORKERS = 4
+
+def load_sentences(path):
+    sentences = []
+    with open(path, "r", encoding="utf-8") as f:
+        for line in f:
+            toks = json.loads(line)
+            sentences.append(toks)
+    return sentences
+
+
+def main():
+    print("Loading sentences...")
+    sents = load_sentences(INPUT_SENTENCES)
+    print(f"Loaded {len(sents)} sentences")
+
+    print("Training Word2Vec...")
+    model = Word2Vec(
+        sentences=sents,
+        vector_size=EMBEDDING_DIM,
+        window=WINDOW,
+        min_count=MIN_COUNT,
+        workers=WORKERS,
+        sg=1
+    )
+
+    print("Saving model...")
+    model.save(OUT_MODEL)
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp1/tsne_player2vec_multi.py b/src/gp1/tsne_player2vec_multi.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ff20e4563523c12f5fd50713740eb70b005a4ad
--- /dev/null
+++ b/src/gp1/tsne_player2vec_multi.py
@@ -0,0 +1,70 @@
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.manifold import TSNE
+
+from embedding_postprocessing import remove_common_components
+
+P2VEC_PATH = "models/gp1/multi_token/player2vec.npy"
+P2VEC_INDEX_PATH = "models/gp1/multi_token/player2vec_index.json"
+PLAYER_NAMES_PATH = "models/gp1/shared/player_id_to_name.json"
+
+OUTPUT_FIG = "models/gp1/multi_token/tsne_player2vec_multi.png"
+
+# =====================
+# LOAD
+# =====================
+
+X = np.load(P2VEC_PATH)
+
+# ---- optional but recommended ----
+X = remove_common_components(X, n_components=1)
+
+# normalize
+X /= np.linalg.norm(X, axis=1, keepdims=True)
+
+with open(P2VEC_INDEX_PATH) as f:
+    player_ids = json.load(f)
+
+with open(PLAYER_NAMES_PATH, encoding="utf-8") as f:
+    id2name = json.load(f)
+
+print("Running t-SNE... this can take a minute...")
+
+tsne = TSNE(
+    n_components=2,
+    perplexity=30,
+    learning_rate="auto",
+    init="random",
+    random_state=42
+)
+
+Y = tsne.fit_transform(X)
+
+# =====================
+# PLOT
+# =====================
+
+plt.figure(figsize=(10, 8))
+plt.scatter(Y[:, 0], Y[:, 1], s=8, alpha=0.7)
+
+# label some famous players
+highlight = [
+    "Lionel Andrés Messi Cuccittini",
+    "Cristiano Ronaldo dos Santos Aveiro",
+    "Sergio Busquets Burgos",
+    "Neymar da Silva Santos Junior",
+    "Luis Alberto Suárez Díaz",
+]
+
+for pid, (x, y) in zip(player_ids, Y):
+    name = id2name.get(str(pid))
+    if name in highlight:
+        plt.text(x, y, name.split()[0], fontsize=8)
+
+plt.title("t-SNE of Player2Vec (Multi-token)")
+plt.tight_layout()
+plt.savefig(OUTPUT_FIG, dpi=300)
+plt.show()
+
+print("Saved figure to", OUTPUT_FIG)
diff --git a/src/gp1/tsne_player2vec_multi_macro.py b/src/gp1/tsne_player2vec_multi_macro.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b47aeb3fd35620919388523b1167cdb905840c5
--- /dev/null
+++ b/src/gp1/tsne_player2vec_multi_macro.py
@@ -0,0 +1,163 @@
+import json
+import glob
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.manifold import TSNE
+
+from embedding_postprocessing import remove_common_components
+
+
+P2VEC = "models/gp1/multi_token/player2vec.npy"
+INDEX = "models/gp1/multi_token/player2vec_index.json"
+PLAYER_ID_TO_NAME = "models/gp1/shared/player_id_to_name.json"
+
+EVENTS_DIR = "data/*.json"
+
+OUT_FIG = "models/gp1/multi_token/tsne_player2vec_multi_macro.png"
+OUT_POINTS = "models/gp1/multi_token/tsne_player2vec_multi_macro.npy"
+
+
+def infer_macro_role(position_name: str):
+    """
+    Convert raw StatsBomb position to GK/DEF/MID/ATT automatically.
+    No external file required.
+    """
+    if not position_name:
+        return "UNKNOWN"
+
+    name = position_name.lower()
+
+    if "keeper" in name:
+        return "GK"
+
+    if "back" in name or "def" in name or "center back" in name:
+        return "DEF"
+
+    if "mid" in name:
+        return "MID"
+
+    # wing, forward, striker, attacker etc.
+    if "wing" in name or "forward" in name or "att" in name or "striker" in name:
+        return "ATT"
+
+    return "UNKNOWN"
+
+
+def build_player_primary_position():
+    """
+    Build mapping:
+        player_id -> most frequent position string
+    directly from StatsBomb event JSON.
+
+    This keeps you future-proof.
+    """
+
+    files = glob.glob(EVENTS_DIR)
+
+    counts = {}
+
+    for path in files:
+        with open(path, encoding="utf-8") as f:
+            events = json.load(f)
+
+        for ev in events:
+            pid = ev.get("player", {}).get("id")
+            pos = ev.get("position", {}).get("name")
+
+            if not pid or not pos:
+                continue
+
+            counts.setdefault(pid, {})
+            counts[pid][pos] = counts[pid].get(pos, 0) + 1
+
+    primary = {}
+
+    for pid, table in counts.items():
+        pos = max(table, key=table.get)
+        primary[pid] = pos
+
+    return primary
+
+
+def load_json(path):
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def main():
+
+    print("Loading Player2Vec…")
+    X = np.load(P2VEC)
+
+    print("Removing dominant global component…")
+    X = remove_common_components(X, n_components=1)
+
+    print("Normalizing…")
+    X /= np.linalg.norm(X, axis=1, keepdims=True)
+
+    print("Loading player index & names…")
+    player_ids = load_json(INDEX)
+    id2name = load_json(PLAYER_ID_TO_NAME)
+
+    print("Extracting player primary positions from event data…")
+    player_pos = build_player_primary_position()
+
+    print("Inferring macro roles…")
+    macro_roles = {}
+
+    for pid in player_ids:
+        pos_name = player_pos.get(int(pid))
+        macro = infer_macro_role(pos_name)
+        macro_roles[int(pid)] = macro
+
+    print("Running t-SNE…")
+    tsne = TSNE(
+        n_components=2,
+        perplexity=30,
+        learning_rate="auto",
+        init="random",
+        random_state=42
+    )
+
+    Y = tsne.fit_transform(X)
+
+    print("Saving t-SNE coordinates…")
+    np.save(OUT_POINTS, Y)
+
+    # color palette
+    colors = {
+        "GK": "gold",
+        "DEF": "red",
+        "MID": "green",
+        "ATT": "blue",
+        "UNKNOWN": "gray"
+    }
+
+    c = [colors[macro_roles.get(int(pid), "UNKNOWN")] for pid in player_ids]
+
+    print("Plotting…")
+    plt.figure(figsize=(10, 8))
+    plt.scatter(Y[:, 0], Y[:, 1], s=8, c=c, alpha=0.8)
+
+    plt.title("t-SNE of Player2Vec (Multi-token) — Macro Roles")
+    plt.xlabel("t-SNE 1")
+    plt.ylabel("t-SNE 2")
+
+    legend_handles = [
+        plt.Line2D([0], [0], marker="o", color="w", label="GK", markerfacecolor="gold", markersize=8),
+        plt.Line2D([0], [0], marker="o", color="w", label="DEF", markerfacecolor="red", markersize=8),
+        plt.Line2D([0], [0], marker="o", color="w", label="MID", markerfacecolor="green", markersize=8),
+        plt.Line2D([0], [0], marker="o", color="w", label="ATT", markerfacecolor="blue", markersize=8),
+        plt.Line2D([0], [0], marker="o", color="w", label="Unknown", markerfacecolor="gray", markersize=8),
+    ]
+    plt.legend(handles=legend_handles, title="Macro Role")
+
+    plt.tight_layout()
+    plt.savefig(OUT_FIG, dpi=300)
+    plt.show()
+
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp1/tsne_player2vec_multi_positions.py b/src/gp1/tsne_player2vec_multi_positions.py
new file mode 100644
index 0000000000000000000000000000000000000000..02779da383a6947f6a372bb98006a54c7e62dabe
--- /dev/null
+++ b/src/gp1/tsne_player2vec_multi_positions.py
@@ -0,0 +1,124 @@
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.manifold import TSNE
+import hdbscan
+
+from embedding_postprocessing import remove_common_components
+
+# =====================
+# PATHS
+# =====================
+P2VEC_PATH = "models/gp1/multi_token/player2vec.npy"
+INDEX_PATH = "models/gp1/multi_token/player2vec_index.json"
+PLAYER_POS_PATH = "models/gp1/shared/player_id_to_primary_position.json"
+
+OUT_TSNE = "models/gp1/multi_token/tsne_player2vec.npy"
+
+# =====================
+# LOAD HELPERS
+# =====================
+def load_json(path):
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+# =====================
+# MAIN
+# =====================
+def main():
+    print("Loading Player2Vec embeddings...")
+    X = np.load(P2VEC_PATH)
+
+    print("Removing dominant global component...")
+    X = remove_common_components(X, n_components=1)
+
+    print("Normalizing vectors...")
+    X /= np.linalg.norm(X, axis=1, keepdims=True)
+
+    player_ids = load_json(INDEX_PATH)
+    player_pos = load_json(PLAYER_POS_PATH)
+
+    print("Running t-SNE...")
+    tsne = TSNE(
+        n_components=2,
+        perplexity=30,
+        init="random",
+        learning_rate="auto",
+        random_state=42
+    )
+    Y = tsne.fit_transform(X)
+    np.save(OUT_TSNE, Y)
+
+    # =====================
+    # POSITION GROUPS
+    # =====================
+    GROUPS = {
+        "Right Backs (RB + RWB)": {
+            "positions": ["Right Back", "Right Wing Back"],
+            "min_cluster_size": 6
+        },
+        "Left Backs (LB + LWB)": {
+            "positions": ["Left Back", "Left Wing Back"],
+            "min_cluster_size": 6
+        },
+        "Goalkeepers": {
+            "positions": ["Goalkeeper"],
+            "min_cluster_size": 4
+        }
+    }
+
+    # =====================
+    # CLUSTER + PLOT
+    # =====================
+    for title, cfg in GROUPS.items():
+        indices = [
+            i for i, pid in enumerate(player_ids)
+            if player_pos.get(str(pid)) in cfg["positions"]
+        ]
+
+        if len(indices) < cfg["min_cluster_size"]:
+            print(f"Skipping {title}: not enough samples")
+            continue
+
+        Y_sub = Y[indices]
+
+        print(f"Clustering {title} ({len(indices)} players)...")
+
+        clusterer = hdbscan.HDBSCAN(
+            min_cluster_size=cfg["min_cluster_size"],
+            metric="euclidean"
+        )
+        labels = clusterer.fit_predict(Y_sub)
+
+        plt.figure(figsize=(6, 6))
+        plt.scatter(
+            Y_sub[:, 0],
+            Y_sub[:, 1],
+            c=labels,
+            cmap="tab10",
+            s=40,
+            edgecolors="black",
+            linewidths=0.3
+        )
+
+        plt.title(f"{title} — Style Sub-Clusters")
+        plt.xlabel("t-SNE 1")
+        plt.ylabel("t-SNE 2")
+
+        n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
+        plt.text(
+            0.02, 0.02,
+            f"Clusters found: {n_clusters}\nNoise points: {(labels == -1).sum()}",
+            transform=plt.gca().transAxes,
+            fontsize=9,
+            verticalalignment="bottom"
+        )
+
+        plt.tight_layout()
+        plt.show()
+
+    print("Done.")
+
+# =====================
+if __name__ == "__main__":
+    main()
diff --git a/src/gp1/tsne_player2vec_single.py b/src/gp1/tsne_player2vec_single.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed4592c2ed58c6a2ea0dacd665a0bfc2298d5ea7
--- /dev/null
+++ b/src/gp1/tsne_player2vec_single.py
@@ -0,0 +1,68 @@
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.manifold import TSNE
+
+from embedding_postprocessing import remove_common_components
+
+P2VEC_PATH = "models/gp1/single_token/player2vec_single.npy"
+P2VEC_INDEX_PATH = "models/gp1/single_token/player2vec_index_single.json"
+PLAYER_NAMES_PATH = "models/gp1/shared/player_id_to_name.json"
+
+OUTPUT_FIG = "models/gp1/single_token/tsne_player2vec_single.png"
+
+# =====================
+# LOAD
+# =====================
+
+X = np.load(P2VEC_PATH)
+
+# remove dominant component (same processing to compare fairly)
+X = remove_common_components(X, n_components=1)
+
+X /= np.linalg.norm(X, axis=1, keepdims=True)
+
+with open(P2VEC_INDEX_PATH) as f:
+    player_ids = json.load(f)
+
+with open(PLAYER_NAMES_PATH, encoding="utf-8") as f:
+    id2name = json.load(f)
+
+print("Running t-SNE...")
+
+tsne = TSNE(
+    n_components=2,
+    perplexity=30,
+    learning_rate="auto",
+    init="random",
+    random_state=42
+)
+
+Y = tsne.fit_transform(X)
+
+# =====================
+# PLOT
+# =====================
+
+plt.figure(figsize=(10, 8))
+plt.scatter(Y[:, 0], Y[:, 1], s=8, alpha=0.7)
+
+highlight = [
+    "Lionel Andrés Messi Cuccittini",
+    "Cristiano Ronaldo dos Santos Aveiro",
+    "Sergio Busquets Burgos",
+    "Neymar da Silva Santos Junior",
+    "Luis Alberto Suárez Díaz",
+]
+
+for pid, (x, y) in zip(player_ids, Y):
+    name = id2name.get(str(pid))
+    if name in highlight:
+        plt.text(x, y, name.split()[0], fontsize=8)
+
+plt.title("t-SNE of Player2Vec (Single-token)")
+plt.tight_layout()
+plt.savefig(OUTPUT_FIG, dpi=300)
+plt.show()
+
+print("Saved figure to", OUTPUT_FIG)
diff --git a/src/gp1/tsne_player2vec_with_roles_multi.py b/src/gp1/tsne_player2vec_with_roles_multi.py
new file mode 100644
index 0000000000000000000000000000000000000000..89c0f21d7a4761f0eab0f0d75c63e35de491a501
--- /dev/null
+++ b/src/gp1/tsne_player2vec_with_roles_multi.py
@@ -0,0 +1,165 @@
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.manifold import TSNE
+
+from embedding_postprocessing import remove_common_components
+
+# ===============================
+# PATHS
+# ===============================
+P2VEC_PATH = "models/gp1/multi_token/player2vec.npy"
+INDEX_PATH = "models/gp1/multi_token/player2vec_index.json"
+PLAYER_POS_PATH = "models/gp1/shared/player_id_to_primary_position.json"
+
+OUT_TSNE = "models/gp1/multi_token/tsne_player2vec_multi_positions.npy"
+OUT_FIG = "models/gp1/multi_token/tsne_player2vec_multi_positions_grouped.png"
+
+# ===============================
+# UTILITIES
+# ===============================
+def load_json(path):
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+# ===============================
+# POSITION → TACTICAL GROUP
+# ===============================
+def map_position_to_group(pos: str) -> str:
+    p = pos.lower()
+
+    # Goalkeeper
+    if "goalkeeper" in p:
+        return "GK"
+
+    # Center backs (LCB, RCB, CB)
+    if "center back" in p or p == "center back":
+        return "CB"
+
+    # Fullbacks / Wingbacks
+    if "right back" in p or "right wing back" in p:
+        return "RB"
+    if "left back" in p or "left wing back" in p:
+        return "LB"
+
+    # Defensive midfield
+    if "defensive midfield" in p:
+        return "DM"
+
+    # Central midfield
+    if "center midfield" in p:
+        return "CM"
+
+    # Attacking midfield
+    if "attacking midfield" in p:
+        return "AM"
+    # Right midfield
+    if "right midfield" in p:
+        return "RM"
+    
+    # Left midfield
+    if "left midfield" in p:
+        return "LM"
+
+    # Wingers
+    if "wing" in p:
+        return "W"
+
+    # Forwards
+    if "center forward" in p or "striker" in p:
+        return "CF"
+
+    return "Other"
+
+# ===============================
+# COLORS (HIGH CONTRAST)
+# ===============================
+GROUP_COLORS = {
+    "GK": "#FFD700",      # gold
+    "CB": "#FF0000",      # red
+    "RB": "#FF7700",      # orange
+    "LB": "#0004FF",      # blue
+    "DM": "#00FF00",      # green
+    "CM": "#00E5FF",      # cyan
+    "AM": "#8400FF",      # purple
+    "W":  "#FF8870",      # brown
+    "CF": "#FF00B3",      # pink
+    "RM": "#000000",    
+    "LM": "#7F7F7F",
+    "Other": "#7F7F7F"     # gray
+}
+
+# ===============================
+# MAIN
+# ===============================
+def main():
+    print("Loading Player2Vec embeddings...")
+    X = np.load(P2VEC_PATH)
+
+    print("Removing dominant global component...")
+    X = remove_common_components(X, n_components=1)
+
+    print("Normalizing embeddings...")
+    X /= np.linalg.norm(X, axis=1, keepdims=True)
+
+    print("Loading indices and positions...")
+    player_ids = load_json(INDEX_PATH)
+    player_primary_pos = load_json(PLAYER_POS_PATH)
+
+    print(f"Players: {len(player_ids)}")
+
+    print("Running t-SNE...")
+    tsne = TSNE(
+        n_components=2,
+        perplexity=30,
+        learning_rate="auto",
+        init="random",
+        random_state=42
+    )
+    Y = tsne.fit_transform(X)
+
+    np.save(OUT_TSNE, Y)
+
+    # ===============================
+    # ASSIGN GROUPS
+    # ===============================
+    groups = []
+    colors = []
+
+    for pid in player_ids:
+        pos = player_primary_pos.get(str(pid), "Unknown")
+        group = map_position_to_group(pos)
+        groups.append(group)
+        colors.append(GROUP_COLORS[group])
+
+    print("Plotting t-SNE...")
+    plt.figure(figsize=(12, 9))
+    plt.scatter(Y[:, 0], Y[:, 1], c=colors, s=18, alpha=0.85)
+
+    plt.title("t-SNE of Player2Vec (Multi-token) — Grouped Tactical Positions")
+    plt.xlabel("t-SNE 1")
+    plt.ylabel("t-SNE 2")
+
+    # Legend
+    handles = [
+        plt.Line2D(
+            [0], [0],
+            marker="o",
+            color="w",
+            label=k,
+            markerfacecolor=v,
+            markersize=8
+        )
+        for k, v in GROUP_COLORS.items()
+    ]
+    plt.legend(handles=handles, title="Tactical Group", loc="best")
+
+    plt.tight_layout()
+    plt.savefig(OUT_FIG, dpi=300)
+    plt.show()
+
+    print(f"Saved figure to {OUT_FIG}")
+    print("Done.")
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp1/visualize_tsne_with_positions.py b/src/gp1/visualize_tsne_with_positions.py
new file mode 100644
index 0000000000000000000000000000000000000000..5555328c225f735f4e73477c6949da943af2832c
--- /dev/null
+++ b/src/gp1/visualize_tsne_with_positions.py
@@ -0,0 +1,148 @@
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+from collections import defaultdict
+
+# ============================
+# CONFIG
+# ============================
+
+EVENTS_DIR = "data"
+PLAYER2VEC_INDEX = "models/gp1/multi_token/player2vec_index.json"   # or single_token path
+TSNE_POINTS = "models/gp1/multi_token/player2vec_tsne.npy"           # your saved tsne
+PLAYER_NAME_FILE = "models/gp1/shared/player_id_to_name.json"
+
+# ============================
+# 1) POSITION MAPPING SCHEME
+# ============================
+
+POSITION_TO_ROLE = {
+    "Goalkeeper": "GK",
+
+    "Left Back": "FB", "Right Back": "FB",
+    "Left Wing Back": "FB", "Right Wing Back": "FB",
+
+    "Left Center Back": "CB", "Right Center Back": "CB",
+    "Center Back": "CB",
+
+    "Center Defensive Midfield": "DM",
+
+    "Left Center Midfield": "CM", "Right Center Midfield": "CM",
+    "Center Midfield": "CM",
+
+    "Left Attacking Midfield": "AM", "Right Attacking Midfield": "AM",
+    "Center Attacking Midfield": "AM",
+
+    "Left Wing": "W", "Right Wing": "W",
+
+    "Center Forward": "CF", "Striker": "CF"
+}
+
+ROLE_COLORS = {
+    "GK": "gold",
+    "CB": "red",
+    "FB": "orangered",
+    "DM": "green",
+    "CM": "lime",
+    "AM": "cyan",
+    "W": "dodgerblue",
+    "CF": "purple",
+    "Unknown": "gray"
+}
+
+
+# ============================
+# 2) LOAD PLAYER POSITIONS
+# ============================
+
+def extract_positions_from_events():
+    player_position = {}
+
+    import os
+    for fname in os.listdir(EVENTS_DIR):
+        if not fname.endswith(".json"):
+            continue
+
+        path = os.path.join(EVENTS_DIR, fname)
+
+        with open(path, "r", encoding="utf-8") as f:
+            events = json.load(f)
+
+        for ev in events:
+            if ev.get("type", {}).get("name") != "Starting XI":
+                continue
+
+            for pl in ev.get("tactics", {}).get("lineup", []):
+                pid = pl["player"]["id"]
+                pos_name = pl["position"]["name"]
+
+                role = POSITION_TO_ROLE.get(pos_name, "Unknown")
+                player_position[pid] = role
+
+    return player_position
+
+
+# ============================
+# 3) LOAD PLAYER2VEC & TSNE
+# ============================
+
+with open(PLAYER2VEC_INDEX, "r") as f:
+    p2v_index = json.load(f)
+
+p2v_index = [int(x) for x in p2v_index]  # ensure ints
+
+tsne_points = np.load(TSNE_POINTS)
+
+with open(PLAYER_NAME_FILE, "r", encoding="utf-8") as f:
+    id_to_name = json.load(f)
+
+player_to_position = extract_positions_from_events()
+
+
+# ============================
+# 4) PLOT
+# ============================
+
+plt.figure(figsize=(10, 8))
+
+for i, pid in enumerate(p2v_index):
+    role = player_to_position.get(pid, "Unknown")
+    color = ROLE_COLORS.get(role, "gray")
+
+    x, y = tsne_points[i]
+    plt.scatter(x, y, c=color, s=12, alpha=0.7)
+
+# --- legend ---
+for role, color in ROLE_COLORS.items():
+    plt.scatter([], [], c=color, label=role)
+
+plt.legend(title="Tactical Role", bbox_to_anchor=(1.05, 1), loc="upper left")
+plt.title("t-SNE of Player2Vec Colored by Position (StatsBomb Starting-XI)")
+plt.xlabel("t-SNE 1")
+plt.ylabel("t-SNE 2")
+
+
+# ============================
+# 5) LABEL FAMOUS PLAYERS
+# ============================
+
+HIGHLIGHTS = {
+    "Lionel": None,
+    "Cristiano": None,
+    "Neymar": None,
+    "Luis": None,
+    "Andrés": None,
+    "Luka": None,
+    "Sergio": None
+}
+
+for i, pid in enumerate(p2v_index):
+    name = id_to_name.get(str(pid), "")
+    for key in HIGHLIGHTS:
+        if key in name:
+            x, y = tsne_points[i]
+            plt.text(x + 0.3, y + 0.3, name.split()[0], fontsize=9)
+
+
+plt.tight_layout()
+plt.show()
diff --git a/src/gp2/__init__.py b/src/gp2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/gp2/evaluation/__init__.py b/src/gp2/evaluation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/gp2/evaluation/mane_case_validation.py b/src/gp2/evaluation/mane_case_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb89c740f44ba891d82f9482b99fa30b3f92c995
--- /dev/null
+++ b/src/gp2/evaluation/mane_case_validation.py
@@ -0,0 +1,201 @@
+"""
+Mané Validation — Regression Test for the Scouting Methodology
+
+Single purpose: verify that our methodology, when given Liverpool's 2015-16 attacking
+profile and asked to find a Klopp-ideal upgrade, ranks Sadio Mané (Liverpool's actual
+2016 signing) inside the top 10 of attacking candidates from 2,211 total players.
+
+Methodology:
+    1. Pool match documents from 7 Liverpool 2015-16 attackers covering different roles
+       (Coutinho, Lallana, Firmino, Sturridge, Origi, Ibe, Benteke).
+    2. Apply Klopp-style interventions (cut_inside, finishing, progression,
+       chance_creation, dribbling, pressing) at validated per-upgrade probabilities.
+    3. Per-match modify-and-infer (Magdaci-style): each match's tokens are modified
+       individually, inferred 10× to smooth Doc2Vec noise, vectors averaged.
+    4. Average source-level vectors → final query vector.
+    5. Rank players by similarity, EXCLUDING defenders (CBs, FBs, GKs, WBs).
+
+Why we exclude defenders here:
+    Klopp's brief was for an attacking signing, so defensive results are not
+    meaningful for this validation. Some ball-playing CBs (Otamendi, Thiago Silva)
+    have onball patterns that overlap with attacking-mids, but they are not
+    candidate replacements for our use case. We post-filter them out so the
+    top-30 reflects what a real scout would actually consider.
+
+Run:
+    python -m src.gp2.evaluation.mane_case_validation
+"""
+
+from .scouting_engine import search_replacements, get_player_summary, find_player_id
+
+
+# ==================================================================================================
+# THE LIVERPOOL 2015-16 ATTACKING POOL
+# ==================================================================================================
+
+LIVERPOOL_2015_16_ATTACKERS = [
+    ##coutinho removed 
+    "Lallana",    # CAM/inside forward
+    "Firmino",    # CF/false 9
+    "Sturridge",  # CF finisher
+    "Origi",      # versatile attacker
+    "Ibe",        # RW/LW pace (note: matches Jordon Ibe)
+    "Benteke",    # target CF
+]
+
+# Validated per-upgrade probabilities used as the regression checkpoint.
+# Drift from these values means something else changed in the pipeline.
+KLOPP_UPGRADES_VALIDATED = {
+    "cut_inside":      0.7,
+    "finishing":       0.5,
+    "progression":     0.4,
+    "chance_creation": 0.4,
+    "dribbling":       0.4,
+    "pressing":        0.7,
+}
+
+
+# ==================================================================================================
+# DEFENDER FILTER
+# ==================================================================================================
+
+# Positions we exclude from the validation output. The Klopp brief was for an
+# attacking signing, so defensive matches (even if behaviorally similar via
+# ball-playing onball patterns) are not meaningful for this test.
+DEFENDER_POSITIONS = {
+    "Goalkeeper",
+    "Center Back",
+    "Left Center Back",
+    "Right Center Back",
+    "Left Back",
+    "Right Back",
+    "Left Wing Back",
+    "Right Wing Back",
+}
+
+
+def is_attacker(candidate: dict) -> bool:
+    """True if the candidate plays an attacking/midfield role (not a pure defender)."""
+    return candidate.get("primary_position") not in DEFENDER_POSITIONS
+
+
+# ==================================================================================================
+# THE VALIDATION
+# ==================================================================================================
+
+def validate():
+    print("=" * 105)
+    print("MANÉ VALIDATION — Regression Test (Defenders Excluded)")
+    print("=" * 105)
+    print("""
+Searches for Klopp's ideal attacking signing using multi-source pooling from
+Liverpool's 2015-16 attacking unit. Defensive positions are excluded from the
+top-30 (Klopp's brief was for an attacker, not a CB).
+""")
+
+    # Confirm Mané is in the dataset
+    mane_id = find_player_id("Mané")
+    if mane_id is None:
+        print("✗ Mané not found in dataset — cannot run validation.")
+        return False
+
+    mane_summary = get_player_summary(mane_id)
+    print(f"  Validation target: {mane_summary['name']}")
+    print(f"    Position: {mane_summary['primary_position']} | Team: {mane_summary['team']}")
+    print(f"    Versatility: {mane_summary['versatility_score']:.3f}")
+    print(f"    Distinct positions played: {mane_summary['num_distinct_positions']}")
+    print()
+
+    # Run the search with the validated per-upgrade probabilities.
+    # We request a larger top_k (60) so that after filtering out defenders we
+    # still have at least 30 attackers to rank in the output.
+    print("  Running search with the Liverpool 2015-16 attacking pool + Klopp upgrades...")
+    print("  Using locked-in per-upgrade probabilities (regression checkpoint).")
+    result = search_replacements(
+        sources=LIVERPOOL_2015_16_ATTACKERS,
+        upgrades=KLOPP_UPGRADES_VALIDATED,
+        top_k=60,
+        seed=42,
+    )
+
+    # Report sources actually resolved
+    print(f"\n  Sources resolved: {len(result['query']['sources'])}/{len(LIVERPOOL_2015_16_ATTACKERS)}")
+    for s in result["query"]["sources"]:
+        print(f"    - {s['name']:<35} {s['primary_position']:<25} matches={s['num_matches']}")
+
+    if result["warnings"]:
+        print("\n  Warnings:")
+        for w in result["warnings"]:
+            print(f"    - {w}")
+
+    # ---------- Filter defenders, re-rank attackers ----------
+    raw_candidates = result["candidates"]
+    attackers = [c for c in raw_candidates if is_attacker(c)]
+    excluded_count = len(raw_candidates) - len(attackers)
+
+    # Re-assign attacker-only ranks
+    for new_rank, c in enumerate(attackers, start=1):
+        c["attacker_rank"] = new_rank
+
+    print(f"\n  Filtered {excluded_count} defender(s) from raw top-{len(raw_candidates)}.")
+
+    # Display top-30 attackers
+    print("\n  Top-30 attackers (defenders excluded):")
+    print(f"  {'Rank':<6}{'Player':<37}{'Position':<27}{'Team':<23}{'Sim':<8}")
+    print("  " + "-" * 100)
+
+    mane_rank = None
+    for c in attackers[:30]:
+        marker = ""
+        if c["player_id"] == mane_id:
+            mane_rank = c["attacker_rank"]
+            marker = "  ← TARGET ✓"
+        print(f"  {c['attacker_rank']:<6}{c['name']:<37}{c['primary_position']:<27}"
+              f"{c['team']:<23}{c['similarity']:.4f}{marker}")
+
+    # If Mané didn't make the top-30 attackers, find his position in the full attacker list
+    if mane_rank is None:
+        for c in attackers:
+            if c["player_id"] == mane_id:
+                mane_rank = c["attacker_rank"]
+                print(f"\n  Mané not in displayed top-30, but ranks #{mane_rank} among all attackers.")
+                break
+
+    # Verdict
+    print()
+    print("=" * 105)
+    print("VERDICT")
+    print("=" * 105)
+    if mane_rank is None:
+        print(f"  ✗ FAIL — Mané did not appear in the attacker shortlist. Methodology has regressed.")
+        return False
+    elif mane_rank <= 5:
+        print(f"  ✓✓✓ EXCELLENT — Mané ranked #{mane_rank} among attackers")
+        return True
+    elif mane_rank <= 10:
+        print(f"  ✓✓ STRONG — Mané ranked #{mane_rank} among attackers")
+        return True
+    elif mane_rank <= 20:
+        print(f"  ✓ ACCEPTABLE — Mané ranked #{mane_rank} among attackers")
+        return True
+    else:
+        print(f"  ⚠ MARGINAL — Mané ranked #{mane_rank} among attackers")
+        return False
+
+
+# ==================================================================================================
+# MAIN
+# ==================================================================================================
+
+def main():
+    success = validate()
+    print()
+    if success:
+        print("Methodology validated. Engine and CLI are safe to use.")
+    else:
+        print("Validation drifted — investigate before deploying.")
+    print()
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/src/gp2/evaluation/mane_search.py b/src/gp2/evaluation/mane_search.py
new file mode 100644
index 0000000000000000000000000000000000000000..e264531d118c239521cfe38519adc1ed1a074433
--- /dev/null
+++ b/src/gp2/evaluation/mane_search.py
@@ -0,0 +1,625 @@
+"""
+The Mané Validation — Modify Liverpool Player Documents, Search for Upgrades
+
+This is the REAL validation of our scouting tool concept.
+
+Approach (Magdaci-style):
+    1. Pick a Liverpool 2015-16 attacker (Coutinho, Lallana, Sturridge)
+    2. Sample N of their match documents
+    3. Apply interventions that move them toward "ideal Klopp attacker":
+        - add_cut_inside (inverted winger behavior)
+        - upgrade_finishing (more clinical)
+        - add_progression (more direct progressive carries)
+        - boost_pressing (gegenpressing)
+    4. Re-infer vectors from modified docs (one vector per modified match)
+    5. Average those vectors → "upgraded Coutinho" target vector
+    6. Search top-50 most similar players in the entire 2015-16 dataset
+    7. Validate: Mané should appear in top-20
+
+Generalization test:
+    Same approach but for Mertens (Napoli LW) and Modrić — does the methodology
+    extend beyond Liverpool? If yes, the tool generalizes.
+"""
+
+import json
+import numpy as np
+import random
+from typing import List
+
+from gensim.models.doc2vec import Doc2Vec
+
+from .modify_doc import (
+    Intervention,
+    add_cut_inside,
+    upgrade_finishing,
+    add_progression,
+    boost_pressing,
+    add_chance_creation,
+    enrich_dribbling,
+    modify_doc,
+)
+from src.gp2.paths import (
+    DOCS_PATH,
+    OFFBALL_MODEL_PATH,
+    ONBALL_MODEL_PATH,
+    PLAYER2VEC_PATH,
+    PLAYER_METADATA_PATH,
+)
+
+
+# ==================================================================================================
+# DATA LOADING
+# ==================================================================================================
+
+def load_everything():
+    """Load Player2Vec + metadata + Doc2Vec models."""
+    data = np.load(PLAYER2VEC_PATH)
+    player_ids = list(data["player_ids"])
+    vectors = data["vectors"]
+
+    with open(PLAYER_METADATA_PATH, "r", encoding="utf-8") as f:
+        metadata = json.load(f)
+
+    print("Loading Doc2Vec models...")
+    onball_model = Doc2Vec.load(str(ONBALL_MODEL_PATH))
+    offball_model = Doc2Vec.load(str(OFFBALL_MODEL_PATH))
+
+    return player_ids, vectors, metadata, onball_model, offball_model
+
+
+def load_player_docs(player_id: str) -> List[dict]:
+    """Load all match docs for a specific player."""
+    docs = []
+    with open(DOCS_PATH, "r", encoding="utf-8") as f:
+        for line in f:
+            d = json.loads(line.strip())
+            if d["player_id"] == player_id:
+                docs.append(d)
+    return docs
+
+
+def find_player_id(name_query: str, metadata: dict, player_ids: list) -> str:
+    """Find player_id by partial name match. Returns None if not found."""
+    query = name_query.lower()
+    matches = []
+    for pid in player_ids:
+        name = metadata.get(pid, {}).get("name", "")
+        if query in name.lower():
+            matches.append((pid, name))
+    if not matches:
+        return None
+    if len(matches) > 1:
+        print(f"  ⚠ Ambiguous '{name_query}', using first: {matches[0][1]}")
+    return matches[0][0]
+
+
+# ==================================================================================================
+# VECTOR INFERENCE FROM MODIFIED DOCS
+# ==================================================================================================
+
+def infer_player_vector_from_docs(
+    docs: List[dict],
+    onball_model: Doc2Vec,
+    offball_model: Doc2Vec,
+    onball_dim: int = 48,
+    offball_dim: int = 16,
+) -> np.ndarray:
+    """
+    Re-infer a player vector from modified docs.
+    For each doc: infer onball + offball vectors, concatenate, then average across docs.
+    """
+    onball_vecs = []
+    offball_vecs = []
+
+    for doc in docs:
+        if doc.get("onball_tokens"):
+            onball_vecs.append(onball_model.infer_vector(doc["onball_tokens"]))
+        if doc.get("offball_tokens"):
+            offball_vecs.append(offball_model.infer_vector(doc["offball_tokens"]))
+
+    if not onball_vecs:
+        raise ValueError("No on-ball tokens to infer from")
+
+    mean_onball = np.mean(onball_vecs, axis=0)
+    mean_offball = (
+        np.mean(offball_vecs, axis=0) if offball_vecs else np.zeros(offball_dim)
+    )
+
+    combined = np.concatenate([mean_onball, mean_offball])
+    return combined / np.linalg.norm(combined)
+
+
+# ==================================================================================================
+# SEARCH
+# ==================================================================================================
+
+def search_similar(
+    target_vec: np.ndarray,
+    vectors: np.ndarray,
+    player_ids: list,
+    metadata: dict,
+    top_k: int = 30,
+    exclude_player_ids: list = None,
+    target_name: str = None,
+    show_target_neighborhood: bool = True,
+):
+    """Find top-K most similar players to target_vec.
+
+    If target_name is given but doesn't appear in top-K, also reports:
+        - The target's actual rank
+        - Its similarity score
+        - Players ranked just above and below it
+    """
+    sims = vectors @ target_vec
+
+    if exclude_player_ids:
+        for pid in exclude_player_ids:
+            if pid in player_ids:
+                sims[player_ids.index(pid)] = -np.inf
+
+    # Full ranking (we use this for diagnostics if target isn't in top-K)
+    full_ranking = np.argsort(sims)[::-1]
+    top_indices = full_ranking[:top_k]
+
+    print(f"\n{'Rank':<6}{'Player':<40}{'Position':<28}{'Team':<24}{'Sim':<8}")
+    print("-" * 105)
+
+    target_rank = None
+    target_idx = None
+    for rank, idx in enumerate(top_indices, 1):
+        pid = player_ids[idx]
+        meta = metadata[pid]
+        marker = ""
+        if target_name and target_name.lower() in meta["name"].lower():
+            target_rank = rank
+            target_idx = idx
+            marker = "  ← TARGET ✓"
+        print(f"{rank:<6}{meta['name']:<40}{meta['primary_position']:<28}{meta['team']:<24}{sims[idx]:.4f}{marker}")
+
+    # If the target wasn't found in top_k, dig deeper to locate it
+    if target_name and target_rank is None and show_target_neighborhood:
+        # Find the target's actual rank in the full ranking
+        target_full_rank = None
+        target_idx_global = None
+        for full_rank_idx, idx in enumerate(full_ranking, 1):
+            pid = player_ids[idx]
+            meta = metadata[pid]
+            if target_name.lower() in meta["name"].lower():
+                target_full_rank = full_rank_idx
+                target_idx_global = idx
+                break
+
+        if target_full_rank is None:
+            print(f"\n  ⚠ Target '{target_name}' not found in dataset at all (excluded or missing).")
+        else:
+            target_meta = metadata[player_ids[target_idx_global]]
+            target_sim = sims[target_idx_global]
+            top_sim = sims[full_ranking[0]]
+            cutoff_sim = sims[full_ranking[top_k - 1]]
+            total_players = len(full_ranking)
+            percentile = 100 * (1 - target_full_rank / total_players)
+
+            print(f"\n  --- Target diagnostic ---")
+            print(f"  Target: {target_meta['name']}")
+            print(f"    Primary position: {target_meta['primary_position']}")
+            print(f"    Team: {target_meta['team']}")
+            print(f"    Versatility: {target_meta.get('versatility_score', 'N/A')}")
+            print(f"  Actual rank: #{target_full_rank} of {total_players} players")
+            print(f"  Percentile: top {100 - percentile:.1f}% (higher rank = closer to top)")
+            print(f"  Target similarity: {target_sim:.4f}")
+            print(f"  Top-1 similarity:  {top_sim:.4f}")
+            print(f"  Top-{top_k} cutoff sim:  {cutoff_sim:.4f}")
+            print(f"  Gap to top-{top_k}:    {cutoff_sim - target_sim:.4f}")
+
+            # Show neighborhood around target rank
+            print(f"\n  --- Players ranked around {target_meta['name']} ---")
+            window_start = max(1, target_full_rank - 3)
+            window_end = min(total_players, target_full_rank + 3)
+            print(f"  {'Rank':<6}{'Player':<40}{'Position':<28}{'Sim':<8}")
+            for r in range(window_start, window_end + 1):
+                idx = full_ranking[r - 1]
+                pid = player_ids[idx]
+                meta = metadata[pid]
+                marker = "  ← TARGET" if idx == target_idx_global else ""
+                print(f"  {r:<6}{meta['name']:<40}{meta['primary_position']:<28}{sims[idx]:.4f}{marker}")
+
+            # Where in the ranking thresholds does the target fall?
+            print(f"\n  --- Target presence at various top-K thresholds ---")
+            thresholds = [10, 20, 30, 50, 100, 200, 500]
+            for t in thresholds:
+                in_or_out = "✓ IN" if target_full_rank <= t else "✗ out"
+                print(f"    Top-{t:<4} ({in_or_out})")
+
+    return target_rank
+
+
+# ==================================================================================================
+# THE MANÉ VALIDATION
+# ==================================================================================================
+
+def validate_mane_search():
+    """
+    The main test. Modify Coutinho's docs to add Mané-like behaviors,
+    search for similar players, expect Mané to appear in top-20.
+    """
+    print("\n" + "█" * 105)
+    print("MANÉ VALIDATION — Replacement Search via Document Modification")
+    print("█" * 105)
+
+    player_ids, vectors, metadata, onball_model, offball_model = load_everything()
+
+    # Find Liverpool's 2015-16 attackers as starting points
+    coutinho_id = find_player_id("Coutinho", metadata, player_ids)
+    if not coutinho_id:
+        print("✗ Coutinho not found in dataset. Aborting.")
+        return
+
+    coutinho_meta = metadata[coutinho_id]
+    print(f"\nStarting point: {coutinho_meta['name']} ({coutinho_meta['team']}, {coutinho_meta['primary_position']})")
+    print(f"Versatility score: {coutinho_meta['versatility_score']}")
+
+    # Load all of Coutinho's match documents
+    coutinho_docs = load_player_docs(coutinho_id)
+    print(f"Coutinho match documents: {len(coutinho_docs)}")
+
+    # Filter to Liverpool docs only (he was at Liverpool in 2015-16, but docs include other teams if any)
+    liverpool_docs = [d for d in coutinho_docs if d.get("primary_position")]
+    print(f"Documents to use: {len(liverpool_docs)}")
+
+    # Sample subset of matches (Magdaci samples random matches for stability)
+    sample_size = min(15, len(liverpool_docs))
+    sampled_docs = random.sample(liverpool_docs, sample_size)
+    print(f"Sampling {sample_size} matches for modification")
+
+    # ============== TEST 1: BASELINE (no modifications) ==============
+    print("\n" + "─" * 105)
+    print("TEST 1 — Baseline: Re-infer Coutinho's vector with NO modifications")
+    print("─" * 105)
+    print("This sanity-checks the inference pipeline. Coutinho should be #1 with sim ≈ 1.")
+
+    baseline_vec = infer_player_vector_from_docs(sampled_docs, onball_model, offball_model)
+    rank = search_similar(
+        baseline_vec, vectors, player_ids, metadata,
+        top_k=10, target_name="Coutinho",
+    )
+    if rank == 1:
+        print("\n  ✓ Inference pipeline works correctly")
+    else:
+        print(f"\n  ⚠ Coutinho ranked #{rank} (expected #1) — high noise in inference")
+
+    # ============== TEST 2: ADD CUT-INSIDE BEHAVIOR ==============
+    print("\n" + "─" * 105)
+    print("TEST 2 — Add inverted-winger behavior (cut_inside_right + cut_inside_left)")
+    print("─" * 105)
+    print("Coutinho already cuts inside but we amplify this trait.")
+
+    interventions_cut_inside = add_cut_inside(probability=0.6)
+    modified_docs_v1 = []
+    for doc in sampled_docs:
+        new_doc = dict(doc)
+        new_doc["onball_tokens"] = modify_doc(doc["onball_tokens"], interventions_cut_inside)
+        modified_docs_v1.append(new_doc)
+
+    target_vec = infer_player_vector_from_docs(modified_docs_v1, onball_model, offball_model)
+    search_similar(
+        target_vec, vectors, player_ids, metadata,
+        top_k=20, target_name="Mané",
+    )
+
+    # ============== TEST 3: FULL "KLOPP IDEAL" PROFILE ==============
+    print("\n" + "─" * 105)
+    print("TEST 3 — Full 'Klopp ideal attacker' profile applied to Coutinho")
+    print("─" * 105)
+    print("Interventions: cut_inside + finishing + progression + pressing + chance creation + dribbling")
+
+    klopp_interventions = (
+        add_cut_inside(probability=0.7) +
+        upgrade_finishing(probability=0.5) +
+        add_progression(probability=0.4) +
+        add_chance_creation(probability=0.4) +
+        enrich_dribbling(probability=0.4)
+    )
+    klopp_offball_interventions = boost_pressing(probability=0.7)
+
+    modified_docs_v2 = []
+    for doc in sampled_docs:
+        new_doc = dict(doc)
+        new_doc["onball_tokens"] = modify_doc(doc["onball_tokens"], klopp_interventions)
+        new_doc["offball_tokens"] = modify_doc(doc["offball_tokens"], klopp_offball_interventions)
+        modified_docs_v2.append(new_doc)
+
+    target_vec = infer_player_vector_from_docs(modified_docs_v2, onball_model, offball_model)
+    target_rank = search_similar(
+        target_vec, vectors, player_ids, metadata,
+        top_k=30,
+        exclude_player_ids=[coutinho_id],  # exclude starting point
+        target_name="Mané",
+    )
+
+    # ============== VERDICT ==============
+    print("\n" + "█" * 105)
+    print("VERDICT")
+    print("█" * 105)
+    if target_rank is None:
+        print("  ✗ Mané did not appear in top-30")
+        print("    The replacement search did not find the actual signing.")
+    elif target_rank <= 5:
+        print(f"  ✓✓✓ EXCELLENT — Mané ranked #{target_rank}")
+        print("    The replacement tool concept works exceptionally well.")
+    elif target_rank <= 10:
+        print(f"  ✓✓ STRONG — Mané ranked #{target_rank}")
+        print("    The replacement tool concept works well.")
+    elif target_rank <= 20:
+        print(f"  ✓ ACCEPTABLE — Mané ranked #{target_rank}")
+        print("    The replacement tool concept works, with room to refine.")
+    else:
+        print(f"  ⚠ WEAK — Mané ranked #{target_rank}")
+        print("    The methodology produces some signal but not strong.")
+
+
+# ==================================================================================================
+# GENERALIZATION TEST — works beyond Liverpool?
+# ==================================================================================================
+
+def validate_generalization():
+    """
+    Test the methodology on a non-Liverpool starting point to prove it generalizes.
+    """
+    print("\n" + "█" * 105)
+    print("GENERALIZATION TEST — Does the methodology work for any team?")
+    print("█" * 105)
+
+    player_ids, vectors, metadata, onball_model, offball_model = load_everything()
+
+    # Pick a versatile midfielder/forward who's NOT at Liverpool
+    test_starts = ["Mertens", "Nainggolan", "Modrić"]
+
+    for name in test_starts:
+        pid = find_player_id(name, metadata, player_ids)
+        if pid is None:
+            print(f"\n  ✗ {name} not in dataset, skipping")
+            continue
+
+        meta = metadata[pid]
+        docs = load_player_docs(pid)
+        if len(docs) < 5:
+            print(f"\n  ✗ {meta['name']} has too few matches ({len(docs)}), skipping")
+            continue
+
+        sample_size = min(10, len(docs))
+        sampled = random.sample(docs, sample_size)
+
+        print(f"\n  {meta['name']} ({meta['team']}, {meta['primary_position']}) — {sample_size} matches")
+
+        # Apply the same Klopp-style enrichment
+        interventions = (
+            add_cut_inside(probability=0.5) +
+            add_progression(probability=0.4) +
+            add_chance_creation(probability=0.3)
+        )
+        modified = []
+        for doc in sampled:
+            new_doc = dict(doc)
+            new_doc["onball_tokens"] = modify_doc(doc["onball_tokens"], interventions)
+            modified.append(new_doc)
+
+        target_vec = infer_player_vector_from_docs(modified, onball_model, offball_model)
+
+        print(f"\n  Top-10 most similar to enriched {meta['name']}:")
+        search_similar(
+            target_vec, vectors, player_ids, metadata,
+            top_k=10, exclude_player_ids=[pid],
+        )
+
+
+# ==================================================================================================
+# APPROACH B — MULTI-SOURCE VALIDATION
+# ==================================================================================================
+#
+# Insight: Mané at Southampton played LW/RW/CAM/CF — he's a multi-zone attacker.
+# Searching with only Coutinho's docs anchors the query in "left winger" space.
+# Solution: Pool documents from Liverpool attackers covering DIFFERENT roles, then
+# apply Klopp-style modifications to the pooled set.
+#
+# This matches how Klopp actually scouted Mané — not as "a left winger replacement"
+# but as "a versatile attacker who can play anywhere in the final third."
+#
+# ==================================================================================================
+
+LIVERPOOL_MULTI_ROLE_SOURCES = [
+    # Format: (name_query, matches_per_source, role_description)
+    ("Coutinho",  8, "LW creator"),
+    ("Lallana",   8, "CAM/inside forward"),
+    ("Firmino",   8, "CF/false 9"),
+    ("Sturridge", 5, "CF finisher"),
+    ("Origi",     5, "Versatile attacker"),
+    ("Ibe",       5, "RW/LW pace"),
+    ("Benteke",   5, "Target CF"),
+]
+
+
+def gather_multi_source_docs(
+    sources_config,
+    metadata: dict,
+    player_ids: list,
+    cap_per_source: int = 8,
+):
+    """
+    Pool match documents from multiple Liverpool attackers covering different roles.
+
+    Returns (pooled_docs, source_summary) where source_summary describes which players contributed.
+    """
+    pooled = []
+    summary = []
+
+    for name, target_count, role_desc in sources_config:
+        pid = find_player_id(name, metadata, player_ids)
+        if pid is None:
+            summary.append((name, role_desc, 0, "not found in dataset"))
+            continue
+
+        meta = metadata[pid]
+        # Only use Liverpool docs — but our metadata has team-level info,
+        # not per-match team. We use the player's primary team.
+        if "Liverpool" not in meta.get("team", ""):
+            # Player primary team isn't Liverpool — skip to avoid noise
+            summary.append((name, role_desc, 0, f"primary team is {meta.get('team', 'Unknown')}"))
+            continue
+
+        docs = load_player_docs(pid)
+        if not docs:
+            summary.append((name, role_desc, 0, "no match docs"))
+            continue
+
+        # Take up to cap_per_source matches
+        n_to_take = min(target_count, cap_per_source, len(docs))
+        sampled = random.sample(docs, n_to_take)
+        pooled.extend(sampled)
+
+        summary.append((meta["name"], role_desc, n_to_take, "OK"))
+
+    return pooled, summary
+
+
+def validate_mane_multi_source():
+    """
+    The Mané search using Approach B: multi-source document pooling from Liverpool attackers.
+
+    Steps:
+        1. Pool docs from Coutinho/Lallana/Firmino/Sturridge/Origi/Ibe (whoever's in dataset)
+        2. Apply Klopp-style interventions to ALL pooled docs
+        3. Re-infer a single target vector from the pooled modified docs
+        4. Search top-30
+        5. Report Mané's rank + position distribution of results
+    """
+    print("\n" + "█" * 105)
+    print("APPROACH B — MULTI-SOURCE VALIDATION")
+    print("Pooling docs from multiple Liverpool attackers covering different roles")
+    print("█" * 105)
+
+    player_ids, vectors, metadata, onball_model, offball_model = load_everything()
+
+    # ----- 1. Gather pooled docs -----
+    print("\nGathering source documents from Liverpool attackers:")
+    pooled_docs, summary = gather_multi_source_docs(
+        LIVERPOOL_MULTI_ROLE_SOURCES, metadata, player_ids, cap_per_source=8,
+    )
+
+    print(f"\n{'Player':<35}{'Role':<28}{'Matches':<10}{'Status':<25}")
+    print("-" * 100)
+    for name, role, count, status in summary:
+        print(f"{name:<35}{role:<28}{count:<10}{status:<25}")
+    print(f"\nTotal pooled documents: {len(pooled_docs)}")
+
+    if len(pooled_docs) < 10:
+        print("⚠ Too few pooled docs — aborting multi-source test")
+        return
+
+    # ----- 2. Apply Klopp interventions to ALL pooled docs -----
+    print("\nApplying Klopp-style interventions to pooled documents...")
+    onball_interventions = (
+        add_cut_inside(probability=0.7)
+        + upgrade_finishing(probability=0.5)
+        + add_progression(probability=0.4)
+        + add_chance_creation(probability=0.4)
+        + enrich_dribbling(probability=0.4)
+    )
+    offball_interventions = boost_pressing(probability=0.7)
+
+    modified_pooled = []
+    for doc in pooled_docs:
+        new_doc = dict(doc)
+        if doc.get("onball_tokens"):
+            new_doc["onball_tokens"] = modify_doc(doc["onball_tokens"], onball_interventions)
+        if doc.get("offball_tokens"):
+            new_doc["offball_tokens"] = modify_doc(doc["offball_tokens"], offball_interventions)
+        modified_pooled.append(new_doc)
+
+    # ----- 3. Re-infer a single target vector -----
+    print("Inferring multi-source target vector...")
+    target_vec = infer_player_vector_from_docs(modified_pooled, onball_model, offball_model)
+
+    # ----- 4. Search -----
+    # Exclude the source players from results so they don't dominate
+    source_pids = []
+    for name, _, _, status in summary:
+        if status == "OK":
+            pid = find_player_id(name, metadata, player_ids)
+            if pid:
+                source_pids.append(pid)
+
+    print(f"\nExcluding {len(source_pids)} source players from results.")
+    print("\nTop-30 most similar players to 'multi-zone Klopp ideal attacker':")
+
+    target_rank = search_similar(
+        target_vec, vectors, player_ids, metadata,
+        top_k=30,
+        exclude_player_ids=source_pids,
+        target_name="Mané",
+    )
+
+    # ----- 5. Position distribution analysis -----
+    print("\n--- Position distribution of top-30 results ---")
+    sims = vectors @ target_vec
+    for pid in source_pids:
+        if pid in player_ids:
+            sims[player_ids.index(pid)] = -np.inf
+    top30_indices = np.argsort(sims)[::-1][:30]
+    position_counts = {}
+    for idx in top30_indices:
+        pos = metadata[player_ids[idx]]["primary_position"]
+        position_counts[pos] = position_counts.get(pos, 0) + 1
+    sorted_positions = sorted(position_counts.items(), key=lambda x: -x[1])
+    for pos, count in sorted_positions:
+        bar = "█" * count
+        print(f"  {pos:<28} {count:<3} {bar}")
+
+    # ----- 6. Versatility check on top-30 -----
+    print("\n--- Versatility scores of top-30 results ---")
+    versatility_scores = []
+    for idx in top30_indices:
+        v = metadata[player_ids[idx]].get("versatility_score", 0.0)
+        versatility_scores.append(v)
+    print(f"  Mean versatility: {np.mean(versatility_scores):.3f}")
+    print(f"  Max versatility:  {np.max(versatility_scores):.3f}")
+    print(f"  Median versatility: {np.median(versatility_scores):.3f}")
+
+    mane_pid = find_player_id("Mané", metadata, player_ids)
+    if mane_pid:
+        mane_versatility = metadata[mane_pid].get("versatility_score", 0.0)
+        print(f"  Mané's versatility (for context): {mane_versatility:.3f}")
+
+    # ----- 7. Verdict -----
+    print("\n" + "█" * 105)
+    print("APPROACH B VERDICT")
+    print("█" * 105)
+    if target_rank is None:
+        print("  ✗ Mané still not in top-30 with multi-source pooling")
+        print("    The query may need per-position vectors (Path B from earlier discussion)")
+    elif target_rank <= 5:
+        print(f"  ✓✓✓ EXCELLENT — Mané ranked #{target_rank}")
+        print("    Multi-source pooling solved the versatility issue. Tool concept validated.")
+    elif target_rank <= 10:
+        print(f"  ✓✓ STRONG — Mané ranked #{target_rank}")
+        print("    Multi-source approach works well for versatile player searches.")
+    elif target_rank <= 20:
+        print(f"  ✓ ACCEPTABLE — Mané ranked #{target_rank}")
+        print("    Methodology produces real signal for versatile players.")
+    else:
+        print(f"  ⚠ MARGINAL — Mané ranked #{target_rank}")
+        print("    Multi-source helps but may need additional refinement.")
+
+
+# ==================================================================================================
+# MAIN
+# ==================================================================================================
+
+def main():
+    random.seed(42)  # reproducibility
+    validate_mane_search()
+    validate_mane_multi_source()
+    validate_generalization()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp2/evaluation/mane_search_v2.py b/src/gp2/evaluation/mane_search_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..59697539b0f3db8e22900953487004a6c69309cb
--- /dev/null
+++ b/src/gp2/evaluation/mane_search_v2.py
@@ -0,0 +1,842 @@
+"""
+The Mané Validation — Modify Liverpool Player Documents, Search for Upgrades
+
+This is the REAL validation of our scouting tool concept.
+
+Approach (Magdaci-style):
+    1. Pick a Liverpool 2015-16 attacker (Coutinho, Lallana, Sturridge)
+    2. Sample N of their match documents
+    3. Apply interventions that move them toward "ideal Klopp attacker":
+        - add_cut_inside (inverted winger behavior)
+        - upgrade_finishing (more clinical)
+        - add_progression (more direct progressive carries)
+        - boost_pressing (gegenpressing)
+    4. Re-infer vectors from modified docs (one vector per modified match)
+    5. Average those vectors → "upgraded Coutinho" target vector
+    6. Search top-50 most similar players in the entire 2015-16 dataset
+    7. Validate: Mané should appear in top-20
+
+Generalization test:
+    Same approach but for Mertens (Napoli LW) and Modrić — does the methodology
+    extend beyond Liverpool? If yes, the tool generalizes.
+"""
+
+import json
+import numpy as np
+import random
+from typing import List
+
+from gensim.models.doc2vec import Doc2Vec
+
+from .modify_doc import (
+    Intervention,
+    add_cut_inside,
+    upgrade_finishing,
+    add_progression,
+    boost_pressing,
+    add_chance_creation,
+    enrich_dribbling,
+    modify_doc,
+)
+from src.gp2.paths import (
+    DOCS_PATH,
+    OFFBALL_MODEL_PATH,
+    ONBALL_MODEL_PATH,
+    PLAYER2VEC_PATH,
+    PLAYER_METADATA_PATH,
+)
+
+
+# ==================================================================================================
+# DATA LOADING
+# ==================================================================================================
+
+def load_everything():
+    """Load Player2Vec + metadata + Doc2Vec models."""
+    data = np.load(PLAYER2VEC_PATH)
+    player_ids = list(data["player_ids"])
+    vectors = data["vectors"]
+
+    with open(PLAYER_METADATA_PATH, "r", encoding="utf-8") as f:
+        metadata = json.load(f)
+
+    print("Loading Doc2Vec models...")
+    onball_model = Doc2Vec.load(str(ONBALL_MODEL_PATH))
+    offball_model = Doc2Vec.load(str(OFFBALL_MODEL_PATH))
+
+    return player_ids, vectors, metadata, onball_model, offball_model
+
+
+def load_player_docs(player_id: str) -> List[dict]:
+    """Load all match docs for a specific player."""
+    docs = []
+    with open(DOCS_PATH, "r", encoding="utf-8") as f:
+        for line in f:
+            d = json.loads(line.strip())
+            if d["player_id"] == player_id:
+                docs.append(d)
+    return docs
+
+
+def find_player_id(name_query: str, metadata: dict, player_ids: list) -> str:
+    """Find player_id by partial name match. Returns None if not found."""
+    query = name_query.lower()
+    matches = []
+    for pid in player_ids:
+        name = metadata.get(pid, {}).get("name", "")
+        if query in name.lower():
+            matches.append((pid, name))
+    if not matches:
+        return None
+    if len(matches) > 1:
+        print(f"  ⚠ Ambiguous '{name_query}', using first: {matches[0][1]}")
+    return matches[0][0]
+
+
+# ==================================================================================================
+# VECTOR INFERENCE FROM MODIFIED DOCS
+# ==================================================================================================
+
+def infer_player_vector_from_docs(
+    docs: List[dict],
+    onball_model: Doc2Vec,
+    offball_model: Doc2Vec,
+    onball_dim: int = 48,
+    offball_dim: int = 16,
+) -> np.ndarray:
+    """
+    Re-infer a player vector from modified docs.
+    For each doc: infer onball + offball vectors, concatenate, then average across docs.
+    """
+    onball_vecs = []
+    offball_vecs = []
+
+    for doc in docs:
+        if doc.get("onball_tokens"):
+            onball_vecs.append(onball_model.infer_vector(doc["onball_tokens"]))
+        if doc.get("offball_tokens"):
+            offball_vecs.append(offball_model.infer_vector(doc["offball_tokens"]))
+
+    if not onball_vecs:
+        raise ValueError("No on-ball tokens to infer from")
+
+    mean_onball = np.mean(onball_vecs, axis=0)
+    mean_offball = (
+        np.mean(offball_vecs, axis=0) if offball_vecs else np.zeros(offball_dim)
+    )
+
+    combined = np.concatenate([mean_onball, mean_offball])
+    return combined / np.linalg.norm(combined)
+
+
+# ==================================================================================================
+# SEARCH
+# ==================================================================================================
+
+def search_similar(
+    target_vec: np.ndarray,
+    vectors: np.ndarray,
+    player_ids: list,
+    metadata: dict,
+    top_k: int = 30,
+    exclude_player_ids: list = None,
+    target_name: str = None,
+    show_target_neighborhood: bool = True,
+):
+    """Find top-K most similar players to target_vec.
+
+    If target_name is given but doesn't appear in top-K, also reports:
+        - The target's actual rank
+        - Its similarity score
+        - Players ranked just above and below it
+    """
+    sims = vectors @ target_vec
+
+    if exclude_player_ids:
+        for pid in exclude_player_ids:
+            if pid in player_ids:
+                sims[player_ids.index(pid)] = -np.inf
+
+    # Full ranking (we use this for diagnostics if target isn't in top-K)
+    full_ranking = np.argsort(sims)[::-1]
+    top_indices = full_ranking[:top_k]
+
+    print(f"\n{'Rank':<6}{'Player':<40}{'Position':<28}{'Team':<24}{'Sim':<8}")
+    print("-" * 105)
+
+    target_rank = None
+    target_idx = None
+    for rank, idx in enumerate(top_indices, 1):
+        pid = player_ids[idx]
+        meta = metadata[pid]
+        marker = ""
+        if target_name and target_name.lower() in meta["name"].lower():
+            target_rank = rank
+            target_idx = idx
+            marker = "  ← TARGET ✓"
+        print(f"{rank:<6}{meta['name']:<40}{meta['primary_position']:<28}{meta['team']:<24}{sims[idx]:.4f}{marker}")
+
+    # If the target wasn't found in top_k, dig deeper to locate it
+    if target_name and target_rank is None and show_target_neighborhood:
+        # Find the target's actual rank in the full ranking
+        target_full_rank = None
+        target_idx_global = None
+        for full_rank_idx, idx in enumerate(full_ranking, 1):
+            pid = player_ids[idx]
+            meta = metadata[pid]
+            if target_name.lower() in meta["name"].lower():
+                target_full_rank = full_rank_idx
+                target_idx_global = idx
+                break
+
+        if target_full_rank is None:
+            print(f"\n  ⚠ Target '{target_name}' not found in dataset at all (excluded or missing).")
+        else:
+            target_meta = metadata[player_ids[target_idx_global]]
+            target_sim = sims[target_idx_global]
+            top_sim = sims[full_ranking[0]]
+            cutoff_sim = sims[full_ranking[top_k - 1]]
+            total_players = len(full_ranking)
+            percentile = 100 * (1 - target_full_rank / total_players)
+
+            print(f"\n  --- Target diagnostic ---")
+            print(f"  Target: {target_meta['name']}")
+            print(f"    Primary position: {target_meta['primary_position']}")
+            print(f"    Team: {target_meta['team']}")
+            print(f"    Versatility: {target_meta.get('versatility_score', 'N/A')}")
+            print(f"  Actual rank: #{target_full_rank} of {total_players} players")
+            print(f"  Percentile: top {100 - percentile:.1f}% (higher rank = closer to top)")
+            print(f"  Target similarity: {target_sim:.4f}")
+            print(f"  Top-1 similarity:  {top_sim:.4f}")
+            print(f"  Top-{top_k} cutoff sim:  {cutoff_sim:.4f}")
+            print(f"  Gap to top-{top_k}:    {cutoff_sim - target_sim:.4f}")
+
+            # Show neighborhood around target rank
+            print(f"\n  --- Players ranked around {target_meta['name']} ---")
+            window_start = max(1, target_full_rank - 3)
+            window_end = min(total_players, target_full_rank + 3)
+            print(f"  {'Rank':<6}{'Player':<40}{'Position':<28}{'Sim':<8}")
+            for r in range(window_start, window_end + 1):
+                idx = full_ranking[r - 1]
+                pid = player_ids[idx]
+                meta = metadata[pid]
+                marker = "  ← TARGET" if idx == target_idx_global else ""
+                print(f"  {r:<6}{meta['name']:<40}{meta['primary_position']:<28}{sims[idx]:.4f}{marker}")
+
+            # Where in the ranking thresholds does the target fall?
+            print(f"\n  --- Target presence at various top-K thresholds ---")
+            thresholds = [10, 20, 30, 50, 100, 200, 500]
+            for t in thresholds:
+                in_or_out = "✓ IN" if target_full_rank <= t else "✗ out"
+                print(f"    Top-{t:<4} ({in_or_out})")
+
+    return target_rank
+
+
+# ==================================================================================================
+# THE MANÉ VALIDATION
+# ==================================================================================================
+
+def validate_mane_search():
+    """
+    The main test. Modify Coutinho's docs to add Mané-like behaviors,
+    search for similar players, expect Mané to appear in top-20.
+    """
+    print("\n" + "█" * 105)
+    print("MANÉ VALIDATION — Replacement Search via Document Modification")
+    print("█" * 105)
+
+    player_ids, vectors, metadata, onball_model, offball_model = load_everything()
+
+    # Find Liverpool's 2015-16 attackers as starting points
+    coutinho_id = find_player_id("Coutinho", metadata, player_ids)
+    if not coutinho_id:
+        print("✗ Coutinho not found in dataset. Aborting.")
+        return
+
+    coutinho_meta = metadata[coutinho_id]
+    print(f"\nStarting point: {coutinho_meta['name']} ({coutinho_meta['team']}, {coutinho_meta['primary_position']})")
+    print(f"Versatility score: {coutinho_meta['versatility_score']}")
+
+    # Load all of Coutinho's match documents
+    coutinho_docs = load_player_docs(coutinho_id)
+    print(f"Coutinho match documents: {len(coutinho_docs)}")
+
+    # Filter to Liverpool docs only (he was at Liverpool in 2015-16, but docs include other teams if any)
+    liverpool_docs = [d for d in coutinho_docs if d.get("primary_position")]
+    print(f"Documents to use: {len(liverpool_docs)}")
+
+    # Sample subset of matches (Magdaci samples random matches for stability)
+    sample_size = min(15, len(liverpool_docs))
+    sampled_docs = random.sample(liverpool_docs, sample_size)
+    print(f"Sampling {sample_size} matches for modification")
+
+    # ============== TEST 1: BASELINE (no modifications) ==============
+    print("\n" + "─" * 105)
+    print("TEST 1 — Baseline: Re-infer Coutinho's vector with NO modifications")
+    print("─" * 105)
+    print("This sanity-checks the inference pipeline. Coutinho should be #1 with sim ≈ 1.")
+
+    baseline_vec = infer_player_vector_from_docs(sampled_docs, onball_model, offball_model)
+    rank = search_similar(
+        baseline_vec, vectors, player_ids, metadata,
+        top_k=10, target_name="Coutinho",
+    )
+    if rank == 1:
+        print("\n  ✓ Inference pipeline works correctly")
+    else:
+        print(f"\n  ⚠ Coutinho ranked #{rank} (expected #1) — high noise in inference")
+
+    # ============== TEST 2: ADD CUT-INSIDE BEHAVIOR ==============
+    print("\n" + "─" * 105)
+    print("TEST 2 — Add inverted-winger behavior (cut_inside_right + cut_inside_left)")
+    print("─" * 105)
+    print("Coutinho already cuts inside but we amplify this trait.")
+
+    interventions_cut_inside = add_cut_inside(probability=0.6)
+    modified_docs_v1 = []
+    for doc in sampled_docs:
+        new_doc = dict(doc)
+        new_doc["onball_tokens"] = modify_doc(doc["onball_tokens"], interventions_cut_inside)
+        modified_docs_v1.append(new_doc)
+
+    target_vec = infer_player_vector_from_docs(modified_docs_v1, onball_model, offball_model)
+    search_similar(
+        target_vec, vectors, player_ids, metadata,
+        top_k=20, target_name="Mané",
+    )
+
+    # ============== TEST 3: FULL "KLOPP IDEAL" PROFILE ==============
+    print("\n" + "─" * 105)
+    print("TEST 3 — Full 'Klopp ideal attacker' profile applied to Coutinho")
+    print("─" * 105)
+    print("Interventions: cut_inside + finishing + progression + pressing + chance creation + dribbling")
+
+    klopp_interventions = (
+        add_cut_inside(probability=0.7) +
+        upgrade_finishing(probability=0.5) +
+        add_progression(probability=0.4) +
+        add_chance_creation(probability=0.4) +
+        enrich_dribbling(probability=0.4)
+    )
+    klopp_offball_interventions = boost_pressing(probability=0.7)
+
+    modified_docs_v2 = []
+    for doc in sampled_docs:
+        new_doc = dict(doc)
+        new_doc["onball_tokens"] = modify_doc(doc["onball_tokens"], klopp_interventions)
+        new_doc["offball_tokens"] = modify_doc(doc["offball_tokens"], klopp_offball_interventions)
+        modified_docs_v2.append(new_doc)
+
+    target_vec = infer_player_vector_from_docs(modified_docs_v2, onball_model, offball_model)
+    target_rank = search_similar(
+        target_vec, vectors, player_ids, metadata,
+        top_k=30,
+        exclude_player_ids=[coutinho_id],  # exclude starting point
+        target_name="Mané",
+    )
+
+    # ============== VERDICT ==============
+    print("\n" + "█" * 105)
+    print("VERDICT")
+    print("█" * 105)
+    if target_rank is None:
+        print("  ✗ Mané did not appear in top-30")
+        print("    The replacement search did not find the actual signing.")
+    elif target_rank <= 5:
+        print(f"  ✓✓✓ EXCELLENT — Mané ranked #{target_rank}")
+        print("    The replacement tool concept works exceptionally well.")
+    elif target_rank <= 10:
+        print(f"  ✓✓ STRONG — Mané ranked #{target_rank}")
+        print("    The replacement tool concept works well.")
+    elif target_rank <= 20:
+        print(f"  ✓ ACCEPTABLE — Mané ranked #{target_rank}")
+        print("    The replacement tool concept works, with room to refine.")
+    else:
+        print(f"  ⚠ WEAK — Mané ranked #{target_rank}")
+        print("    The methodology produces some signal but not strong.")
+
+
+# ==================================================================================================
+# GENERALIZATION TEST — works beyond Liverpool?
+# ==================================================================================================
+
+def validate_generalization():
+    """
+    Test the methodology on a non-Liverpool starting point to prove it generalizes.
+    """
+    print("\n" + "█" * 105)
+    print("GENERALIZATION TEST — Does the methodology work for any team?")
+    print("█" * 105)
+
+    player_ids, vectors, metadata, onball_model, offball_model = load_everything()
+
+    # Pick a versatile midfielder/forward who's NOT at Liverpool
+    test_starts = ["Mertens", "Nainggolan", "Modrić"]
+
+    for name in test_starts:
+        pid = find_player_id(name, metadata, player_ids)
+        if pid is None:
+            print(f"\n  ✗ {name} not in dataset, skipping")
+            continue
+
+        meta = metadata[pid]
+        docs = load_player_docs(pid)
+        if len(docs) < 5:
+            print(f"\n  ✗ {meta['name']} has too few matches ({len(docs)}), skipping")
+            continue
+
+        sample_size = min(10, len(docs))
+        sampled = random.sample(docs, sample_size)
+
+        print(f"\n  {meta['name']} ({meta['team']}, {meta['primary_position']}) — {sample_size} matches")
+
+        # Apply the same Klopp-style enrichment
+        interventions = (
+            add_cut_inside(probability=0.5) +
+            add_progression(probability=0.4) +
+            add_chance_creation(probability=0.3)
+        )
+        modified = []
+        for doc in sampled:
+            new_doc = dict(doc)
+            new_doc["onball_tokens"] = modify_doc(doc["onball_tokens"], interventions)
+            modified.append(new_doc)
+
+        target_vec = infer_player_vector_from_docs(modified, onball_model, offball_model)
+
+        print(f"\n  Top-10 most similar to enriched {meta['name']}:")
+        search_similar(
+            target_vec, vectors, player_ids, metadata,
+            top_k=10, exclude_player_ids=[pid],
+        )
+
+
+# ==================================================================================================
+# APPROACH B — MULTI-SOURCE VALIDATION
+# ==================================================================================================
+#
+# Insight: Mané at Southampton played LW/RW/CAM/CF — he's a multi-zone attacker.
+# Searching with only Coutinho's docs anchors the query in "left winger" space.
+# Solution: Pool documents from Liverpool attackers covering DIFFERENT roles, then
+# apply Klopp-style modifications to the pooled set.
+#
+# This matches how Klopp actually scouted Mané — not as "a left winger replacement"
+# but as "a versatile attacker who can play anywhere in the final third."
+#
+# ==================================================================================================
+
+LIVERPOOL_MULTI_ROLE_SOURCES = [
+    # Format: (name_query, matches_per_source, role_description)
+    ("Coutinho",  8, "LW creator"),
+    ("Lallana",   8, "CAM/inside forward"),
+    ("Firmino",   8, "CF/false 9"),
+    ("Sturridge", 5, "CF finisher"),
+    ("Origi",     5, "Versatile attacker"),
+    ("Ibe",       5, "RW/LW pace"),
+    ("Benteke",   5, "Target CF"),
+]
+
+
+# ==================================================================================================
+# TOKEN-BUDGET POOLING — equalized contribution per role
+# ==================================================================================================
+
+ROLE_TOKEN_BUDGETS = {
+    # Each role contributes the same total tokens to the pooled query,
+    # regardless of how many matches the player has or how event-dense they were.
+    # This prevents Coutinho (high event volume) from dominating the multi-source query.
+    "onball_per_role":  400,
+    "offball_per_role": 100,
+}
+
+
+def gather_multi_source_docs_equalized(
+    sources_config,
+    metadata: dict,
+    player_ids: list,
+    onball_budget: int = 400,
+    offball_budget: int = 100,
+):
+    """
+    Pool documents from multiple sources but cap total tokens per role.
+
+    For each source player:
+        - Use ALL their available match docs
+        - Concatenate all their onball tokens, shuffle, truncate to onball_budget
+        - Same for offball
+    This produces ONE merged "role document" per source player, all contributing
+    equally to the final pooled query.
+
+    Returns (pooled_role_docs, summary) where each role_doc is a synthetic
+    document representing one role's equalized contribution.
+    """
+    pooled_role_docs = []
+    summary = []
+
+    for name, _target_count_unused, role_desc in sources_config:
+        pid = find_player_id(name, metadata, player_ids)
+        if pid is None:
+            summary.append((name, role_desc, 0, 0, "not found"))
+            continue
+
+        meta = metadata[pid]
+        if "Liverpool" not in meta.get("team", ""):
+            summary.append((name, role_desc, 0, 0, f"primary team is {meta.get('team', 'Unknown')}"))
+            continue
+
+        docs = load_player_docs(pid)
+        if not docs:
+            summary.append((name, role_desc, 0, 0, "no match docs"))
+            continue
+
+        # Concatenate ALL onball and offball tokens from ALL matches
+        all_onball = []
+        all_offball = []
+        for doc in docs:
+            all_onball.extend(doc.get("onball_tokens", []) or [])
+            all_offball.extend(doc.get("offball_tokens", []) or [])
+
+        # Shuffle so the truncation isn't biased toward early-match tokens
+        random.shuffle(all_onball)
+        random.shuffle(all_offball)
+
+        # Truncate to the role budget — equalized contribution
+        truncated_onball = all_onball[:onball_budget]
+        truncated_offball = all_offball[:offball_budget]
+
+        # Build one synthetic "role document"
+        role_doc = {
+            "player_id": pid,
+            "match_id": f"{pid}_pooled",
+            "primary_position": meta.get("primary_position", "Unknown"),
+            "onball_tokens": truncated_onball,
+            "offball_tokens": truncated_offball,
+        }
+        pooled_role_docs.append(role_doc)
+
+        summary.append((
+            meta["name"],
+            role_desc,
+            len(truncated_onball),
+            len(truncated_offball),
+            f"OK ({len(docs)} matches, {len(all_onball)} total onball)"
+        ))
+
+    return pooled_role_docs, summary
+
+
+def validate_mane_multi_source_equalized():
+    """
+    Approach B-Equalized: Same multi-source pooling as Approach B, but every role
+    contributes the same total tokens. This neutralizes Coutinho's volume bias.
+    """
+    print("\n" + "█" * 105)
+    print("APPROACH B-EQUALIZED — Multi-Source with Equalized Token Contribution Per Role")
+    print("Each Liverpool attacker contributes the same number of tokens, regardless of match count")
+    print("█" * 105)
+
+    player_ids, vectors, metadata, onball_model, offball_model = load_everything()
+
+    # ----- 1. Gather equalized role docs -----
+    print("\nGathering equalized role documents:")
+    role_docs, summary = gather_multi_source_docs_equalized(
+        LIVERPOOL_MULTI_ROLE_SOURCES,
+        metadata,
+        player_ids,
+        onball_budget=ROLE_TOKEN_BUDGETS["onball_per_role"],
+        offball_budget=ROLE_TOKEN_BUDGETS["offball_per_role"],
+    )
+
+    print(f"\n{'Player':<35}{'Role':<28}{'Onball':<10}{'Offball':<10}{'Status':<40}")
+    print("-" * 125)
+    for name, role, ob, off, status in summary:
+        print(f"{name:<35}{role:<28}{ob:<10}{off:<10}{status:<40}")
+
+    if len(role_docs) < 3:
+        print("⚠ Too few role docs — aborting equalized test")
+        return
+
+    print(f"\nTotal role docs: {len(role_docs)}")
+    print(f"Each role contributes ≤ {ROLE_TOKEN_BUDGETS['onball_per_role']} onball + "
+          f"≤ {ROLE_TOKEN_BUDGETS['offball_per_role']} offball tokens")
+
+    # ----- 2. Apply Klopp interventions -----
+    print("\nApplying Klopp-style interventions to equalized role documents...")
+    onball_interventions = (
+        add_cut_inside(probability=0.7)
+        + upgrade_finishing(probability=0.5)
+        + add_progression(probability=0.4)
+        + add_chance_creation(probability=0.4)
+        + enrich_dribbling(probability=0.4)
+    )
+    offball_interventions = boost_pressing(probability=0.7)
+
+    modified_role_docs = []
+    for doc in role_docs:
+        new_doc = dict(doc)
+        if doc.get("onball_tokens"):
+            new_doc["onball_tokens"] = modify_doc(doc["onball_tokens"], onball_interventions)
+        if doc.get("offball_tokens"):
+            new_doc["offball_tokens"] = modify_doc(doc["offball_tokens"], offball_interventions)
+        modified_role_docs.append(new_doc)
+
+    # ----- 3. Re-infer target vector -----
+    print("Inferring equalized multi-source target vector...")
+    target_vec = infer_player_vector_from_docs(modified_role_docs, onball_model, offball_model)
+
+    # ----- 4. Search -----
+    source_pids = []
+    for name, _, _, _, status in summary:
+        if status.startswith("OK"):
+            pid = find_player_id(name, metadata, player_ids)
+            if pid:
+                source_pids.append(pid)
+
+    print(f"\nExcluding {len(source_pids)} source players from results.")
+    print("\nTop-30 most similar players to 'EQUALIZED multi-zone Klopp ideal attacker':")
+
+    target_rank = search_similar(
+        target_vec, vectors, player_ids, metadata,
+        top_k=30,
+        exclude_player_ids=source_pids,
+        target_name="Mané",
+    )
+
+    # ----- 5. Position distribution -----
+    print("\n--- Position distribution of top-30 results ---")
+    sims = vectors @ target_vec
+    for pid in source_pids:
+        if pid in player_ids:
+            sims[player_ids.index(pid)] = -np.inf
+    top30_indices = np.argsort(sims)[::-1][:30]
+    position_counts = {}
+    for idx in top30_indices:
+        pos = metadata[player_ids[idx]]["primary_position"]
+        position_counts[pos] = position_counts.get(pos, 0) + 1
+    sorted_positions = sorted(position_counts.items(), key=lambda x: -x[1])
+    for pos, count in sorted_positions:
+        bar = "█" * count
+        print(f"  {pos:<28} {count:<3} {bar}")
+
+    # ----- 6. Versatility check -----
+    print("\n--- Versatility scores of top-30 results ---")
+    versatility_scores = []
+    for idx in top30_indices:
+        v = metadata[player_ids[idx]].get("versatility_score", 0.0)
+        versatility_scores.append(v)
+    print(f"  Mean versatility:   {np.mean(versatility_scores):.3f}")
+    print(f"  Max versatility:    {np.max(versatility_scores):.3f}")
+    print(f"  Median versatility: {np.median(versatility_scores):.3f}")
+
+    mane_pid = find_player_id("Mané", metadata, player_ids)
+    if mane_pid:
+        mane_versatility = metadata[mane_pid].get("versatility_score", 0.0)
+        print(f"  Mané's versatility: {mane_versatility:.3f}")
+
+    # ----- 7. Verdict -----
+    print("\n" + "█" * 105)
+    print("APPROACH B-EQUALIZED VERDICT")
+    print("█" * 105)
+    if target_rank is None:
+        print("  Mané not in top-30. See diagnostic above for actual rank.")
+    elif target_rank <= 5:
+        print(f"  ✓✓✓ EXCELLENT — Mané ranked #{target_rank}")
+    elif target_rank <= 10:
+        print(f"  ✓✓ STRONG — Mané ranked #{target_rank}")
+    elif target_rank <= 20:
+        print(f"  ✓ GOOD — Mané ranked #{target_rank}")
+    elif target_rank <= 30:
+        print(f"  ⚠ MARGINAL — Mané ranked #{target_rank}")
+    else:
+        print(f"  ⚠ Mané ranked #{target_rank}")
+
+
+# ==================================================================================================
+# MAIN
+# ==================================================================================================
+
+def main():
+    random.seed(42)  # reproducibility
+    validate_mane_search()
+    validate_mane_multi_source_equalized()
+    validate_generalization()
+
+
+if __name__ == "__main__":
+    main()
+
+
+def gather_multi_source_docs(
+    sources_config,
+    metadata: dict,
+    player_ids: list,
+    cap_per_source: int = 8,
+):
+    """
+    Pool match documents from multiple Liverpool attackers covering different roles.
+
+    Returns (pooled_docs, source_summary) where source_summary describes which players contributed.
+    """
+    pooled = []
+    summary = []
+
+    for name, target_count, role_desc in sources_config:
+        pid = find_player_id(name, metadata, player_ids)
+        if pid is None:
+            summary.append((name, role_desc, 0, "not found in dataset"))
+            continue
+
+        meta = metadata[pid]
+        # Only use Liverpool docs — but our metadata has team-level info,
+        # not per-match team. We use the player's primary team.
+        if "Liverpool" not in meta.get("team", ""):
+            # Player primary team isn't Liverpool — skip to avoid noise
+            summary.append((name, role_desc, 0, f"primary team is {meta.get('team', 'Unknown')}"))
+            continue
+
+        docs = load_player_docs(pid)
+        if not docs:
+            summary.append((name, role_desc, 0, "no match docs"))
+            continue
+
+        # Take up to cap_per_source matches
+        n_to_take = min(target_count, cap_per_source, len(docs))
+        sampled = random.sample(docs, n_to_take)
+        pooled.extend(sampled)
+
+        summary.append((meta["name"], role_desc, n_to_take, "OK"))
+
+    return pooled, summary
+
+
+def validate_mane_multi_source():
+    """
+    The Mané search using Approach B: multi-source document pooling from Liverpool attackers.
+
+    Steps:
+        1. Pool docs from Coutinho/Lallana/Firmino/Sturridge/Origi/Ibe (whoever's in dataset)
+        2. Apply Klopp-style interventions to ALL pooled docs
+        3. Re-infer a single target vector from the pooled modified docs
+        4. Search top-30
+        5. Report Mané's rank + position distribution of results
+    """
+    print("\n" + "█" * 105)
+    print("APPROACH B — MULTI-SOURCE VALIDATION")
+    print("Pooling docs from multiple Liverpool attackers covering different roles")
+    print("█" * 105)
+
+    player_ids, vectors, metadata, onball_model, offball_model = load_everything()
+
+    # ----- 1. Gather pooled docs -----
+    print("\nGathering source documents from Liverpool attackers:")
+    pooled_docs, summary = gather_multi_source_docs(
+        LIVERPOOL_MULTI_ROLE_SOURCES, metadata, player_ids, cap_per_source=8,
+    )
+
+    print(f"\n{'Player':<35}{'Role':<28}{'Matches':<10}{'Status':<25}")
+    print("-" * 100)
+    for name, role, count, status in summary:
+        print(f"{name:<35}{role:<28}{count:<10}{status:<25}")
+    print(f"\nTotal pooled documents: {len(pooled_docs)}")
+
+    if len(pooled_docs) < 10:
+        print("⚠ Too few pooled docs — aborting multi-source test")
+        return
+
+    # ----- 2. Apply Klopp interventions to ALL pooled docs -----
+    print("\nApplying Klopp-style interventions to pooled documents...")
+    onball_interventions = (
+        add_cut_inside(probability=0.7)
+        + upgrade_finishing(probability=0.5)
+        + add_progression(probability=0.4)
+        + add_chance_creation(probability=0.4)
+        + enrich_dribbling(probability=0.4)
+    )
+    offball_interventions = boost_pressing(probability=0.7)
+
+    modified_pooled = []
+    for doc in pooled_docs:
+        new_doc = dict(doc)
+        if doc.get("onball_tokens"):
+            new_doc["onball_tokens"] = modify_doc(doc["onball_tokens"], onball_interventions)
+        if doc.get("offball_tokens"):
+            new_doc["offball_tokens"] = modify_doc(doc["offball_tokens"], offball_interventions)
+        modified_pooled.append(new_doc)
+
+    # ----- 3. Re-infer a single target vector -----
+    print("Inferring multi-source target vector...")
+    target_vec = infer_player_vector_from_docs(modified_pooled, onball_model, offball_model)
+
+    # ----- 4. Search -----
+    # Exclude the source players from results so they don't dominate
+    source_pids = []
+    for name, _, _, status in summary:
+        if status == "OK":
+            pid = find_player_id(name, metadata, player_ids)
+            if pid:
+                source_pids.append(pid)
+
+    print(f"\nExcluding {len(source_pids)} source players from results.")
+    print("\nTop-30 most similar players to 'multi-zone Klopp ideal attacker':")
+
+    target_rank = search_similar(
+        target_vec, vectors, player_ids, metadata,
+        top_k=30,
+        exclude_player_ids=source_pids,
+        target_name="Mané",
+    )
+
+    # ----- 5. Position distribution analysis -----
+    print("\n--- Position distribution of top-30 results ---")
+    sims = vectors @ target_vec
+    for pid in source_pids:
+        if pid in player_ids:
+            sims[player_ids.index(pid)] = -np.inf
+    top30_indices = np.argsort(sims)[::-1][:30]
+    position_counts = {}
+    for idx in top30_indices:
+        pos = metadata[player_ids[idx]]["primary_position"]
+        position_counts[pos] = position_counts.get(pos, 0) + 1
+    sorted_positions = sorted(position_counts.items(), key=lambda x: -x[1])
+    for pos, count in sorted_positions:
+        bar = "█" * count
+        print(f"  {pos:<28} {count:<3} {bar}")
+
+    # ----- 6. Versatility check on top-30 -----
+    print("\n--- Versatility scores of top-30 results ---")
+    versatility_scores = []
+    for idx in top30_indices:
+        v = metadata[player_ids[idx]].get("versatility_score", 0.0)
+        versatility_scores.append(v)
+    print(f"  Mean versatility: {np.mean(versatility_scores):.3f}")
+    print(f"  Max versatility:  {np.max(versatility_scores):.3f}")
+    print(f"  Median versatility: {np.median(versatility_scores):.3f}")
+
+    mane_pid = find_player_id("Mané", metadata, player_ids)
+    if mane_pid:
+        mane_versatility = metadata[mane_pid].get("versatility_score", 0.0)
+        print(f"  Mané's versatility (for context): {mane_versatility:.3f}")
+
+    # ----- 7. Verdict -----
+    print("\n" + "█" * 105)
+    print("APPROACH B VERDICT")
+    print("█" * 105)
+    if target_rank is None:
+        print("  ✗ Mané still not in top-30 with multi-source pooling")
+        print("    The query may need per-position vectors (Path B from earlier discussion)")
+    elif target_rank <= 5:
+        print(f"  ✓✓✓ EXCELLENT — Mané ranked #{target_rank}")
+        print("    Multi-source pooling solved the versatility issue. Tool concept validated.")
+    elif target_rank <= 10:
+        print(f"  ✓✓ STRONG — Mané ranked #{target_rank}")
+        print("    Multi-source approach works well for versatile player searches.")
+    elif target_rank <= 20:
+        print(f"  ✓ ACCEPTABLE — Mané ranked #{target_rank}")
+        print("    Methodology produces real signal for versatile players.")
+    else:
+        print(f"  ⚠ MARGINAL — Mané ranked #{target_rank}")
+        print("    Multi-source helps but may need additional refinement.")
+
+
+# end of file
diff --git a/src/gp2/evaluation/modify_doc.py b/src/gp2/evaluation/modify_doc.py
new file mode 100644
index 0000000000000000000000000000000000000000..313981b4e066c90682183243491fdbbe5986f75d
--- /dev/null
+++ b/src/gp2/evaluation/modify_doc.py
@@ -0,0 +1,365 @@
+"""
+Document Modification Engine — Adapted from Magdaci's modify_doc/enrich_doc
+
+Two operations supported:
+    1. SUBSTITUTE: change a field value in a token (e.g., shot outcome 'on' → 'goal')
+    2. INSERT: add a rare modifier token next to base tokens (e.g., add 'cut_inside_right' after wide carries)
+
+Token format reminder (V2):
+    shot|z18|on|r|open|xg_l|close          ← shot in z18, outcome 'on', right foot, open play, xg low, close
+    pass|z9_z14|s|r|g|short                 ← pass from z9 to z14, success, right foot, ground, short
+    carry|z9_z14|fwd                        ← carry forward
+    pressure|z21|cp                         ← pressure in z21, counter-press
+    progressive_pass                        ← rare modifier (no fields)
+    cut_inside_right                        ← rare modifier (no fields)
+
+Field positions per event type are fixed and known.
+"""
+
+import random
+from copy import deepcopy
+from typing import List, Dict, Callable, Optional
+
+
+# ==================================================================================================
+# FIELD SCHEMAS — position of each field in tokens
+# ==================================================================================================
+
+FIELD_SCHEMAS = {
+    "shot":          {"zone": 1, "outcome": 2, "body": 3, "type": 4, "xg": 5, "dist": 6},
+    "pass":          {"zones": 1, "outcome": 2, "body": 3, "height": 4, "length": 5},
+    "carry":         {"zones": 1, "direction": 2},
+    "dribble":       {"zone": 1, "outcome": 2},
+    "pressure":      {"zone": 1, "type": 2},  # type = 'cp' or 'reg'
+    "duel":          {"zone": 1, "type": 2, "outcome": 3},
+    "interception":  {"zone": 1, "outcome": 2},
+    "ball_recovery": {"zone": 1, "outcome": 2},
+    "clearance":     {"zone": 1, "body": 2},
+}
+
+
+def parse_token(token: str) -> Optional[Dict]:
+    """
+    Parse a base token into its event type and fields.
+    Returns None for rare modifiers (no pipe character) or unknown tokens.
+    """
+    if "|" not in token:
+        return None  # rare modifier, no fields
+
+    parts = token.split("|")
+    event_type = parts[0]
+
+    if event_type not in FIELD_SCHEMAS:
+        return None
+
+    return {
+        "event": event_type,
+        "parts": parts,
+        "schema": FIELD_SCHEMAS[event_type],
+    }
+
+
+def rebuild_token(parsed: Dict) -> str:
+    """Reverse of parse_token."""
+    return "|".join(parsed["parts"])
+
+
+# ==================================================================================================
+# INTERVENTION TYPES
+# ==================================================================================================
+
+class Intervention:
+    """Base class for all document interventions."""
+
+    def __init__(self, probability: float = 1.0, name: str = ""):
+        self.probability = probability
+        self.name = name
+
+    def apply(self, doc: List[str]) -> List[str]:
+        raise NotImplementedError
+
+
+class SubstituteField(Intervention):
+    """
+    Change a field value in matching tokens.
+
+    Example: SubstituteField('shot', 'outcome', 'on', 'goal', probability=0.5)
+        → Half of 'on' shots become 'goal' shots
+
+    Optional `condition` lets you gate substitution on additional fields.
+    Example: only substitute lost AERIAL duels (not ground ones):
+        SubstituteField('duel', 'outcome', 'lost', 'won',
+                        probability=0.5,
+                        condition=lambda p: p['parts'][2] == 'aerial')
+    """
+
+    def __init__(
+        self,
+        event: str,
+        field: str,
+        from_value: str,
+        to_value: str,
+        probability: float = 1.0,
+        name: str = "",
+        condition: Optional[Callable[[Dict], bool]] = None,
+    ):
+        super().__init__(probability, name or f"sub_{event}_{field}_{from_value}_to_{to_value}")
+        self.event = event
+        self.field = field
+        self.from_value = from_value
+        self.to_value = to_value
+        self.condition = condition
+
+        if event not in FIELD_SCHEMAS or field not in FIELD_SCHEMAS[event]:
+            raise ValueError(f"Unknown field: {event}.{field}")
+
+        self.field_idx = FIELD_SCHEMAS[event][field]
+
+    def apply(self, doc: List[str]) -> List[str]:
+        new_doc = []
+        for token in doc:
+            parsed = parse_token(token)
+            if parsed is None or parsed["event"] != self.event:
+                new_doc.append(token)
+                continue
+
+            if parsed["parts"][self.field_idx] != self.from_value:
+                new_doc.append(token)
+                continue
+
+            # Apply optional condition gate
+            if self.condition is not None:
+                try:
+                    if not self.condition(parsed):
+                        new_doc.append(token)
+                        continue
+                except (KeyError, IndexError):
+                    new_doc.append(token)
+                    continue
+
+            if random.random() < self.probability:
+                parsed["parts"][self.field_idx] = self.to_value
+                new_doc.append(rebuild_token(parsed))
+            else:
+                new_doc.append(token)
+
+        return new_doc
+
+
+class InsertModifier(Intervention):
+    """
+    Insert a rare modifier token next to tokens matching a condition.
+
+    Example: InsertModifier('cut_inside_right', condition=lambda t: ...)
+        → Adds 'cut_inside_right' token after qualifying tokens
+
+    The condition is a function that receives a parsed token and returns bool.
+    """
+
+    def __init__(
+        self,
+        modifier_token: str,
+        condition: Callable[[Dict], bool],
+        probability: float = 1.0,
+        name: str = "",
+    ):
+        super().__init__(probability, name or f"insert_{modifier_token}")
+        self.modifier_token = modifier_token
+        self.condition = condition
+
+    def apply(self, doc: List[str]) -> List[str]:
+        new_doc = []
+        for token in doc:
+            new_doc.append(token)
+            parsed = parse_token(token)
+            if parsed is None:
+                continue
+
+            try:
+                matches = self.condition(parsed)
+            except (KeyError, IndexError):
+                matches = False
+
+            if matches and random.random() < self.probability:
+                new_doc.append(self.modifier_token)
+
+        return new_doc
+
+
+# ==================================================================================================
+# PRE-DEFINED INTERVENTIONS — composable building blocks for searches
+# ==================================================================================================
+
+def upgrade_finishing(probability: float = 0.5) -> List[Intervention]:
+    """Convert non-goal shots to goals. Models 'more clinical finishing'."""
+    return [
+        SubstituteField("shot", "outcome", "on",  "goal", probability, "upgrade_shots_on"),
+        SubstituteField("shot", "outcome", "off", "goal", probability, "upgrade_shots_off"),
+    ]
+
+
+def add_cut_inside(probability: float = 0.6) -> List[Intervention]:
+    """
+    Insert cut_inside_right after carries that start in right wide area (z5/10/15/20/25).
+    Insert cut_inside_left after carries that start in left wide area (z1/6/11/16/21).
+
+    Approximates 'add inverted winger behavior'.
+    """
+    # Right-side wide zones in our 5x5 grid (column 5 = rightmost)
+    right_wide = {"5", "10", "15", "20", "25"}
+    left_wide  = {"1", "6", "11", "16", "21"}
+
+    def is_right_wide_carry(parsed):
+        if parsed["event"] != "carry":
+            return False
+        zones = parsed["parts"][1]  # e.g., "z5_z14"
+        start_zone = zones.split("_")[0].replace("z", "")
+        return start_zone in right_wide
+
+    def is_left_wide_carry(parsed):
+        if parsed["event"] != "carry":
+            return False
+        zones = parsed["parts"][1]
+        start_zone = zones.split("_")[0].replace("z", "")
+        return start_zone in left_wide
+
+    return [
+        InsertModifier("cut_inside_right", is_right_wide_carry, probability, "add_cut_inside_right"),
+        InsertModifier("cut_inside_left",  is_left_wide_carry,  probability, "add_cut_inside_left"),
+    ]
+
+
+def add_progression(probability: float = 0.4) -> List[Intervention]:
+    """Boost progressive carries and passes. Models 'more direct ball progression'."""
+
+    def is_forward_carry(parsed):
+        return parsed["event"] == "carry" and parsed["parts"][2] == "fwd"
+
+    def is_pass(parsed):
+        return parsed["event"] == "pass" and parsed["parts"][2] == "s"  # successful only
+
+    return [
+        InsertModifier("progressive_carry", is_forward_carry, probability, "add_progressive_carry"),
+        InsertModifier("progressive_pass",  is_pass,          probability, "add_progressive_pass"),
+    ]
+
+
+def boost_pressing(probability: float = 0.7) -> List[Intervention]:
+    """Convert regular pressure to counter-press. Models 'Klopp-style gegenpressing'."""
+    return [
+        SubstituteField("pressure", "type", "reg", "cp", probability, "boost_pressure_to_cp"),
+    ]
+
+
+def add_chance_creation(probability: float = 0.4) -> List[Intervention]:
+    """Insert key_pass after successful passes to box-adjacent zones."""
+    box_zones = {"23", "24", "25", "18", "19", "20"}  # final third zones in our 5x5
+
+    def is_pass_to_box_area(parsed):
+        if parsed["event"] != "pass" or parsed["parts"][2] != "s":
+            return False
+        zones = parsed["parts"][1]  # e.g., "z9_z23"
+        end_zone = zones.split("_")[1].replace("z", "")
+        return end_zone in box_zones
+
+    return [
+        InsertModifier("key_pass", is_pass_to_box_area, probability, "add_key_pass"),
+    ]
+
+
+def enrich_dribbling(probability: float = 0.5) -> List[Intervention]:
+    """Convert failed dribbles to successful, add dribble_in_box modifiers."""
+
+    def is_in_box_dribble(parsed):
+        if parsed["event"] != "dribble":
+            return False
+        zone = parsed["parts"][1].replace("z", "")
+        return zone in {"23", "24", "25"}
+
+    return [
+        SubstituteField("dribble", "outcome", "f", "s", probability, name="complete_dribbles"),
+        InsertModifier("dribble_in_box", is_in_box_dribble, probability, "add_dribble_in_box"),
+    ]
+
+
+# ==================================================================================================
+# DEFENSIVE INTERVENTIONS
+# ==================================================================================================
+
+def aerial_dominance(probability: float = 0.5) -> List[Intervention]:
+    """
+    Convert lost aerial duels to won. Models 'aerial threat / defensive heading specialist'.
+
+    Use case: Find target strikers (Benteke, Giroud), aerial-strong CBs, set-piece threats.
+
+    Implementation note: We use a condition gate to ONLY affect aerial duels.
+    Ground duels stay untouched — those are a different skill (ball winning).
+    """
+    def is_aerial(parsed):
+        # Field 2 is duel type; aerial vs ground
+        return parsed["parts"][2] == "aerial"
+
+    return [
+        SubstituteField(
+            "duel", "outcome", "lost", "won",
+            probability=probability,
+            condition=is_aerial,
+            name="win_aerial_duels",
+        ),
+    ]
+
+
+def ball_winning(probability: float = 0.5) -> List[Intervention]:
+    """
+    Convert lost ground defensive actions to won. Models 'defensive midfielder ball-winner'.
+
+    Affects:
+        - Ground duels (field 2 = ground)
+        - Interceptions
+        - Ball recoveries
+
+    Use case: Find ball-winners like Kanté, Casemiro, Wanyama, Vidal-types.
+
+    Note: This is partially a quality bump (winning more) but it's also a tactical
+    profile — some players are TASKED with winning the ball, others aren't. The
+    intervention shifts the search toward players whose token distributions look
+    like ball-winners.
+    """
+    def is_ground(parsed):
+        return parsed["parts"][2] == "ground"
+
+    return [
+        SubstituteField(
+            "duel", "outcome", "lost", "won",
+            probability=probability,
+            condition=is_ground,
+            name="win_ground_duels",
+        ),
+        SubstituteField(
+            "interception", "outcome", "lost", "won",
+            probability=probability,
+            name="win_interceptions",
+        ),
+        SubstituteField(
+            "ball_recovery", "outcome", "f", "s",
+            probability=probability,
+            name="successful_recoveries",
+        ),
+    ]
+
+
+# ==================================================================================================
+# APPLY INTERVENTIONS TO A DOCUMENT
+# ==================================================================================================
+
+def modify_doc(doc: List[str], interventions: List[Intervention]) -> List[str]:
+    """Apply a list of interventions sequentially to a document."""
+    result = list(doc)
+    for intervention in interventions:
+        result = intervention.apply(result)
+    return result
+
+
+def modify_docs(docs: List[List[str]], interventions: List[Intervention]) -> List[List[str]]:
+    """Apply interventions to a batch of documents."""
+    return [modify_doc(doc, interventions) for doc in docs]
\ No newline at end of file
diff --git a/src/gp2/evaluation/player_analogies.py b/src/gp2/evaluation/player_analogies.py
new file mode 100644
index 0000000000000000000000000000000000000000..9886eda6101e9205bbd32968bee62d896b46909c
--- /dev/null
+++ b/src/gp2/evaluation/player_analogies.py
@@ -0,0 +1,184 @@
+"""
+Player Analogies — Vector Arithmetic Validation
+
+Tests if the Player2Vec embedding space encodes football semantics meaningfully.
+Inspired by Magdaci's PlayersAnalogies and the classic word2vec
+"king - man + woman ≈ queen" demonstration.
+
+Examples:
+    Suárez - Messi + Neymar ≈ ?     (forward who plays alongside playmakers)
+    Alba   - Piqué + Ramos  ≈ ?     (modern fullback)
+    Iniesta - Xavi + Modrić ≈ ?     (creative midfielder)
+
+If results make football sense, the embedding space is meaningful.
+If results are random, something is structurally wrong.
+"""
+
+import json
+import numpy as np
+from typing import List, Optional
+
+from src.gp2.paths import PLAYER2VEC_PATH, PLAYER_METADATA_PATH
+
+
+# ==================================================================================================
+# DATA LOADING
+# ==================================================================================================
+
+def load_player2vec():
+    """Returns (player_ids, vectors, metadata_dict, name_to_idx)."""
+    data = np.load(PLAYER2VEC_PATH)
+    player_ids = list(data["player_ids"])
+    vectors = data["vectors"]
+
+    with open(PLAYER_METADATA_PATH, "r", encoding="utf-8") as f:
+        metadata = json.load(f)
+
+    # Build name → index lookup (lowercase substring matching)
+    name_to_idx = {}
+    for i, pid in enumerate(player_ids):
+        name = metadata.get(pid, {}).get("name", "")
+        if name:
+            name_to_idx[name.lower()] = i
+
+    return player_ids, vectors, metadata, name_to_idx
+
+
+def find_player(name_query: str, name_to_idx: dict) -> Optional[int]:
+    """Find player index by partial name match. Returns None if not found or ambiguous."""
+    query = name_query.lower()
+
+    # Exact match first
+    if query in name_to_idx:
+        return name_to_idx[query]
+
+    # Substring match
+    matches = [(name, idx) for name, idx in name_to_idx.items() if query in name]
+    if len(matches) == 1:
+        return matches[0][1]
+    elif len(matches) > 1:
+        print(f"  ⚠ Ambiguous: '{name_query}' matches {len(matches)} players. Showing first 3:")
+        for name, idx in matches[:3]:
+            print(f"      - {name}")
+        return matches[0][1]  # use first match
+    else:
+        return None
+
+
+# ==================================================================================================
+# ANALOGY COMPUTATION
+# ==================================================================================================
+
+def analogy(
+    player_a: str,
+    player_minus: str,
+    player_plus: str,
+    top_k: int = 10,
+    exclude_inputs: bool = True,
+):
+    """
+    Compute: vec(A) - vec(MINUS) + vec(PLUS), find top-K most similar players.
+    Reads as: "A is to MINUS as ? is to PLUS"
+    Or:       "A but in PLUS's context instead of MINUS's"
+    """
+    player_ids, vectors, metadata, name_to_idx = load_player2vec()
+
+    # Find input players
+    idx_a = find_player(player_a, name_to_idx)
+    idx_minus = find_player(player_minus, name_to_idx)
+    idx_plus = find_player(player_plus, name_to_idx)
+
+    missing = []
+    if idx_a is None: missing.append(player_a)
+    if idx_minus is None: missing.append(player_minus)
+    if idx_plus is None: missing.append(player_plus)
+    if missing:
+        print(f"  ✗ Could not find: {', '.join(missing)}")
+        return None
+
+    # Vector arithmetic
+    target = vectors[idx_a] - vectors[idx_minus] + vectors[idx_plus]
+    target = target / np.linalg.norm(target)
+
+    # Cosine similarity (vectors are already normalized post-dominant-removal)
+    sims = vectors @ target
+
+    # Exclude input players
+    if exclude_inputs:
+        sims[idx_a] = -np.inf
+        sims[idx_minus] = -np.inf
+        sims[idx_plus] = -np.inf
+
+    top_indices = np.argsort(sims)[::-1][:top_k]
+
+    a_meta = metadata[player_ids[idx_a]]
+    m_meta = metadata[player_ids[idx_minus]]
+    p_meta = metadata[player_ids[idx_plus]]
+
+    print(f"\n{'='*100}")
+    print(f"  {a_meta['name']} ({a_meta['primary_position']}) "
+          f"- {m_meta['name']} ({m_meta['primary_position']}) "
+          f"+ {p_meta['name']} ({p_meta['primary_position']}) ≈ ?")
+    print(f"{'='*100}")
+    print(f"{'Rank':<6}{'Player':<40}{'Position':<28}{'Team':<24}{'Sim':<8}")
+    print("-" * 105)
+
+    results = []
+    for rank, idx in enumerate(top_indices, 1):
+        meta = metadata[player_ids[idx]]
+        print(f"{rank:<6}{meta['name']:<40}{meta['primary_position']:<28}{meta['team']:<24}{sims[idx]:.4f}")
+        results.append({
+            "rank": rank,
+            "name": meta["name"],
+            "position": meta["primary_position"],
+            "team": meta["team"],
+            "similarity": float(sims[idx]),
+        })
+
+    return results
+
+
+# ==================================================================================================
+# PRESET ANALOGY SUITES
+# ==================================================================================================
+
+ANALOGY_TESTS = [
+    # Position translation tests
+    ("Suárez",  "Messi",   "Neymar"),       # forward in different attacking context
+    ("Alba",    "Piqué",   "Ramos"),        # left back to right back-ish via Ramos
+    ("Iniesta", "Xavi",    "Modrić"),       # midfielder swap
+
+    # Team-context translation
+    ("Neymar",  "Messi",   "Suárez"),       # winger when paired with finisher instead of playmaker
+    ("Coutinho","Henderson","Wijnaldum"),  # Liverpool internal swap
+
+    # Style translation
+    ("Mané",    "Lallana", "Coutinho"),     # versatile attacker swapped between Liverpool styles
+
+    # Simple sanity (player should map close to similar role players)
+    ("Ronaldo", "Messi",   "Neymar"),       # if both Ronaldos exist this might be ambiguous
+]
+
+
+def run_analogy_suite():
+    """Run a battery of analogies and print results."""
+    print("\n" + "█" * 100)
+    print("PLAYER ANALOGIES — Vector Arithmetic Sanity Check")
+    print("█" * 100)
+    print("\nTesting whether the embedding space captures football semantics.")
+    print("Good results = related/sensible players appear at the top.\n")
+
+    for a, minus, plus in ANALOGY_TESTS:
+        analogy(a, minus, plus, top_k=5)
+
+
+# ==================================================================================================
+# MAIN
+# ==================================================================================================
+
+def main():
+    run_analogy_suite()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp2/evaluation/scout.py b/src/gp2/evaluation/scout.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb21e6d4937c34b71593a0948d1dc2526a1aa957
--- /dev/null
+++ b/src/gp2/evaluation/scout.py
@@ -0,0 +1,327 @@
+"""
+Interactive CLI for the Scouting Engine
+
+Wraps scouting_engine.py with a simple terminal UI for testing and experimentation.
+Same engine the web app will use — this just gives a terminal way to drive it.
+
+Run:
+    python -m src.gp2.evaluation.scout
+"""
+
+from typing import List, Dict, Optional
+
+from .scouting_engine import (
+    search_replacements,
+    list_available_players,
+    list_available_upgrades,
+    list_available_filters,
+    get_player_summary,
+    find_player_id,
+    DEFAULT_UPGRADE_INTENSITY,
+)
+
+
+# ==================================================================================================
+# DISPLAY HELPERS
+# ==================================================================================================
+
+def _hr(char: str = "─", width: int = 100):
+    print(char * width)
+
+
+def _section(title: str):
+    print()
+    _hr("█")
+    print(f"  {title}")
+    _hr("█")
+
+
+def _print_player(p: Dict, prefix: str = ""):
+    print(f"{prefix}{p['name']:<35} {p['primary_position']:<28} {p['team']:<25} "
+          f"matches={p['num_matches']:<4} versatility={p['versatility_score']:.3f}")
+
+
+# ==================================================================================================
+# INTERACTIVE PROMPTS
+# ==================================================================================================
+
+def prompt_player(prompt_label: str = "Player") -> Optional[str]:
+    """
+    Prompt for a player by name. Shows matches, lets user pick by number.
+    Returns player_id, or None if user cancels.
+    """
+    while True:
+        query = input(f"  {prompt_label} (name, partial OK; blank to cancel): ").strip()
+        if not query:
+            return None
+
+        matches = list_available_players(query=query, limit=10)
+        if not matches:
+            print(f"    No players matching '{query}'. Try again.")
+            continue
+
+        if len(matches) == 1:
+            chosen = matches[0]
+            _print_player(chosen, prefix="  ✓ Selected: ")
+            return chosen["player_id"]
+
+        print(f"    Multiple matches ({len(matches)}):")
+        for i, m in enumerate(matches, 1):
+            print(f"      [{i}] {m['name']:<35} {m['primary_position']:<25} {m['team']:<22}")
+        print(f"      [0] Cancel / search again")
+
+        choice = input("    Pick number: ").strip()
+        if not choice.isdigit():
+            print("    Invalid input.")
+            continue
+        choice_int = int(choice)
+        if choice_int == 0:
+            continue
+        if 1 <= choice_int <= len(matches):
+            chosen = matches[choice_int - 1]
+            _print_player(chosen, prefix="  ✓ Selected: ")
+            return chosen["player_id"]
+        print("    Out of range.")
+
+
+def prompt_sources() -> List[str]:
+    """Loop to collect 1+ source players."""
+    print("\nSelect source player(s). 1 = single upgrade, 2+ = multi-source pooling (recommended for versatile profiles).")
+    sources = []
+    while True:
+        label = f"Source #{len(sources) + 1}" if sources else "Source player"
+        pid = prompt_player(prompt_label=label)
+        if pid is None:
+            if not sources:
+                print("  At least one source is required.")
+                continue
+            break
+        sources.append(pid)
+        more = input("  Add another source? (y/N): ").strip().lower()
+        if more != "y":
+            break
+    return sources
+
+
+def prompt_upgrades() -> List[str]:
+    """Show upgrade catalog and let user pick multiple."""
+    catalog = list_available_upgrades()
+    print("\nAvailable upgrades (apply changes to source profile before searching):")
+    for i, u in enumerate(catalog, 1):
+        print(f"  [{i}] {u['label']:<35}  ({u['applies_to']})")
+        print(f"      {u['description']}")
+    print(f"  [0] Done — no more upgrades")
+
+    selected = []
+    while True:
+        choice = input("  Pick upgrade by number (0 to finish): ").strip()
+        if not choice.isdigit():
+            print("    Invalid input.")
+            continue
+        choice_int = int(choice)
+        if choice_int == 0:
+            break
+        if 1 <= choice_int <= len(catalog):
+            key = catalog[choice_int - 1]["key"]
+            if key in selected:
+                print(f"    Already selected. Skipping.")
+            else:
+                selected.append(key)
+                print(f"    + {catalog[choice_int - 1]['label']}")
+        else:
+            print("    Out of range.")
+
+    return selected
+
+
+def prompt_intensity_mode(selected_upgrades: List[str]):
+    """
+    Ask how the user wants to apply intensity.
+
+    Returns a tuple (upgrades_param, intensity_param):
+        - upgrades_param is either the original list (uniform mode) or a dict (per-upgrade)
+        - intensity_param is the float (used only with the list form)
+    """
+    if not selected_upgrades:
+        return [], DEFAULT_UPGRADE_INTENSITY
+
+    print("\nIntensity mode:")
+    print(f"  [1] Uniform intensity for all upgrades  (default — moderate value {DEFAULT_UPGRADE_INTENSITY})")
+    print(f"  [2] Custom per-upgrade probabilities    (advanced)")
+    choice = input("  Pick (default 1): ").strip()
+
+    if choice == "2":
+        # Per-upgrade tuning
+        per_upgrade = {}
+        print("\n  Set probability for each upgrade (0.0 to 1.0). Press Enter to use 0.5.")
+        for key in selected_upgrades:
+            raw = input(f"    {key}: ").strip()
+            if not raw:
+                per_upgrade[key] = DEFAULT_UPGRADE_INTENSITY
+            else:
+                try:
+                    val = float(raw)
+                    val = max(0.0, min(1.0, val))
+                    per_upgrade[key] = val
+                except ValueError:
+                    print(f"      Invalid number, using {DEFAULT_UPGRADE_INTENSITY}.")
+                    per_upgrade[key] = DEFAULT_UPGRADE_INTENSITY
+        return per_upgrade, DEFAULT_UPGRADE_INTENSITY
+
+    # Uniform mode (default)
+    raw = input(f"  Intensity (0.0 to 1.0, default {DEFAULT_UPGRADE_INTENSITY}): ").strip()
+    if not raw:
+        return selected_upgrades, DEFAULT_UPGRADE_INTENSITY
+    try:
+        val = float(raw)
+        val = max(0.0, min(1.0, val))
+        return selected_upgrades, val
+    except ValueError:
+        print(f"    Invalid number, using {DEFAULT_UPGRADE_INTENSITY}.")
+        return selected_upgrades, DEFAULT_UPGRADE_INTENSITY
+
+
+def prompt_filters() -> Dict:
+    """Optionally collect filters."""
+    apply_filters = input("\nApply filters? (y/N): ").strip().lower()
+    if apply_filters != "y":
+        return {}
+
+    filters = {}
+
+    # Position filter
+    pos_input = input("  Filter by positions? Comma-separated (e.g., 'Left Wing, Right Wing'). Blank = all: ").strip()
+    if pos_input:
+        filters["positions"] = [p.strip() for p in pos_input.split(",") if p.strip()]
+
+    # Exclude teams
+    excl_input = input("  Exclude teams? Comma-separated. Blank = none: ").strip()
+    if excl_input:
+        filters["exclude_teams"] = [t.strip() for t in excl_input.split(",") if t.strip()]
+
+    # Versatility floor
+    minver = input("  Minimum versatility score? (e.g., 0.5; blank = no minimum): ").strip()
+    if minver:
+        try:
+            filters["min_versatility"] = float(minver)
+        except ValueError:
+            print("    Invalid number, skipping.")
+
+    # Min matches
+    minmatches = input("  Minimum matches in dataset? (e.g., 10; blank = 5): ").strip()
+    if minmatches:
+        try:
+            filters["min_matches"] = int(minmatches)
+        except ValueError:
+            print("    Invalid number, skipping.")
+
+    return filters
+
+
+def prompt_top_k(default: int = 20) -> int:
+    raw = input(f"\nHow many results to show? (default {default}): ").strip()
+    if not raw:
+        return default
+    try:
+        return max(1, int(raw))
+    except ValueError:
+        return default
+
+
+# ==================================================================================================
+# DISPLAY RESULTS
+# ==================================================================================================
+
+def display_results(result: Dict):
+    """Pretty-print the engine's response."""
+    _section("QUERY SUMMARY")
+
+    sources = result["query"]["sources"]
+    print(f"  Source player(s): {len(sources)}")
+    for s in sources:
+        _print_player(s, prefix="    - ")
+
+    upgrades = result["query"]["upgrades"]
+    if upgrades:
+        intensity = result["query"]["upgrade_intensity"]
+        if isinstance(upgrades, dict):
+            print(f"\n  Upgrades applied (per-upgrade probabilities):")
+            for k, v in upgrades.items():
+                print(f"    - {k:<25} (p={v})")
+        else:
+            print(f"\n  Upgrades applied (uniform intensity={intensity}):")
+            for u in upgrades:
+                print(f"    - {u}")
+    else:
+        print("\n  No upgrades applied (pure similarity search)")
+
+    filters = result["query"]["filters"]
+    if filters:
+        print(f"\n  Filters:")
+        for k, v in filters.items():
+            print(f"    - {k}: {v}")
+
+    if result["warnings"]:
+        print("\n  ⚠ Warnings:")
+        for w in result["warnings"]:
+            print(f"    - {w}")
+
+    _section("CANDIDATES")
+    candidates = result["candidates"]
+    if not candidates:
+        print("  No candidates returned. Filters may be too restrictive.")
+        return
+
+    print(f"  {'Rank':<6}{'Player':<35}{'Position':<27}{'Team':<23}{'Sim':<8}{'Vers':<6}")
+    _hr("-")
+    for c in candidates:
+        print(f"  {c['rank']:<6}{c['name']:<35}{c['primary_position']:<27}"
+              f"{c['team']:<23}{c['similarity']:.4f}  {c['versatility_score']:<6.3f}")
+
+    # Summary stats
+    print()
+    positions = [c["primary_position"] for c in candidates]
+    pos_counts = {}
+    for p in positions:
+        pos_counts[p] = pos_counts.get(p, 0) + 1
+    sorted_pos = sorted(pos_counts.items(), key=lambda x: -x[1])
+    print(f"  Position distribution: {', '.join(f'{p}({n})' for p, n in sorted_pos)}")
+
+
+# ==================================================================================================
+# MAIN INTERACTIVE FLOW
+# ==================================================================================================
+
+def main():
+    _section("SCOUTING ENGINE — Interactive CLI")
+    print("\nThis tool searches for replacement / upgrade candidates based on a source profile.")
+    print("Steps: pick source(s) → pick upgrades → optionally filter → see results.\n")
+
+    while True:
+        sources = prompt_sources()
+        upgrades_selected = prompt_upgrades()
+        upgrades_param, intensity_param = prompt_intensity_mode(upgrades_selected)
+        filters = prompt_filters()
+        top_k = prompt_top_k()
+
+        try:
+            print("\n  Running search...")
+            result = search_replacements(
+                sources=sources,
+                upgrades=upgrades_param,
+                upgrade_intensity=intensity_param,
+                top_k=top_k,
+                filters=filters,
+            )
+            display_results(result)
+        except ValueError as e:
+            print(f"\n  ✗ Error: {e}")
+
+        again = input("\n\nRun another search? (y/N): ").strip().lower()
+        if again != "y":
+            print("\n  Done.\n")
+            break
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/src/gp2/evaluation/scouting_engine.py b/src/gp2/evaluation/scouting_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc767f8a8b07ceda4b9674f676619d78b965cfaf
--- /dev/null
+++ b/src/gp2/evaluation/scouting_engine.py
@@ -0,0 +1,651 @@
+"""
+Scouting Engine — Reusable Player Replacement / Upgrade Search
+
+Pure engine module: takes inputs, returns ranked candidates. No I/O beyond returns.
+Consumed by both the CLI (scout.py) and the future web app.
+
+Public API:
+    search_replacements(...)      → ranked list of candidate dicts
+    list_available_players(...)   → autocomplete-style player search
+    list_available_upgrades()     → upgrade catalog with descriptions
+    list_available_filters()      → filter spec
+    get_player_summary(...)       → quick info card for a player
+
+Design notes (V3 — fixed inference):
+    - For pure similarity (no upgrades): use the STORED player vector directly.
+      No re-inference needed. Fast and exact.
+    - For modifications/upgrades: use Magdaci's per-match approach. Each match is
+      modified individually, inferred 10 times to smooth Doc2Vec noise, then
+      vectors are averaged. Tokens stay in their natural per-match documents.
+    - Multi-source: pool match-level vectors (NOT tokens) across source players.
+    - Default upgrade intensity is 0.5 — kept moderate to avoid the
+      offball-saturation bias that surfaces defenders at high values.
+    - All randomness uses a per-call seed for reproducibility (web app needs this).
+
+Why we changed: The previous implementation pooled all of a player's tokens into
+one shuffled, truncated synthetic doc, then ran ONE inference on it. This produced
+vectors that didn't live in the same region of vector space as the stored ones,
+causing Suárez/Benzema searches to return Center Backs. The new approach matches
+how Magdaci's reference implementation works.
+"""
+
+import json
+import random
+from collections import defaultdict
+from typing import List, Dict, Optional
+
+import numpy as np
+from gensim.models.doc2vec import Doc2Vec
+
+from .modify_doc import (
+    Intervention,
+    add_cut_inside,
+    upgrade_finishing,
+    add_progression,
+    add_chance_creation,
+    enrich_dribbling,
+    boost_pressing,
+    aerial_dominance,
+    ball_winning,
+    modify_doc,
+)
+from src.gp2.paths import (
+    DOCS_PATH,
+    OFFBALL_MODEL_PATH,
+    ONBALL_MODEL_PATH,
+    PLAYER2VEC_PATH,
+    PLAYER_METADATA_PATH,
+)
+
+
+# ==================================================================================================
+# PATHS
+# ==================================================================================================
+
+# ==================================================================================================
+# CONFIGURATION
+# ==================================================================================================
+
+DEFAULT_UPGRADE_INTENSITY = 0.5
+ONBALL_DIM = 48
+OFFBALL_DIM = 16
+
+# Inference parameters (per Magdaci's approach)
+# - INFERENCE_STEPS: epochs per single inference call. More = more accurate but slower.
+# - INFERENCE_REPS: how many times to re-infer each doc and average. Smooths Doc2Vec
+#   stochasticity. Magdaci uses 10. Lower = faster, higher = more stable.
+INFERENCE_STEPS = 10
+INFERENCE_REPS = 10
+
+# Optional cap on matches per source to limit search latency.
+# 0 or None = use all matches the player has.
+MAX_MATCHES_PER_SOURCE = 25
+
+
+# ==================================================================================================
+# UPGRADE CATALOG — what users can choose from
+# ==================================================================================================
+
+UPGRADE_CATALOG = {
+    "finishing": {
+        "label": "More Clinical Finishing",
+        "description": "Converts more shot attempts into goals. Use when you want a more clinical striker.",
+        "applies_to": "onball",
+        "builder": upgrade_finishing,
+    },
+    "cut_inside": {
+        "label": "Inverted Winger / Cut Inside",
+        "description": "Adds cut-inside behavior from wide areas. Use for inverted wingers like Mané or Salah.",
+        "applies_to": "onball",
+        "builder": add_cut_inside,
+    },
+    "progression": {
+        "label": "Direct Ball Progression",
+        "description": "More progressive carries and forward passes. Use when you want a more direct, vertical player.",
+        "applies_to": "onball",
+        "builder": add_progression,
+    },
+    "chance_creation": {
+        "label": "Chance Creation",
+        "description": "More key passes and final-third deliveries. Use when you need a creator.",
+        "applies_to": "onball",
+        "builder": add_chance_creation,
+    },
+    "dribbling": {
+        "label": "Better Dribbling",
+        "description": "More successful dribbles, especially in the box. Use for 1v1 specialists.",
+        "applies_to": "onball",
+        "builder": enrich_dribbling,
+    },
+    "pressing": {
+        "label": "Counterpressing Intensity",
+        "description": "Converts regular pressure into counterpress (Klopp-style gegenpressing).",
+        "applies_to": "offball",
+        "builder": boost_pressing,
+    },
+    "aerial_dominance": {
+        "label": "Aerial Dominance",
+        "description": "Wins more aerial duels. Use for target strikers, headed-goal threats, or aerially strong center backs.",
+        "applies_to": "offball",
+        "builder": aerial_dominance,
+    },
+    "ball_winning": {
+        "label": "Ball-Winning",
+        "description": "Wins more ground duels, interceptions, and recoveries. Use for defensive midfielders or pressing midfielders (Kanté/Casemiro types).",
+        "applies_to": "offball",
+        "builder": ball_winning,
+    },
+}
+
+
+# ==================================================================================================
+# FILTER CATALOG — supported filters in v1
+# ==================================================================================================
+
+def list_available_filters() -> Dict:
+    """Returns supported filter spec for UI building."""
+    return {
+        "positions": {
+            "type": "multi_select",
+            "description": "Filter by primary position. If empty, all positions allowed.",
+            "field": "primary_position",
+        },
+        "exclude_teams": {
+            "type": "multi_select",
+            "description": "Exclude players from these teams (e.g., your own).",
+            "field": "team",
+        },
+        "min_versatility": {
+            "type": "float",
+            "description": "Minimum versatility score (Shannon entropy across positions).",
+            "default": 0.0,
+        },
+        "max_versatility": {
+            "type": "float",
+            "description": "Maximum versatility score.",
+            "default": None,
+        },
+        "min_matches": {
+            "type": "int",
+            "description": "Minimum number of matches in the dataset.",
+            "default": 5,
+        },
+    }
+
+
+# ==================================================================================================
+# DATA LOADING — module-level cache
+# ==================================================================================================
+
+_cache = {
+    "player_ids": None,
+    "vectors": None,
+    "metadata": None,
+    "name_to_id": None,
+    "onball_model": None,
+    "offball_model": None,
+}
+
+
+def _ensure_loaded():
+    """Idempotent: loads everything once, caches in module state."""
+    if _cache["player_ids"] is not None:
+        return
+
+    data = np.load(PLAYER2VEC_PATH)
+    _cache["player_ids"] = list(data["player_ids"])
+    _cache["vectors"] = data["vectors"]
+
+    with open(PLAYER_METADATA_PATH, "r", encoding="utf-8") as f:
+        _cache["metadata"] = json.load(f)
+
+    # Lowercase name-to-id index for fast lookup
+    name_to_id = {}
+    for pid in _cache["player_ids"]:
+        name = _cache["metadata"].get(pid, {}).get("name", "")
+        if name:
+            name_to_id[name.lower()] = pid
+    _cache["name_to_id"] = name_to_id
+
+    _cache["onball_model"] = Doc2Vec.load(str(ONBALL_MODEL_PATH))
+    _cache["offball_model"] = Doc2Vec.load(str(OFFBALL_MODEL_PATH))
+
+
+# ==================================================================================================
+# PLAYER LOOKUP
+# ==================================================================================================
+
+def list_available_players(query: str = "", limit: int = 20) -> List[Dict]:
+    """
+    Autocomplete-style player search. Returns matching players with their IDs.
+    Empty query returns first `limit` players alphabetically.
+    """
+    _ensure_loaded()
+    metadata = _cache["metadata"]
+    player_ids = _cache["player_ids"]
+
+    query_lower = query.lower().strip()
+    matches = []
+    for pid in player_ids:
+        meta = metadata.get(pid, {})
+        name = meta.get("name", "")
+        if not query_lower or query_lower in name.lower():
+            matches.append({
+                "player_id": pid,
+                "name": name,
+                "primary_position": meta.get("primary_position", "Unknown"),
+                "team": meta.get("team", "Unknown"),
+                "versatility_score": meta.get("versatility_score", 0.0),
+                "num_matches": meta.get("num_matches", 0),
+            })
+
+    matches.sort(key=lambda m: m["name"])
+    return matches[:limit]
+
+
+def find_player_id(query: str) -> Optional[str]:
+    """
+    Resolve a single player by name query. Prefers exact match.
+    Returns None if not found, returns first match if multiple.
+    """
+    _ensure_loaded()
+    name_to_id = _cache["name_to_id"]
+    query_lower = query.lower().strip()
+
+    if query_lower in name_to_id:
+        return name_to_id[query_lower]
+
+    # Partial match
+    for name, pid in name_to_id.items():
+        if query_lower in name:
+            return pid
+    return None
+
+
+def get_player_summary(player_id_or_name: str) -> Optional[Dict]:
+    """Quick info card for a player. Accepts ID or name."""
+    _ensure_loaded()
+    metadata = _cache["metadata"]
+
+    pid = player_id_or_name if player_id_or_name in metadata else find_player_id(player_id_or_name)
+    if pid is None or pid not in metadata:
+        return None
+
+    meta = metadata[pid]
+    return {
+        "player_id": pid,
+        "name": meta.get("name"),
+        "primary_position": meta.get("primary_position"),
+        "team": meta.get("team"),
+        "versatility_score": meta.get("versatility_score", 0.0),
+        "num_distinct_positions": meta.get("num_distinct_positions", 1),
+        "num_matches": meta.get("num_matches", 0),
+        "position_distribution": meta.get("position_distribution", {}),
+    }
+
+
+# ==================================================================================================
+# UPGRADE CATALOG ACCESS
+# ==================================================================================================
+
+def list_available_upgrades() -> List[Dict]:
+    """Returns the upgrade catalog, formatted for UI display."""
+    return [
+        {
+            "key": key,
+            "label": spec["label"],
+            "description": spec["description"],
+            "applies_to": spec["applies_to"],
+        }
+        for key, spec in UPGRADE_CATALOG.items()
+    ]
+
+
+def _build_interventions(upgrades, intensity: float):
+    """
+    Build the actual intervention objects from upgrade specifications.
+
+    Args:
+        upgrades:  Either a list of upgrade keys (using uniform `intensity`),
+                   or a dict mapping keys to per-upgrade probabilities.
+        intensity: Uniform probability used when `upgrades` is a list.
+                   Ignored when `upgrades` is a dict.
+
+    Returns:
+        (onball_interventions, offball_interventions)
+    """
+    onball = []
+    offball = []
+
+    if isinstance(upgrades, dict):
+        upgrade_specs = upgrades.items()  # (key, probability) pairs
+    else:
+        upgrade_specs = [(key, intensity) for key in upgrades]
+
+    for key, prob in upgrade_specs:
+        if key not in UPGRADE_CATALOG:
+            raise ValueError(f"Unknown upgrade: {key}. Available: {list(UPGRADE_CATALOG.keys())}")
+        spec = UPGRADE_CATALOG[key]
+        built = spec["builder"](probability=prob)
+        if spec["applies_to"] == "onball":
+            onball.extend(built)
+        else:
+            offball.extend(built)
+
+    return onball, offball
+
+
+# ==================================================================================================
+# DOCUMENT LOADING & MODIFICATION
+# ==================================================================================================
+
+def _load_player_docs(player_id: str) -> List[dict]:
+    """Stream the corpus and pick out one player's match documents."""
+    docs = []
+    with open(DOCS_PATH, "r", encoding="utf-8") as f:
+        for line in f:
+            d = json.loads(line.strip())
+            if d["player_id"] == player_id:
+                docs.append(d)
+    return docs
+
+
+def _sample_player_matches(
+    player_id: str,
+    rng: random.Random,
+    max_matches: int = MAX_MATCHES_PER_SOURCE,
+) -> List[dict]:
+    """
+    Load all of a player's match docs, optionally cap to max_matches via random sampling.
+    Each doc keeps its natural per-match structure — we do NOT pool tokens.
+    """
+    docs = _load_player_docs(player_id)
+    if not docs:
+        return []
+    if max_matches and len(docs) > max_matches:
+        docs = rng.sample(docs, max_matches)
+    return docs
+
+
+# ==================================================================================================
+# VECTOR INFERENCE — Magdaci-style per-match approach
+# ==================================================================================================
+# Key principle: each match document stays intact. We infer one vector PER MATCH, with multiple
+# repetitions to smooth Doc2Vec inference noise, then average across matches.
+# We never pool tokens across matches into one synthetic super-doc.
+# ==================================================================================================
+
+
+def _estimate_match_vector(
+    onball_tokens: List[str],
+    offball_tokens: List[str],
+    reps: int = INFERENCE_REPS,
+    steps: int = INFERENCE_STEPS,
+) -> np.ndarray:
+    """
+    Infer the 64D vector for ONE match document.
+    Runs `reps` inferences and averages to reduce Doc2Vec stochastic noise.
+    Returns an unnormalized 64D vector.
+    """
+    onball_model = _cache["onball_model"]
+    offball_model = _cache["offball_model"]
+
+    on_reps = []
+    off_reps = []
+
+    if onball_tokens:
+        for _ in range(reps):
+            on_reps.append(onball_model.infer_vector(onball_tokens, epochs=steps))
+    if offball_tokens:
+        for _ in range(reps):
+            off_reps.append(offball_model.infer_vector(offball_tokens, epochs=steps))
+
+    mean_on = np.mean(on_reps, axis=0) if on_reps else np.zeros(ONBALL_DIM)
+    mean_off = np.mean(off_reps, axis=0) if off_reps else np.zeros(OFFBALL_DIM)
+
+    return np.concatenate([mean_on, mean_off])
+
+
+def _player_vector_via_per_match_inference(
+    player_id: str,
+    rng: random.Random,
+    onball_interventions: list = None,
+    offball_interventions: list = None,
+    max_matches: int = MAX_MATCHES_PER_SOURCE,
+) -> Optional[np.ndarray]:
+    """
+    Build a player's 64D vector by re-inferring each of their matches, then averaging.
+
+    If interventions are provided, each match's tokens are modified BEFORE inference.
+    Each match keeps its natural document structure (no token pooling, no shuffling).
+
+    Returns the averaged 64D vector (unnormalized), or None if no usable matches.
+    """
+    docs = _sample_player_matches(player_id, rng, max_matches)
+    if not docs:
+        return None
+
+    match_vectors = []
+    for doc in docs:
+        on_tokens = list(doc.get("onball_tokens") or [])
+        off_tokens = list(doc.get("offball_tokens") or [])
+
+        # Apply interventions per-match if requested (Magdaci-style)
+        if onball_interventions and on_tokens:
+            on_tokens = modify_doc(on_tokens, onball_interventions)
+        if offball_interventions and off_tokens:
+            off_tokens = modify_doc(off_tokens, offball_interventions)
+
+        if not on_tokens:
+            continue  # skip docs with no onball signal at all
+
+        match_vec = _estimate_match_vector(on_tokens, off_tokens)
+        match_vectors.append(match_vec)
+
+    if not match_vectors:
+        return None
+
+    return np.mean(match_vectors, axis=0)
+
+
+# ==================================================================================================
+# FILTERING
+# ==================================================================================================
+
+def _passes_filters(meta: dict, filters: Dict) -> bool:
+    """Check if a player's metadata passes the user-specified filters."""
+    if not filters:
+        return True
+
+    if "positions" in filters and filters["positions"]:
+        if meta.get("primary_position") not in filters["positions"]:
+            return False
+
+    if "exclude_teams" in filters and filters["exclude_teams"]:
+        if meta.get("team") in filters["exclude_teams"]:
+            return False
+
+    if "min_versatility" in filters and filters["min_versatility"] is not None:
+        if meta.get("versatility_score", 0.0) < filters["min_versatility"]:
+            return False
+
+    if "max_versatility" in filters and filters["max_versatility"] is not None:
+        if meta.get("versatility_score", 0.0) > filters["max_versatility"]:
+            return False
+
+    if "min_matches" in filters and filters["min_matches"] is not None:
+        if meta.get("num_matches", 0) < filters["min_matches"]:
+            return False
+
+    return True
+
+
+# ==================================================================================================
+# THE MAIN SEARCH FUNCTION
+# ==================================================================================================
+
+def search_replacements(
+    sources: List[str],
+    upgrades=None,
+    upgrade_intensity: float = DEFAULT_UPGRADE_INTENSITY,
+    top_k: int = 30,
+    filters: Dict = None,
+    exclude_sources: bool = True,
+    seed: int = 42,
+) -> Dict:
+    """
+    Search for replacement / upgrade candidates.
+
+    Args:
+        sources:           One or more player names or IDs to use as the search base.
+                           1 source = single-player upgrade. 2+ = multi-source pooling.
+        upgrades:          Either:
+                             - List[str] of upgrade keys (uniform intensity), e.g.
+                               ["cut_inside", "finishing"]
+                             - Dict[str, float] mapping keys to per-upgrade probabilities,
+                               e.g. {"cut_inside": 0.7, "finishing": 0.5}
+                             - None or empty list = no modification.
+        upgrade_intensity: Uniform probability when `upgrades` is a list. Default 0.5.
+                           Ignored when `upgrades` is a dict.
+        top_k:             Number of candidates to return.
+        filters:           Optional dict of filters (see list_available_filters()).
+        exclude_sources:   If True, exclude the source players from results.
+        seed:              RNG seed for reproducibility.
+
+    Returns:
+        {
+            "query": {
+                "sources": [...resolved player info...],
+                "upgrades": [...] or {...},
+                "upgrade_intensity": 0.5,
+                "filters": {...},
+            },
+            "candidates": [
+                {"rank": 1, "player_id": ..., "name": ..., "primary_position": ...,
+                 "team": ..., "similarity": ..., "versatility_score": ...},
+                ...
+            ],
+            "warnings": [...]   // any non-fatal issues
+        }
+    """
+    _ensure_loaded()
+    rng = random.Random(seed)
+
+    if not sources:
+        raise ValueError("At least one source player is required.")
+
+    # Normalize upgrades input
+    if upgrades is None:
+        upgrades = []
+    filters = filters or {}
+    warnings = []
+
+    # ---------- Resolve source players ----------
+    resolved_sources = []
+    source_pids = []
+    for src in sources:
+        pid = src if src in _cache["metadata"] else find_player_id(src)
+        if pid is None:
+            warnings.append(f"Source not found: '{src}' — skipping.")
+            continue
+        resolved_sources.append(get_player_summary(pid))
+        source_pids.append(pid)
+
+    if not resolved_sources:
+        raise ValueError("No valid source players resolved. Aborting.")
+
+    # ---------- Build interventions (if any) ----------
+    has_upgrades = bool(upgrades) and (len(upgrades) > 0 if isinstance(upgrades, (list, dict)) else False)
+    if has_upgrades:
+        onball_interventions, offball_interventions = _build_interventions(upgrades, upgrade_intensity)
+    else:
+        onball_interventions, offball_interventions = [], []
+
+    # ---------- Build target vector ----------
+    # Two paths:
+    #   (1) No upgrades → use the STORED player vector(s) directly. Skip inference entirely.
+    #       Stored vectors are the result of training; using them is exact and fast.
+    #   (2) With upgrades → use Magdaci's per-match approach. Each source player's matches
+    #       are modified individually, inferred with multiple repetitions, then averaged.
+    vectors = _cache["vectors"]
+    player_ids = _cache["player_ids"]
+    metadata = _cache["metadata"]
+
+    if not has_upgrades:
+        # FAST PATH — use stored vectors. Average across multiple sources if given.
+        per_source_vectors = []
+        for pid in source_pids:
+            if pid not in player_ids:
+                warnings.append(f"Source {pid} not in stored player2vec — skipping.")
+                continue
+            per_source_vectors.append(vectors[player_ids.index(pid)])
+        if not per_source_vectors:
+            raise ValueError("None of the sources have stored vectors.")
+        target_vec = np.mean(per_source_vectors, axis=0)
+    else:
+        # MODIFICATION PATH — Magdaci-style per-match inference per source, then average.
+        per_source_vectors = []
+        for pid in source_pids:
+            v = _player_vector_via_per_match_inference(
+                pid, rng,
+                onball_interventions=onball_interventions,
+                offball_interventions=offball_interventions,
+                max_matches=MAX_MATCHES_PER_SOURCE,
+            )
+            if v is None:
+                warnings.append(f"No usable docs for source: {pid}")
+                continue
+            per_source_vectors.append(v)
+        if not per_source_vectors:
+            raise ValueError("None of the sources had usable documents.")
+        target_vec = np.mean(per_source_vectors, axis=0)
+
+    # Normalize target vector (stored vectors are unit-length, so this matches)
+    target_vec = target_vec / np.clip(np.linalg.norm(target_vec), 1e-8, None)
+
+    # ---------- Search ----------
+    sims = vectors @ target_vec
+
+    # Exclude sources from results
+    if exclude_sources:
+        for pid in source_pids:
+            if pid in player_ids:
+                sims[player_ids.index(pid)] = -np.inf
+
+    # Apply filters by setting non-passing players to -inf
+    if filters:
+        for i, pid in enumerate(player_ids):
+            meta = metadata.get(pid, {})
+            if not _passes_filters(meta, filters):
+                sims[i] = -np.inf
+
+    # Rank and assemble candidates
+    ranked_indices = np.argsort(sims)[::-1]
+    candidates = []
+    for rank, idx in enumerate(ranked_indices[:top_k], 1):
+        if not np.isfinite(sims[idx]):
+            break  # all remaining are filtered out
+        pid = player_ids[idx]
+        meta = metadata[pid]
+        candidates.append({
+            "rank": rank,
+            "player_id": pid,
+            "name": meta.get("name"),
+            "primary_position": meta.get("primary_position"),
+            "team": meta.get("team"),
+            "similarity": float(sims[idx]),
+            "versatility_score": meta.get("versatility_score", 0.0),
+            "num_matches": meta.get("num_matches", 0),
+        })
+
+    return {
+        "query": {
+            "sources": resolved_sources,
+            "upgrades": upgrades,
+            "upgrade_intensity": upgrade_intensity,
+            "filters": filters,
+            "seed": seed,
+        },
+        "candidates": candidates,
+        "warnings": warnings,
+    }
diff --git a/src/gp2/evaluation/visualize_umap.py b/src/gp2/evaluation/visualize_umap.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bf7e0aa3908be1bf2e2c98a4484c61eb964fe5b
--- /dev/null
+++ b/src/gp2/evaluation/visualize_umap.py
@@ -0,0 +1,294 @@
+"""
+UMAP Visualization of Player2Vec embeddings.
+
+Reduces 32D player vectors to 2D using UMAP and colors by position.
+This is Magdaci's primary validation — if the model learned football
+semantics, positions should cluster naturally without ever being told
+what position a player plays.
+
+Expected result:
+    - Goalkeepers form tight cluster separate from everyone
+    - Defenders cluster together
+    - Attackers cluster together
+    - Midfielders in between
+    - Full-backs bridge defenders and midfielders
+"""
+
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+import umap
+from collections import Counter
+
+from src.gp2.paths import PLAYER2VEC_PATH, PLAYER2VEC_UMAP_PATH, PLAYER_INFO_PATH
+
+
+# ==================================================================================================
+# POSITION GROUPING & COLORS
+# ==================================================================================================
+
+# Map StatsBomb's positions to tactical role groups (LEFT/RIGHT SEPARATED)
+POSITION_GROUPS = {
+    # Goalkeepers
+    "Goalkeeper": "GK",
+    
+    # Central Defenders (no left/right distinction needed)
+    "Left Center Back": "Center Back",
+    "Center Back": "Center Back",
+    "Right Center Back": "Center Back",
+    
+    # Fullbacks (SEPARATED by side)
+    "Left Back": "Left Back",
+    "Right Back": "Right Back",
+    "Left Wing Back": "Left Wing Back",
+    "Right Wing Back": "Right Wing Back",
+    
+    # Defensive Midfielders
+    "Left Defensive Midfield": "Defensive Mid",
+    "Center Defensive Midfield": "Defensive Mid",
+    "Right Defensive Midfield": "Defensive Mid",
+    
+    # Central Midfielders
+    "Left Midfield": "Central Mid",
+    "Left Center Midfield": "Central Mid",
+    "Center Midfield": "Central Mid",
+    "Right Center Midfield": "Central Mid",
+    "Right Midfield": "Central Mid",
+    
+    # Attacking Midfielders
+    "Left Attacking Midfield": "Attacking Mid",
+    "Center Attacking Midfield": "Attacking Mid",
+    "Right Attacking Midfield": "Attacking Mid",
+    
+    # Wingers (SEPARATED by side)
+    "Left Wing": "Left Wing",
+    "Right Wing": "Right Wing",
+    
+    # Strikers
+    "Left Center Forward": "Striker",
+    "Center Forward": "Striker",
+    "Right Center Forward": "Striker",
+    "Secondary Striker": "Striker",
+}
+
+# Color scheme — distinct colors for each tactical role
+POSITION_COLORS = {
+    "GK": "#FFD700",              # Gold
+    "Center Back": "#1E3A8A",     # Dark Blue
+    "Left Back": "#FF0095",       # Medium Blue
+    "Right Back": "#8C00FF",      # Light Blue  
+    "Left Wing Back": "#FF0095",  # Very Light Blue
+    "Right Wing Back": "#8C00FF", # Pale Blue
+    "Defensive Mid": "#000000",   # Dark Green
+    "Central Mid": "#09FF00",     # Medium Green
+    "Attacking Mid": "#EEFF00",   # Light Green
+    "Left Wing": "#00FFF2FF",       # Red
+    "Right Wing": "#FF5E00",      # Light Red
+    "Striker": "#FF0000",         # Dark Red
+    "Unknown": "#808080",         # Gray
+}
+
+
+# ==================================================================================================
+# LOAD DATA
+# ==================================================================================================
+
+def load_data():
+    """Load player vectors and metadata."""
+    print("Loading Player2Vec...")
+    data = np.load(PLAYER2VEC_PATH)
+    player_ids = data["player_ids"]
+    vectors = data["vectors"]
+    print(f"Loaded {len(player_ids)} players with {vectors.shape[1]}D vectors")
+
+    print(f"\nLoading player info...")
+    with open(PLAYER_INFO_PATH, "r", encoding="utf-8") as f:
+        player_info = json.load(f)
+    print(f"Loaded info for {len(player_info)} players")
+
+    return player_ids, vectors, player_info
+
+
+# ==================================================================================================
+# UMAP PROJECTION
+# ==================================================================================================
+
+def compute_umap(vectors, n_neighbors=15, min_dist=0.1, random_state=42):
+    """
+    Reduce 32D vectors to 2D using UMAP.
+    
+    Args:
+        n_neighbors: local neighborhood size (15 is good default)
+        min_dist:    minimum distance between points (0.1 = moderate spread)
+    """
+    print(f"\nComputing UMAP projection...")
+    print(f"  n_neighbors={n_neighbors}, min_dist={min_dist}")
+    
+    reducer = umap.UMAP(
+        n_components=2,
+        n_neighbors=n_neighbors,
+        min_dist=min_dist,
+        random_state=random_state,
+        metric="cosine",  # same metric as similarity search
+    )
+    
+    embedding_2d = reducer.fit_transform(vectors)
+    print(f"  Projection complete: {embedding_2d.shape}")
+    
+    return embedding_2d
+
+
+# ==================================================================================================
+# VISUALIZATION
+# ==================================================================================================
+
+def visualize(player_ids, embedding_2d, player_info):
+    """Create scatter plot colored by position."""
+    
+    # Map each player to position group
+    position_groups = []
+    position_names = []
+    
+    for pid in player_ids:
+        info = player_info.get(pid, {})
+        pos = info.get("position", "Unknown")
+        pos_group = POSITION_GROUPS.get(pos, "Unknown")
+        position_groups.append(pos_group)
+        position_names.append(pos)
+    
+    # Count distribution
+    pos_counts = Counter(position_groups)
+    print(f"\nPosition distribution:")
+    for pos, count in sorted(pos_counts.items()):
+        print(f"  {pos:<12}: {count}")
+    
+    # Create figure
+    fig, ax = plt.subplots(figsize=(14, 10))
+    
+    # Plot each position group separately for clean legend
+    for pos_group in ["GK", "Center Back", "Left Back", "Right Back",
+                      "Left Wing Back", "Right Wing Back",
+                      "Defensive Mid", "Central Mid", "Attacking Mid",
+                      "Left Wing", "Right Wing", "Striker", "Unknown"]:
+        mask = np.array([pg == pos_group for pg in position_groups])
+        if mask.sum() == 0:
+            continue
+        
+        ax.scatter(
+            embedding_2d[mask, 0],
+            embedding_2d[mask, 1],
+            c=POSITION_COLORS.get(pos_group, "#808080"),
+            label=f"{pos_group} (n={mask.sum()})",
+            alpha=0.6,
+            s=30,
+            edgecolors="white",
+            linewidth=0.3,
+        )
+    
+    ax.set_title(
+        "Player2Vec — UMAP Projection (Colored by Position)\n"
+        "Natural clustering validates model learned football semantics",
+        fontsize=14,
+        fontweight="bold",
+    )
+    ax.set_xlabel("UMAP Dimension 1", fontsize=12)
+    ax.set_ylabel("UMAP Dimension 2", fontsize=12)
+    ax.legend(loc="best", fontsize=10, framealpha=0.9)
+    ax.grid(True, alpha=0.3, linestyle="--")
+    
+    plt.tight_layout()
+    
+    # Save
+    PLAYER2VEC_UMAP_PATH.parent.mkdir(parents=True, exist_ok=True)
+    plt.savefig(PLAYER2VEC_UMAP_PATH, dpi=300, bbox_inches="tight")
+    print(f"\nVisualization saved to: {PLAYER2VEC_UMAP_PATH}")
+    
+    plt.show()
+
+
+# ==================================================================================================
+# CLUSTER QUALITY METRICS
+# ==================================================================================================
+
+def analyze_clustering(player_ids, embedding_2d, player_info):
+    """
+    Quantitative analysis of position clustering.
+    If model learned semantics, same-position players should be closer
+    in embedding space than different-position players.
+    """
+    print("\n" + "="*60)
+    print("CLUSTER QUALITY ANALYSIS")
+    print("="*60)
+    
+    # Build position mapping
+    pid_to_group = {}
+    for pid in player_ids:
+        pos = player_info.get(pid, {}).get("position", "Unknown")
+        pid_to_group[pid] = POSITION_GROUPS.get(pos, "Unknown")
+    
+    # For each position group, compute intra-cluster vs inter-cluster distances
+    from scipy.spatial.distance import pdist, cdist
+    
+    results = {}
+    
+    # Analyze all tactical role groups (left/right separated)
+    for target_group in ["GK", "Center Back", "Left Back", "Right Back",
+                         "Left Wing Back", "Right Wing Back",
+                         "Defensive Mid", "Central Mid", "Attacking Mid",
+                         "Left Wing", "Right Wing", "Striker"]:
+        # Get indices of this group
+        target_mask = np.array([pid_to_group[pid] == target_group for pid in player_ids])
+        
+        if target_mask.sum() < 2:
+            continue
+        
+        target_points = embedding_2d[target_mask]
+        other_points = embedding_2d[~target_mask]
+        
+        # Intra-cluster distances (within same position)
+        if len(target_points) > 1:
+            intra_dists = pdist(target_points, metric="euclidean")
+            mean_intra = intra_dists.mean()
+        else:
+            mean_intra = 0
+        
+        # Inter-cluster distances (to other positions)
+        if len(other_points) > 0:
+            inter_dists = cdist(target_points, other_points, metric="euclidean")
+            mean_inter = inter_dists.mean()
+        else:
+            mean_inter = 0
+        
+        # Ratio — higher is better (positions are more separated)
+        ratio = mean_inter / mean_intra if mean_intra > 0 else 0
+        
+        results[target_group] = {
+            "intra": mean_intra,
+            "inter": mean_inter,
+            "ratio": ratio,
+        }
+        
+        print(f"\n{target_group}:")
+        print(f"  Mean intra-cluster distance: {mean_intra:.3f}")
+        print(f"  Mean inter-cluster distance: {mean_inter:.3f}")
+        print(f"  Separation ratio:            {ratio:.3f}  {'✓ Good' if ratio > 1.2 else '✗ Poor'}")
+    
+    print("\nInterpretation:")
+    print("  Ratio > 1.5  → Excellent clustering (positions well separated)")
+    print("  Ratio > 1.2  → Good clustering")
+    print("  Ratio < 1.0  → Poor clustering (positions mixed)")
+
+
+# ==================================================================================================
+# MAIN
+# ==================================================================================================
+
+def main():
+    player_ids, vectors, player_info = load_data()
+    embedding_2d = compute_umap(vectors)
+    visualize(player_ids, embedding_2d, player_info)
+    analyze_clustering(player_ids, embedding_2d, player_info)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp2/model/__init__.py b/src/gp2/model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/gp2/model/build_player2vec_split.py b/src/gp2/model/build_player2vec_split.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b64418f804a92bc48a6f709c9e8ee350e2679ce
--- /dev/null
+++ b/src/gp2/model/build_player2vec_split.py
@@ -0,0 +1,358 @@
+"""
+Build 64D Player2Vec from Asymmetric On-Ball + Off-Ball Vectors — V2
+
+V2 Architecture:
+    - On-ball vectors:  48D (rich on-ball signal)
+    - Off-ball vectors: 16D (sparser off-ball signal)
+    - Concatenated:     64D
+    - GKs zero-padded with 16D zeros for off-ball component
+
+Pipeline:
+    1. Load both Doc2Vec models (onball 48D + offball 16D)
+    2. For each player:
+        - Average match-level on-ball vectors  → 48D player on-ball
+        - Average match-level off-ball vectors → 16D player off-ball
+        - Concatenate → 64D raw player vector
+    3. Aggregate position metadata (primary position, distribution, versatility)
+    4. Remove dominant direction (PCA, n_components=1)
+    5. Normalize → final 64D Player2Vec
+
+Outputs:
+    - models/gp2/player2vec_64d.npz       (player_ids + 64D vectors)
+    - models/gp2/player_metadata_v2.json  (name, team, position info, versatility)
+"""
+
+import json
+import math
+from collections import Counter, defaultdict
+from pathlib import Path
+
+import numpy as np
+from gensim.models.doc2vec import Doc2Vec
+
+from src.gp2.paths import (
+    OFFBALL_MODEL_PATH,
+    ONBALL_MODEL_PATH,
+    PLAYER2VEC_PATH,
+    PLAYER_INFO_PATH,
+    PLAYER_MATCH_DOCS_PATH,
+    PLAYER_METADATA_PATH,
+)
+
+
+# ==================================================================================================
+# DIMENSIONS
+# ==================================================================================================
+
+ONBALL_DIM = 48
+OFFBALL_DIM = 16
+TOTAL_DIM = ONBALL_DIM + OFFBALL_DIM  # 64
+
+MIN_MATCHES = 5  # minimum matches required to include player
+
+
+# ==================================================================================================
+# DOMINANT VECTOR REMOVAL (unchanged from V1 — proven technique)
+# ==================================================================================================
+
+def remove_dominant_direction(vectors, n_components=1):
+    """
+    Remove top-N principal components (Mu et al. 2018, "All-but-the-Top").
+    Critical for spreading similarity scores from compressed range.
+    """
+    mean_vec = vectors.mean(axis=0, keepdims=True)
+    centered = vectors - mean_vec
+
+    _, _, Vt = np.linalg.svd(centered, full_matrices=False)
+    top_components = Vt[:n_components]
+
+    for component in top_components:
+        projection = (centered @ component).reshape(-1, 1) * component.reshape(1, -1)
+        centered = centered - projection
+
+    norms = np.linalg.norm(centered, axis=1, keepdims=True)
+    norms = np.clip(norms, 1e-8, None)
+    normalized = centered / norms
+
+    return normalized
+
+
+# ==================================================================================================
+# POSITION METADATA AGGREGATION
+# ==================================================================================================
+
+def aggregate_position_metadata(docs_path):
+    """
+    Read corpus and aggregate per-player position info across all matches.
+    Returns dict: player_id → {
+        primary_position: str,
+        position_distribution: {position: total_event_count},
+        versatility_score: float (Shannon entropy),
+        num_matches: int,
+        num_distinct_positions: int  # positions with >=15 events total
+    }
+    """
+    player_positions = defaultdict(Counter)  # player_id → {position: total events}
+    player_match_count = Counter()           # player_id → number of matches
+
+    with open(docs_path, "r", encoding="utf-8") as f:
+        for line in f:
+            doc = json.loads(line.strip())
+            pid = doc["player_id"]
+            player_match_count[pid] += 1
+
+            # Sum event counts across matches
+            for pos, count in doc.get("position_distribution", {}).items():
+                player_positions[pid][pos] += count
+
+    # Compute aggregate metadata per player
+    metadata = {}
+    for pid, position_counts in player_positions.items():
+        total_events = sum(position_counts.values())
+        if total_events == 0:
+            continue
+
+        # Primary position = most events
+        primary_position = position_counts.most_common(1)[0][0]
+
+        # Versatility via Shannon entropy
+        # 0 = single position, higher = more spread across positions
+        entropy = 0.0
+        for count in position_counts.values():
+            p = count / total_events
+            if p > 0:
+                entropy -= p * math.log(p)
+
+        # Distinct positions (with at least 15 events total — meaningful playtime)
+        distinct = sum(1 for count in position_counts.values() if count >= 15)
+
+        metadata[pid] = {
+            "primary_position": primary_position,
+            "position_distribution": dict(position_counts),
+            "versatility_score": round(entropy, 4),
+            "num_distinct_positions": distinct,
+            "num_matches": player_match_count[pid],
+        }
+
+    return metadata
+
+
+# ==================================================================================================
+# BUILD 64D PLAYER2VEC
+# ==================================================================================================
+
+def build_player2vec(min_matches=MIN_MATCHES, n_components=1):
+    """Aggregate match vectors into player vectors and clean."""
+
+    # ---------------- LOAD MODELS ----------------
+    print("Loading Doc2Vec models...")
+    onball_model = Doc2Vec.load(str(ONBALL_MODEL_PATH))
+    offball_model = Doc2Vec.load(str(OFFBALL_MODEL_PATH))
+
+    # Sanity check dimensions
+    actual_onball_dim = onball_model.dv[onball_model.dv.index_to_key[0]].shape[0]
+    actual_offball_dim = offball_model.dv[offball_model.dv.index_to_key[0]].shape[0]
+
+    print(f"  On-ball model:  {len(onball_model.dv):,} doc vectors, dim={actual_onball_dim}")
+    print(f"  Off-ball model: {len(offball_model.dv):,} doc vectors, dim={actual_offball_dim}")
+
+    if actual_onball_dim != ONBALL_DIM or actual_offball_dim != OFFBALL_DIM:
+        raise ValueError(
+            f"Dimension mismatch! Expected {ONBALL_DIM}+{OFFBALL_DIM}, "
+            f"got {actual_onball_dim}+{actual_offball_dim}. "
+            f"Did train_playermatch2vec.py run with the right dimensions?"
+        )
+
+    # ---------------- GROUP MATCH VECTORS BY PLAYER ----------------
+    print("\nGrouping match vectors by player...")
+    player_onball = defaultdict(list)
+    player_offball = defaultdict(list)
+
+    for doc_tag in onball_model.dv.index_to_key:
+        player_id = doc_tag.split("_")[0]
+        player_onball[player_id].append(onball_model.dv[doc_tag])
+
+    for doc_tag in offball_model.dv.index_to_key:
+        player_id = doc_tag.split("_")[0]
+        player_offball[player_id].append(offball_model.dv[doc_tag])
+
+    print(f"  Players with on-ball data:  {len(player_onball):,}")
+    print(f"  Players with off-ball data: {len(player_offball):,}")
+
+    # ---------------- LOAD POSITION METADATA ----------------
+    print("\nAggregating position metadata across matches...")
+    position_metadata = aggregate_position_metadata(PLAYER_MATCH_DOCS_PATH)
+    print(f"  Players with position metadata: {len(position_metadata):,}")
+
+    # ---------------- LOAD EXISTING PLAYER INFO (name, team) ----------------
+    if Path(PLAYER_INFO_PATH).exists():
+        with open(PLAYER_INFO_PATH, "r", encoding="utf-8") as f:
+            player_info = json.load(f)
+        print(f"  Loaded existing player_info: {len(player_info):,} players")
+    else:
+        print(f"  ⚠ {PLAYER_INFO_PATH} not found — name/team will be missing")
+        player_info = {}
+
+    # ---------------- BUILD 64D VECTORS ----------------
+    valid_ids = []
+    raw_vectors = []
+    excluded_too_few = 0
+    gk_zero_padded = 0
+
+    for player_id in player_onball.keys():
+        onball_vecs = player_onball[player_id]
+
+        if len(onball_vecs) < min_matches:
+            excluded_too_few += 1
+            continue
+
+        mean_onball = np.mean(onball_vecs, axis=0)  # 48D
+
+        # Handle missing off-ball (goalkeepers, mostly)
+        if player_id not in player_offball:
+            mean_offball = np.zeros(OFFBALL_DIM)
+            gk_zero_padded += 1
+        else:
+            offball_vecs = player_offball[player_id]
+            if len(offball_vecs) < min_matches:
+                # Has off-ball but too few matches — still zero-pad rather than exclude
+                mean_offball = np.zeros(OFFBALL_DIM)
+                gk_zero_padded += 1
+            else:
+                mean_offball = np.mean(offball_vecs, axis=0)  # 16D
+
+        combined = np.concatenate([mean_onball, mean_offball])  # 64D
+        valid_ids.append(player_id)
+        raw_vectors.append(combined)
+
+    raw_vectors = np.array(raw_vectors)
+
+    print(f"\nPlayers in final Player2Vec: {len(valid_ids):,}")
+    print(f"  Zero-padded off-ball (GKs/few matches): {gk_zero_padded}")
+    print(f"  Excluded (too few on-ball matches):     {excluded_too_few}")
+    print(f"  Vector shape: {raw_vectors.shape}")
+
+    # ---------------- DOMINANT VECTOR REMOVAL ----------------
+    print(f"\nRemoving {n_components} dominant component(s)...")
+
+    # Stats before
+    norms_before = np.linalg.norm(raw_vectors, axis=1, keepdims=True)
+    normalized_before = raw_vectors / np.clip(norms_before, 1e-8, None)
+    sims_before = normalized_before @ normalized_before.T
+    np.fill_diagonal(sims_before, 0)
+
+    print(f"\nBefore removal:")
+    print(f"  Mean similarity:  {sims_before.mean():.4f}")
+    print(f"  Min similarity:   {sims_before.min():.4f}")
+    print(f"  Max similarity:   {sims_before.max():.4f}")
+
+    cleaned_vectors = remove_dominant_direction(raw_vectors, n_components)
+
+    # Stats after
+    sims_after = cleaned_vectors @ cleaned_vectors.T
+    np.fill_diagonal(sims_after, 0)
+
+    print(f"\nAfter removal:")
+    print(f"  Mean similarity:  {sims_after.mean():.4f}")
+    print(f"  Min similarity:   {sims_after.min():.4f}")
+    print(f"  Max similarity:   {sims_after.max():.4f}")
+
+    # ---------------- SAVE PLAYER2VEC ----------------
+    player_ids_array = np.array(valid_ids)
+    np.savez(
+        PLAYER2VEC_PATH,
+        player_ids=player_ids_array,
+        vectors=cleaned_vectors,
+    )
+    print(f"\n✓ Player2Vec ({TOTAL_DIM}D) saved: {PLAYER2VEC_PATH}")
+    print(f"  Shape: {cleaned_vectors.shape}")
+
+    # ---------------- SAVE ENRICHED METADATA ----------------
+    print(f"\nBuilding enriched metadata...")
+    enriched_metadata = {}
+    for pid in valid_ids:
+        info = player_info.get(pid, {})
+        pos_meta = position_metadata.get(pid, {})
+
+        enriched_metadata[pid] = {
+            "name": info.get("name", "Unknown"),
+            "team": info.get("team", "Unknown"),
+            "primary_position": pos_meta.get("primary_position", info.get("position", "Unknown")),
+            "position_distribution": pos_meta.get("position_distribution", {}),
+            "versatility_score": pos_meta.get("versatility_score", 0.0),
+            "num_distinct_positions": pos_meta.get("num_distinct_positions", 1),
+            "num_matches": pos_meta.get("num_matches", 0),
+        }
+
+    with open(PLAYER_METADATA_PATH, "w", encoding="utf-8") as f:
+        json.dump(enriched_metadata, f, ensure_ascii=False, indent=2)
+    print(f"✓ Enriched metadata saved: {PLAYER_METADATA_PATH}")
+
+    # ---------------- SANITY CHECKS ----------------
+    print("\n" + "=" * 80)
+    print("SANITY CHECK — Top 5 similar players for sample players")
+    print("=" * 80)
+
+    sample_names = ["Suárez", "Neymar", "Mané", "Messi", "Iniesta"]
+    name_to_idx = {}
+    for i, pid in enumerate(valid_ids):
+        name = enriched_metadata[pid]["name"]
+        for sample in sample_names:
+            if sample in name:
+                name_to_idx[sample] = i
+                break
+
+    for sample, idx in name_to_idx.items():
+        target_vec = cleaned_vectors[idx]
+        sims = cleaned_vectors @ target_vec
+        sims[idx] = -1  # exclude self
+
+        top_indices = np.argsort(sims)[::-1][:5]
+
+        target_meta = enriched_metadata[valid_ids[idx]]
+        print(
+            f"\n{target_meta['name']} ({target_meta['primary_position']}, "
+            f"{target_meta['team']}, versatility={target_meta['versatility_score']}):"
+        )
+        for i in top_indices:
+            sim_meta = enriched_metadata[valid_ids[i]]
+            print(
+                f"  {sim_meta['name']:<35} "
+                f"({sim_meta['primary_position']:<25}, {sim_meta['team']:<22}) "
+                f"→ {sims[i]:.4f}"
+            )
+
+    # ---------------- VERSATILITY HIGHLIGHTS ----------------
+    print("\n" + "=" * 80)
+    print("VERSATILITY HIGHLIGHTS — Most versatile players (entropy + distinct positions)")
+    print("=" * 80)
+
+    versatile_players = sorted(
+        enriched_metadata.items(),
+        key=lambda kv: (kv[1]["num_distinct_positions"], kv[1]["versatility_score"]),
+        reverse=True,
+    )[:15]
+
+    print(f"\n{'Player':<35} {'Primary Pos':<25} {'Distinct':<10} {'Entropy':<10}")
+    print("-" * 90)
+    for pid, meta in versatile_players:
+        print(
+            f"{meta['name']:<35} {meta['primary_position']:<25} "
+            f"{meta['num_distinct_positions']:<10} {meta['versatility_score']:<10.4f}"
+        )
+
+
+# ==================================================================================================
+# MAIN
+# ==================================================================================================
+
+def main():
+    print("=" * 80)
+    print(f"BUILDING {TOTAL_DIM}D PLAYER2VEC (V2)")
+    print(f"  On-ball:  {ONBALL_DIM}D  +  Off-ball: {OFFBALL_DIM}D")
+    print("=" * 80)
+    build_player2vec(min_matches=MIN_MATCHES, n_components=1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp2/model/train_action2vec.py b/src/gp2/model/train_action2vec.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f37fbf578093b72c4171d2aa368c90a90c9f370
--- /dev/null
+++ b/src/gp2/model/train_action2vec.py
@@ -0,0 +1,64 @@
+import json
+import multiprocessing
+from gensim.models import Word2Vec
+
+from src.gp2.paths import ACTION_SENTENCES_PATH, ACTION2VEC_PATH
+
+
+class SentenceIterator:
+    """
+    Memory-safe iterator for large corpus
+    """
+    def __init__(self, file_path):
+        self.file_path = file_path
+
+    def __iter__(self):
+        with open(self.file_path, "r", encoding="utf-8") as f:
+            for line in f:
+                yield json.loads(line.strip())
+
+
+def main():
+    print("🚀 Starting Action2Vec training...")
+
+    sentences = SentenceIterator(ACTION_SENTENCES_PATH)
+
+    # ⚙️ SAFE CPU usage (VERY IMPORTANT)
+    total_cores = multiprocessing.cpu_count()
+    workers = max(1, total_cores - 2)  # leave 2 cores free
+
+    print(f"🧠 CPU cores: {total_cores} | Using workers: {workers}")
+
+    model = Word2Vec(
+        sentences=sentences,
+        vector_size=48,
+        window=5,
+        min_count=10,
+        sg=1,                 # Skip-gram (important)
+        negative=10,
+        workers=workers,
+        epochs=5,             # safe start
+        compute_loss=True
+    )
+
+    ACTION2VEC_PATH.parent.mkdir(parents=True, exist_ok=True)
+    model.save(str(ACTION2VEC_PATH))
+
+    print("✅ Training complete!")
+    print(f"💾 Model saved to: {ACTION2VEC_PATH}")
+
+    # 🔎 Debug: show similar tokens
+    print("\n🔍 Sample token similarities:")
+    print(f"Vocabulary size: {len(model.wv)}")
+
+    # Pick actual tokens from vocabulary
+    sample_tokens = list(model.wv.key_to_index.keys())[:3]
+
+    for word in sample_tokens:
+        print(f"\nTop similar to '{word}':")
+        for sim_word, score in model.wv.most_similar(word, topn=5):
+            print(f"  {sim_word} → {score:.4f}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp2/model/train_playermatch2vec.py b/src/gp2/model/train_playermatch2vec.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f2b2768cbb59ecb22ed9342b08c66b76c337f14
--- /dev/null
+++ b/src/gp2/model/train_playermatch2vec.py
@@ -0,0 +1,205 @@
+"""
+Train Separate On-Ball and Off-Ball Doc2Vec Models — V2
+
+V2 Changes:
+    - Onball Doc2Vec:  48D (inherits word vectors from Action2Vec)
+    - Offball Doc2Vec: 16D (trains from scratch — no inheritance, dimensions don't match)
+
+Why asymmetric dimensions:
+    - On-ball events are 3-5x more frequent → richer signal → more dimensions
+    - Off-ball events are sparser but distinctive → 16D is enough
+    - Total concatenated: 64D (same as before)
+
+Output:
+    - playermatch2vec_onball.model  (48D)
+    - playermatch2vec_offball.model (16D)
+"""
+
+import json
+import multiprocessing
+from pathlib import Path
+
+from gensim.models import Word2Vec
+from gensim.models.doc2vec import Doc2Vec, TaggedDocument
+
+from src.gp2.paths import (
+    ACTION2VEC_PATH,
+    OFFBALL_MODEL_PATH,
+    ONBALL_MODEL_PATH,
+    PLAYER_MATCH_DOCS_PATH,
+)
+
+
+# ==================================================================================================
+# DIMENSIONS — V2 ASYMMETRIC
+# ==================================================================================================
+
+ONBALL_DIM = 48      # Inherits from Action2Vec (must match Action2Vec dim)
+OFFBALL_DIM = 16     # Trains from scratch (no inheritance)
+
+
+# ==================================================================================================
+# DOCUMENT ITERATORS
+# ==================================================================================================
+
+class OnBallIterator:
+    """Streams on-ball documents only."""
+
+    def __init__(self, file_path):
+        self.file_path = file_path
+
+    def __iter__(self):
+        with open(self.file_path, "r", encoding="utf-8") as f:
+            for line in f:
+                doc = json.loads(line.strip())
+                if not doc.get("onball_tokens"):
+                    continue
+                tag = f"{doc['player_id']}_{doc['match_id']}"
+                yield TaggedDocument(words=doc["onball_tokens"], tags=[tag])
+
+
+class OffBallIterator:
+    """Streams off-ball documents only."""
+
+    def __init__(self, file_path):
+        self.file_path = file_path
+
+    def __iter__(self):
+        with open(self.file_path, "r", encoding="utf-8") as f:
+            for line in f:
+                doc = json.loads(line.strip())
+                if not doc.get("offball_tokens"):
+                    continue
+                tag = f"{doc['player_id']}_{doc['match_id']}"
+                yield TaggedDocument(words=doc["offball_tokens"], tags=[tag])
+
+
+# ==================================================================================================
+# TRAINING
+# ==================================================================================================
+
+def train_doc2vec_model(iterator, model_path, name, vector_size, action2vec=None):
+    """
+    Train one Doc2Vec model.
+
+    Args:
+        iterator:        OnBallIterator or OffBallIterator
+        model_path:      where to save the trained model
+        name:            display name for logging
+        vector_size:     dimensionality of doc vectors
+        action2vec:      optional Word2Vec model to inherit word vectors from.
+                         Must have matching vector_size or inheritance is skipped.
+    """
+    print(f"\n{'='*80}")
+    print(f"Training {name} Model — {vector_size}D")
+    print(f"{'='*80}")
+
+    # Count documents (iterator re-opens file each time, so this is safe)
+    doc_count = sum(1 for _ in iterator)
+    print(f"Total documents: {doc_count:,}")
+
+    # CPU safety
+    total_cores = multiprocessing.cpu_count()
+    workers = max(1, total_cores - 2)
+    print(f"CPU cores: {total_cores} | Using workers: {workers}")
+
+    # Build Doc2Vec model
+    model = Doc2Vec(
+        vector_size=vector_size,
+        window=2,
+        min_count=5,
+        dm=1,
+        negative=10,
+        workers=workers,
+        epochs=20,
+    )
+
+    # Build vocabulary
+    print("Building vocabulary...")
+    model.build_vocab(iterator)
+    print(f"Vocabulary: {len(model.wv)} tokens")
+
+    # Inherit word vectors from Action2Vec (if dimensions match)
+    if action2vec is not None:
+        if action2vec.wv.vector_size != vector_size:
+            print(
+                f"⚠ Action2Vec dim ({action2vec.wv.vector_size}) != Doc2Vec dim ({vector_size}). "
+                f"Skipping inheritance — will train from scratch."
+            )
+        else:
+            copied = 0
+            for word in model.wv.key_to_index:
+                if word in action2vec.wv:
+                    model.wv[word] = action2vec.wv[word]
+                    copied += 1
+            print(f"Copied {copied}/{len(model.wv)} word vectors from Action2Vec")
+    else:
+        print("No Action2Vec inheritance — training word vectors from scratch")
+
+    # Train
+    print("Training...")
+    model.train(
+        iterator,
+        total_examples=model.corpus_count,
+        epochs=model.epochs,
+    )
+
+    # Save
+    Path(model_path).parent.mkdir(parents=True, exist_ok=True)
+    model.save(str(model_path))
+    print(f"Saved to: {model_path}")
+
+    return model
+
+
+def main():
+    print("=" * 80)
+    print("TRAINING ASYMMETRIC ON-BALL / OFF-BALL DOC2VEC MODELS (V2)")
+    print("=" * 80)
+    print(f"\nArchitecture:")
+    print(f"  On-ball  Doc2Vec: {ONBALL_DIM}D (inherits from Action2Vec)")
+    print(f"  Off-ball Doc2Vec: {OFFBALL_DIM}D (from scratch)")
+    print(f"  Final concat:     {ONBALL_DIM + OFFBALL_DIM}D")
+
+    # Load pretrained Action2Vec
+    print(f"\nLoading Action2Vec from: {ACTION2VEC_PATH}")
+    action2vec = Word2Vec.load(str(ACTION2VEC_PATH))
+    print(f"Action2Vec vocabulary: {len(action2vec.wv)} tokens, dim: {action2vec.wv.vector_size}")
+
+    # Sanity check: Action2Vec must match ONBALL_DIM for inheritance to work
+    if action2vec.wv.vector_size != ONBALL_DIM:
+        print(
+            f"\n⚠ WARNING: Action2Vec dim ({action2vec.wv.vector_size}) != ONBALL_DIM ({ONBALL_DIM}). "
+            f"On-ball model will train from scratch instead of inheriting."
+        )
+
+    # Train on-ball model (with inheritance)
+    onball_iter = OnBallIterator(PLAYER_MATCH_DOCS_PATH)
+    train_doc2vec_model(
+        iterator=onball_iter,
+        model_path=ONBALL_MODEL_PATH,
+        name="On-Ball",
+        vector_size=ONBALL_DIM,
+        action2vec=action2vec,
+    )
+
+    # Train off-ball model (no inheritance — different dimensionality)
+    offball_iter = OffBallIterator(PLAYER_MATCH_DOCS_PATH)
+    train_doc2vec_model(
+        iterator=offball_iter,
+        model_path=OFFBALL_MODEL_PATH,
+        name="Off-Ball",
+        vector_size=OFFBALL_DIM,
+        action2vec=None,
+    )
+
+    print("\n" + "=" * 80)
+    print("TRAINING COMPLETE")
+    print("=" * 80)
+    print(f"\nOn-ball model:  {ONBALL_MODEL_PATH}  ({ONBALL_DIM}D)")
+    print(f"Off-ball model: {OFFBALL_MODEL_PATH} ({OFFBALL_DIM}D)")
+    print(f"\nNext step: Run build_player2vec.py to create {ONBALL_DIM + OFFBALL_DIM}D Player2Vec")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp2/paths.py b/src/gp2/paths.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1aa4f3f1b9a4ade15456ff64dcc2bdbc883de2e
--- /dev/null
+++ b/src/gp2/paths.py
@@ -0,0 +1,26 @@
+from pathlib import Path
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+DATA_DIR = PROJECT_ROOT / "data"
+MODELS_DIR = PROJECT_ROOT / "models"
+VISUALIZATIONS_DIR = PROJECT_ROOT / "visualizations"
+
+GP2_MODELS_DIR = MODELS_DIR / "gp2"
+TOP5_DATA_DIR = DATA_DIR / "Top5_Leagues_1516"
+FULL_MATCHES_DIR = DATA_DIR / "Full_Matches"
+
+ACTION_SENTENCES_PATH = GP2_MODELS_DIR / "action_sentences.jsonl"
+ACTION2VEC_PATH = GP2_MODELS_DIR / "action2vec.model"
+PLAYER_INFO_PATH = GP2_MODELS_DIR / "player_info.json"
+PLAYER_MATCH_DOCS_PATH = GP2_MODELS_DIR / "player_match_docs_split.jsonl"
+PLAYER_MATCH_DOCS_LEGACY_PATH = GP2_MODELS_DIR / "player_match_docs.jsonl"
+PLAYER2VEC_PATH = GP2_MODELS_DIR / "player2vec_64d.npz"
+PLAYER_METADATA_PATH = GP2_MODELS_DIR / "player_metadata_v2.json"
+ONBALL_MODEL_PATH = GP2_MODELS_DIR / "playermatch2vec_onball.model"
+OFFBALL_MODEL_PATH = GP2_MODELS_DIR / "playermatch2vec_offball.model"
+
+PLAYER2VEC_UMAP_PATH = VISUALIZATIONS_DIR / "player2vec_umap.png"
+
+# Backward-compatible names used by older GP2 scripts.
+DOCS_PATH = PLAYER_MATCH_DOCS_PATH
diff --git a/src/gp2/pipeline/__init__.py b/src/gp2/pipeline/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/gp2/pipeline/build_action_corpus.py b/src/gp2/pipeline/build_action_corpus.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e1b2f6422de837b571a4219fadb8613d76bfb25
--- /dev/null
+++ b/src/gp2/pipeline/build_action_corpus.py
@@ -0,0 +1,48 @@
+import json
+
+from src.gp2.preprocess.tokens import event_to_tokens
+from src.gp2.preprocess.sequence_builder import build_possession_sequences
+from src.gp2.preprocess.prepare_sequences import flatten_sequences, filter_sequences_by_length
+from src.gp2.paths import ACTION_SENTENCES_PATH, FULL_MATCHES_DIR
+
+
+def main():
+    print("Building Action2Vec corpus (streaming mode)...")
+
+    ACTION_SENTENCES_PATH.parent.mkdir(parents=True, exist_ok=True)
+
+    total_sequences = 0
+
+    with open(ACTION_SENTENCES_PATH, "w", encoding="utf-8") as out_f:
+
+        for i, file in enumerate(FULL_MATCHES_DIR.glob("*.json")):
+            match_id = file.stem
+
+            with open(file, "r", encoding="utf-8") as f:
+                events = json.load(f)
+
+                # inject match_id
+                for ev in events:
+                    ev["match_id"] = match_id
+
+            # process this match only
+            sequences = build_possession_sequences(events, event_to_tokens)
+            flat_sequences = flatten_sequences(sequences)
+            flat_sequences = filter_sequences_by_length(flat_sequences, min_len=10, max_len=200)
+
+            # save immediately
+            for seq in flat_sequences:
+                out_f.write(json.dumps(seq) + "\n")
+
+            total_sequences += len(flat_sequences)
+
+            # progress log every 50 matches
+            if i % 50 == 0:
+                print(f"Processed {i} matches | total sequences: {total_sequences}")
+
+    print("✅ Done")
+    print(f"Total sequences: {total_sequences}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp2/pipeline/build_player_match_corpus.py b/src/gp2/pipeline/build_player_match_corpus.py
new file mode 100644
index 0000000000000000000000000000000000000000..4232f236a3b36b9be7b49e53d89c9ca7ee06acab
--- /dev/null
+++ b/src/gp2/pipeline/build_player_match_corpus.py
@@ -0,0 +1,233 @@
+"""
+Build Player-Match Corpus with Separate On-Ball and Off-Ball Documents — V2
+
+V2 Changes:
+    - Uses updated tokens.py (V2) with semantic markers (cut_inside, to_box, key_pass, etc.)
+    - Single document per player-match (no position segmentation)
+      Following Magdaci's proven approach — position info will be tracked separately
+      for downstream filtering/analysis, not encoded in document boundaries.
+    - GKs handled with empty offball lists (zero-padded later in build_player2vec)
+    - Token budgets unchanged (proven to balance event types correctly)
+
+Output format per line (JSONL):
+{
+    "player_id": "5246",
+    "match_id": "265839",
+    "primary_position": "Center Forward",   # most-frequent position in this match
+    "position_distribution": {              # for versatility analysis later
+        "Center Forward": 45,
+        "Left Wing": 12
+    },
+    "onball_tokens": [...],                 # on-ball actions (passes, carries, shots, dribbles)
+    "offball_tokens": [...]                 # defensive/pressing actions (can be empty for GKs)
+}
+"""
+
+import json
+import random
+from collections import defaultdict, Counter
+from pathlib import Path
+
+from src.gp2.preprocess.tokens import event_to_tokens
+from src.gp2.paths import PLAYER_MATCH_DOCS_PATH, TOP5_DATA_DIR
+
+
+# ==================================================================================================
+# EVENT CLASSIFICATION (unchanged from V1)
+# ==================================================================================================
+
+ONBALL_EVENTS = {
+    "Pass",
+    "Carry",
+    "Dribble",
+    "Shot",
+    "Foul Committed",
+    "Ball Recovery",
+}
+
+OFFBALL_EVENTS = {
+    "Pressure",
+    "Block",
+    "Dribbled Past",
+    "Dispossessed",
+    "Clearance",
+    "Interception",
+    "Duel",
+}
+
+
+# ==================================================================================================
+# TOKEN BUDGETING (unchanged — proven to work)
+# ==================================================================================================
+
+ONBALL_BUDGET = {
+    "Pass":           80,
+    "Carry":          50,
+    "Shot":           30,
+    "Dribble":        25,
+    "Ball Recovery":  20,
+    "Foul Committed": 15,
+}
+
+OFFBALL_BUDGET = {
+    "Pressure":       50,
+    "Duel":           30,
+    "Clearance":      25,
+    "Interception":   25,
+    "Block":          20,
+    "Dribbled Past":  15,
+    "Dispossessed":   15,
+}
+
+
+def build_balanced_doc(tokens_by_type, event_set, budget_map):
+    """
+    Build balanced document from tokens grouped by event type.
+    Caps each event type at its budget to prevent dominant types
+    (e.g., Pass) from drowning out distinctive ones (e.g., Shot).
+    """
+    doc = []
+
+    for event_type, tokens in tokens_by_type.items():
+        if event_type not in event_set:
+            continue
+
+        budget = budget_map.get(event_type, 20)
+        doc.extend(tokens[:budget])
+
+    random.shuffle(doc)
+    return doc
+
+
+# ==================================================================================================
+# MATCH PROCESSING
+# ==================================================================================================
+
+def process_match(file_path):
+    """
+    Process one match. Returns: dict {player_id: doc_dict, ...}, match_id
+
+    For each player in the match:
+      - Collect tokens by event type
+      - Track which positions they played (for metadata)
+      - Build separate onball + offball documents
+    """
+    with open(file_path, "r", encoding="utf-8") as f:
+        events = json.load(f)
+
+    match_id = Path(file_path).stem
+
+    # Per player: tokens grouped by event type, position counter
+    player_type_tokens = defaultdict(lambda: defaultdict(list))
+    player_position_counts = defaultdict(Counter)
+
+    for event in events:
+        # Skip events without a player (e.g., team-level events)
+        if "player" not in event or event["player"] is None:
+            continue
+
+        player_id = event["player"]["id"]
+        event_type = event.get("type", {}).get("name", "unknown")
+
+        # Track position (from event metadata)
+        position = event.get("position", {}).get("name") if event.get("position") else None
+        if position:
+            player_position_counts[player_id][position] += 1
+
+        # Tokenize and collect
+        tokens = event_to_tokens(event)
+        if len(tokens) >= 1:
+            player_type_tokens[player_id][event_type].extend(tokens)
+
+    # Build documents per player
+    player_docs = {}
+
+    for player_id, type_tokens in player_type_tokens.items():
+        onball = build_balanced_doc(type_tokens, ONBALL_EVENTS, ONBALL_BUDGET)
+        offball = build_balanced_doc(type_tokens, OFFBALL_EVENTS, OFFBALL_BUDGET)
+
+        # Require minimum on-ball tokens (off-ball can be empty for GKs)
+        if len(onball) < 10:
+            continue
+
+        # Determine primary position (most events) and full distribution
+        position_dist = dict(player_position_counts[player_id])
+        primary_position = (
+            max(position_dist, key=position_dist.get) if position_dist else "Unknown"
+        )
+
+        player_docs[player_id] = {
+            "onball": onball,
+            "offball": offball,
+            "primary_position": primary_position,
+            "position_distribution": position_dist,
+        }
+
+    return player_docs, match_id
+
+
+# ==================================================================================================
+# MAIN
+# ==================================================================================================
+
+def main():
+    PLAYER_MATCH_DOCS_PATH.parent.mkdir(parents=True, exist_ok=True)
+
+    total_docs = 0
+    total_onball_tokens = 0
+    total_offball_tokens = 0
+    docs_with_no_offball = 0
+    position_distribution = Counter()
+
+    print("Building player-match corpus (V2)...")
+    print(f"Reading from: {TOP5_DATA_DIR}")
+    print(f"Writing to:   {PLAYER_MATCH_DOCS_PATH}\n")
+
+    with open(PLAYER_MATCH_DOCS_PATH, "w", encoding="utf-8") as out_file:
+        match_files = list(TOP5_DATA_DIR.glob("*.json"))
+        total_matches = len(match_files)
+
+        for i, file in enumerate(match_files):
+            player_docs, match_id = process_match(file)
+
+            for player_id, docs in player_docs.items():
+                doc = {
+                    "player_id": str(player_id),
+                    "match_id": match_id,
+                    "primary_position": docs["primary_position"],
+                    "position_distribution": docs["position_distribution"],
+                    "onball_tokens": docs["onball"],
+                    "offball_tokens": docs["offball"],
+                }
+                json.dump(doc, out_file)
+                out_file.write("\n")
+
+                total_docs += 1
+                total_onball_tokens += len(docs["onball"])
+                total_offball_tokens += len(docs["offball"])
+
+                if len(docs["offball"]) == 0:
+                    docs_with_no_offball += 1
+
+                position_distribution[docs["primary_position"]] += 1
+
+            if i % 50 == 0:
+                print(f"Processed {i}/{total_matches} matches | docs: {total_docs}")
+
+    print("\n" + "=" * 60)
+    print("CORPUS BUILD COMPLETE")
+    print("=" * 60)
+    print(f"Total player-match documents: {total_docs}")
+    print(f"Total on-ball tokens:  {total_onball_tokens:,}")
+    print(f"Total off-ball tokens: {total_offball_tokens:,}")
+    print(f"Avg on-ball per doc:   {total_onball_tokens/total_docs:.1f}")
+    print(f"Avg off-ball per doc:  {total_offball_tokens/total_docs:.1f}")
+    print(f"Docs with NO off-ball: {docs_with_no_offball} (likely GKs/subs)")
+
+    print(f"\nPosition distribution (top 15):")
+    for pos, count in position_distribution.most_common(15):
+        print(f"  {pos:<35} {count}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp2/pipeline/extract_players.py b/src/gp2/pipeline/extract_players.py
new file mode 100644
index 0000000000000000000000000000000000000000..f013d8a7b52511b20b7f33a9a1a9dd15590ac446
--- /dev/null
+++ b/src/gp2/pipeline/extract_players.py
@@ -0,0 +1,96 @@
+"""
+Extract player_id → name + position from Top5 Leagues 1516 data.
+Outputs a JSON file for use by run_search.py and visualization scripts.
+
+Output format:
+{
+    "5246": {"name": "Luis Alberto Suárez Díaz", "position": "Center Forward"},
+    "4320": {"name": "Neymar da Silva Santos Junior", "position": "Left Wing"},
+    ...
+}
+"""
+
+import json
+from collections import defaultdict, Counter
+
+from src.gp2.paths import PLAYER_INFO_PATH, TOP5_DATA_DIR
+
+
+def main():
+    print("Extracting player info from match data...")
+
+    # Track name, position, and team occurrences per player
+    player_names = {}
+    player_positions = defaultdict(Counter)
+    player_teams = defaultdict(Counter)
+
+    match_count = 0
+
+    for file in TOP5_DATA_DIR.glob("*.json"):
+        with open(file, "r", encoding="utf-8") as f:
+            events = json.load(f)
+
+        for ev in events:
+            player = ev.get("player")
+            position = ev.get("position")
+            team = ev.get("team")
+
+            if player is None or player.get("id") is None:
+                continue
+
+            pid = str(player["id"])
+
+            # Store name (overwrite is fine — same player same name)
+            player_names[pid] = player["name"]
+
+            # Count position occurrences to find primary position
+            if position and position.get("name"):
+                player_positions[pid][position["name"]] += 1
+            
+            # Count team occurrences to find primary team
+            if team and team.get("name"):
+                player_teams[pid][team["name"]] += 1
+
+        match_count += 1
+        if match_count % 200 == 0:
+            print(f"Processed {match_count} matches | {len(player_names)} unique players")
+
+    # Build final lookup — use most frequent position and team per player
+    player_info = {}
+
+    for pid, name in player_names.items():
+        pos_counter = player_positions.get(pid, Counter())
+        team_counter = player_teams.get(pid, Counter())
+        
+        primary_position = pos_counter.most_common(1)[0][0] if pos_counter else "Unknown"
+        primary_team = team_counter.most_common(1)[0][0] if team_counter else "Unknown"
+
+        player_info[pid] = {
+            "name": name,
+            "position": primary_position,
+            "team": primary_team,
+        }
+
+    # Save
+    PLAYER_INFO_PATH.parent.mkdir(parents=True, exist_ok=True)
+    with open(PLAYER_INFO_PATH, "w", encoding="utf-8") as f:
+        json.dump(player_info, f, ensure_ascii=False, indent=2)
+
+    print(f"\nTotal players extracted: {len(player_info)}")
+    print(f"Saved to: {PLAYER_INFO_PATH}")
+
+    # Position distribution
+    positions = Counter(info["position"] for info in player_info.values())
+    print(f"\nPosition distribution:")
+    for pos, count in positions.most_common():
+        print(f"  {pos:<30}: {count}")
+    
+    # Team distribution (top 20)
+    teams = Counter(info["team"] for info in player_info.values())
+    print(f"\nTop 20 teams by player count:")
+    for team, count in teams.most_common(20):
+        print(f"  {team:<30}: {count}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gp2/pipeline/sanitycheck.py b/src/gp2/pipeline/sanitycheck.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c1a3e4c4c4c7349b72de7e149f082731da18103
--- /dev/null
+++ b/src/gp2/pipeline/sanitycheck.py
@@ -0,0 +1,35 @@
+import json
+from collections import defaultdict
+
+from src.gp2.paths import PLAYER_MATCH_DOCS_LEGACY_PATH
+
+file_path = PLAYER_MATCH_DOCS_LEGACY_PATH
+
+lengths = []
+player_doc_counts = defaultdict(int)
+
+with open(file_path, "r", encoding="utf-8") as f:
+    for line in f:
+        doc = json.loads(line)
+        lengths.append(len(doc["tokens"]))
+        player_doc_counts[doc["player_id"]] += 1
+
+print(f"Total docs: {len(lengths)}")
+print(f"Min length: {min(lengths)}")
+print(f"Max length: {max(lengths)}")
+print(f"Average length: {sum(lengths)/len(lengths):.2f}")
+
+short  = sum(1 for l in lengths if l < 20)
+normal = sum(1 for l in lengths if 20 <= l <= 300)
+long   = sum(1 for l in lengths if l > 300)
+
+print(f"\nToken distribution:")
+print(f"Too short  (<20):    {short}")
+print(f"Normal     (20–300): {normal}")
+print(f"Too long   (>300):   {long}")
+
+print(f"\nPlayer-level stats:")
+print(f"Unique players: {len(player_doc_counts)}")
+print(f"Players with <5 match docs:   {sum(1 for v in player_doc_counts.values() if v < 5)}")
+print(f"Players with 5–30 match docs: {sum(1 for v in player_doc_counts.values() if 5 <= v <= 30)}")
+print(f"Players with >30 match docs:  {sum(1 for v in player_doc_counts.values() if v > 30)}")
diff --git a/src/gp2/pipeline/test.py b/src/gp2/pipeline/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b2d1c05e4233e0721bbcb18303ace5c22b9dde3
--- /dev/null
+++ b/src/gp2/pipeline/test.py
@@ -0,0 +1,17 @@
+from collections import Counter
+from src.gp2.preprocess.tokens import event_to_tokens
+from src.gp2.paths import TOP5_DATA_DIR
+import json
+
+with open(TOP5_DATA_DIR / "265839.json") as f:
+    events = json.load(f)
+
+type_lengths = Counter()
+for ev in events:
+    tokens = event_to_tokens(ev)
+    event_type = ev.get("type", {}).get("name", "unknown")
+    type_lengths[event_type] += len(tokens)
+
+# See which event types are generating the most tokens
+for event_type, total in type_lengths.most_common(15):
+    print(f"{event_type}: {total} total tokens")
diff --git a/src/gp2/preprocess/__init__.py b/src/gp2/preprocess/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/gp2/preprocess/prepare_sequences.py b/src/gp2/preprocess/prepare_sequences.py
new file mode 100644
index 0000000000000000000000000000000000000000..83dc90b002f9e9ccb336f0c29afae107c531ead7
--- /dev/null
+++ b/src/gp2/preprocess/prepare_sequences.py
@@ -0,0 +1,29 @@
+# prepare_sequences.py
+
+def flatten_sequences(sequences):
+
+    flat_sequences = []
+
+    for seq in sequences:
+        # NEW FORMAT
+        if isinstance(seq, dict):
+            tokens = seq["tokens"]
+        else:
+            # fallback (just in case)
+            tokens = seq
+
+        if tokens:
+            flat_sequences.append(tokens)
+
+    return flat_sequences  # player_ids not needed for Word2Vec, can be added later for Doc2Vec
+
+
+def filter_sequences_by_length(sequences, min_len=5, max_len=200):
+
+    filtered = []
+
+    for seq in sequences:
+        if min_len <= len(seq) <= max_len:
+            filtered.append(seq)
+
+    return filtered
\ No newline at end of file
diff --git a/src/gp2/preprocess/sequence_builder.py b/src/gp2/preprocess/sequence_builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..b025980ad2cf664086ccfdfb3ef4c2ecdcca80a0
--- /dev/null
+++ b/src/gp2/preprocess/sequence_builder.py
@@ -0,0 +1,81 @@
+def build_possession_sequences(events, event_to_tokens , min_seq_len=10):
+    sequences = []
+
+    current_tokens = []
+    current_events = []
+    current_key = None  # (possession_id, team_id)
+    current_period = None
+
+    for ev in events:
+        possession_id = ev.get("possession")
+        team_id = ev.get("possession_team", {}).get("id")
+        period = ev.get("period")
+
+        if possession_id is None or team_id is None:
+            continue
+
+        key = (possession_id, team_id)
+
+        event_team_id = ev.get("team", {}).get("id")
+
+        if event_team_id != team_id:   # team_id is possession_team id
+            continue                    # skip defending team's actions from possession sentence
+
+        current_period = period
+
+        # New possession
+        if key != current_key:
+            if current_tokens:
+                sequences.append({
+                    "tokens": current_tokens,
+                    "events": current_events,
+                    "period": current_period
+                })
+
+            current_tokens = []
+            current_events = []
+            current_key = key
+
+        tokens = event_to_tokens(ev)
+
+        if tokens:
+            current_tokens.extend(tokens)
+            current_events.append(ev)
+            
+
+    # Last sequence
+    if current_tokens:
+        sequences.append({
+            "tokens": current_tokens,
+            "events": current_events,
+            "period": current_period
+        })
+
+    merged = []
+    buffer_tokens = []
+    buffer_events = []
+    prev_period = None
+
+    for seq in sequences:
+        if seq["period"] != prev_period and buffer_tokens:
+            merged.append({"tokens": buffer_tokens, "events": buffer_events})
+            buffer_tokens = []
+            buffer_events = []
+        prev_period = seq["period"]
+
+        buffer_tokens.extend(seq["tokens"])
+        buffer_events.extend(seq["events"])
+
+        if len(buffer_tokens) >= min_seq_len:      
+            merged.append({"tokens": buffer_tokens, "events": buffer_events})
+            buffer_tokens = []
+            buffer_events = []
+
+    if buffer_tokens:
+        if merged:
+            merged[-1]["tokens"].extend(buffer_tokens)
+            merged[-1]["events"].extend(buffer_events)
+        else:
+            merged.append({"tokens": buffer_tokens, "events": buffer_events})
+
+    return merged  # ← was: return sequences
diff --git a/src/gp2/preprocess/tokens.py b/src/gp2/preprocess/tokens.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bbca8ff17c0eecd7b1bf1983b94eff2139d1564
--- /dev/null
+++ b/src/gp2/preprocess/tokens.py
@@ -0,0 +1,564 @@
+"""
+Updated Tokenization for Player2Vec — V2
+
+NEW SEMANTIC TOKENS (added to existing base tokens):
+
+  CARRY:
+    - cut_inside_right    : started touchline-right (y>60), ended central (25-55), forward
+    - cut_inside_left     : started touchline-left  (y<20), ended central (25-55), forward
+    - carry_to_box        : ended in penalty area (x>102, 18<=y<=62)
+    - carry_to_final_third: ended in attacking third (x>80) but not box
+
+  PASS:
+    - pass_to_box         : ended in penalty area
+    - pass_to_final_third : crossed into final third (started x<80, ended x>80)
+    - key_pass            : StatsBomb shot_assist=True (created shot)
+    - assist              : StatsBomb goal_assist=True (created goal)
+
+  DRIBBLE:
+    - dribble_right_wide  : performed in right wing zone (y>60)
+    - dribble_left_wide   : performed in left wing zone (y<20)
+    - dribble_in_box      : performed in penalty area
+
+  PRESSURE:
+    - Now base token includes counterpress: "pressure|z21|cp" or "pressure|z21|reg"
+    - Counterpress = Klopp's gegenpressing signature
+
+These are RARE MODIFIERS (except pressure where it's part of base token)
+that capture distinctive behaviors without bloating vocabulary.
+"""
+
+from .zones import xy_to_zone
+
+
+# ==================================================================================================
+# CONFIG
+# ==================================================================================================
+
+PASS_PROGRESSIVE_THRESHOLD = 10
+CARRY_PROGRESSIVE_THRESHOLD = 10
+
+# Spatial thresholds (StatsBomb 120x80 pitch)
+BOX_X_MIN = 102          # Penalty area starts at 18 yards from goal
+BOX_Y_MIN = 18           # Penalty area top
+BOX_Y_MAX = 62           # Penalty area bottom
+FINAL_THIRD_X = 80       # Final third starts at x=80
+
+# Cut-inside detection
+WIDE_RIGHT_Y_MIN = 60    # Right touchline area
+WIDE_LEFT_Y_MAX = 20     # Left touchline area
+CENTRAL_Y_MIN = 25       # Half-space / center start
+CENTRAL_Y_MAX = 55       # Half-space / center end
+
+
+# ==================================================================================================
+# HELPERS
+# ==================================================================================================
+
+def _zone(x, y):
+    """Returns just the zone number as a string."""
+    z = xy_to_zone(x, y)
+    return z.split("_")[-1]
+
+
+def _body(data):
+    """Extracts body part abbreviation from a StatsBomb sub-dict."""
+    b = data.get("body_part", {}).get("name", "").lower()
+    if "right" in b:  return "r"
+    if "left"  in b:  return "l"
+    if "head"  in b:  return "h"
+    return "o"
+
+
+def _height(data):
+    """Extracts pass height abbreviation."""
+    h = data.get("height", {}).get("name", "").lower()
+    if "ground" in h: return "g"
+    if "low"    in h: return "l"
+    if "high"   in h: return "h"
+    return "g"
+
+
+def _length_bin(length):
+    """Bins pass length into short / medium / long."""
+    if length is None:   return "short"
+    if length < 10:      return "short"
+    if length < 30:      return "med"
+    return "long"
+
+
+# ==================================================================================================
+# V2 NEW: SPATIAL HELPERS FOR SEMANTIC TOKENS
+# ==================================================================================================
+
+def _is_in_box(x, y):
+    """True if (x, y) is in the penalty area."""
+    return x > BOX_X_MIN and BOX_Y_MIN <= y <= BOX_Y_MAX
+
+
+def _is_in_final_third(x):
+    """True if x is in the final third."""
+    return x > FINAL_THIRD_X
+
+
+def _is_wide_right(y):
+    """True if y is on the right touchline area."""
+    return y > WIDE_RIGHT_Y_MIN
+
+
+def _is_wide_left(y):
+    """True if y is on the left touchline area."""
+    return y < WIDE_LEFT_Y_MAX
+
+
+def _is_central(y):
+    """True if y is in central / half-space area."""
+    return CENTRAL_Y_MIN <= y <= CENTRAL_Y_MAX
+
+
+def _detect_cut_inside(start_x, start_y, end_x, end_y):
+    """
+    Detect cut-inside behavior (inverted winger signature).
+    Returns: 'cut_inside_right', 'cut_inside_left', or None
+    """
+    # Must move forward to count as cut-inside
+    if end_x <= start_x:
+        return None
+
+    # Right side cut inside
+    if _is_wide_right(start_y) and _is_central(end_y):
+        return "cut_inside_right"
+
+    # Left side cut inside
+    if _is_wide_left(start_y) and _is_central(end_y):
+        return "cut_inside_left"
+
+    return None
+
+
+# ==================================================================================================
+# MAIN ENTRY POINT
+# ==================================================================================================
+
+def event_to_tokens(ev):
+    ev_type = ev.get("type", {}).get("name", "").lower()
+
+    if   ev_type == "pass":           return tokenize_pass(ev)
+    elif ev_type == "carry":          return tokenize_carry(ev)
+    elif ev_type == "dribble":        return tokenize_dribble(ev)
+    elif ev_type == "shot":           return tokenize_shot(ev)
+    elif ev_type == "duel":           return tokenize_duel(ev)
+    elif ev_type == "interception":   return tokenize_interception(ev)
+    elif ev_type == "ball recovery":  return tokenize_ball_recovery(ev)
+    elif ev_type == "clearance":      return tokenize_clearance(ev)
+    elif ev_type == "foul committed": return tokenize_foul_committed(ev)
+    elif ev_type == "pressure":       return tokenize_pressure(ev)
+    elif ev_type == "block":          return tokenize_block(ev)
+    elif ev_type == "dribbled past":  return tokenize_dribbled_past(ev)
+    elif ev_type == "dispossessed":   return tokenize_dispossessed(ev)
+    else:
+        return []
+
+
+# ==================================================================================================
+# PASS (V2 — added to_box, to_final_third, key_pass, assist)
+# ==================================================================================================
+
+def tokenize_pass(ev):
+    """
+    Base token:  pass|z{start}_z{end}|{outcome}|{body}|{height}|{length}
+    Example:     pass|z9_z7|s|r|g|short
+
+    Rare modifiers:
+        progressive_pass, cross, through_ball, switch, cutback, under_pressure
+        [V2 NEW] pass_to_box, pass_to_final_third, key_pass, assist
+    """
+    if not ev.get("location") or not ev.get("pass"):
+        return ["pass|unknown"]
+
+    start_loc = ev["location"]
+    end_loc   = ev["pass"].get("end_location")
+
+    if not end_loc:
+        return ["pass|unknown"]
+
+    start_x, start_y = start_loc
+    end_x,   end_y   = end_loc
+
+    sz      = _zone(start_x, start_y)
+    ez      = _zone(end_x, end_y)
+    outcome = "s" if ev["pass"].get("outcome") is None else "f"
+    body    = _body(ev["pass"])
+    height  = _height(ev["pass"])
+    length  = _length_bin(ev["pass"].get("length"))
+
+    base = f"pass|z{sz}_z{ez}|{outcome}|{body}|{height}|{length}"
+    tokens = [base]
+
+    # --- Existing rare modifiers ---
+    if (end_x - start_x) >= PASS_PROGRESSIVE_THRESHOLD:
+        tokens.append("progressive_pass")
+
+    if ev["pass"].get("cross"):
+        tokens.append("cross")
+
+    if ev["pass"].get("through_ball"):
+        tokens.append("through_ball")
+
+    if ev["pass"].get("switch"):
+        tokens.append("switch")
+
+    if ev["pass"].get("cut_back"):
+        tokens.append("cutback")
+
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    # --- V2 NEW: Spatial destination modifiers (only for successful passes) ---
+    if outcome == "s":
+        if _is_in_box(end_x, end_y):
+            tokens.append("pass_to_box")
+        elif _is_in_final_third(end_x) and not _is_in_final_third(start_x):
+            tokens.append("pass_to_final_third")
+
+    # --- V2 NEW: Quality modifiers (key_pass, assist) ---
+    if ev["pass"].get("shot_assist"):
+        tokens.append("key_pass")
+
+    if ev["pass"].get("goal_assist"):
+        tokens.append("assist")
+
+    return tokens
+
+
+# ==================================================================================================
+# CARRY (V2 — added cut_inside, to_box, to_final_third)
+# ==================================================================================================
+
+def tokenize_carry(ev):
+    """
+    Base token:  carry|z{start}_z{end}|{direction}
+    Example:     carry|z9_z13|fwd
+
+    Rare modifiers:
+        progressive_carry, under_pressure
+        [V2 NEW] cut_inside_right, cut_inside_left, carry_to_box, carry_to_final_third
+    """
+    start = ev.get("location")
+    end   = ev.get("carry", {}).get("end_location")
+
+    if not start or not end:
+        return ["carry|unknown"]
+
+    sx, sy = start
+    ex, ey = end
+
+    sz  = _zone(sx, sy)
+    ez  = _zone(ex, ey)
+    dx  = ex - sx
+
+    if   dx > 5:  direction = "fwd"
+    elif dx < -5: direction = "bwd"
+    else:         direction = "lat"
+
+    base = f"carry|z{sz}_z{ez}|{direction}"
+    tokens = [base]
+
+    # --- Existing rare modifiers ---
+    if dx >= CARRY_PROGRESSIVE_THRESHOLD:
+        tokens.append("progressive_carry")
+
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    # --- V2 NEW: Cut-inside detection (Mané/Salah signature) ---
+    cut_inside = _detect_cut_inside(sx, sy, ex, ey)
+    if cut_inside:
+        tokens.append(cut_inside)
+
+    # --- V2 NEW: Spatial destination modifiers ---
+    if _is_in_box(ex, ey):
+        tokens.append("carry_to_box")
+    elif _is_in_final_third(ex) and not _is_in_final_third(sx):
+        tokens.append("carry_to_final_third")
+
+    return tokens
+
+
+# ==================================================================================================
+# DRIBBLE (V2 — added wide-area + in-box modifiers)
+# ==================================================================================================
+
+def tokenize_dribble(ev):
+    """
+    Base token:  dribble|z{zone}|{outcome}
+    Example:     dribble|z9|s
+
+    Rare modifiers:
+        under_pressure
+        [V2 NEW] dribble_right_wide, dribble_left_wide, dribble_in_box
+    """
+    loc = ev.get("location")
+    if not loc:
+        return ["dribble|unknown"]
+
+    x, y    = loc
+    z       = _zone(x, y)
+    outcome = ev.get("dribble", {}).get("outcome", {}).get("name", "").lower()
+    o       = "s" if "complete" in outcome else "f"
+
+    base   = f"dribble|z{z}|{o}"
+    tokens = [base]
+
+    # --- Existing modifier ---
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    # --- V2 NEW: Spatial context for dribbles ---
+    if _is_wide_right(y):
+        tokens.append("dribble_right_wide")
+    elif _is_wide_left(y):
+        tokens.append("dribble_left_wide")
+
+    if _is_in_box(x, y):
+        tokens.append("dribble_in_box")
+
+    return tokens
+
+
+# ==================================================================================================
+# SHOT (unchanged from V1 — already excellent)
+# ==================================================================================================
+
+def tokenize_shot(ev):
+    """
+    Base token:  shot|z{zone}|{outcome}|{body}|{type}|{xg_bin}|{dist_bin}
+    Example:     shot|z15|goal|r|open|xg_h|close
+    """
+    shot = ev.get("shot", {})
+    loc  = ev.get("location")
+
+    if not loc:
+        return ["shot|unknown"]
+
+    x, y = loc
+    z    = _zone(x, y)
+
+    outcome_str = shot.get("outcome", {}).get("name", "").lower()
+    if "goal"  in outcome_str:                            o = "goal"
+    elif "saved" in outcome_str or "post" in outcome_str: o = "on"
+    else:                                                  o = "off"
+
+    body = _body(shot)
+
+    shot_type = shot.get("type", {}).get("name", "").lower()
+    if   "open play" in shot_type: t = "open"
+    elif "penalty"   in shot_type: t = "pen"
+    else:                          t = "set"
+
+    xg = shot.get("statsbomb_xg")
+    if   xg is None:  xg_bin = "xg_u"
+    elif xg < 0.1:    xg_bin = "xg_l"
+    elif xg < 0.3:    xg_bin = "xg_m"
+    else:             xg_bin = "xg_h"
+
+    dist = ((120 - x) ** 2 + (40 - y) ** 2) ** 0.5
+    if   dist < 10: dist_bin = "close"
+    elif dist < 20: dist_bin = "mid"
+    else:           dist_bin = "long"
+
+    base   = f"shot|z{z}|{o}|{body}|{t}|{xg_bin}|{dist_bin}"
+    tokens = [base]
+
+    if shot.get("one_on_one"):  tokens.append("one_on_one")
+    if shot.get("open_goal"):   tokens.append("open_goal")
+    if shot.get("first_time"):  tokens.append("first_time")
+
+    return tokens
+
+
+# ==================================================================================================
+# DUEL (unchanged)
+# ==================================================================================================
+
+def tokenize_duel(ev):
+    duel = ev.get("duel", {})
+    loc  = ev.get("location")
+
+    if not loc:
+        return ["duel|unknown"]
+
+    x, y = loc
+    z    = _zone(x, y)
+
+    duel_type = duel.get("type", {}).get("name", "").lower()
+    t = "aerial" if "aerial" in duel_type else "ground"
+
+    outcome = duel.get("outcome", {}).get("name", "").lower()
+    o = "won" if "won" in outcome else "lost"
+
+    base   = f"duel|z{z}|{t}|{o}"
+    tokens = [base]
+
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    return tokens
+
+
+# ==================================================================================================
+# INTERCEPTION (unchanged)
+# ==================================================================================================
+
+def tokenize_interception(ev):
+    loc = ev.get("location")
+    if not loc:
+        return ["interception|unknown"]
+
+    x, y    = loc
+    z       = _zone(x, y)
+    outcome = ev.get("interception", {}).get("outcome", {}).get("name", "").lower()
+    o       = "won" if "won" in outcome else "lost"
+
+    base   = f"interception|z{z}|{o}"
+    tokens = [base]
+
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    return tokens
+
+
+# ==================================================================================================
+# BALL RECOVERY (unchanged)
+# ==================================================================================================
+
+def tokenize_ball_recovery(ev):
+    loc = ev.get("location")
+    if not loc:
+        return ["ball_recovery|unknown"]
+
+    x, y = loc
+    z    = _zone(x, y)
+    o    = "f" if ev.get("ball_recovery", {}).get("recovery_failure") else "s"
+
+    base   = f"ball_recovery|z{z}|{o}"
+    tokens = [base]
+
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    return tokens
+
+
+# ==================================================================================================
+# CLEARANCE (unchanged)
+# ==================================================================================================
+
+def tokenize_clearance(ev):
+    loc = ev.get("location")
+    if not loc:
+        return ["clearance|unknown"]
+
+    x, y = loc
+    z    = _zone(x, y)
+    body = _body(ev.get("clearance", {}))
+
+    base   = f"clearance|z{z}|{body}"
+    tokens = [base]
+
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    return tokens
+
+
+# ==================================================================================================
+# FOUL COMMITTED (unchanged)
+# ==================================================================================================
+
+def tokenize_foul_committed(ev):
+    loc = ev.get("location")
+    if not loc:
+        return ["foul_committed|unknown"]
+
+    x, y = loc
+    z    = _zone(x, y)
+
+    card = ev.get("foul_committed", {}).get("card", {}).get("name", "").lower()
+    if   "yellow" in card: card_token = "yellow"
+    elif "red"    in card: card_token = "red"
+    else:                  card_token = "none"
+
+    base   = f"foul_committed|z{z}|{card_token}"
+    tokens = [base]
+
+    if ev.get("foul_committed", {}).get("penalty"):
+        tokens.append("foul_penalty")
+
+    return tokens
+
+
+# ==================================================================================================
+# PRESSURE (V2 — counterpress now part of base token)
+# ==================================================================================================
+
+def tokenize_pressure(ev):
+    """
+    Base token: pressure|z{zone}|{cp_or_reg}
+    Examples:
+        pressure|z21|cp     (counterpress — Klopp's gegenpressing signature)
+        pressure|z21|reg    (regular pressure)
+
+    Counterpress is encoded in base token (not as rare modifier) because
+    it's a fundamental quality distinction we want the model to learn.
+    """
+    loc = ev.get("location")
+    if not loc:
+        return ["pressure|unknown"]
+    x, y = loc
+    z = _zone(x, y)
+
+    cp = "cp" if ev.get("counterpress") else "reg"
+
+    return [f"pressure|z{z}|{cp}"]
+
+
+# ==================================================================================================
+# BLOCK (unchanged)
+# ==================================================================================================
+
+def tokenize_block(ev):
+    loc = ev.get("location")
+    if not loc:
+        return ["block|unknown"]
+    x, y = loc
+    z = _zone(x, y)
+    deflection = "def" if ev.get("block", {}).get("deflection") else "blk"
+    return [f"block|z{z}|{deflection}"]
+
+
+# ==================================================================================================
+# DRIBBLED PAST (unchanged)
+# ==================================================================================================
+
+def tokenize_dribbled_past(ev):
+    loc = ev.get("location")
+    if not loc:
+        return ["dribbled_past|unknown"]
+    x, y = loc
+    z = _zone(x, y)
+    return [f"dribbled_past|z{z}"]
+
+
+# ==================================================================================================
+# DISPOSSESSED (unchanged)
+# ==================================================================================================
+
+def tokenize_dispossessed(ev):
+    loc = ev.get("location")
+    if not loc:
+        return ["dispossessed|unknown"]
+    x, y = loc
+    z = _zone(x, y)
+    return [f"dispossessed|z{z}"]
\ No newline at end of file
diff --git a/src/gp2/preprocess/zones.py b/src/gp2/preprocess/zones.py
new file mode 100644
index 0000000000000000000000000000000000000000..6240752fdfd990244ee2aef09ddaf00d66a516a4
--- /dev/null
+++ b/src/gp2/preprocess/zones.py
@@ -0,0 +1,118 @@
+"""
+zones.py
+--------
+
+Handles conversion between (x, y) coordinates and pitch zones.
+
+StatsBomb pitch:
+    x ∈ [0, 120]
+    y ∈ [0, 80]
+"""
+
+# =====================
+# CONFIG
+# =====================
+
+PITCH_LENGTH = 120.0
+PITCH_WIDTH  = 80.0
+
+NUM_X_BINS = 6   # horizontal divisions
+NUM_Y_BINS = 3   # vertical divisions
+
+
+# =====================
+# CORE FUNCTIONS
+# =====================
+
+def xy_to_zone(x, y, num_x=NUM_X_BINS, num_y=NUM_Y_BINS):
+    """
+    Convert (x, y) → zone_id
+
+    Zones are indexed row-wise:
+        zone_0 ... zone_(num_x*num_y - 1)
+
+    Example (6x3):
+        zone_0 zone_1 zone_2 ...
+    """
+    if x is None or y is None:
+        return "zone_unknown"
+
+    # Clamp values
+    x = max(0.0, min(PITCH_LENGTH, float(x)))
+    y = max(0.0, min(PITCH_WIDTH, float(y)))
+
+    bx = min(int((x / PITCH_LENGTH) * num_x), num_x - 1)
+    by = min(int((y / PITCH_WIDTH) * num_y), num_y - 1)
+
+    return f"{by * num_x + bx}"
+
+
+def get_start_zone(ev):
+    """
+    Extract start zone from event
+    """
+    loc = ev.get("location")
+    if not loc:
+        return "zone_unknown"
+
+    return xy_to_zone(loc[0], loc[1])
+
+
+def get_end_zone(ev):
+    """
+    Extract end zone depending on event type
+    """
+    ev_type = ev.get("type", {}).get("name", "").lower()
+
+    if ev_type == "pass":
+        end = ev.get("pass", {}).get("end_location")
+
+    elif ev_type == "carry":
+        end = ev.get("carry", {}).get("end_location")
+
+    elif ev_type == "shot":
+        end = ev.get("shot", {}).get("end_location")
+
+    else:
+        return "zone_unknown"
+
+    if not end:
+        return "zone_unknown"
+
+    return xy_to_zone(end[0], end[1])
+
+
+# =====================
+# OPTIONAL UTILITIES
+# =====================
+
+def zone_center(zone_id, num_x=NUM_X_BINS, num_y=NUM_Y_BINS):
+    """
+    Convert zone_id → approximate (x, y) center
+    Useful for distance / progressive logic later
+    """
+    if not zone_id.startswith("zone_"):
+        return None, None
+
+    zid = int(zone_id.split("_")[1])
+
+    bx = zid % num_x
+    by = zid // num_x
+
+    x = (bx + 0.5) * (PITCH_LENGTH / num_x)
+    y = (by + 0.5) * (PITCH_WIDTH / num_y)
+
+    return x, y
+
+
+def zone_distance(z1, z2):
+    """
+    Euclidean distance between two zones (center-based)
+    """
+    x1, y1 = zone_center(z1)
+    x2, y2 = zone_center(z2)
+
+    if x1 is None or x2 is None:
+        return 0.0
+
+    return ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5
\ No newline at end of file
diff --git a/src/model/__init__.py b/src/model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/model/extract_embeddings.py b/src/model/extract_embeddings.py
new file mode 100644
index 0000000000000000000000000000000000000000..0107fa8f8bcc14e7a7549c117f6b627628e29bda
--- /dev/null
+++ b/src/model/extract_embeddings.py
@@ -0,0 +1,21 @@
+import torch
+
+def extract_embeddings(model, padded_sequences, batch_size=64):
+    device = next(model.parameters()).device
+
+    model.eval()
+
+    data = torch.tensor(padded_sequences, dtype=torch.long)
+    dataset = torch.utils.data.TensorDataset(data)
+    loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size)
+
+    all_embeddings = []
+
+    with torch.no_grad():
+        for batch in loader:
+            batch = batch[0].to(device)
+
+            emb = model.get_embeddings(batch)  # (B, D)
+            all_embeddings.append(emb.cpu())
+
+    return torch.cat(all_embeddings, dim=0)  # (N, D)
\ No newline at end of file
diff --git a/src/model/search.py b/src/model/search.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb64370963b54322f6edf064577a7670ff41a025
--- /dev/null
+++ b/src/model/search.py
@@ -0,0 +1,26 @@
+import torch
+
+def load_embeddings(path="player_embeddings.pt"):
+    data = torch.load(path, map_location="cpu")
+    
+    # handle both formats
+    if isinstance(data, dict) and "embeddings" in data:
+        return data["embeddings"]
+    
+    return data
+
+def find_similar_players(player_id, player_embeddings, top_k=5):
+    if player_id not in player_embeddings:
+        print("Player not found")
+        return []
+
+    target_vec = player_embeddings[player_id]
+
+    ids = [pid for pid in player_embeddings if pid != player_id]
+    matrix = torch.stack([player_embeddings[pid] for pid in ids])
+
+    sims = torch.matmul(matrix, target_vec)
+
+    top_k_idx = torch.topk(sims, k=min(top_k, len(ids))).indices
+
+    return [(ids[i], sims[i].item()) for i in top_k_idx]
\ No newline at end of file
diff --git a/src/model/similarity.py b/src/model/similarity.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea15ce4b9fb3019b7c6d4d2b176ad5bf9538b736
--- /dev/null
+++ b/src/model/similarity.py
@@ -0,0 +1,22 @@
+import torch
+import torch.nn.functional as F
+
+
+def cosine_similarity(vec1, vec2):
+    return F.cosine_similarity(vec1.unsqueeze(0), vec2.unsqueeze(0)).item()
+
+
+def find_similar_players(player_id, player_embeddings, top_k=5):
+    if player_id not in player_embeddings:
+        return []
+
+    target_vec = player_embeddings[player_id]
+
+    ids = [pid for pid in player_embeddings if pid != player_id]
+    matrix = torch.stack([player_embeddings[pid] for pid in ids])
+
+    sims = torch.matmul(matrix, target_vec)
+
+    top_k_idx = torch.topk(sims, k=min(top_k, len(ids))).indices
+
+    return [(ids[i], sims[i].item()) for i in top_k_idx]
\ No newline at end of file
diff --git a/src/model/test_trans.py b/src/model/test_trans.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3773bb7ba50ff49b7d9bda260e60e5269b62aea
--- /dev/null
+++ b/src/model/test_trans.py
@@ -0,0 +1,10 @@
+from src.model.transformer import FootballTransformer
+import torch
+
+model = FootballTransformer(vocab_size=110)
+
+x = torch.randint(0, 110, (4, 50))  # batch of 4, seq_len=50
+
+out = model(x)
+
+print(out.shape)
\ No newline at end of file
diff --git a/src/model/train.py b/src/model/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebcb5ea9732248c40c8755289a343b576762c509
--- /dev/null
+++ b/src/model/train.py
@@ -0,0 +1,129 @@
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, TensorDataset
+from src.model.transformer import FootballTransformer
+
+
+def mask_tokens(inputs, vocab_size, mask_token_id=2, pad_token_id=0, mask_prob=0.2):
+    inputs = inputs.clone()  
+    labels = inputs.clone()
+
+    probability_matrix = torch.full(labels.shape, mask_prob)
+
+    special_mask = inputs.eq(pad_token_id)
+    probability_matrix.masked_fill_(special_mask, value=0.0)
+
+    mask = torch.bernoulli(probability_matrix).bool()
+
+    labels[~mask] = -100  # ignore non-masked tokens
+
+    # 80% replace with MASK
+    indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & mask
+    inputs[indices_replaced] = mask_token_id
+
+    # 10% random token
+    indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & mask & ~indices_replaced
+    random_tokens = torch.randint(vocab_size, labels.shape, dtype=torch.long)
+    inputs[indices_random] = random_tokens[indices_random]
+
+    # 10% unchanged
+
+    return inputs, labels
+    
+def train_model(padded_sequences, vocab_size, epochs=5, lr=1e-3):
+    device = torch.device("cpu")
+    print("Using device:", device)
+
+    # Model
+    model = FootballTransformer(vocab_size=vocab_size).to(device)
+
+    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
+
+    criterion = nn.CrossEntropyLoss(ignore_index=-100)
+
+    # Data
+    data = torch.tensor(padded_sequences, dtype=torch.long)
+    dataset = TensorDataset(data)
+
+    # 🔥 Train / Validation split
+    val_size = int(0.1 * len(dataset))
+    train_size = len(dataset) - val_size
+    torch.manual_seed(42)
+    train_dataset, val_dataset = torch.utils.data.random_split(
+        dataset, [train_size, val_size]
+    )
+
+    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
+    val_loader = DataLoader(val_dataset, batch_size=64)
+
+    # Training loop
+    for epoch in range(epochs):
+        model.train()
+        total_loss = 0
+
+        for batch in train_loader:
+            batch = batch[0].to(device)
+
+            # 🔥 Masked LM (no clone needed now)
+            inputs, labels = mask_tokens(batch, vocab_size)
+
+            inputs = inputs.to(device)
+            labels = labels.to(device)
+
+            outputs = model(inputs)
+
+            outputs = outputs.reshape(-1, vocab_size)
+            labels = labels.reshape(-1)
+
+            loss = criterion(outputs, labels)
+
+            optimizer.zero_grad()
+            loss.backward()
+
+            # 🔥 Prevent exploding gradients
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+
+            optimizer.step()
+
+            total_loss += loss.item()
+
+        # 🔥 Validation
+        model.eval()
+        val_loss = 0
+
+        with torch.no_grad():
+            for batch in val_loader:
+                batch = batch[0].to(device)
+
+                inputs, labels = mask_tokens(batch, vocab_size)
+
+                inputs = inputs.to(device)
+                labels = labels.to(device)
+
+                outputs = model(inputs)
+
+                outputs = outputs.reshape(-1, vocab_size)
+                labels = labels.reshape(-1)
+
+                loss = criterion(outputs, labels)
+                val_loss += loss.item()
+
+        scheduler.step()
+
+        print(
+            f"Epoch {epoch+1}/{epochs} | "
+            f"Train Loss: {total_loss / len(train_loader):.4f} | "
+            f"Val Loss: {val_loss / len(val_loader):.4f}"
+        )
+
+    # 🔥 Save model
+    torch.save(model.state_dict(), "football_transformer.pt")
+
+    return model
+
+def load_model(path, vocab_size, device):
+    model = FootballTransformer(vocab_size=vocab_size).to(device)
+    model.load_state_dict(torch.load(path, map_location=device))
+    model.eval()
+    return model
diff --git a/src/model/transformer.py b/src/model/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d0dba23f42df59633492fb3b95f0752a0147272
--- /dev/null
+++ b/src/model/transformer.py
@@ -0,0 +1,99 @@
+import torch
+import torch.nn as nn
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=500):
+        super().__init__()
+
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len).unsqueeze(1)
+
+        div_term = torch.exp(
+            torch.arange(0, d_model, 2) * (-torch.log(torch.tensor(10000.0)) / d_model)
+        )
+
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+
+        self.pe = pe.unsqueeze(0)  # (1, max_len, d_model)
+
+    def forward(self, x):
+        return x + self.pe[:, :x.size(1)].to(x.device)
+
+class FootballTransformer(nn.Module):
+    def __init__(
+        self,
+        vocab_size,
+        d_model=128,
+        n_heads=4,
+        n_layers=2,
+        dim_feedforward=256,
+        max_len=200,
+        dropout=0.1,
+    ):
+        super().__init__()
+
+        self.embedding = nn.Embedding(vocab_size, d_model)
+        self.pos_encoding = PositionalEncoding(d_model, max_len)
+
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=d_model,
+            nhead=n_heads,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
+            batch_first=True  # IMPORTANT
+        )
+
+        self.transformer = nn.TransformerEncoder(
+            encoder_layer,
+            num_layers=n_layers
+        )
+
+        self.fc_out = nn.Linear(d_model, vocab_size)
+
+    def forward(self, x):
+        # x: (B, L)
+
+        mask = (x == 0)
+
+        x = self.embedding(x)        # (B, L, D)
+        x = self.pos_encoding(x)     # (B, L, D)
+
+        x = self.transformer(
+            x,
+            src_key_padding_mask=mask
+        )                            # (B, L, D)
+
+        logits = self.fc_out(x)
+
+        return logits
+    
+    def get_embeddings(self, x):
+        """
+        x: (B, L)
+        returns: (B, D)
+        """
+        mask = (x == 0)  # (B, L)
+
+        x = self.embedding(x)        # (B, L, D)
+        x = self.pos_encoding(x)     # (B, L, D)
+
+        # 🚀 NO TRANSPOSE HERE
+        x = self.transformer(
+            x,
+            src_key_padding_mask=mask
+        )  # (B, L, D)
+
+        # 🔥 mean pooling (ignore padding)
+        valid_mask = (~mask).unsqueeze(-1)
+
+        x = x * valid_mask
+        summed = x.sum(dim=1)
+        counts = valid_mask.sum(dim=1)
+
+        embeddings = summed / counts.clamp(min=1)
+
+        return embeddings  # (B, D)
+    
+      
+
diff --git a/src/preprocess/__init__.py b/src/preprocess/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/preprocess/prepare_sequences.py b/src/preprocess/prepare_sequences.py
new file mode 100644
index 0000000000000000000000000000000000000000..4338f2b562b3d6debf886dabbaa8de42c5023bb0
--- /dev/null
+++ b/src/preprocess/prepare_sequences.py
@@ -0,0 +1,67 @@
+def flatten_sequences(sequences):
+    flat_sequences = []
+    player_ids = []
+
+    for seq in sequences:
+        flat_seq = []
+        seq_players = []
+
+        for event in seq:
+            flat_seq.extend(event["tokens"])
+            seq_players.append(event["player_id"])
+
+        flat_sequences.append(flat_seq)
+        player_ids.append(seq_players)
+
+    return flat_sequences, player_ids
+
+def build_vocab(sequences, min_freq=1):
+    from collections import Counter
+
+    counter = Counter()
+
+    for seq in sequences:
+        counter.update(seq)
+
+    vocab = {
+        "<PAD>": 0,
+        "<UNK>": 1,
+        "<MASK>": 2,
+    }
+
+    idx = 3
+
+    for token, freq in counter.items():
+        if freq >= min_freq:
+            vocab[token] = idx
+            idx += 1
+
+    return vocab
+
+def encode_sequences(sequences, vocab):
+    encoded = []
+
+    for seq in sequences:
+        encoded_seq = [
+            vocab.get(token, vocab["<UNK>"])
+            for token in seq
+        ]
+        encoded.append(encoded_seq)
+
+    return encoded
+
+def pad_sequences(sequences, max_len=100):
+    padded = []
+
+    for seq in sequences:
+        # 1. CLIP first
+        if len(seq) > max_len:
+            seq = seq[:max_len]
+
+        # 2. PAD after
+        if len(seq) < max_len:
+            seq = seq + [0] * (max_len - len(seq))  # PAD = 0
+
+        padded.append(seq)
+
+    return padded
\ No newline at end of file
diff --git a/src/preprocess/sequence_builder.py b/src/preprocess/sequence_builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..b220cf9661e4458f9259326ce1c2fa640cdcc89a
--- /dev/null
+++ b/src/preprocess/sequence_builder.py
@@ -0,0 +1,41 @@
+def build_possession_sequences(events, event_to_tokens):
+    sequences = []
+
+    current_sequence = []
+    current_key = None  # (possession_id, team_id)
+
+    for ev in events:
+        # --- extract possession info ---
+        possession_id = ev.get("possession")
+        team_id = ev.get("possession_team", {}).get("id")
+
+        # skip if missing (rare but safe)
+        if possession_id is None or team_id is None:
+            continue
+
+        key = (possession_id, team_id)
+
+        # --- new possession starts ---
+        if key != current_key:
+            if current_sequence:
+                sequences.append(current_sequence)
+
+            current_sequence = []
+            current_key = key
+
+        # --- tokenize event ---
+        tokens = event_to_tokens(ev)
+
+        player_id = ev.get("player", {}).get("id")
+
+        if tokens and player_id is not None:
+            current_sequence.append({
+                "player_id": player_id,
+                "tokens": tokens
+            })
+
+    # --- last sequence ---
+    if current_sequence:
+        sequences.append(current_sequence)
+
+    return sequences
\ No newline at end of file
diff --git a/src/preprocess/tempfile.py b/src/preprocess/tempfile.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfcb6d1afc2d7eb11fb4e0195d31904fe6ae6d9f
--- /dev/null
+++ b/src/preprocess/tempfile.py
@@ -0,0 +1,5 @@
+from src.preprocess.zones import xy_to_zone
+
+print(xy_to_zone(0, 0))        # should be zone_0
+print(xy_to_zone(120, 80))     # last zone
+print(xy_to_zone(60, 40))      # middle
\ No newline at end of file
diff --git a/src/preprocess/test_pipeline.py b/src/preprocess/test_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c23989ef55431c67dfcf1f785680e0c6d6aca10
--- /dev/null
+++ b/src/preprocess/test_pipeline.py
@@ -0,0 +1,164 @@
+import os
+import json
+import numpy as np
+import random
+import torch
+from src.preprocess.tokens import event_to_tokens
+from src.preprocess.sequence_builder import build_possession_sequences
+from src.model.extract_embeddings import extract_embeddings
+from src.model.train import train_model
+from src.model.similarity import find_similar_players
+from collections import Counter
+import torch.nn.functional as F
+from src.preprocess.prepare_sequences import (
+    flatten_sequences,
+    build_vocab,
+    encode_sequences,
+    pad_sequences
+)
+
+DATA_DIR = "data"
+MAX_MATCHES = None   # 🔥 change to None to use ALL matches
+
+
+def load_matches(data_dir, max_matches=None):
+    files = [f for f in os.listdir(data_dir) if f.endswith(".json")]
+    random.shuffle(files)
+    if max_matches:
+        files = files[:max_matches]
+
+    all_events = []
+
+    for file in files:
+        path = os.path.join(data_dir, file)
+        with open(path, "r", encoding="utf-8") as f:
+            events = json.load(f)
+            all_events.extend(events)
+
+    return all_events
+
+
+# 1. Load data
+events = load_matches(DATA_DIR, MAX_MATCHES)
+
+print(f"Loaded events: {len(events)}")
+
+# 2. Build sequences
+sequences = build_possession_sequences(events, event_to_tokens)
+
+# 3. Flatten
+flat_sequences, player_ids = flatten_sequences(sequences)
+
+# 4. Vocab
+vocab = build_vocab(flat_sequences)
+
+# 5. Encode
+encoded = encode_sequences(flat_sequences, vocab)
+
+# 6. Length stats (IMPORTANT 🔥)
+lengths = [len(seq) for seq in flat_sequences]
+
+print("\n--- LENGTH STATS ---")
+print("Min:", min(lengths))
+print("Max:", max(lengths))
+print("Avg:", sum(lengths)/len(lengths))
+print("95th percentile:", int(np.percentile(lengths, 95)))
+
+# 7. Padding (temporary value for now)
+MAX_LEN = int(np.percentile(lengths, 95))
+padded = pad_sequences(encoded, max_len=MAX_LEN)
+
+
+
+MODEL_PATH = "football_transformer.pt"
+
+from src.model.transformer import FootballTransformer
+
+device = torch.device("cpu")
+
+if os.path.exists(MODEL_PATH):
+    print("Loading saved model...")
+    model = FootballTransformer(vocab_size=len(vocab)).to(device)
+    model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
+    model.eval()
+else:
+    print("Training new model...")
+    model = train_model(padded, vocab_size=len(vocab), epochs=5, lr=1e-3)
+
+
+embeddings = extract_embeddings(model, padded)
+
+player_embeddings = {}
+
+for i, seq_players in enumerate(player_ids):
+    emb = embeddings[i]
+
+    counts = Counter(seq_players)
+    for pid, count in counts.items():
+        if pid not in player_embeddings:
+            player_embeddings[pid] = []
+
+        player_embeddings[pid].append(emb * (count / len(seq_players)))
+
+
+
+
+
+# average per player (NO normalization yet)
+for pid in player_embeddings:
+    vec = torch.stack(player_embeddings[pid]).mean(dim=0)
+    player_embeddings[pid] = vec
+
+# =========================
+# 🔥 GLOBAL VECTOR REMOVAL
+# =========================
+all_vecs = torch.stack(list(player_embeddings.values()))  # (num_players, D)
+global_vec = all_vecs.mean(dim=0)
+
+for pid in player_embeddings:
+    player_embeddings[pid] = player_embeddings[pid] - global_vec
+
+# =========================
+# ✅ Normalize AFTER removal
+# =========================
+for pid in player_embeddings:
+    player_embeddings[pid] = F.normalize(player_embeddings[pid], dim=0)
+
+
+
+
+sample_vec = next(iter(player_embeddings.values()))
+
+torch.save({
+    "embeddings": player_embeddings,
+    "vocab_size": len(vocab),
+    "num_players": len(player_embeddings),
+    "embedding_dim": sample_vec.shape[0]
+}, "player_embeddings.pt")
+
+print("Saved player embeddings")
+
+  
+
+print("Number of players:", len(player_embeddings))
+
+print("Embeddings shape:", embeddings.shape)
+# 8. Debug prints
+print("\n--- DATASET INFO ---")
+print("Number of sequences:", len(flat_sequences))
+print("Vocab size:", len(vocab))
+print("Max len used:", MAX_LEN)
+
+print("\nExample sequence (tokens):", flat_sequences[0][:20])
+print("Example sequence (encoded):", padded[0][:20])
+
+
+
+# pick random player
+sample_player = list(player_embeddings.keys())[0]
+
+similar = find_similar_players(sample_player, player_embeddings, top_k=5)
+
+print(f"\nTop similar players to {sample_player}:")
+for pid, score in similar:
+    print(f"Player {pid} → similarity: {score:.4f}")  
\ No newline at end of file
diff --git a/src/preprocess/test_tokens.py b/src/preprocess/test_tokens.py
new file mode 100644
index 0000000000000000000000000000000000000000..544469dce64aa1689abc763037793605fb699e8d
--- /dev/null
+++ b/src/preprocess/test_tokens.py
@@ -0,0 +1,16 @@
+import json
+from src.preprocess.tokens import event_to_tokens
+from src.preprocess.sequence_builder import build_possession_sequences
+
+with open("data/laliga1516/265839.json", "r", encoding="utf-8") as f:
+    events = json.load(f)
+
+sequences = build_possession_sequences(events, event_to_tokens)
+
+print("Total sequences:", len(sequences))
+
+# print first 3 sequences
+for i, seq in enumerate(sequences[:3]):
+    print(f"\nSequence {i+1}:")
+    for event_tokens in seq:
+        print(event_tokens)
\ No newline at end of file
diff --git a/src/preprocess/test_torch.py b/src/preprocess/test_torch.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b181c581cd3eeb8466060354c3137fb2f3e1518
--- /dev/null
+++ b/src/preprocess/test_torch.py
@@ -0,0 +1,3 @@
+import torch
+print(torch.cuda.is_available())
+print(torch.cuda.get_device_name(0) if torch.cuda.is_available() else "No GPU")
\ No newline at end of file
diff --git a/src/preprocess/tokens.py b/src/preprocess/tokens.py
new file mode 100644
index 0000000000000000000000000000000000000000..5aef6f0c0b9d60068e3c835473570f8f1e08f6a3
--- /dev/null
+++ b/src/preprocess/tokens.py
@@ -0,0 +1,418 @@
+from .zones import xy_to_zone
+
+
+# ==================================================================================================
+# CONFIG
+# ==================================================================================================
+
+PASS_PROGRESSIVE_THRESHOLD = 10
+CARRY_PROGRESSIVE_THRESHOLD = 10
+
+
+# ==================================================================================================
+# DESIGN PHILOSOPHY
+# ==================================================================================================
+#
+# Each event produces:
+#   1. ONE composite base token  — encodes the full identity of the action
+#      Format: "event_type|key_attribute_1|key_attribute_2|..."
+#      Example: "pass|z9_z7|s|r|g|short"
+#                         ↑     ↑ ↑ ↑ ↑
+#              zones   outcome body height length
+#
+#   2. ZERO or MORE rare modifier tokens — only emitted when truly present
+#      These are kept separate because they carry meaning in SEQUENCE CONTEXT:
+#      e.g. "cross → shot" is a real pattern Word2Vec should learn.
+#      If merged into the base token they'd appear too rarely to learn embeddings.
+#
+# WHY composite base tokens?
+#   With atomic tokens (old design), Word2Vec window=3 was consumed by attributes
+#   of the SAME event (body_right, pass_short, pass_ground all for one pass).
+#   The model learned attribute co-occurrence within events, not transitions between events.
+#   With one composite token per event, window=3 always spans 3 actual football actions.
+#
+# VOCAB GROWTH:
+#   25 zones × 25 zones × 2 outcomes × 3 body parts × 3 heights × 3 lengths
+#   → ~10,000+ unique pass tokens alone (Magdaci's ~19K becomes achievable)
+#
+# ==================================================================================================
+
+
+# ==================================================================================================
+# HELPERS
+# ==================================================================================================
+
+def _zone(x, y):
+    """
+    Returns just the zone number as a string (e.g. "9") by stripping
+    the "zone_" prefix that xy_to_zone returns.
+    This keeps composite tokens readable: "pass|z9_z7|..." not "pass|zone_9_zone_7|..."
+    """
+    z = xy_to_zone(x, y)           # returns "zone_9"
+    return z.split("_")[-1]        # returns "9"
+
+
+def _body(data):
+    """Extracts body part abbreviation from a StatsBomb sub-dict."""
+    b = data.get("body_part", {}).get("name", "").lower()
+    if "right" in b:  return "r"
+    if "left"  in b:  return "l"
+    if "head"  in b:  return "h"
+    return "o"                      # other (chest, no touch, etc.)
+
+
+def _height(data):
+    """Extracts pass height abbreviation."""
+    h = data.get("height", {}).get("name", "").lower()
+    if "ground" in h: return "g"
+    if "low"    in h: return "l"
+    if "high"   in h: return "h"
+    return "g"                      # default ground if missing
+
+
+def _length_bin(length):
+    """Bins pass length into short / medium / long."""
+    if length is None:   return "short"
+    if length < 10:      return "short"
+    if length < 30:      return "med"
+    return "long"
+
+
+# ==================================================================================================
+# MAIN ENTRY POINT
+# ==================================================================================================
+
+def event_to_tokens(ev):
+    ev_type = ev.get("type", {}).get("name", "").lower()
+
+    if   ev_type == "pass":           return tokenize_pass(ev)
+    elif ev_type == "carry":          return tokenize_carry(ev)
+    elif ev_type == "dribble":        return tokenize_dribble(ev)
+    elif ev_type == "shot":           return tokenize_shot(ev)
+    elif ev_type == "duel":           return tokenize_duel(ev)
+    elif ev_type == "interception":   return tokenize_interception(ev)
+    elif ev_type == "ball recovery":  return tokenize_ball_recovery(ev)
+    elif ev_type == "clearance":      return tokenize_clearance(ev)
+    else:
+        return []
+
+
+# ==================================================================================================
+# TOKENIZATION FUNCTIONS
+# ==================================================================================================
+
+# ================================
+# PASS
+# ================================
+
+def tokenize_pass(ev):
+    """
+    Base token:  pass|z{start}_z{end}|{outcome}|{body}|{height}|{length}
+    Example:     pass|z9_z7|s|r|g|short
+                                   ↑ success, right foot, ground pass, short
+
+    Rare modifiers (kept separate for sequence context learning):
+        progressive_pass, cross, through_ball, switch, cutback, under_pressure
+    """
+    if not ev.get("location") or not ev.get("pass"):
+        return ["pass|unknown"]
+
+    start_loc = ev["location"]
+    end_loc   = ev["pass"].get("end_location")
+
+    if not end_loc:
+        return ["pass|unknown"]
+
+    start_x, start_y = start_loc
+    end_x,   end_y   = end_loc
+
+    sz      = _zone(start_x, start_y)
+    ez      = _zone(end_x, end_y)
+    outcome = "s" if ev["pass"].get("outcome") is None else "f"
+    body    = _body(ev["pass"])
+    height  = _height(ev["pass"])
+    length  = _length_bin(ev["pass"].get("length"))
+
+    base = f"pass|z{sz}_z{ez}|{outcome}|{body}|{height}|{length}"
+    tokens = [base]
+
+    # --- Rare modifiers ---
+    if (end_x - start_x) >= PASS_PROGRESSIVE_THRESHOLD:
+        tokens.append("progressive_pass")
+
+    if ev["pass"].get("cross"):
+        tokens.append("cross")
+
+    if ev["pass"].get("through_ball"):
+        tokens.append("through_ball")
+
+    if ev["pass"].get("switch"):
+        tokens.append("switch")
+
+    if ev["pass"].get("cut_back"):
+        tokens.append("cutback")
+
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    return tokens
+
+
+# ================================
+# CARRY
+# ================================
+
+def tokenize_carry(ev):
+    """
+    Base token:  carry|z{start}_z{end}|{direction}
+    Example:     carry|z9_z13|fwd
+
+    Direction: fwd (forward), bwd (backward), lat (lateral)
+
+    Rare modifiers: progressive_carry, under_pressure
+    """
+    start = ev.get("location")
+    end   = ev.get("carry", {}).get("end_location")
+
+    if not start or not end:
+        return ["carry|unknown"]
+
+    sx, sy = start
+    ex, ey = end
+
+    sz  = _zone(sx, sy)
+    ez  = _zone(ex, ey)
+    dx  = ex - sx
+
+    if   dx > 5:  direction = "fwd"
+    elif dx < -5: direction = "bwd"
+    else:         direction = "lat"
+
+    base = f"carry|z{sz}_z{ez}|{direction}"
+    tokens = [base]
+
+    # --- Rare modifiers ---
+    if dx >= CARRY_PROGRESSIVE_THRESHOLD:
+        tokens.append("progressive_carry")
+
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    return tokens
+
+
+# ================================
+# DRIBBLE
+# ================================
+
+def tokenize_dribble(ev):
+    """
+    Base token:  dribble|z{zone}|{outcome}
+    Example:     dribble|z9|s
+
+    Rare modifiers: under_pressure
+    """
+    loc = ev.get("location")
+    if not loc:
+        return ["dribble|unknown"]
+
+    x, y    = loc
+    z       = _zone(x, y)
+    outcome = ev.get("dribble", {}).get("outcome", {}).get("name", "").lower()
+    o       = "s" if "complete" in outcome else "f"
+
+    base   = f"dribble|z{z}|{o}"
+    tokens = [base]
+
+    # --- Rare modifiers ---
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    return tokens
+
+
+# ================================
+# SHOT
+# ================================
+
+def tokenize_shot(ev):
+    """
+    Base token:  shot|z{zone}|{outcome}|{body}|{type}|{xg_bin}|{dist_bin}
+    Example:     shot|z15|goal|r|open|xg_h|close
+
+    outcome: goal / on (on target) / off (off target)
+    type:    open / pen (penalty) / set (set piece)
+    xg_bin:  xg_l (<0.1) / xg_m (<0.3) / xg_h (>=0.3)
+    dist:    close (<10m) / mid (<20m) / long (>=20m)
+
+    Rare modifiers: one_on_one, open_goal, first_time
+    """
+    shot = ev.get("shot", {})
+    loc  = ev.get("location")
+
+    if not loc:
+        return ["shot|unknown"]
+
+    x, y = loc
+    z    = _zone(x, y)
+
+    # outcome
+    outcome_str = shot.get("outcome", {}).get("name", "").lower()
+    if "goal"  in outcome_str:                          o = "goal"
+    elif "saved" in outcome_str or "post" in outcome_str: o = "on"
+    else:                                                   o = "off"
+
+    body = _body(shot)
+
+    # shot type
+    shot_type = shot.get("type", {}).get("name", "").lower()
+    if   "open play" in shot_type: t = "open"
+    elif "penalty"   in shot_type: t = "pen"
+    else:                          t = "set"
+
+    # xG bin
+    xg = shot.get("statsbomb_xg")
+    if   xg is None:  xg_bin = "xg_u"   # unknown
+    elif xg < 0.1:    xg_bin = "xg_l"
+    elif xg < 0.3:    xg_bin = "xg_m"
+    else:             xg_bin = "xg_h"
+
+    # distance to goal (goal centre at 120, 40)
+    dist = ((120 - x) ** 2 + (40 - y) ** 2) ** 0.5
+    if   dist < 10: dist_bin = "close"
+    elif dist < 20: dist_bin = "mid"
+    else:           dist_bin = "long"
+
+    base   = f"shot|z{z}|{o}|{body}|{t}|{xg_bin}|{dist_bin}"
+    tokens = [base]
+
+    # --- Rare modifiers ---
+    if shot.get("one_on_one"):  tokens.append("one_on_one")
+    if shot.get("open_goal"):   tokens.append("open_goal")
+    if shot.get("first_time"):  tokens.append("first_time")
+
+    return tokens
+
+
+# ================================
+# DUEL
+# ================================
+
+def tokenize_duel(ev):
+    """
+    Base token:  duel|z{zone}|{type}|{outcome}
+    Example:     duel|z9|ground|won
+
+    Rare modifiers: under_pressure
+    """
+    duel = ev.get("duel", {})
+    loc  = ev.get("location")
+
+    if not loc:
+        return ["duel|unknown"]
+
+    x, y = loc
+    z    = _zone(x, y)
+
+    duel_type = duel.get("type", {}).get("name", "").lower()
+    t = "aerial" if "aerial" in duel_type else "ground"
+
+    outcome = duel.get("outcome", {}).get("name", "").lower()
+    o = "won" if "won" in outcome else "lost"
+
+    base   = f"duel|z{z}|{t}|{o}"
+    tokens = [base]
+
+    # --- Rare modifiers ---
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    return tokens
+
+
+# ================================
+# INTERCEPTION
+# ================================
+
+def tokenize_interception(ev):
+    """
+    Base token:  interception|z{zone}|{outcome}
+    Example:     interception|z5|won
+
+    Rare modifiers: under_pressure
+    """
+    loc = ev.get("location")
+    if not loc:
+        return ["interception|unknown"]
+
+    x, y    = loc
+    z       = _zone(x, y)
+    outcome = ev.get("interception", {}).get("outcome", {}).get("name", "").lower()
+    o       = "won" if "won" in outcome else "lost"
+
+    base   = f"interception|z{z}|{o}"
+    tokens = [base]
+
+    # --- Rare modifiers ---
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    return tokens
+
+
+# ================================
+# BALL RECOVERY
+# ================================
+
+def tokenize_ball_recovery(ev):
+    """
+    Base token:  ball_recovery|z{zone}|{outcome}
+    Example:     ball_recovery|z3|s
+
+    Rare modifiers: under_pressure
+    """
+    loc = ev.get("location")
+    if not loc:
+        return ["ball_recovery|unknown"]
+
+    x, y = loc
+    z    = _zone(x, y)
+    o    = "f" if ev.get("ball_recovery", {}).get("recovery_failure") else "s"
+
+    base   = f"ball_recovery|z{z}|{o}"
+    tokens = [base]
+
+    # --- Rare modifiers ---
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    return tokens
+
+
+# ================================
+# CLEARANCE
+# ================================
+
+def tokenize_clearance(ev):
+    """
+    Base token:  clearance|z{zone}|{body}
+    Example:     clearance|z3|h
+
+    Rare modifiers: under_pressure
+    """
+    loc = ev.get("location")
+    if not loc:
+        return ["clearance|unknown"]
+
+    x, y = loc
+    z    = _zone(x, y)
+    body = _body(ev.get("clearance", {}))
+
+    base   = f"clearance|z{z}|{body}"
+    tokens = [base]
+
+    # --- Rare modifiers ---
+    if ev.get("under_pressure"):
+        tokens.append("under_pressure")
+
+    return tokens
\ No newline at end of file
diff --git a/src/preprocess/zones.py b/src/preprocess/zones.py
new file mode 100644
index 0000000000000000000000000000000000000000..6240752fdfd990244ee2aef09ddaf00d66a516a4
--- /dev/null
+++ b/src/preprocess/zones.py
@@ -0,0 +1,118 @@
+"""
+zones.py
+--------
+
+Handles conversion between (x, y) coordinates and pitch zones.
+
+StatsBomb pitch:
+    x ∈ [0, 120]
+    y ∈ [0, 80]
+"""
+
+# =====================
+# CONFIG
+# =====================
+
+PITCH_LENGTH = 120.0
+PITCH_WIDTH  = 80.0
+
+NUM_X_BINS = 6   # horizontal divisions
+NUM_Y_BINS = 3   # vertical divisions
+
+
+# =====================
+# CORE FUNCTIONS
+# =====================
+
+def xy_to_zone(x, y, num_x=NUM_X_BINS, num_y=NUM_Y_BINS):
+    """
+    Convert (x, y) → zone_id
+
+    Zones are indexed row-wise:
+        zone_0 ... zone_(num_x*num_y - 1)
+
+    Example (6x3):
+        zone_0 zone_1 zone_2 ...
+    """
+    if x is None or y is None:
+        return "zone_unknown"
+
+    # Clamp values
+    x = max(0.0, min(PITCH_LENGTH, float(x)))
+    y = max(0.0, min(PITCH_WIDTH, float(y)))
+
+    bx = min(int((x / PITCH_LENGTH) * num_x), num_x - 1)
+    by = min(int((y / PITCH_WIDTH) * num_y), num_y - 1)
+
+    return f"{by * num_x + bx}"
+
+
+def get_start_zone(ev):
+    """
+    Extract start zone from event
+    """
+    loc = ev.get("location")
+    if not loc:
+        return "zone_unknown"
+
+    return xy_to_zone(loc[0], loc[1])
+
+
+def get_end_zone(ev):
+    """
+    Extract end zone depending on event type
+    """
+    ev_type = ev.get("type", {}).get("name", "").lower()
+
+    if ev_type == "pass":
+        end = ev.get("pass", {}).get("end_location")
+
+    elif ev_type == "carry":
+        end = ev.get("carry", {}).get("end_location")
+
+    elif ev_type == "shot":
+        end = ev.get("shot", {}).get("end_location")
+
+    else:
+        return "zone_unknown"
+
+    if not end:
+        return "zone_unknown"
+
+    return xy_to_zone(end[0], end[1])
+
+
+# =====================
+# OPTIONAL UTILITIES
+# =====================
+
+def zone_center(zone_id, num_x=NUM_X_BINS, num_y=NUM_Y_BINS):
+    """
+    Convert zone_id → approximate (x, y) center
+    Useful for distance / progressive logic later
+    """
+    if not zone_id.startswith("zone_"):
+        return None, None
+
+    zid = int(zone_id.split("_")[1])
+
+    bx = zid % num_x
+    by = zid // num_x
+
+    x = (bx + 0.5) * (PITCH_LENGTH / num_x)
+    y = (by + 0.5) * (PITCH_WIDTH / num_y)
+
+    return x, y
+
+
+def zone_distance(z1, z2):
+    """
+    Euclidean distance between two zones (center-based)
+    """
+    x1, y1 = zone_center(z1)
+    x2, y2 = zone_center(z2)
+
+    if x1 is None or x2 is None:
+        return 0.0
+
+    return ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5
\ No newline at end of file
diff --git a/src/run_search.py b/src/run_search.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfeb9d0e3060705bf7b4cfede05c283bd106e83e
--- /dev/null
+++ b/src/run_search.py
@@ -0,0 +1,46 @@
+from src.model.similarity import find_similar_players
+import torch
+import json
+
+def load_player_names(path):
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+    
+def get_player_name(pid, player_map):
+    return player_map.get(str(pid), f"Unknown ({pid})")    
+# Load embeddings
+data = torch.load("player_embeddings.pt")
+player_embeddings = data["embeddings"]
+
+# Load names
+player_map = load_player_names("models/gp1/shared/player_id_to_name.json")
+
+while True:
+    try:
+        query = input("\nEnter player ID (or 'q' to quit): ")
+
+        if query.lower() == 'q':
+            print("Exiting search...")
+            break
+
+        query_id = int(query)
+
+    except ValueError:
+        print("Invalid input! Please enter a number.")
+        continue
+
+    if query_id not in player_embeddings:
+        print("Player not found!")
+        continue
+
+    # Print query player
+    print(f"\n🔎 Query Player:")
+    print(f"{query_id} → {get_player_name(query_id, player_map)}")
+
+    # Get similar players
+    similar = find_similar_players(query_id, player_embeddings, top_k=5)
+
+    print("\nTop similar players:\n")
+    for pid, score in similar:
+        name = get_player_name(pid, player_map)
+        print(f"{pid} → {name} | similarity: {score:.4f}")
\ No newline at end of file