Add institutional dataset card

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - tabular-regression
+  - feature-extraction
+  - other
+language:
+  - en
+tags:
+  - cs2
+  - counter-strike
+  - esports
+  - sports-analytics
+  - competitive-gaming
+  - machine-learning
+  - elo
+  - feature-engineering
+  - win-prediction
+  - sports-betting
+  - tabular-data
+size_categories:
+  - 10K<n<100K
+pretty_name: CounterQuant CS2 GoldLite
+---
+
+# CounterQuant CS2 GoldLite
+
+**ML-ready feature tables for professional CS2 match outcome modelling — Elo ratings, team form, head-to-head records, and event context. No black boxes, no proprietary signals.**
+
+> ⭐ If you use this dataset in research, a product, or any publication,
+> please **cite the author** (see [Citation](#citation) below).
+
+GoldLite is the open, reproducible subset of CounterQuant's internal feature pipeline. It provides pre-computed, match-level features derived entirely from public match results and event metadata — no demo parsing required, no market data, no model outputs. Train your own win-prediction model on a decade of professional CS2.
+
+Curated and maintained by **[Eimantas Kulbe (KEDevO)](https://counterquant.com)** as part of the **CounterQuant** esports intelligence platform.
+
+---
+
+## The CounterQuant Data Stack
+
+GoldLite is the top public-facing tier of a four-layer architecture:
+
+| Tier | Dataset | Contents | Status |
+|------|---------|----------|--------|
+| **Raw demos** | [CounterQuant CS2 Demos](https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-Demos) | Raw `.dem` files | Live |
+| **Bronze** | [CounterQuant CS2 Bronze](https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-Bronze) | Tick-level events: kills, damages, flashes, utility | Live, growing |
+| **Silver** | [CounterQuant CS2 Silver](https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-Silver) | Match results, map scores, player stats, rosters | Staging for export |
+| **Gold Lite** ← *this dataset* | — | Elo, form, H2H, event context — ML features | Staging for export |
+
+The full internal Gold layer (demo-derived features, market signals, 509-feature ML vectors) is proprietary and powers the CounterQuant prediction API. GoldLite is the public, reproducible subset of that pipeline.
+
+---
+
+## Dataset at a Glance
+
+| Metric | Value |
+|--------|-------|
+| **Team Elo records in DB** | 45,502 (per team, per map pool) |
+| **Matches with features** | Growing — 2024 first public batch |
+| **Year range (planned)** | 2012 – 2024 (first release) |
+| **Feature count (GoldLite)** | ~50 pre-match features |
+| **Full pipeline feature count** | 509 (internal, not released) |
+| **Format** | Parquet (Snappy compression) |
+| **License** | CC BY 4.0 |
+
+---
+
+## Current State & Release Schedule
+
+> **GoldLite is not yet published to HuggingFace as Parquet files.**
+> Elo ratings are computed and live in CounterQuant's database.
+> The pre-match feature export is in progress, with 2024 as the first public batch.
+
+| Release | Coverage | Target Date | Notes |
+|---------|----------|-------------|-------|
+| **v1 — 2024** | Jan 2024 – Dec 2024 | Q3 2026 | First public release, alongside Silver |
+| **Backfill — 2012–2023** | 2012 – 2023 | Q4 2026 | Historical Elo + features |
+| **v2 — 2025** | 2025 | 2027 | 2-year delay policy |
+| **2026+** | 2026+ | 2028+ | 2-year delay minimum |
+
+---
+
+## What's in GoldLite
+
+GoldLite contains **pre-match features computed from historical match results** — the information that would be available to a predictor at kick-off. All features are constructed from public sources only.
+
+### Feature categories
+
+| Category | Features | Source |
+|----------|----------|--------|
+| **Elo ratings** | Global team Elo, per-map Elo, Elo uncertainty, Elo differential | Computed from Silver match results |
+| **Recent form** | Win rate last 5/10/20 matches, map differential last 10, days since last match | Silver matches |
+| **Head-to-head** | All-time H2H record, last 12 months H2H, H2H on specific map | Silver matches |
+| **Map pool** | Team win rate per map (last 30 games), map familiarity score, most/least played maps | Silver maps |
+| **Event context** | Prize pool tier, LAN vs online, event stage (groups/playoffs/final), days to event end | Silver events |
+| **Roster stability** | Avg days together, roster change flag (last 30 days) | Silver rosters |
+| **Player aggregate** | Avg team rating last 20 maps, avg ADR last 20 maps, avg KAST last 20 maps | Silver player_stats |
+
+### What GoldLite does NOT include
+
+| Excluded | Where it lives |
+|----------|---------------|
+| Demo-derived features (eco win rates, KAST, trade kills, utility efficiency) | Internal Gold (CounterQuant API) |
+| Polymarket / betting market odds | Private, never released |
+| XGBoost/LightGBM model output probabilities | Private (CounterQuant predictions) |
+| 509-feature full ML vectors | Private (CounterQuant API) |
+| Live round-by-round probability updates | Private (CounterQuant live feed) |
+| Proprietary tactical embeddings | Private, never released |
+
+If you need demo-derived features, use [Bronze](https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-Bronze) and compute them yourself — that's exactly what CounterQuant does internally.
+
+---
+
+## Planned File Structure
+
+```
+data/
+├── team_elo/
+│   ├── team_elo_2024.parquet           # Elo snapshot at each match played in 2024
+│   ├── team_elo_2012_2023.parquet      # Historical backfill
+│   └── team_elo_current.parquet        # Latest Elo per team per map (updated)
+├── match_features/
+│   ├── match_features_2024.parquet     # Pre-match feature vector per 2024 match
+│   └── match_features_2012_2023.parquet
+├── player_rolling_stats/
+│   ├── player_rolling_2024.parquet     # Rolling 5/10/20-match averages per player
+│   └── ...
+└── map_pool/
+    ├── map_pool_stats_2024.parquet     # Team win rates per map, as of each match
+    └── ...
+```
+
+---
+
+## Schema Reference
+
+### `team_elo_YYYY.parquet`
+
+Elo rating for each team at the point of each match. One row per team per match.
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `match_id` | int64 | Match ID |
+| `team_id` | int64 | Team ID |
+| `team_name` | string | Team name (denormalized) |
+| `map_name` | string | `all` for global Elo; CS2 map name for map-specific Elo |
+| `elo_before` | float32 | Elo rating entering this match |
+| `elo_after` | float32 | Elo rating after this match |
+| `elo_change` | float32 | Delta (positive = won, negative = lost) |
+| `uncertainty` | float32 | Elo uncertainty / confidence interval |
+| `matches_played` | int32 | Total matches used to compute this rating |
+| `date` | date | Match date |
+
+---
+
+### `match_features_YYYY.parquet`
+
+Pre-match feature snapshot. One row per match, all features computed from data available **before** kick-off. Safe for train/test split — no data leakage.
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `match_id` | int64 | HLTV match ID |
+| `date` | date | Match date |
+| `tier` | int16 | Match tier (1/2/3) |
+| `is_lan` | bool | LAN event |
+| `prize_pool_usd` | int32 | Event prize pool in USD |
+| `team1_id` | int64 | Team 1 ID |
+| `team2_id` | int64 | Team 2 ID |
+| `team1_elo` | float32 | Global Elo entering this match |
+| `team2_elo` | float32 | Global Elo entering this match |
+| `elo_diff` | float32 | team1_elo − team2_elo |
+| `team1_form_5` | float32 | Win rate last 5 matches |
+| `team2_form_5` | float32 | Win rate last 5 matches |
+| `team1_form_10` | float32 | Win rate last 10 matches |
+| `team2_form_10` | float32 | Win rate last 10 matches |
+| `team1_map_diff_10` | float32 | Map differential (maps won − lost) last 10 |
+| `team2_map_diff_10` | float32 | Map differential last 10 |
+| `h2h_team1_wins` | int16 | All-time H2H wins for team 1 |
+| `h2h_team2_wins` | int16 | All-time H2H wins for team 2 |
+| `h2h_team1_wins_12m` | int16 | H2H wins last 12 months |
+| `h2h_team2_wins_12m` | int16 | H2H wins last 12 months |
+| `team1_days_since_match` | int16 | Days since team 1's last match |
+| `team2_days_since_match` | int16 | Days since team 2's last match |
+| `team1_avg_rating_20` | float32 | Avg team player rating last 20 maps |
+| `team2_avg_rating_20` | float32 | Avg team player rating last 20 maps |
+| `team1_avg_adr_20` | float32 | Avg team ADR last 20 maps |
+| `team2_avg_adr_20` | float32 | Avg team ADR last 20 maps |
+| `roster_change_team1_30d` | bool | Team 1 had a roster change in last 30 days |
+| `roster_change_team2_30d` | bool | Team 2 had a roster change in last 30 days |
+| `team1_won` | bool | **Label** — did team 1 win? (null = match not completed) |
+
+---
+
+### `player_rolling_YYYY.parquet`
+
+Rolling player performance windows, computed at each match. Useful for player-level predictions and ranking models.
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `match_id` | int64 | Match ID |
+| `player_id` | int64 | Player ID |
+| `player_ign` | string | IGN |
+| `team_id` | int64 | Team ID at this match |
+| `maps_played_total` | int16 | Career maps played at this point |
+| `rolling_kills_5` | float32 | Avg kills per map last 5 maps |
+| `rolling_kills_10` | float32 | Avg kills per map last 10 maps |
+| `rolling_adr_10` | float32 | Avg ADR last 10 maps |
+| `rolling_adr_20` | float32 | Avg ADR last 20 maps |
+| `rolling_kast_10` | float32 | Avg KAST last 10 maps |
+| `rolling_rating_20` | float32 | Avg HLTV Rating 2.0 last 20 maps |
+| `rating_trend_10` | float32 | Linear slope of rating over last 10 maps (form direction) |
+| `days_since_last_match` | int16 | Days since player's last match |
+
+---
+
+### `map_pool_stats_YYYY.parquet`
+
+Team win rates per map, computed at each match date (so you can join to match_features for map-specific models).
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `as_of_match_id` | int64 | The match this snapshot was computed for |
+| `as_of_date` | date | Date of the snapshot |
+| `team_id` | int64 | Team ID |
+| `map_name` | string | CS2 map name |
+| `maps_played` | int16 | Maps played on this map (last 2 years) |
+| `maps_won` | int16 | Maps won |
+| `win_rate` | float32 | Win rate (0–1) |
+| `maps_played_tier1` | int16 | Maps played at Tier 1 only |
+| `win_rate_tier1` | float32 | Win rate at Tier 1 |
+
+---
+
+## Quick Start (Preview)
+
+Once 2024 data is published:
+
+```python
+import polars as pl
+from huggingface_hub import hf_hub_download
+
+# Download pre-match features for 2024
+path = hf_hub_download(
+    repo_id="KEDevO/CounterQuant-CS2-GoldLite",
+    repo_type="dataset",
+    filename="data/match_features/match_features_2024.parquet",
+)
+df = pl.read_parquet(path)
+
+# Drop rows with incomplete data (ongoing / no-result matches)
+df = df.filter(pl.col("team1_won").is_not_null())
+
+X = df.drop(["match_id", "date", "team1_id", "team2_id", "team1_won"])
+y = df["team1_won"].cast(pl.Int8)
+print(f"Training samples: {len(X)}, Features: {X.width}")
+```
+
+```python
+from sklearn.model_selection import train_test_split
+from xgboost import XGBClassifier
+from sklearn.metrics import brier_score_loss, roc_auc_score
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X.to_pandas(), y.to_pandas(),
+    test_size=0.2, shuffle=False  # time-ordered split
+)
+
+model = XGBClassifier(
+    n_estimators=300,
+    max_depth=5,
+    learning_rate=0.05,
+    subsample=0.8,
+    colsample_bytree=0.8,
+    use_label_encoder=False,
+    eval_metric="logloss",
+)
+model.fit(X_train, y_train)
+
+probs = model.predict_proba(X_test)[:, 1]
+print(f"Brier score: {brier_score_loss(y_test, probs):.4f}")  # target ≤ 0.21
+print(f"AUC-ROC:     {roc_auc_score(y_test, probs):.4f}")    # target ≥ 0.67
+```
+
+```python
+# Plot team Elo history
+import polars as pl
+import matplotlib.pyplot as plt
+
+elo = pl.read_parquet("data/team_elo/team_elo_2024.parquet")
+team = elo.filter((pl.col("team_name") == "NAVI") & (pl.col("map_name") == "all"))
+
+plt.plot(team["date"].to_list(), team["elo_before"].to_list())
+plt.title("NAVI Global Elo — 2024")
+plt.xlabel("Date")
+plt.ylabel("Elo")
+plt.show()
+```
+
+---
+
+## Methodology Notes
+
+### Elo model
+The Elo ratings in GoldLite are computed using a custom variant of the standard Elo formula:
+
+- **K-factor**: Variable by tier (T1 = 32, T2 = 24, T3 = 20) and match format (bo5 weighted higher)
+- **Map-specific Elo**: Separate rating maintained per CS2 map (de_mirage, de_dust2, etc.)
+- **Initial Elo**: All teams start at 1500. Teams with fewer than 10 matches have higher uncertainty.
+- **Update frequency**: Recomputed from all historical results on demand.
+
+The full GoldLite Elo model is a **baseline** — it does not incorporate demo-derived features, player-level signals, or market information. It is calibrated to be reproducible and interpretable, not to maximize prediction accuracy.
+
+CounterQuant's production model (internal, not released) additionally incorporates 509 features including economy statistics, KAST, utility efficiency, and market-implied probabilities.
+
+### No data leakage guarantee
+All features in `match_features_YYYY.parquet` are constructed from data available strictly **before** the match date. The `team1_won` column is the label — it is derived from the match result after the fact. There are no post-match statistics in the feature columns.
+
+---
+
+## Prediction Targets & Baselines
+
+| Model | Target | GoldLite Baseline | Production Target |
+|-------|--------|------------------|-------------------|
+| Match outcome | team1_won (binary) | Brier ≤ 0.22 | Brier ≤ 0.21 |
+| — | — | AUC-ROC ≥ 0.65 | AUC-ROC ≥ 0.67 |
+| Map outcome | map winner (binary) | Brier ≤ 0.24 | TBD |
+
+A random classifier scores Brier = 0.25. An Elo-only model should reach approximately 0.228. The GoldLite feature set adds form, H2H, and event context on top of Elo and should reach around 0.22.
+
+---
+
+## Related Datasets
+
+| Dataset | Contents | Relationship |
+|---------|----------|--------------|
+| [CounterQuant CS2 Demos](https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-Demos) | Raw `.dem` files | Source layer |
+| [CounterQuant CS2 Bronze](https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-Bronze) | Tick-level events: kills, damages, flashes, utility | Compute demo-derived features to extend GoldLite |
+| [CounterQuant CS2 Silver](https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-Silver) | Match results, map scores, player stats | GoldLite is derived from Silver — join on `match_id` |
+| [CounterQuant Platform](https://counterquant.com) | Live analytics, predictions, API | Production system built on the full 509-feature pipeline |
+| [CounterQuant API](https://counterquant.com/api/docs/) | REST API access | Access to live predictions and extended features |
+
+---
+
+## Data Release Policy
+
+| Layer | Public? | Timeline |
+|-------|---------|----------|
+| **GoldLite 2024** (Elo + form + H2H + event) | ✅ CC BY 4.0 | Q3 2026 |
+| **GoldLite 2012–2023** (backfill) | ✅ CC BY 4.0 | Q4 2026 |
+| **GoldLite 2025** | ✅ CC BY 4.0 | 2027 (2-year delay) |
+| Demo-derived features (eco, KAST, utility) | ❌ Private | CounterQuant API only |
+| Full 509-feature vectors | ❌ Private | CounterQuant API only |
+| Polymarket / market odds | ❌ Private | Never released |
+| Model output probabilities | ❌ Private | CounterQuant API only |
+
+---
+
+## Citation
+
+If you use this dataset in research, analytics products, or publications, please cite:
+
+### BibTeX
+
+```bibtex
+@dataset{kulbe2026counterquantgoldlite,
+  author       = {Eimantas Kulbe},
+  title        = {CounterQuant CS2 GoldLite: ML-Ready Match Features for Professional CS2},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-GoldLite},
+  note         = {Pre-match Elo ratings, team form, head-to-head records, and event
+                  context features for professional CS2 match outcome modelling.
+                  Covers 2012--2024 (first release). 2-year delay for recent data.}
+}
+```
+
+### APA
+
+Kulbe, E. (2026). *CounterQuant CS2 GoldLite: ML-Ready Match Features for Professional CS2* [Dataset]. Hugging Face. https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-GoldLite
+
+### Acknowledgement (for papers/articles)
+
+> Pre-match features and Elo ratings sourced from *CounterQuant CS2 GoldLite* (Kulbe, 2026), available at https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-GoldLite under CC BY 4.0.
+
+---
+
+## License
+
+**Creative Commons Attribution 4.0 International (CC BY 4.0)**
+
+You are free to:
+- **Share** — copy and redistribute the material in any medium or format
+- **Adapt** — remix, transform, and build upon the material for any purpose, including commercial
+
+Under the condition that you **give appropriate credit** to **Eimantas Kulbe** and link back to this dataset.
+
+Full license text: https://creativecommons.org/licenses/by/4.0/
+
+---
+
+*Dataset maintained by [Eimantas Kulbe](https://counterquant.com). For questions, issues, or collaboration: open a discussion on this dataset page or reach out via [CounterQuant](https://counterquant.com).*