Duplicate from ClementeH/statsbomb-open-data-shots

Co-authored-by: Clemente Henriquez <ClementeH@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-sa-4.0
+task_categories:
+- tabular-classification
+- tabular-regression
+language:
+- en
+tags:
+- football
+- soccer
+- xG
+- expected-goals
+- sports-analytics
+- statsbomb
+- messi
+- analytics
+pretty_name: StatsBomb Open Data — Football Shots (xG)
+size_categories:
+- 10K<n<100K
+dataset_info:
+  features:
+  - name: match_id
+    dtype: int64
+  - name: competition_name
+    dtype: string
+  - name: season_name
+    dtype: string
+  - name: match_date
+    dtype: string
+  - name: team
+    dtype: string
+  - name: player
+    dtype: string
+  - name: x
+    dtype: float64
+  - name: y
+    dtype: float64
+  - name: distance_to_goal
+    dtype: float64
+  - name: angle_to_goal
+    dtype: float64
+  - name: under_pressure
+    dtype: bool
+  - name: shot_outcome_name
+    dtype: string
+  - name: shot_body_part_name
+    dtype: string
+  - name: shot_technique_name
+    dtype: string
+  - name: shot_type_name
+    dtype: string
+  - name: play_pattern_name
+    dtype: string
+  - name: xg_statsbomb
+    dtype: float64
+  - name: is_goal
+    dtype: int64
+  splits:
+  - name: train
+    num_examples: 70418
+  - name: validation
+    num_examples: 8802
+  - name: test
+    num_examples: 8803
+---
+
+# StatsBomb Open Data — Football Shots (xG)
+
+**88,023 shot-level football records** extracted from [StatsBomb Open Data](https://github.com/statsbomb/open-data),
+spanning **67 years of football** (1958–2025) across **21 competitions**, **48 seasons**, **308 teams**, and **6,147 players**.
+
+Includes StatsBomb's own xG values as labels, making this the most complete open football shot dataset
+available on HuggingFace for training Expected Goals models.
+
+## Highlights
+
+- 🏆 **Lionel Messi** — 2,670 shots, the most of any player in the dataset (18 La Liga seasons at Barcelona)
+- 🌍 **Historical depth** — FIFA World Cup data from 1958 to 2022
+- ⚽ **Both genders** — FA Women's Super League, Women's World Cup, UEFA Women's Euro included
+- 📊 **StatsBomb xG labels** — use as regression target or benchmark for your own model
+- 🎯 **Benchmark** — A GradientBoosting model trained on this dataset achieves ROC-AUC 0.8047, reaching 98% of StatsBomb's professional xG model performance (ROC-AUC 0.8198) using only 9 features
+
+## Dataset Statistics
+
+| Metric | Value |
+|--------|-------|
+| Total shots | 88,023 |
+| Goals | 9,790 (11.1%) |
+| Unique players | 6,147 |
+| Unique teams | 308 |
+| Competitions | 21 |
+| Seasons | 48 |
+| Date range | 1958-06-24 → 2025-07-27 |
+| Mean xG per shot | 0.107 |
+
+## Coverage by Competition
+
+| Competition | Shots | Goals | Players | Seasons |
+|-------------|-------|-------|---------|---------|
+| La Liga | 21,210 | 2,658 | 1,312 | 18 |
+| Premier League | 10,837 | 1,082 | 641 | 2 |
+| Ligue 1 | 10,346 | 1,105 | 739 | 3 |
+| Serie A | 10,033 | 955 | 475 | 2 |
+| 1. Bundesliga | 8,747 | 947 | 560 | 2 |
+| FA Women's Super League | 8,321 | 962 | 330 | 3 |
+| FIFA World Cup | 3,904 | 443 | 890 | 8 |
+| Women's World Cup | 2,994 | 327 | 576 | 2 |
+| UEFA Euro | 2,629 | 281 | 547 | 2 |
+| Champions League | 594 | 74 | 193 | 18 |
+| *+ 11 more competitions* | | | | |
+
+## Splits
+
+| Split | Shots | Goals |
+|-------|-------|-------|
+| train | 70,418 | 7,825 (11.1%) |
+| validation | 8,802 | 979 (11.1%) |
+| test | 8,803 | 986 (11.2%) |
+
+Stratified by `is_goal` to preserve goal rate across splits.
+
+## Features
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `x` | float | Shot x-coordinate (StatsBomb pitch: 0–120, attacking direction) |
+| `y` | float | Shot y-coordinate (StatsBomb pitch: 0–80) |
+| `distance_to_goal` | float | Euclidean distance to goal center (120, 40) |
+| `angle_to_goal` | float | Angle to goal in degrees |
+| `under_pressure` | bool | Shooter was under defensive pressure |
+| `shot_body_part_name` | string | `Right Foot` / `Left Foot` / `Head` / `Other` |
+| `shot_technique_name` | string | `Normal` / `Volley` / `Half Volley` / `Lob` / `Overhead Kick` / `Backheel` |
+| `shot_type_name` | string | `Open Play` / `Free Kick` / `Corner` / `Penalty` |
+| `play_pattern_name` | string | `Regular Play` / `From Corner` / `From Free Kick` / etc. |
+| `shot_outcome_name` | string | `Goal` / `Saved` / `Blocked` / `Off T` / `Wayward` / `Post` |
+| `xg_statsbomb` | float | StatsBomb's official xG value — use as regression label or benchmark |
+| `is_goal` | int | 1 if goal, 0 otherwise — binary classification label |
+| `player` | string | Player full name |
+| `team` | string | Team name |
+| `competition_name` | string | Competition name |
+| `season_name` | string | Season (e.g. `2019/2020`) |
+| `match_date` | string | Match date (YYYY-MM-DD) |
+| `match_id` | int | StatsBomb match identifier |
+
+## Pitch Coordinates
+
+StatsBomb uses a 120×80 coordinate system:
+- `x=0` is the defensive goal line, `x=120` is the attacking goal line
+- `y=0` is the left touchline, `y=80` is the right touchline
+- Goal center is at `(120, 40)`
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("ClementeH/statsbomb-open-data-shots")
+df = ds["train"].to_pandas()
+
+# All Messi shots
+messi = df[df["player"] == "Lionel Andrés Messi Cuccittini"]
+print(f"Messi: {len(messi)} shots, {messi['is_goal'].sum()} goals, mean xG {messi['xg_statsbomb'].mean():.3f}")
+
+# Train a simple xG model
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.preprocessing import LabelEncoder
+
+features = ["x", "y", "distance_to_goal", "angle_to_goal", "under_pressure"]
+X = df[features].astype(float)
+y = df["is_goal"]
+
+model = GradientBoostingClassifier(n_estimators=200, max_depth=4)
+model.fit(X, y)
+```
+
+## Benchmark: xG Model Performance
+
+A GradientBoosting model trained on this dataset (9 features) vs StatsBomb's professional xG:
+
+| Model | Brier Score ↓ | ROC-AUC ↑ |
+|-------|-------------|----------|
+| Logistic Regression (baseline) | 0.0858 | 0.7706 |
+| GradientBoosting (this dataset) | 0.0803 | **0.8047** |
+| StatsBomb xG (professional) | 0.0770 | 0.8198 |
+
+The open model reaches **98% of professional xG performance** using only position, body part, technique, shot type, and pressure.
+
+See [`ClementeH/football-xg`](https://huggingface.co/ClementeH/football-xg) for the trained model *(coming soon)*.
+
+## Attribution
+
+> **Data provided by StatsBomb via [StatsBomb Open Data](https://github.com/statsbomb/open-data).**
+> Licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
+>
+> You are free to share and adapt this data for any purpose, provided you give appropriate credit
+> to StatsBomb and distribute your contributions under the same license.
+>
+> This dataset was processed and packaged for HuggingFace by [ClementeH](https://huggingface.co/ClementeH).
+
+## Related Resources
+
+- [statsbomb/open-data](https://github.com/statsbomb/open-data) — original raw JSON data
+- [statsbombpy](https://github.com/statsbomb/statsbombpy) — official Python library
+- [`ClementeH/football-xg`](https://huggingface.co/ClementeH/football-xg) — xG model trained on this dataset *(coming soon)*
+- [`ClementeH/football-xg-analyzer`](https://huggingface.co/spaces/ClementeH/football-xg-analyzer) — interactive Space *(coming soon)*

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