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ClementeH/statsbomb-open-data-shots

StatsBomb Open Data — Football Shots (xG) 88,023 shot-level football records extracted from 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… See the full description on the dataset page: https://huggingface.co/datasets/ClementeH/statsbomb-open-data-shots.

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Dataset Card

StatsBomb Open Data — Football Shots (xG)

88,023 shot-level football records extracted from 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` 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. 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.

Related Resources

—statsbomb/open-data — original raw JSON data
—statsbombpy — official Python library
—`ClementeH/football-xg` — xG model trained on this dataset (coming soon)
—`ClementeH/football-xg-analyzer` — interactive Space (coming soon)