luxury-lakehouse/statsbomb-shots-on-target

StatsBomb On-Target Shots — Goalmouth Coordinates

~15K on-target shots from StatsBomb Open Data with goalmouth coordinates (end_location_y, end_location_z). Primary training input for the PSxG model used in goalkeeper shot-stopping evaluation.

Part of the (Right! Luxury!) Lakehouse soccer analytics platform.

Quick Start

from datasets import load_dataset

ds = load_dataset("luxury-lakehouse/statsbomb-shots-on-target")
df = ds["train"].to_pandas()
print(f"{len(df)} on-target shots")

# Goal rate by goalmouth zone
df["height_zone"] = df["end_location_z"].apply(
    lambda z: "high" if z > 0.6 else ("mid" if z > 0.3 else "low")
)
df.groupby("height_zone")["is_goal"].mean()

Explore interactively: HF Space demo

What Is This Dataset?

This dataset contains every on-target shot from the StatsBomb open data collection that includes goalmouth coordinates. It is the training corpus for the PSxG (Post-Shot Expected Goals) model, which estimates the probability that an on-target shot becomes a goal given where it was headed.

Only shots with shot_outcome in {Saved, Goal, Post} are included. Blocked shots and wayward shots are excluded because they never reach the goalkeeper.

Data Fields

Column	Type	Description
event_id	string	Unique StatsBomb event identifier
match_id	Int64	Match identifier
player_id	Int64	Shooter player identifier
end_location_y	float64	Normalized horizontal goalmouth position [0, 1] (0 = left post, 1 = right post)
end_location_z	float64	Normalized vertical goalmouth position [0, 1] (0 = ground level, 1 = crossbar)
shot_outcome	string	Outcome: Saved, Goal, or Post
is_goal	bool	Target variable — true if shot_outcome = 'Goal'

Coordinate System

Goalmouth coordinates use the StatsBomb 360 coordinate system, normalized to [0, 1]:

• end_location_y: Raw StatsBomb y is 36–44 yards (goalpost to goalpost, 8 yards wide). Normalized: (y − 36) / 8.
• end_location_z: Raw StatsBomb z is 0–2.44 meters (ground to crossbar). Normalized: z / 2.44.

Values outside [0, 1] (shots that miss via height or width but are still classified as "Saved") are clipped at 0 and 1.

Data Sources

Source	On-Target Shots	License
StatsBomb Open Data	~15K	CC-BY 4.0

Coverage includes the Premier League, La Liga, Serie A, Bundesliga, Ligue 1, Champions League, World Cup, and more (StatsBomb 360-enabled competitions only, as end_location_z requires 360 data).

Use Cases

• PSxG model training: Primary training input for the PSxG model
• Goalkeeper benchmarking: Analyze shot difficulty distributions faced by individual goalkeepers
• Shot analysis: Visualize goalmouth heatmaps by outcome, competition, or position
• Custom models: Train alternative PSxG architectures (e.g., kernel density, neural nets) on the same standardized dataset

Limitations

• StatsBomb only: end_location_z is not available in Wyscout or other open providers. This dataset is StatsBomb-only.
• 360-enabled competitions: Only StatsBomb competitions with 360 data have goalmouth z-coordinates. Earlier StatsBomb open data lacks the z-dimension.
• Open data only: Contains only publicly available StatsBomb shots. Commercial datasets cover more competitions and seasons.
• No keeper position: This dataset does not include the goalkeeper's starting position or movement. For freeze-frame context, see xG Freeze Frame Data.
• Clipped coordinates: A small fraction of "Saved" shots have raw coordinates outside the goalmouth geometry (e.g., diving saves). These are clipped to [0, 1].

Citation

If you use this dataset, please cite:

@misc{statsbomb2024opendata,
  title={StatsBomb Open Data},
  author={{StatsBomb}},
  year={2024},
  url={https://github.com/statsbomb/open-data},
  note={CC-BY 4.0}
}

@article{butcher2025xgot,
  title={An Expected Goals On Target (xGOT) Model},
  author={Butcher, J. and others},
  journal={Big Data and Cognitive Computing},
  volume={9},
  number={3},
  pages={64},
  year={2025},
  publisher={MDPI},
  url={https://www.mdpi.com/2504-2289/9/3/64}
}

@software{nielsen2026psxg,
  title={PSxG Model: Post-Shot Expected Goals for Goalkeeper Evaluation},
  author={Nielsen, Karsten Skytt},
  year={2026},
  url={https://github.com/karsten-s-nielsen/luxury-lakehouse}
}

Companion Resources

Resource	Description
PSxG Model	Logistic regression PSxG model trained on this dataset
PSxG Predictions	Per-shot PSxG predictions with player and match identifiers
xG Shot Data	Full shot dataset with pre-shot features (StatsBomb + Wyscout)
xG Freeze Frame Data	Player positions at shot time for context-conditioned xG models

More Information

Explore interactively: HF Space demo

• Model repo: luxury-lakehouse/psxg-model
• License: CC-BY 4.0 (StatsBomb Open Data)
• Platform: Luxury Lakehouse Soccer Analytics