luxury-lakehouse/xg-shot-data

xG Shot Data — StatsBomb + Wyscout

~131K professional soccer shots from StatsBomb Open Data (88K) and Wyscout (43K), with geometric features, categorical context, and goal labels. Partitioned by data_source for selective loading.

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

Quick Start

from datasets import load_dataset

ds = load_dataset("luxury-lakehouse/xg-shot-data")
df = ds["train"].to_pandas()

# Goal conversion rate by body part
df.groupby("shot_body_part")["is_goal"].mean().sort_values(ascending=False)

Explore interactively: HF Space demo

What Is This Dataset?

This dataset contains every shot from the StatsBomb and Wyscout open data collections, enriched with pre-computed geometric features (distance to goal, shot angle) and unified to a common coordinate system. It serves as the primary training input for both xG models in the platform:

Model	Repo	Architecture
xG v1	xg-model-statsbomb-wyscout	Calibrated XGBoost (13 tabular features)
xG v2	xg-v2-model-set-encoder	Deep Sets encoder + MLP with MC Dropout (tabular + freeze-frame context)

Data Fields

Column	Type	Description
shot_id	string	Surrogate key (deterministic hash via dbt_utils.generate_surrogate_key)
match_id	Int64	Match identifier
competition_id	Int64	Competition identifier (NULL for Wyscout)
season_id	Int64	Season identifier (NULL for Wyscout)
player_id	Int64	Player identifier
team_id	Int64	Team identifier
period	Int64	Match period (1 = first half, 2 = second half, 3+ = extra time)
minute	Int64	Match minute
second	Int64	Second within the minute
location_x	float64	Shot x-coordinate (StatsBomb: 0–120 yards, attacking direction)
location_y	float64	Shot y-coordinate (StatsBomb: 0–80 yards)
end_location_x	float64	Shot destination x-coordinate
end_location_y	float64	Shot destination y-coordinate
shot_outcome	string	Categorical outcome: Goal, Saved, Blocked, Off T, Wayward, Post
shot_body_part	string	Body part used: Right Foot, Left Foot, Head, Other
shot_technique	string	Technique: Normal, Volley, Half Volley, Lob, Overhead Kick, Backheel, Diving Header
shot_type	string	Context: Open Play, Free Kick, Corner, Penalty, Kick Off
is_goal	bool	Target variable — true if shot_outcome = 'Goal'
distance_to_goal	float64	Euclidean distance from shot location to goal center (yards)
shot_angle	float64	Angle subtended by the goal posts from the shot location (radians)
is_first_time	bool	Shot taken first-time (no prior control touch)
play_pattern	string	Build-up pattern: Regular Play, From Counter, From Corner, From Free Kick, From Keeper, etc.
statsbomb_xg	float64	StatsBomb proprietary xG (NULL for Wyscout shots; useful as a benchmark label)
data_source	string	Partition key: statsbomb or wyscout

Coordinate System

All spatial features use the StatsBomb coordinate system:

• Pitch dimensions: 120 yards (length) × 80 yards (width)
• Origin: top-left corner of the pitch
• Attacking direction: left to right (x increases toward opponent goal)
• Goal center: approximately (120, 40)

Wyscout coordinates (0–100% scale) are converted to StatsBomb coordinates at the dbt staging layer.

Data Sources

Source	Shots	Matches	License
StatsBomb Open Data	~88K	~3,000	CC-BY 4.0
Wyscout Public Dataset	~43K	~1,900	CC-BY-NC 4.0
Total	~131K		CC-BY-NC 4.0 (most restrictive applies)

Coverage includes the Premier League, La Liga, Serie A, Bundesliga, Ligue 1, Champions League, World Cup, and more.

Partitioning

Data is Hive-partitioned by data_source:

data/
  data_source=statsbomb/data.parquet
  data_source=wyscout/data.parquet

Load a single source efficiently:

import pandas as pd
from huggingface_hub import hf_hub_download

# Load only StatsBomb shots
path = hf_hub_download(
    "luxury-lakehouse/xg-shot-data",
    "data/data_source=statsbomb/data.parquet",
    repo_type="dataset",
)
df_sb = pd.read_parquet(path)

Use Cases

• xG model training: Primary input for training expected goals models (logistic regression, XGBoost, Deep Sets, or custom architectures)
• Shot analysis: Visualize shot maps, compare conversion rates by body part, technique, or play pattern
• Benchmarking: Compare custom xG models against the included statsbomb_xg column on the StatsBomb subset
• Feature engineering: Pre-computed distance_to_goal and shot_angle ready for modeling; categorical columns ready for one-hot encoding
• Cross-source research: Study differences in shot event classification between StatsBomb and Wyscout

Limitations

• Open data only: Contains only publicly available StatsBomb and Wyscout shots. Commercial datasets cover additional leagues and seasons.
• No freeze frames: This dataset contains tabular shot features only. For player positions at the moment of each shot (used by xG v2), see the companion xG Freeze Frame Data dataset.
• Wyscout NULL columns: competition_id, season_id, and statsbomb_xg are NULL for all Wyscout shots due to the absence of a cross-source match join for those fields.
• Coordinate conversion: Wyscout coordinates are converted from percentage-based (0–100) to StatsBomb yards (0–120, 0–80) at the dbt staging layer. Subtle conversion artifacts may exist at pitch boundaries.
• Class imbalance: Goals are relatively rare (~9–10% of shots). Models should account for this imbalance during training.

Citation

If you use this dataset, please cite the data providers:

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

@misc{pappalardo2019public,
  title={A public data set of spatio-temporal match events in soccer competitions},
  author={Pappalardo, Luca and Cintia, Paolo and Rossi, Alessio and Massucco, Emanuele
          and Ferragina, Paolo and Pedreschi, Dino and Giannotti, Fosca},
  journal={Scientific Data},
  volume={6},
  number={1},
  pages={1--15},
  year={2019},
  publisher={Nature Publishing Group}
}

@software{nielsen2026xgshotdata,
  title={xG Shot Data: StatsBomb + Wyscout Open Data Shot Features},
  author={Nielsen, Karsten Skytt},
  year={2026},
  url={https://github.com/karsten-s-nielsen/luxury-lakehouse}
}

Companion Resources

Resource	Type	Description
xG Model v1	Model	Calibrated XGBoost + logistic baseline (13 features)
xG v2 Set Encoder	Model	Deep Sets + MLP with MC Dropout uncertainty
xG Freeze Frame Data	Dataset	Player positions at shot time (15.58M rows, 323 matches)
SPADL/VAEP Action Values	Dataset	Per-action VAEP valuations (~9.5M actions)
VAEP Model	Model	P(scores) + P(concedes) XGBClassifiers

Demo

Try the interactive Soccer Analytics Explorer — explore shot maps with xG overlays, filter by competition, and compare custom xG against StatsBomb.

Explore interactively: HF Space demo

More Information

• License: CC-BY-NC 4.0 (inherited from Wyscout data)
• Publishing script: scripts/publish_xg_shots_hf.py (PEP 723 standalone)
• Platform: Luxury Lakehouse Soccer Analytics