luxury-lakehouse/football2vec-training-data

Football2Vec Training Data — SPADL Action Sequences

Tokenized SPADL action sequences for training the Football2Vec v2 transformer encoder. One row per player-match, covering ~87,000 sequences across ~3,000 professional soccer matches from StatsBomb Open Data and Wyscout.

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

Quick Start

from datasets import load_dataset

ds = load_dataset("luxury-lakehouse/football2vec-training-data")
df = ds["train"].to_pandas()
print(f"{len(df)} player-match sequences")

# Inspect one sequence
row = df.iloc[0]
print(f"Player: {row['canonical_player_id']}, Match: {row['match_id']}")
print(f"Actions: {len(row['actions'])} events")
print(f"First action: {row['actions'][0]}")  # {'action_type': 0, 'x': 0.52, 'y': 0.34, 'result': 1}

Explore interactively: HF Space demo

What Is This Dataset?

Each row represents one player's actions in one match, serialized as a struct array of SPADL-tokenized events. The 23-type SPADL vocabulary provides a unified action taxonomy across StatsBomb and Wyscout data sources. Continuous spatial coordinates (x, y) are normalized to [0, 1] on a 105×68m pitch.

This dataset is the training corpus for Football2Vec v2. It is exported from the platform's fct_action_values Delta table via the export_embeddings_training_data entry point and published here for reproducibility.

Data Fields

Column	Type	Description
canonical_player_id	string	Unified player identifier (from entity resolution across data sources)
match_id	string	Match identifier
competition_id	int	Competition identifier (used as adversarial target in Stage 2 training)
season_id	int	Season identifier
position_group	string (nullable)	Player position group: GK, Def, Mid, Fwd (from dim_players)
actions	array<struct>	Ordered sequence of tokenized SPADL actions

Action Struct Schema

Each element in the actions array:

Field	Type	Description
action_type	int	SPADL action type ID (0–22, 23 action types)
x	float	Normalized x coordinate [0, 1] on 105m pitch
y	float	Normalized y coordinate [0, 1] on 68m pitch
result	int	Binary outcome: 1 = success, 0 = failure

SPADL Action Vocabulary (23 types)

ID	Action	ID	Action	ID	Action
0	pass	8	foul	16	keeper_punch
1	cross	9	tackle	17	keeper_pick_up
2	throw_in	10	interception	18	clearance
3	freekick_crossed	11	shot	19	bad_touch
4	freekick_short	12	shot_penalty	20	non_action
5	corner_crossed	13	shot_freekick	21	dribble
6	corner_short	14	keeper_save	22	goalkick
7	take_on	15	keeper_claim

Data Sources

Source	Matches	License
StatsBomb Open Data	~3,000	CC-BY 4.0
Wyscout Public Dataset	~1,900	CC-BY-NC 4.0

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

Freshness

Metric	Value
Freshness SLA	168 hours (7 days)
Refresh trigger	Re-exported when upstream fct_action_values is updated with new match data
Publish script	src/ingestion/export_embeddings_training_data.py (entry point: export_embeddings_training_data)

Use Cases

• Transformer training: Primary training corpus for Football2Vec v2 (masked language modeling + adversarial debiasing)
• Custom embedding models: Train your own player embedding model on standardized SPADL sequences
• Sequence analysis: Study per-player action patterns, spatial tendencies, and decision sequences
• Vocabulary research: Compare action distributions across competitions, positions, or eras

Limitations

• Event-based only: Contains on-ball action sequences. Off-ball movement, pressing, and positioning are not represented.
• Open data only: Derived from publicly available StatsBomb and Wyscout data. Coverage is uneven across leagues and seasons.
• Coordinate normalization: All coordinates are normalized to [0, 1] on a 105×68m pitch (SPADL standard). Original provider-specific coordinate systems are not preserved.
• NULL position_group: Players not matched via entity resolution or lacking position metadata have position_group = NULL.

Citation

If you use this dataset, please cite the SPADL framework and the Football2Vec v2 model:

@inproceedings{decroos2019actions,
  title={Actions Speak Louder than Goals: Valuing Player Actions in Soccer},
  author={Decroos, Tom and Bransen, Lotte and Van Haaren, Jan and Davis, Jesse},
  booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={1851--1861},
  year={2019},
  publisher={ACM}
}

@software{nielsen2026football2vec_v2,
  title={Football2Vec v2: Transformer Player Embeddings with Adversarial Team Debiasing},
  author={Nielsen, Karsten Skytt},
  year={2026},
  url={https://github.com/karsten-s-nielsen/luxury-lakehouse}
}

Companion Resources

Resource	Description
Football2Vec v2 Model	128-dim transformer encoder trained on this data
Football2Vec v1 Model	32-dim Doc2Vec baseline
Player Embeddings	Pre-computed vectors (career/season/match)
SPADL/VAEP Action Values	Per-action offensive/defensive VAEP valuations

More Information

Explore interactively: HF Space demo

• Model repo: luxury-lakehouse/football2vec-v2
• License: CC-BY-NC 4.0 (inherited from Wyscout data)