language:
- en
license: cc-by-nc-4.0
task_categories:
- feature-extraction
- sentence-similarity
tags:
- sports-analytics
- soccer
- football
- player-sequences
- transformer
- scoutgpt
- statsbomb
- wyscout
size_categories:
- 100K<n<1M
configs:
- config_name: default
data_files:
- split: train
path: data/*.parquet
ScoutGPT Training Data — Player Action Sequences
Per-player match-level action sequences unified across StatsBomb Open Data and Wyscout open data. Each row is one player-match's ordered SPADL action sequence with contextual tokens (competition, season, score state, half, opponent), serialized as the token stream that the ScoutGPT transformer consumes during training and at inference.
Part of the (Right! Luxury!) Lakehouse soccer analytics platform.
Quick Start
from datasets import load_dataset
ds = load_dataset("luxury-lakehouse/scoutgpt-training-data")
df = ds["train"].to_pandas()
print(f"{len(df):,} player-match sequences, {df['player_id'].nunique():,} unique players")
Explore interactively: Soccer Analytics App
What Is This Dataset?
ScoutGPT is a transformer decoder trained to produce per-player season embeddings by modelling the sequential structure of on-ball actions within a match. Unlike per-action bag-of-features embeddings (Football2Vec v1), ScoutGPT sees the order of actions — so it can capture tempo, build-up patterns, and decision-making over a possession.
This dataset is the serialized per-player-match training corpus produced by wf-scoutgpt-export from the gold-layer fct_action_values table.
Schema
| Column | Type | Description |
|---|---|---|
player_id |
Int64 |
Canonical player identifier (cross-source resolved) |
match_id |
Int64 |
Provider-native match identifier |
data_source |
string |
Origin (statsbomb or wyscout) |
competition_id |
Int64 |
Competition identifier (NULL for Wyscout) |
season_id |
Int64 |
Season identifier (NULL for Wyscout) |
team_id |
Int64 |
Player's team in this match |
token_ids |
list<int32> |
Tokenized action-sequence for this player in this match |
sequence_length |
Int64 |
Number of tokens in the sequence |
Data Sources
| Source | Matches | License |
|---|---|---|
| StatsBomb Open Data | ~3,000 | CC-BY 4.0 |
| Wyscout Public Dataset | ~1,900 | CC-BY-NC 4.0 |
Inherits the more restrictive CC-BY-NC 4.0 license via Wyscout.
Use Cases
- ScoutGPT training: primary training corpus for the ScoutGPT transformer
- Sequence-aware embedding research: evaluate new architectures (cross-attention, Fourier position encodings, RoPE) against a common corpus
- Downstream fine-tuning: task-specific heads (player-type classification, next-action prediction) on top of pre-trained ScoutGPT checkpoints
Limitations
- Open data only: commercial datasets cover additional leagues and seasons
- Season-level aggregation: per-match sequences are independent — cross-match context is not captured in a single row
- Derived from SPADL: downstream of the SPADL conversion; any SPADL-adapter issue (see spadl-vaep-action-values) propagates here
Companion Resources
| Resource | Type | Description |
|---|---|---|
| ScoutGPT | Model | Transformer decoder trained on this dataset |
| ScoutGPT variants (rope) | Model | Ablation checkpoint with RoPE position encoding |
| SPADL/VAEP Action Values | Dataset | Upstream source — per-action valuations |
License
CC-BY-NC 4.0 (inherited from Wyscout).