---
license: cc-by-nc-4.0
language:
  - en
tags:
  - sports
  - football
  - soccer
  - match-data
  - match-statistics
  - player-statistics
pretty_name: Gamblistics Sport Statistics
size_categories:
  - 10K<n<100K
---

# Football Match Data

Match-level, team-level, and player-level football data collected from
various sources, intended for research on match outcome and predictive-market
modeling (final score, half-time score, corners, over/under goals, time of
first goal, etc.).

Data is provided as JSON Lines (`.jsonl`) — one JSON object per line.

## Files & Row Shapes

Row counts reflect the current snapshot; the dataset may grow over time.

### `matches.jsonl` — 1,140 rows
One row per match.
- `id` — match UUID (join key for `match_stats`, `match_player_stats`)
- `kickOff` — Unix epoch **seconds** (int)
- `homeTeam`, `awayTeam` — team names (denormalized)
- `homeTeamId`, `awayTeamId` — join to `teams.id`
- `homeScore`, `awayScore` — full-time score (**strings**, cast to int)
- `homeScore1st`, `awayScore1st` — 1st-half score (may be string or int)
- `homeScore2nd`, `awayScore2nd` — 2nd-half score
- `homeScore90`, `awayScore90` — score at 90 min (before extra time, if any)
- `status` — integer enum; `3` = full-time (finished)
- `leagueId` → `leagues.id`; `tournamentId` → `tournaments.id`; `stageId` → `tournament_stages.id`
- `roundName` — e.g. `"Round 24"`

### `match_stats.jsonl` — 760 rows
Team-level stats per match, split into three periods.
- `id` — same UUID as the matching `matches.id`
- `matchId` — duplicate of `id`
- `overall`, `first_half`, `second_half` — each is `{metric: {home, away}}`
- Metrics observed: `xg`, `possession`, `shots_total`, `shots_on_target`,
  `shots_off_target`, `shots_blocked`, `corners`, `passes_total`,
  `passes_accurate`, `yellow_cards`, `offsides`, `free_kicks`, `throw_ins`,
  `fouls`, `gk_saves`.

### `match_lineup.jsonl` — 47,837 rows
**One row per (match, player)** — the raw upstream form (one doc per match
with an embedded player array) has been exploded.
- `id` — `"<matchId>_<playerId>"` (composite)
- `matchId`, `playerId`, `teamId`
- `homeFormation`, `awayFormation` — match-level, repeated on every row
- `side` — `"home"` or `"away"`
- `isStarter`, `isCaptain`, `isGoalkeeper` — booleans
- `playerType` — `1` = player, `2` = coach, `3` = goalkeeper
- `jerseyNumber`, `position`, `formationSlot`, `pitchRow`
- `playerName`, `playerCountry`, `playerCountryId`

Note: `minuteOn`/`minuteOff` are absent for full-90 starters and must be
inferred as `(0, 90 + stoppage)` downstream.

### `match_player_stats.jsonl` — 442 rows
Per-player stats per match. Coverage is heavily biased toward top leagues
and matches from **2024-25 season onward** — many matches will not have a
row here. Model design must account for MNAR (missing-not-at-random) gaps.

- `id` — same UUID as the matching `matches.id`
- `players` — array of `{playerId, teamId, side, stats: {METRIC: value}}`

Stat keys are `SCREAMING_SNAKE_CASE` — e.g. `EXPECTED_GOALS`, `SHOTS_TOTAL`,
`PASSES_ACCURATE`, `PASSES_OPEN_PLAY_ACCURACY`, `DUELS_EFFICIENCY`,
`FS_RATING`. Up to ~103 keys per player; **every player carries the full
dictionary with `0`s for non-events** (e.g. `PENALTY_SHOOTOUT_*` in a
regular league match). Do not treat these `0`s as observed absence of the
event — drop constant-zero columns or emit a `metric_observed` mask.

### Dimension tables
- `players.jsonl` (1,296) — `id, name, country, countryId, primaryPosition, playerType, dateOfBirth`
- `teams.jsonl` (23) — `id, name, countryId, countryName, stadium, city, capacity`
- `leagues.jsonl` (1) — `id, name, countryId, sportId` — permanent competition (e.g. "Premier League")
- `tournaments.jsonl` (115) — `id, leagueId, season` — one season of a league
- `tournament_stages.jsonl` (115) — `id, tournamentId, leagueId, typeId, match_count`
- `sports.jsonl` (1) — `id, name`

## Join Keys

- `matches.id == match_stats.id == match_player_stats.id` for the same match.
- `match_lineup.id == "<matchId>_<playerId>"`; use `matchId` to join to `matches.id`.
- `matches.homeTeamId` / `awayTeamId` → `teams.id`.
- `match_lineup.playerId` / `match_player_stats.players[].playerId` → `players.id`.
- `matches.leagueId` → `leagues.id`; `matches.tournamentId` → `tournaments.id`.
- `players.countryId` / `teams.countryId` → country UUID (country dimension
  not included in this release).

## Known Data Quality Caveats

- **`match_stats` and `match_player_stats` do not cover every match** — they
  are gated by upstream availability; expect ~two-thirds of matches to lack
  team stats and most to lack per-player stats.
- **Score fields in `matches` are strings** in some rows and ints in others —
  cast to nullable int before use.
- **Timestamps** are Unix epoch **seconds** (not milliseconds).
- **Metric naming is inconsistent** between `match_stats` (`snake_case`) and
  `match_player_stats.players[].stats` (`SCREAMING_SNAKE_CASE`). Normalize
  in your loader.
- The proprietary source format is reverse-engineered and can shift silently
  — sanity-check field distributions before every training run.

## Suggested Uses

- Predicting final score as home/away Poisson rates (λ_home, λ_away)
- 1st-half score and half-time result markets
- Over/Under 1.5 & 2.5 goals
- Total corners
- Time of first goal
- Player-level xG / shot / rating modeling

Any classification/regression targets used for betting markets should be
followed by **calibration** (Platt or isotonic) on a held-out set —
uncalibrated probabilities are misleading for expected-value calculations.

## Source & Licensing

Data was collected from various sources. This release is provided under
**CC-BY-NC-4.0** — non-commercial, with attribution — for research and
educational purposes only. If you build on this dataset, please credit the
uploader and note the original source.

No personal data beyond publicly displayed player and coach names,
nationalities, and dates of birth is included.

## Citation

If you use this dataset, please cite:

```
@dataset{gamblistics_sport_statistics,
  title  = {Gamblistics Sport Statistics},
  author = {Nguyen Thuc Tuyen},
  year   = {2026},
  url    = {https://huggingface.co/datasets/gamblistics-lab/sport-statistics/}
}
```