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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 17 new columns ({'added_time', 'tournament_id', 'player_id', 'description', 'assist_name', 'date', 'minute', 'away_score_after', 'player_name', 'home_score_after', 'assist_id', 'incident_class', 'season_id', 'is_home', 'incident_type', 'round', 'team_id'}) and 6 missing columns ({'h2h_away_goals', 'h2h_total', 'h2h_home_goals', 'h2h_draws', 'h2h_home_wins', 'h2h_away_wins'}).
This happened while the csv dataset builder was generating data using
hf://datasets/adibmed/football-dataset/data/worldcup/incidents.csv (at revision 4612e73080f67e00290d9334081cd447f9dff7c6), [/tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/h2h_records.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/h2h_records.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/incidents.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/incidents.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/lineups.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/lineups.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/match_stats.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/match_stats.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/matches.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/matches.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/players.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/players.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/standings.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/standings.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/teams.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/teams.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/output/01_matches_all.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/output/01_matches_all.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/output/02_matches_with_odds.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/output/02_matches_with_odds.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/output/03_wc_history.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/output/03_wc_history.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/output/04_wc2026_teams.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/output/04_wc2026_teams.csv)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
match_id: int64
tournament_id: int64
season_id: int64
round: int64
date: string
home_team_id: int64
away_team_id: int64
minute: int64
added_time: double
incident_type: string
incident_class: string
is_home: bool
team_id: int64
player_id: double
player_name: string
assist_id: double
assist_name: string
home_score_after: double
away_score_after: double
description: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2647
to
{'match_id': Value('int64'), 'home_team_id': Value('int64'), 'away_team_id': Value('int64'), 'h2h_total': Value('float64'), 'h2h_home_wins': Value('float64'), 'h2h_draws': Value('float64'), 'h2h_away_wins': Value('float64'), 'h2h_home_goals': Value('float64'), 'h2h_away_goals': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1348, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 890, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 951, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 17 new columns ({'added_time', 'tournament_id', 'player_id', 'description', 'assist_name', 'date', 'minute', 'away_score_after', 'player_name', 'home_score_after', 'assist_id', 'incident_class', 'season_id', 'is_home', 'incident_type', 'round', 'team_id'}) and 6 missing columns ({'h2h_away_goals', 'h2h_total', 'h2h_home_goals', 'h2h_draws', 'h2h_home_wins', 'h2h_away_wins'}).
This happened while the csv dataset builder was generating data using
hf://datasets/adibmed/football-dataset/data/worldcup/incidents.csv (at revision 4612e73080f67e00290d9334081cd447f9dff7c6), [/tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/h2h_records.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/h2h_records.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/incidents.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/incidents.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/lineups.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/lineups.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/match_stats.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/match_stats.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/matches.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/matches.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/players.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/players.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/standings.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/standings.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/teams.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/data/worldcup/teams.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/output/01_matches_all.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/output/01_matches_all.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/output/02_matches_with_odds.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/output/02_matches_with_odds.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/output/03_wc_history.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/output/03_wc_history.csv), /tmp/hf-datasets-cache/medium/datasets/44998497282186-config-parquet-and-info-adibmed-football-dataset-d9a26bc4/hub/datasets--adibmed--football-dataset/snapshots/4612e73080f67e00290d9334081cd447f9dff7c6/output/04_wc2026_teams.csv (origin=hf://datasets/adibmed/football-dataset@4612e73080f67e00290d9334081cd447f9dff7c6/output/04_wc2026_teams.csv)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
match_id int64 | home_team_id int64 | away_team_id int64 | h2h_total null | h2h_home_wins float64 | h2h_draws float64 | h2h_away_wins float64 | h2h_home_goals null | h2h_away_goals null |
|---|---|---|---|---|---|---|---|---|
14,859,314 | 1,134,875 | 1,134,852 | null | 0 | 0 | 2 | null | null |
14,859,334 | 1,134,877 | 1,134,858 | null | 2 | 1 | 3 | null | null |
14,859,372 | 305,262 | 1,134,858 | null | 2 | 4 | 0 | null | null |
14,859,323 | 1,134,877 | 1,134,850 | null | 2 | 1 | 3 | null | null |
14,859,644 | 1,134,850 | 445,555 | null | 3 | 1 | 2 | null | null |
14,859,335 | 445,555 | 1,134,876 | null | 4 | 0 | 0 | null | null |
14,859,401 | 1,134,849 | 390,850 | null | 2 | 1 | 2 | null | null |
14,859,445 | 445,555 | 305,262 | null | 5 | 1 | 0 | null | null |
14,859,315 | 1,134,876 | 1,134,851 | null | 0 | 0 | 2 | null | null |
14,859,374 | 1,134,852 | 1,134,850 | null | 0 | 0 | 2 | null | null |
14,859,464 | 1,134,876 | 1,134,858 | null | 1 | 1 | 1 | null | null |
14,859,324 | 1,134,875 | 390,850 | null | 1 | 0 | 3 | null | null |
14,859,649 | 1,134,852 | 1,134,876 | null | 1 | 1 | 0 | null | null |
14,859,338 | 390,850 | 1,134,852 | null | 0 | 1 | 1 | null | null |
14,859,404 | 1,134,875 | 1,134,850 | null | 0 | 2 | 2 | null | null |
14,859,449 | 1,134,877 | 1,134,852 | null | 0 | 1 | 1 | null | null |
14,859,384 | 390,850 | 1,134,851 | null | 1 | 0 | 3 | null | null |
14,859,467 | 1,134,875 | 1,134,877 | null | 0 | 1 | 3 | null | null |
14,859,316 | 305,262 | 1,134,877 | null | 1 | 0 | 4 | null | null |
14,859,325 | 1,134,849 | 445,555 | null | 1 | 2 | 2 | null | null |
14,859,651 | 1,134,851 | 1,134,875 | null | 4 | 0 | 0 | null | null |
14,859,351 | 1,134,850 | 1,134,849 | null | 5 | 1 | 0 | null | null |
14,859,407 | 1,134,877 | 1,134,876 | null | 1 | 1 | 1 | null | null |
14,859,453 | 1,134,851 | 1,134,849 | null | 1 | 2 | 1 | null | null |
14,859,379 | 1,134,876 | 1,134,849 | null | 1 | 0 | 3 | null | null |
14,859,469 | 305,262 | 1,134,850 | null | 0 | 0 | 6 | null | null |
14,859,321 | 1,134,858 | 1,134,850 | null | 0 | 2 | 4 | null | null |
14,859,327 | 1,134,851 | 1,134,858 | null | 0 | 2 | 2 | null | null |
14,859,655 | 390,850 | 1,134,858 | null | 5 | 1 | 0 | null | null |
14,859,347 | 305,262 | 1,134,875 | null | 3 | 1 | 0 | null | null |
14,859,415 | 305,262 | 1,134,851 | null | 1 | 1 | 2 | null | null |
14,859,458 | 1,134,876 | 390,850 | null | 0 | 0 | 4 | null | null |
14,859,387 | 445,555 | 1,134,877 | null | 2 | 3 | 1 | null | null |
14,859,476 | 445,555 | 1,134,851 | null | 1 | 0 | 3 | null | null |
14,859,322 | 390,850 | 445,555 | null | 2 | 2 | 2 | null | null |
14,859,330 | 1,134,852 | 305,262 | null | 1 | 1 | 0 | null | null |
14,859,657 | 1,134,877 | 1,134,849 | null | 1 | 3 | 2 | null | null |
14,859,412 | 445,555 | 1,134,852 | null | 1 | 0 | 1 | null | null |
14,859,456 | 1,134,858 | 1,134,875 | null | 3 | 0 | 1 | null | null |
14,859,473 | 1,134,849 | 1,134,852 | null | 1 | 0 | 1 | null | null |
14,859,668 | 1,134,858 | 1,134,852 | null | 0 | 0 | 2 | null | null |
14,859,689 | 1,134,851 | 1,134,877 | null | 1 | 3 | 0 | null | null |
14,859,706 | 1,134,849 | 1,134,858 | null | 2 | 3 | 1 | null | null |
14,859,739 | 390,850 | 305,262 | null | 2 | 3 | 0 | null | null |
14,859,751 | 445,555 | 1,134,858 | null | 2 | 3 | 1 | null | null |
14,859,763 | 390,850 | 1,134,877 | null | 2 | 2 | 2 | null | null |
14,859,786 | 1,134,849 | 1,134,877 | null | 2 | 3 | 1 | null | null |
14,859,691 | 390,850 | 1,134,850 | null | 1 | 2 | 3 | null | null |
14,859,709 | 445,555 | 1,134,875 | null | 3 | 1 | 0 | null | null |
14,859,810 | 1,134,852 | 1,134,849 | null | 1 | 0 | 1 | null | null |
14,859,742 | 1,134,876 | 1,134,850 | null | 2 | 0 | 2 | null | null |
14,859,752 | 1,134,849 | 1,134,875 | null | 1 | 1 | 2 | null | null |
14,859,766 | 1,134,851 | 1,134,850 | null | 0 | 2 | 2 | null | null |
14,859,789 | 1,134,858 | 390,850 | null | 0 | 1 | 5 | null | null |
14,859,694 | 1,134,875 | 1,134,849 | null | 2 | 1 | 1 | null | null |
14,859,713 | 1,134,852 | 1,134,851 | null | 2 | 0 | 0 | null | null |
14,859,670 | 1,134,876 | 1,134,875 | null | 0 | 1 | 1 | null | null |
14,859,813 | 1,134,851 | 445,555 | null | 3 | 0 | 1 | null | null |
14,859,746 | 1,134,858 | 1,134,849 | null | 1 | 3 | 2 | null | null |
14,859,753 | 305,262 | 1,134,876 | null | 3 | 0 | 1 | null | null |
14,859,768 | 1,134,875 | 1,134,876 | null | 1 | 1 | 0 | null | null |
14,859,793 | 1,134,875 | 1,134,851 | null | 0 | 0 | 4 | null | null |
14,859,696 | 1,134,876 | 305,262 | null | 1 | 0 | 3 | null | null |
14,859,716 | 1,134,850 | 1,134,876 | null | 2 | 0 | 2 | null | null |
14,859,675 | 305,262 | 1,134,849 | null | 2 | 1 | 3 | null | null |
14,859,748 | 1,134,875 | 445,555 | null | 0 | 1 | 3 | null | null |
14,859,755 | 1,134,877 | 1,134,851 | null | 0 | 3 | 1 | null | null |
14,859,767 | 1,134,849 | 305,262 | null | 3 | 1 | 2 | null | null |
14,859,815 | 1,134,850 | 305,262 | null | 6 | 0 | 0 | null | null |
14,859,790 | 1,134,876 | 1,134,852 | null | 0 | 1 | 1 | null | null |
14,859,700 | 1,134,858 | 445,555 | null | 1 | 3 | 2 | null | null |
14,859,719 | 305,262 | 390,850 | null | 0 | 3 | 2 | null | null |
14,859,678 | 1,134,850 | 1,134,851 | null | 2 | 2 | 0 | null | null |
14,859,750 | 1,134,851 | 1,134,852 | null | 0 | 0 | 2 | null | null |
14,859,759 | 1,134,850 | 390,850 | null | 3 | 2 | 1 | null | null |
14,859,769 | 1,134,852 | 1,134,858 | null | 2 | 0 | 0 | null | null |
14,859,819 | 1,134,858 | 1,134,876 | null | 1 | 1 | 1 | null | null |
14,859,797 | 445,555 | 1,134,850 | null | 2 | 1 | 3 | null | null |
14,859,682 | 1,134,877 | 390,850 | null | 2 | 2 | 2 | null | null |
14,859,817 | 1,134,877 | 1,134,875 | null | 3 | 1 | 0 | null | null |
14,859,831 | 1,134,875 | 1,134,858 | null | 1 | 0 | 3 | null | null |
14,859,842 | 1,134,851 | 305,262 | null | 2 | 1 | 1 | null | null |
14,859,857 | 1,134,850 | 1,134,852 | null | 2 | 0 | 0 | null | null |
14,859,877 | 1,134,875 | 305,262 | null | 0 | 1 | 3 | null | null |
14,859,891 | 390,850 | 1,134,875 | null | 3 | 0 | 1 | null | null |
14,859,833 | 1,134,849 | 1,134,851 | null | 1 | 2 | 1 | null | null |
14,859,845 | 1,134,852 | 445,555 | null | 1 | 0 | 1 | null | null |
14,859,860 | 1,134,849 | 1,134,876 | null | 3 | 0 | 1 | null | null |
14,859,908 | 1,134,850 | 1,134,858 | null | 4 | 2 | 0 | null | null |
14,859,880 | 1,134,876 | 445,555 | null | 0 | 0 | 4 | null | null |
14,859,893 | 305,262 | 1,134,852 | null | 0 | 1 | 1 | null | null |
14,859,836 | 1,134,852 | 1,134,877 | null | 1 | 1 | 0 | null | null |
14,859,847 | 1,134,876 | 1,134,877 | null | 1 | 1 | 1 | null | null |
14,859,861 | 1,134,877 | 445,555 | null | 1 | 3 | 2 | null | null |
14,859,909 | 1,134,852 | 1,134,875 | null | 2 | 0 | 0 | null | null |
14,859,882 | 1,134,849 | 1,134,850 | null | 0 | 1 | 5 | null | null |
14,859,838 | 305,262 | 445,555 | null | 0 | 1 | 5 | null | null |
14,859,852 | 1,134,850 | 1,134,875 | null | 2 | 2 | 0 | null | null |
14,859,863 | 1,134,858 | 305,262 | null | 0 | 4 | 2 | null | null |
14,859,910 | 1,134,851 | 1,134,876 | null | 2 | 0 | 0 | null | null |
End of preview.
Football Predictions Dataset
International football match dataset used to train an ML prediction system (CatBoost + Poisson/LightGBM ensemble) that predicts win/draw/loss probabilities and expected goals for any match between two national teams.
Used to generate World Cup 2026 predictions — both pure ML and LLM consensus (Claude, GPT-5.5, Gemini, DeepSeek, Grok, Qwen).
Dataset contents
output/ — ML-ready engineered features
| File | Rows | Description |
|---|---|---|
01_matches_all.csv |
~49,000 | Full feature matrix used for training. All international matches 1872–2026 with 191 engineered features. |
02_matches_with_odds.csv |
~8,000 | Subset with bookmaker odds attached. |
03_wc_history.csv |
~900 | All World Cup matches with full stats. |
04_wc2026_teams.csv |
48 | WC 2026 qualified teams with current squad data. |
data/worldcup/ — Raw World Cup data
| File | Description |
|---|---|
matches.csv |
All WC matches with scores, venue, attendance |
match_stats.csv |
xG, shots, possession, passes, cards per match |
lineups.csv |
Starting XIs and substitutes |
incidents.csv |
Goals, cards, subs with minute timestamps |
players.csv |
Player-level data with ratings |
standings.csv |
Group stage standings by tournament |
teams.csv |
Team metadata |
h2h_records.csv |
Head-to-head records between all WC teams |
tournaments.json |
Tournament metadata |
Features (01_matches_all.csv — 191 columns)
Key feature groups:
- ELO ratings — pre-match ELO for both teams, delta over last 5/10 matches
- FIFA rankings — monthly rank + points, rank differential
- Form — last 3/5/10 match W-D-L, goals for/against
- Squad — Transfermarkt market value, squad size, avg caps
- Player ELO — top-5 and top-11 player ratings (pelo)
- H2H — historical head-to-head record
- Match context — neutral venue, tournament type, days rest
- xG — expected goals from SofaScore (WC matches)
- Bookmaker odds — pre-match odds from
8 bookmakers (8k matches)
Model performance
Trained on ~49,000 matches (1872–2026), backtested on 6 World Cup tournaments:
| Model | Accuracy | Log Loss |
|---|---|---|
| CatBoost (multiclass) | 59.7% | 0.913 |
| Poisson / LightGBM xG | 60.4% | 0.909 |
| Bookmaker baseline | ~58–62% | — |
| Random baseline | 33.3% | — |
World Cup 2026 predictions
Generated June 2026 using this dataset:
ML model: Spain 22.1% 🏆 · Argentina 14.9% · France 9.6%
LLM consensus (6 frontier models): Argentina 28.7% 🏆 · Spain 12.0% · England 11.6%
Full results: github.com/adibmed/football_predictions
Data sources
| Source | Content |
|---|---|
| International matches | All international results 1872–2026 |
| ELO ratings | eloratings.net |
| FIFA rankings | Monthly rankings + points |
| Transfermarkt | Squad market values, caps |
| SofaScore | xG, lineups, match stats, player ratings |
| Bookmaker odds | Pre-match odds ~8k matches |
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