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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 1 new columns ({'market_key'}) and 3 missing columns ({'outcomes', 'time_to_start', 'market_type'}).
This happened while the csv dataset builder was generating data using
hf://datasets/edushinka/bookmaker-margin-panel/betbetter_prop_margin_panel.csv (at revision c4600807b86ea8f8943871431da4e32a68a992f6), ['hf://datasets/edushinka/bookmaker-margin-panel@c4600807b86ea8f8943871431da4e32a68a992f6/betbetter_margin_panel.csv', 'hf://datasets/edushinka/bookmaker-margin-panel@c4600807b86ea8f8943871431da4e32a68a992f6/betbetter_prop_margin_panel.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/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/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/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
snap_date: string
sport: string
region: string
market_key: string
mean_margin_pct: double
n_markets: int64
n_bookmakers: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1107
to
{'snap_date': Value('string'), 'sport': Value('string'), 'region': Value('string'), 'market_type': Value('string'), 'outcomes': Value('int64'), 'time_to_start': Value('string'), 'mean_margin_pct': Value('float64'), 'n_markets': Value('int64'), 'n_bookmakers': Value('int64')}
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 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
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 1 new columns ({'market_key'}) and 3 missing columns ({'outcomes', 'time_to_start', 'market_type'}).
This happened while the csv dataset builder was generating data using
hf://datasets/edushinka/bookmaker-margin-panel/betbetter_prop_margin_panel.csv (at revision c4600807b86ea8f8943871431da4e32a68a992f6), ['hf://datasets/edushinka/bookmaker-margin-panel@c4600807b86ea8f8943871431da4e32a68a992f6/betbetter_margin_panel.csv', 'hf://datasets/edushinka/bookmaker-margin-panel@c4600807b86ea8f8943871431da4e32a68a992f6/betbetter_prop_margin_panel.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.
snap_date string | sport string | region string | market_type string | outcomes int64 | time_to_start string | mean_margin_pct float64 | n_markets int64 | n_bookmakers int64 |
|---|---|---|---|---|---|---|---|---|
2026-06-29 | americanfootball_nfl | offshore_intl | h2h | 2 | 4_over7d | 4.362 | 194 | 6 |
2026-06-29 | americanfootball_nfl | us | h2h | 2 | 4_over7d | 4.675 | 656 | 12 |
2026-06-29 | americanfootball_nfl | us | spread | 2 | 4_over7d | 5.223 | 656 | 12 |
2026-06-29 | americanfootball_nfl | offshore_intl | spread | 2 | 4_over7d | 4.451 | 290 | 6 |
2026-06-29 | americanfootball_nfl | us | total | 2 | 4_over7d | 5.209 | 656 | 12 |
2026-06-29 | americanfootball_nfl | offshore_intl | total | 2 | 4_over7d | 4.593 | 292 | 6 |
2026-06-29 | aussierules_afl | au | h2h | 2 | 2_1-3d | 5.542 | 18 | 9 |
2026-06-29 | aussierules_afl | au | h2h | 2 | 3_3-7d | 5.615 | 144 | 9 |
2026-06-29 | aussierules_afl | offshore_intl | h2h | 2 | 3_3-7d | 5.302 | 16 | 1 |
2026-06-29 | aussierules_afl | au | spread | 2 | 2_1-3d | 5.637 | 12 | 6 |
2026-06-29 | aussierules_afl | offshore_intl | spread | 2 | 3_3-7d | 5.684 | 16 | 1 |
2026-06-29 | aussierules_afl | au | spread | 2 | 3_3-7d | 5.637 | 96 | 6 |
2026-06-29 | baseball_mlb | us | h2h | 2 | 1_6-24h | 4.618 | 408 | 12 |
2026-06-29 | baseball_mlb | au | h2h | 2 | 1_6-24h | 4.828 | 339 | 9 |
2026-06-29 | baseball_mlb | offshore_intl | h2h | 2 | 1_6-24h | 3.756 | 342 | 9 |
2026-06-29 | baseball_mlb | us | spread | 2 | 1_6-24h | 5.057 | 438 | 12 |
2026-06-29 | baseball_mlb | offshore_intl | spread | 2 | 1_6-24h | 4.126 | 303 | 8 |
2026-06-29 | baseball_mlb | au | spread | 2 | 1_6-24h | 4.897 | 153 | 5 |
2026-06-29 | baseball_mlb | au | total | 2 | 1_6-24h | 5.583 | 147 | 4 |
2026-06-29 | baseball_mlb | us | total | 2 | 1_6-24h | 5.101 | 441 | 12 |
2026-06-29 | baseball_mlb | offshore_intl | total | 2 | 1_6-24h | 4.685 | 303 | 8 |
2026-06-29 | soccer_epl | us | h2h | 3 | 4_over7d | 7.439 | 180 | 9 |
2026-06-29 | soccer_epl | offshore_intl | h2h | 3 | 4_over7d | 8.003 | 378 | 19 |
2026-06-29 | soccer_epl | offshore_intl | spread | 2 | 4_over7d | 3.436 | 40 | 2 |
2026-06-29 | soccer_epl | us | total | 2 | 4_over7d | 7.356 | 80 | 4 |
2026-06-29 | soccer_epl | offshore_intl | total | 2 | 4_over7d | 5.571 | 100 | 5 |
2026-06-29 | soccer_italy_serie_a | offshore_intl | h2h | 3 | 4_over7d | 9.478 | 264 | 14 |
2026-06-29 | soccer_italy_serie_a | us | h2h | 3 | 4_over7d | 9.39 | 112 | 6 |
2026-06-29 | soccer_italy_serie_a | us | total | 2 | 4_over7d | 7.787 | 60 | 3 |
2026-06-29 | soccer_italy_serie_a | offshore_intl | total | 2 | 4_over7d | 7.787 | 60 | 3 |
2026-06-29 | tennis_atp_wimbledon | us | h2h | 2 | 0_under6h | 5.499 | 98 | 10 |
2026-06-29 | tennis_atp_wimbledon | offshore_intl | h2h | 2 | 0_under6h | 4.755 | 34 | 4 |
2026-06-29 | tennis_atp_wimbledon | us | h2h | 2 | 1_6-24h | 5.704 | 448 | 10 |
2026-06-29 | tennis_atp_wimbledon | offshore_intl | h2h | 2 | 1_6-24h | 5.176 | 196 | 4 |
2026-06-29 | tennis_atp_wimbledon | us | h2h | 2 | 2_1-3d | 5.626 | 96 | 8 |
2026-06-29 | tennis_atp_wimbledon | offshore_intl | h2h | 2 | 2_1-3d | 4.993 | 48 | 4 |
2026-06-29 | tennis_wta_wimbledon | offshore_intl | h2h | 2 | 0_under6h | 4.994 | 186 | 5 |
2026-06-29 | tennis_wta_wimbledon | us | h2h | 2 | 0_under6h | 5.487 | 388 | 10 |
2026-06-29 | tennis_wta_wimbledon | offshore_intl | h2h | 2 | 1_6-24h | 5.312 | 292 | 5 |
2026-06-29 | tennis_wta_wimbledon | us | h2h | 2 | 1_6-24h | 5.718 | 520 | 10 |
2026-06-29 | tennis_wta_wimbledon | us | h2h | 2 | 2_1-3d | 4.224 | 24 | 6 |
2026-06-30 | americanfootball_nfl | us | h2h | 2 | 4_over7d | 4.672 | 328 | 12 |
2026-06-30 | americanfootball_nfl | offshore_intl | h2h | 2 | 4_over7d | 4.388 | 97 | 6 |
2026-06-30 | americanfootball_nfl | us | spread | 2 | 4_over7d | 5.228 | 328 | 12 |
2026-06-30 | americanfootball_nfl | offshore_intl | spread | 2 | 4_over7d | 4.451 | 145 | 6 |
2026-06-30 | americanfootball_nfl | offshore_intl | total | 2 | 4_over7d | 4.593 | 146 | 6 |
2026-06-30 | americanfootball_nfl | us | total | 2 | 4_over7d | 5.209 | 328 | 12 |
2026-06-30 | aussierules_afl | au | h2h | 2 | 2_1-3d | 5.5 | 27 | 9 |
2026-06-30 | aussierules_afl | au | h2h | 2 | 3_3-7d | 5.561 | 54 | 9 |
2026-06-30 | aussierules_afl | offshore_intl | h2h | 2 | 3_3-7d | 5.309 | 11 | 2 |
2026-06-30 | aussierules_afl | au | spread | 2 | 2_1-3d | 5.45 | 18 | 6 |
2026-06-30 | aussierules_afl | au | spread | 2 | 3_3-7d | 5.529 | 36 | 6 |
2026-06-30 | aussierules_afl | offshore_intl | spread | 2 | 3_3-7d | 5.518 | 11 | 2 |
2026-06-30 | aussierules_afl | au | total | 2 | 2_1-3d | 6.105 | 12 | 4 |
2026-06-30 | aussierules_afl | au | total | 2 | 3_3-7d | 6.081 | 22 | 4 |
2026-06-30 | baseball_mlb | offshore_intl | h2h | 2 | 0_under6h | 4.018 | 27 | 9 |
2026-06-30 | baseball_mlb | us | h2h | 2 | 0_under6h | 4.734 | 34 | 12 |
2026-06-30 | baseball_mlb | au | h2h | 2 | 0_under6h | 4.754 | 27 | 9 |
2026-06-30 | baseball_mlb | au | h2h | 2 | 1_6-24h | 4.823 | 99 | 9 |
2026-06-30 | baseball_mlb | us | h2h | 2 | 1_6-24h | 4.685 | 129 | 12 |
2026-06-30 | baseball_mlb | offshore_intl | h2h | 2 | 1_6-24h | 3.854 | 96 | 9 |
2026-06-30 | baseball_mlb | au | spread | 2 | 0_under6h | 4.823 | 12 | 4 |
2026-06-30 | baseball_mlb | us | spread | 2 | 0_under6h | 5.078 | 36 | 12 |
2026-06-30 | baseball_mlb | offshore_intl | spread | 2 | 0_under6h | 4.229 | 24 | 8 |
2026-06-30 | baseball_mlb | au | spread | 2 | 1_6-24h | 4.878 | 47 | 5 |
2026-06-30 | baseball_mlb | us | spread | 2 | 1_6-24h | 5.02 | 132 | 12 |
2026-06-30 | baseball_mlb | offshore_intl | spread | 2 | 1_6-24h | 4.164 | 84 | 8 |
2026-06-30 | baseball_mlb | au | total | 2 | 0_under6h | 5.501 | 12 | 4 |
2026-06-30 | baseball_mlb | offshore_intl | total | 2 | 0_under6h | 4.713 | 24 | 8 |
2026-06-30 | baseball_mlb | us | total | 2 | 0_under6h | 5.132 | 36 | 12 |
2026-06-30 | baseball_mlb | offshore_intl | total | 2 | 1_6-24h | 4.67 | 84 | 8 |
2026-06-30 | baseball_mlb | us | total | 2 | 1_6-24h | 5.081 | 132 | 12 |
2026-06-30 | baseball_mlb | au | total | 2 | 1_6-24h | 5.462 | 44 | 4 |
2026-06-30 | soccer_epl | offshore_intl | h2h | 3 | 4_over7d | 7.999 | 189 | 19 |
2026-06-30 | soccer_epl | us | h2h | 3 | 4_over7d | 7.438 | 90 | 9 |
2026-06-30 | soccer_epl | offshore_intl | spread | 2 | 4_over7d | 3.436 | 20 | 2 |
2026-06-30 | soccer_epl | offshore_intl | total | 2 | 4_over7d | 5.571 | 50 | 5 |
2026-06-30 | soccer_epl | us | total | 2 | 4_over7d | 7.356 | 40 | 4 |
2026-06-30 | soccer_fifa_world_cup | offshore_intl | h2h | 3 | 0_under6h | 5.99 | 24 | 24 |
2026-06-30 | soccer_fifa_world_cup | us | h2h | 3 | 0_under6h | 5.421 | 11 | 11 |
2026-06-30 | soccer_fifa_world_cup | us | h2h | 3 | 1_6-24h | 5.672 | 22 | 11 |
2026-06-30 | soccer_fifa_world_cup | offshore_intl | h2h | 3 | 1_6-24h | 5.829 | 50 | 25 |
2026-06-30 | soccer_fifa_world_cup | us | h2h | 3 | 2_1-3d | 5.601 | 55 | 11 |
2026-06-30 | soccer_fifa_world_cup | offshore_intl | h2h | 3 | 2_1-3d | 5.922 | 125 | 25 |
2026-06-30 | soccer_fifa_world_cup | offshore_intl | h2h | 3 | 3_3-7d | 5.459 | 93 | 24 |
2026-06-30 | soccer_fifa_world_cup | us | h2h | 3 | 3_3-7d | 5.533 | 44 | 11 |
2026-06-30 | soccer_fifa_world_cup | offshore_intl | spread | 2 | 1_6-24h | 3.94 | 12 | 6 |
2026-06-30 | soccer_fifa_world_cup | offshore_intl | spread | 2 | 2_1-3d | 4.249 | 30 | 6 |
2026-06-30 | soccer_fifa_world_cup | offshore_intl | spread | 2 | 3_3-7d | 4.301 | 21 | 6 |
2026-06-30 | soccer_fifa_world_cup | offshore_intl | total | 2 | 1_6-24h | 5.279 | 19 | 10 |
2026-06-30 | soccer_fifa_world_cup | us | total | 2 | 1_6-24h | 6.225 | 10 | 5 |
2026-06-30 | soccer_fifa_world_cup | offshore_intl | total | 2 | 2_1-3d | 5.415 | 48 | 10 |
2026-06-30 | soccer_fifa_world_cup | us | total | 2 | 2_1-3d | 6.234 | 25 | 5 |
2026-06-30 | soccer_fifa_world_cup | offshore_intl | total | 2 | 3_3-7d | 5.754 | 34 | 10 |
2026-06-30 | soccer_fifa_world_cup | us | total | 2 | 3_3-7d | 6.205 | 20 | 5 |
2026-06-30 | soccer_italy_serie_a | us | h2h | 3 | 4_over7d | 9.39 | 56 | 6 |
2026-06-30 | soccer_italy_serie_a | offshore_intl | h2h | 3 | 4_over7d | 9.48 | 132 | 14 |
2026-06-30 | soccer_italy_serie_a | offshore_intl | total | 2 | 4_over7d | 7.787 | 30 | 3 |
2026-06-30 | soccer_italy_serie_a | us | total | 2 | 4_over7d | 7.787 | 30 | 3 |
2026-06-30 | tennis_atp_wimbledon | us | h2h | 2 | 1_6-24h | 5.61 | 125 | 10 |
Bookmaker Margin Panel — daily measured betting margins across 22 leagues and 3 market types
A daily panel of measured bookmaker margins (overround/vigorish) across head-to-head, spread and totals markets: one row per day × league × region × market type, aggregated from over 4 million individually recorded odds observations (2.7 million cleaned markets).
To our knowledge this is the only openly published bookmaker margin panel. It quantifies the price of sports betting — the amount by which a market's implied probabilities exceed 100% — across operators, sports and time.
| Files | Main panel: 3,494 cells (day × league × region × market type × time-to-start) · Props panel: 1,148 cells |
| Underlying observations | 2,002,681 cleaned main markets + 699,733 two-sided player-prop markets |
| Bookmakers aggregated | 52 (not identified individually — see Limitations; exchanges excluded) |
| Leagues | 22 competition keys across 15 sports, including 9 soccer leagues |
| Market types | head-to-head (2- and 3-way), spread, total |
| Period | 29 June – 2 August 2026, 35 days (collection continues; quarterly updates planned) |
| Licence | CC BY 4.0 |
Columns
| Column | Meaning |
|---|---|
snap_date |
Observation date (UTC) |
sport |
League/competition key, e.g. baseball_mlb, soccer_epl |
region |
au, us or offshore_intl — the licensing region of the operators aggregated in the cell |
market_type |
h2h, spread or total (main panel); specific prop market key in the props file |
time_to_start |
Hours from observation to scheduled start, bucketed (under 6h to over 7 days) — main panel |
outcomes |
Number of priced outcomes (2, or 3 for soccer-style h2h with the draw) |
mean_margin_pct |
Mean margin that day: sum of the outcomes' implied probabilities minus 100, in percent |
n_markets |
Number of distinct market observations in the cell (cells with fewer than 10 are excluded) |
n_bookmakers |
Number of distinct operators aggregated in the cell |
Method
For every pre-match market observation, all priced outcomes were converted to implied probabilities (1 ÷ decimal odds) and summed; the excess over 100% is the margin. Two-way and three-way markets are kept in separate cells because their margins are not comparable. Excluded: in-play observations, implausible margins (below 0%, or above 20% two-way / 35% three-way — suspended or near-settled prices), and betting exchanges. Full methodology: https://betbetter.world/studies/bookmaker-margins
Headline findings from this panel
- Player props average a 7.93% margin — 66% more than main head-to-head markets (4.77%). To our knowledge this is the first public measurement of prop-market margins (699,733 two-sided prop markets, MLB/NHL/soccer/basketball prop families).
- The market-type price ladder: h2h 4.77% → spreads 4.90% → totals 5.45% → three-way soccer h2h 7.67% → player props 7.93%.
- Margin has a term structure: it peaks 1–3 days before the start (5.23%) and compresses to 4.71% in the final six hours — betting early costs measurably more.
- Australian-licensed bookmakers average 5.25%, US-licensed 4.72%, offshore/international 4.55% — Australians pay roughly 11% more margin than Americans for equivalent bets.
- Individual operators are not identified in these files; operator-level statistics (a 3.16% to 5.78% spread between the cheapest and dearest bookmaker) are published in the accompanying study: https://betbetter.world/studies/bookmaker-margins
Limitations
One month of collection (a longer panel accumulates with each planned update). Individual bookmakers are deliberately not identified in the files: cells aggregate across all operators in a region (n_bookmakers reports how many), so no operator's prices or price patterns are recoverable — the files are irreversible statistics, not a data feed in any form. Head-to-head is the tightest market type bookmakers offer; margins on multis and player props are materially higher, so these figures are a floor for the cost of betting generally. No individual odds, prices or market observations are included — both files are aggregate statistics derived from a licensed feed that does not permit redistribution of the underlying prices.
Licence and citation
CC BY 4.0 — free to use, including commercially, with attribution.
Bet Better (2026). Bookmaker Margin Panel: daily measured betting margins across 22 leagues, three market types and player-prop markets. https://betbetter.world/studies/bookmaker-margins
Maintained by Bet Better, which also publishes a free sports model API and an open AFL dataset.
This dataset contains no odds, no prices and no wagering data — aggregate statistics only. 18+. Most people lose money gambling. If gambling is causing you harm: gamblinghelponline.org.au.
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