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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 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
End of preview.

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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