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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 4 new columns ({'mean_abs_point_move', 'pct_line_changed', 'market_type', 'max_point_move'}) and 3 missing columns ({'favourite_signed_drift_pp', 'mean_abs_move_pp', 'pct_moved_gt2pp'}).
This happened while the csv dataset builder was generating data using
hf://datasets/edushinka/line-movement/betbetter_line_movement_points.csv (at revision 453ee83212e1196414223a8e26ff66471cf2d675), ['hf://datasets/edushinka/line-movement@453ee83212e1196414223a8e26ff66471cf2d675/betbetter_line_movement_h2h.csv', 'hf://datasets/edushinka/line-movement@453ee83212e1196414223a8e26ff66471cf2d675/betbetter_line_movement_points.csv', 'hf://datasets/edushinka/line-movement@453ee83212e1196414223a8e26ff66471cf2d675/betbetter_distance_to_close.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
sport: string
market_type: string
observations: int64
mean_lead_hours: int64
mean_abs_point_move: double
pct_line_changed: double
max_point_move: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1159
to
{'sport': Value('string'), 'observations': Value('int64'), 'mean_lead_hours': Value('int64'), 'mean_abs_move_pp': Value('float64'), 'pct_moved_gt2pp': Value('float64'), 'favourite_signed_drift_pp': 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 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 4 new columns ({'mean_abs_point_move', 'pct_line_changed', 'market_type', 'max_point_move'}) and 3 missing columns ({'favourite_signed_drift_pp', 'mean_abs_move_pp', 'pct_moved_gt2pp'}).
This happened while the csv dataset builder was generating data using
hf://datasets/edushinka/line-movement/betbetter_line_movement_points.csv (at revision 453ee83212e1196414223a8e26ff66471cf2d675), ['hf://datasets/edushinka/line-movement@453ee83212e1196414223a8e26ff66471cf2d675/betbetter_line_movement_h2h.csv', 'hf://datasets/edushinka/line-movement@453ee83212e1196414223a8e26ff66471cf2d675/betbetter_line_movement_points.csv', 'hf://datasets/edushinka/line-movement@453ee83212e1196414223a8e26ff66471cf2d675/betbetter_distance_to_close.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.
sport string | observations int64 | mean_lead_hours int64 | mean_abs_move_pp float64 | pct_moved_gt2pp float64 | favourite_signed_drift_pp float64 |
|---|---|---|---|---|---|
americanfootball_ncaaf | 1,258 | 1,232 | 0.74 | 14.1 | -0.19 |
americanfootball_nfl | 1,720 | 2,471 | 0.41 | 4.2 | -0.02 |
aussierules_afl | 1,250 | 137 | 2.52 | 44 | 0.5 |
baseball_mlb | 22,960 | 11 | 0.6 | 6.1 | 0.07 |
basketball_wnba | 2,450 | 35 | 1.81 | 32 | 0.44 |
icehockey_nhl | 352 | 1,796 | 0.3 | 5.7 | 0 |
mma_mixed_martial_arts | 2,564 | 214 | 2.76 | 53.4 | 0.8 |
tennis_atp_canadian_open | 836 | 11 | 0.45 | 6 | -0.11 |
tennis_atp_washington_open | 668 | 16 | 1.55 | 28.7 | 0.13 |
tennis_atp_wimbledon | 1,978 | 19 | 0.69 | 11 | 0.21 |
tennis_wta_canadian_open | 554 | 12 | 0.7 | 7.9 | 0.04 |
tennis_wta_washington_open | 388 | 13 | 0.76 | 13.9 | -0.09 |
tennis_wta_wimbledon | 2,024 | 18 | 0.78 | 14.4 | 0.27 |
americanfootball_ncaaf | 2,024 | 1,242 | null | null | null |
americanfootball_ncaaf | 1,986 | 1,229 | null | null | null |
americanfootball_nfl | 1,996 | 2,499 | null | null | null |
americanfootball_nfl | 1,998 | 2,499 | null | null | null |
aussierules_afl | 830 | 130 | null | null | null |
aussierules_afl | 504 | 91 | null | null | null |
baseball_mlb | 18,014 | 11 | null | null | null |
baseball_mlb | 18,202 | 11 | null | null | null |
basketball_wnba | 2,062 | 38 | null | null | null |
basketball_wnba | 2,030 | 38 | null | null | null |
icehockey_nhl | 280 | 1,795 | null | null | null |
icehockey_nhl | 320 | 1,770 | null | null | null |
mma_mixed_martial_arts | 660 | 94 | null | null | null |
soccer_brazil_campeonato | 408 | 168 | null | null | null |
soccer_efl_champ | 214 | 433 | null | null | null |
soccer_epl | 240 | 1,260 | null | null | null |
soccer_fifa_world_cup | 348 | 67 | null | null | null |
soccer_fifa_world_cup | 780 | 70 | null | null | null |
soccer_france_ligue_one | 260 | 906 | null | null | null |
soccer_germany_bundesliga | 240 | 1,178 | null | null | null |
soccer_italy_serie_a | 332 | 950 | null | null | null |
soccer_spain_la_liga | 350 | 827 | null | null | null |
soccer_usa_mls | 208 | 89 | null | null | null |
soccer_usa_mls | 626 | 162 | null | null | null |
americanfootball_ncaaf | 206,312 | null | null | null | null |
americanfootball_nfl | 318,866 | null | null | null | null |
aussierules_afl | 15,948 | null | null | null | null |
aussierules_afl | 1,192 | null | null | null | null |
aussierules_afl | 19,116 | null | null | null | null |
aussierules_afl | 6,978 | null | null | null | null |
aussierules_afl | 4,552 | null | null | null | null |
baseball_mlb | 390 | null | null | null | null |
baseball_mlb | 38,992 | null | null | null | null |
baseball_mlb | 84,696 | null | null | null | null |
basketball_wnba | 12,670 | null | null | null | null |
basketball_wnba | 4,230 | null | null | null | null |
basketball_wnba | 4,734 | null | null | null | null |
basketball_wnba | 14,932 | null | null | null | null |
icehockey_nhl | 53,140 | null | null | null | null |
mma_mixed_martial_arts | 36,290 | null | null | null | null |
mma_mixed_martial_arts | 5,044 | null | null | null | null |
mma_mixed_martial_arts | 38,874 | null | null | null | null |
mma_mixed_martial_arts | 17,224 | null | null | null | null |
mma_mixed_martial_arts | 26,584 | null | null | null | null |
soccer_brazil_campeonato | 510 | null | null | null | null |
soccer_germany_bundesliga | 114 | null | null | null | null |
soccer_usa_mls | 284 | null | null | null | null |
tennis_atp_canadian_open | 596 | null | null | null | null |
tennis_atp_canadian_open | 1,464 | null | null | null | null |
tennis_atp_canadian_open | 7,306 | null | null | null | null |
tennis_atp_washington_open | 444 | null | null | null | null |
tennis_atp_washington_open | 3,522 | null | null | null | null |
tennis_atp_washington_open | 4,014 | null | null | null | null |
tennis_atp_wimbledon | 312 | null | null | null | null |
tennis_atp_wimbledon | 470 | null | null | null | null |
tennis_atp_wimbledon | 2,794 | null | null | null | null |
tennis_wta_canadian_open | 354 | null | null | null | null |
tennis_wta_canadian_open | 1,708 | null | null | null | null |
tennis_wta_canadian_open | 5,480 | null | null | null | null |
tennis_wta_washington_open | 2,120 | null | null | null | null |
tennis_wta_washington_open | 2,878 | null | null | null | null |
tennis_wta_wimbledon | 724 | null | null | null | null |
tennis_wta_wimbledon | 3,210 | null | null | null | null |
Line Movement Dataset — how much betting lines actually move
Aggregate measurements of pre-match betting line movement across 13+ sports and three market types, built from 11.7 million odds snapshots recorded between 29 June and 3 August 2026 across 52 online bookmakers. To our knowledge the first open, multi-sport measurement of line movement magnitude — the canonical public evidence on closing-line value rests on a single operator's blog study.
| Source observations | 11,740,751 pre-match odds snapshots, ~hourly cadence |
| Window | 29 June – 3 August 2026 (36 days) |
| Books / competitions | 52 bookmakers, 22 competitions |
| Files | 3 aggregate CSVs (sport × market × time-bucket grain) |
| Licence | CC BY 4.0 |
Files
betbetter_line_movement_h2h.csv — open-to-close probability movement
One row per sport. Head-to-head (two-outcome) markets: the outcome's de-vigged implied
probability at the first recorded snapshot vs the last pre-match snapshot, for markets first seen
at least 6 hours before start. Columns: observations, mean_lead_hours, mean_abs_move_pp,
pct_moved_gt2pp, favourite_signed_drift_pp (positive = favourites shorten toward the close).
betbetter_line_movement_points.csv — spread & total line movement
One row per sport × market type. The quoted line itself (points/handicap), first vs last
pre-match snapshot: mean_abs_point_move, pct_line_changed, max_point_move.
betbetter_distance_to_close.csv — where price discovery happens
One row per sport × time-to-start bucket (7d_plus, 3-7d, 1-3d, 6-24h, 1-6h, under_1h): the mean
absolute gap, in probability points, between the de-vigged price at that time and the same
outcome's closing price. Cells with fewer than 100 observations are suppressed.
Headline findings
- Movement is sport-specific, by an order of magnitude. Mean open-to-close move: UFC/MMA 2.76 pts (53% of outcomes move >2 pts), AFL 2.52, WNBA 1.81 — against MLB 0.60 and ATP-tour tennis ~0.5–0.8.
- Price discovery happens 1–7 days out, not at the death. In-season markets sit ~2.2 pts from their close 3–7 days before start, ~1.4 pts at 1–3 days, 0.6 pts at 6–24h and only 0.33 pts in the final 1–6 hours.
- Favourites shorten. The opening favourite's probability drifts up toward the close in almost every sport (MMA +0.8 pts, AFL +0.5, WNBA +0.44) — consistent with openers overpricing longshots and the close correcting it.
- Spread/total lines move where scoring is high: AFL handicaps move 2.55 pts on average (76% of lines change; max observed 14), WNBA totals 1.69 pts (77% change) — while MLB run lines and NHL puck lines are essentially static.
- Pre-season lines barely move: NFL and NHL markets posted months ahead move ~0.3–0.4 pts — lines move when information flows, not with the passage of time.
Construction and caveats
- Probabilities are de-vigged (margin removed across the market's outcomes), so movement is price relocation, not margin change. Points (spreads/totals) are the quoted lines themselves.
- "Open" = first snapshot in our archive, bounded by the 36-day window — for far-posted markets (NFL) the true open predates the window, so movement figures are floors for those sports. In-season sports (MLB, AFL, WNBA, MMA, tennis, MLS) are fully captured.
- Aggregates only: no odds, prices, bookmaker identities, teams, players or event identifiers appear in the data, and no underlying odds feed can be reconstructed from it.
- Snapshot cadence is approximately hourly and varies;
under_1hcells are thin.
Licence and citation
CC BY 4.0 — free to use, including commercially, with attribution.
Bet Better (2026). Line Movement Dataset: how much betting lines actually move, measured across 11.7M odds snapshots. https://betbetter.world/studies/line-movement
Maintained by Bet Better, which also publishes the Bookmaker Margin Panel, the Market Calibration Dataset, a free sports model API and an open AFL dataset.
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