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Datasets:
luxury-lakehouse
/
space-creation-values

Tags:
soccer
football
space-creation
pitch-control
obso
analytics
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
grid_nx: int64
grid_ny: int64
cell_area_m2: double
frame_rate: int64
frame_sample_step: int64
effective_fps: int64
n_frames_processed: int64
n_frames_skipped: int64
n_player_frame_rows: int64
n_matches: int64
match_ids: list<item: string>
  child 0, item: string
n_unique_players: int64
jax_used: bool
total_elapsed_seconds: double
data_sources: struct<tracking: string, trained_grids: string>
  child 0, tracking: string
  child 1, trained_grids: string
tracking_dataset_commit: string
elapsed_seconds: double
rate_usd_per_hour: double
estimated_cost_usd: double
workflow_id: string
workflow_phase: string
row_count: int64
phase: string
state: string
started_at: string
hf_job_id: null
updated_at: string
ended_at: string
duration_seconds: double
to
{'workflow_id': Value('string'), 'phase': Value('string'), 'started_at': Value('string'), 'rate_usd_per_hour': Value('float64'), 'hf_job_id': Value('null'), 'updated_at': Value('string'), 'state': Value('string'), 'ended_at': Value('string'), 'duration_seconds': Value('float64'), 'estimated_cost_usd': Value('float64'), 'row_count': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 265, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 120, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              grid_nx: int64
              grid_ny: int64
              cell_area_m2: double
              frame_rate: int64
              frame_sample_step: int64
              effective_fps: int64
              n_frames_processed: int64
              n_frames_skipped: int64
              n_player_frame_rows: int64
              n_matches: int64
              match_ids: list<item: string>
                child 0, item: string
              n_unique_players: int64
              jax_used: bool
              total_elapsed_seconds: double
              data_sources: struct<tracking: string, trained_grids: string>
                child 0, tracking: string
                child 1, trained_grids: string
              tracking_dataset_commit: string
              elapsed_seconds: double
              rate_usd_per_hour: double
              estimated_cost_usd: double
              workflow_id: string
              workflow_phase: string
              row_count: int64
              phase: string
              state: string
              started_at: string
              hf_job_id: null
              updated_at: string
              ended_at: string
              duration_seconds: double
              to
              {'workflow_id': Value('string'), 'phase': Value('string'), 'started_at': Value('string'), 'rate_usd_per_hour': Value('float64'), 'hf_job_id': Value('null'), 'updated_at': Value('string'), 'state': Value('string'), 'ended_at': Value('string'), 'duration_seconds': Value('float64'), 'estimated_cost_usd': Value('float64'), 'row_count': Value('int64')}
              because column names don't match

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Space Creation Values

Per-player per-frame space creation metrics computed via JAX-accelerated pitch control with player removal on A10G GPU. 726,210 player-frame rows across 7 IDSSE Bundesliga matches.

Method

Space creation quantifies each player's contribution to off-ball scoring opportunity (OBSO) by measuring the change in OBSO surface when that player is hypothetically removed from the pitch (Fernandez & Bornn 2018).

For each sampled frame:

  1. Compute baseline pitch control surface with all players via JAX
  2. Compute N player-removal variants via jax.vmap (one GPU dispatch per frame)
  3. Convert each pitch control surface to OBSO surface
  4. space_created_m2: sum of cells where OBSO increased due to player presence
  5. space_destroyed_m2: sum of cells where OBSO decreased due to player presence
  6. net_space_m2: total OBSO contribution in square meters

Parameters

  • Grid resolution: 52 x 34 cells (1,768 total)
  • Cell area: 4.04 m^2
  • Frame sampling: 1 fps (every 25th frame)
  • Coordinate system: StatsBomb 120x80

Contents

  • data/space_creation.parquet -- Per-player per-frame values (726,210 rows)
  • metadata.json -- Computation parameters, timing, and data provenance

Data Fields

Column Type Description
match_id string Match identifier (idsse_J03...)
frame_id int Tracking frame number
player_id string DFL PersonId
team string Player's team (home / away)
period int Match half (1 or 2)
space_created_m2 double OBSO area added by player presence (m^2, >= 0)
space_destroyed_m2 double OBSO area removed by player presence (m^2, <= 0)
net_space_m2 double Net OBSO contribution (m^2, positive = beneficial)

Input Data

References

  • Fernandez, J. & Bornn, L. (2018). "Wide Open Spaces." MIT Sloan.
  • Spearman, W. (2018). "Beyond Expected Goals." MIT Sloan.
  • Bassek et al. (2025). "An integrated dataset of spatiotemporal and event data in elite soccer." Sci. Data.

License

MIT -- computed from IDSSE open data (CC-BY 4.0).
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