Datasets:

luxury-lakehouse
/

spadl-action-context

action_context_id string	match_key int64	team_key float64	player_key float64	defending_gk_player_key float64	action_id int64	data_source string	period_id int64	time_seconds float64	type_name string	start_x float64	start_y float64	end_x float64	end_y float64	frame_id int64	time_offset_seconds float64	link_quality_score float64	n_candidate_frames int64	defending_gk_player_id_native string	pre_shot_gk_x float64	pre_shot_gk_y float64	pre_shot_gk_distance_to_goal float64	pre_shot_gk_distance_to_shot float64	pre_shot_gk_angle_to_shot_trajectory float64	pre_shot_gk_angle_off_goal_line float64	nearest_defender_distance float64	actor_speed float64	receiver_zone_density int64	defenders_in_triangle_to_goal int64	actor_arc_length_pre_window float64	actor_displacement_pre_window float64	pressure_on_actor__andrienko_oval float64	pressure_on_actor__link_zones float64	pressure_on_actor__bekkers_pi float64	pitch_control_at_target__spearman float64	pitch_control_at_target__fernandez_bornn float64	pitch_control_at_target__voronoi float64	defensive_line_x float64	back_line_high_x float64	compactness_x float64	lateral_width float64	max_lateral_gap float64	back_n_count float64	line_break bool	n_attackers_behind_line float64	n_off_ball_runners_pre_window float64	max_off_ball_run_displacement_pre_window float64	mean_off_ball_run_speed_pre_window float64	n_off_ball_runners_toward_goal_pre_window float64	line_break__ward bool	lines_broken__ward float64	line_breaking_type__ward string	team_shape_centroid_x_attacking float64	team_shape_centroid_y_attacking float64	team_shape_convex_hull_area_attacking float64	team_shape_team_length_attacking float64	team_shape_team_width_attacking float64	team_shape_stretch_index_attacking float64	team_shape_n_outfield_players_attacking float64	team_shape_centroid_x_defending float64	team_shape_centroid_y_defending float64	team_shape_convex_hull_area_defending float64	team_shape_team_length_defending float64	team_shape_team_width_defending float64	team_shape_stretch_index_defending float64	team_shape_n_outfield_players_defending float64	das_team float64	das_opponent float64	das_diff float64	gk_pitch_control_share_weighted float64	gk_reachable_area_m2 float64	gk_closing_time_mean_s__six_yard_box float64	gk_closing_time_min_s__six_yard_box float64	gk_closing_time_mean_s__near_post float64	gk_closing_time_min_s__near_post float64	gk_closing_time_mean_s__far_post float64	gk_closing_time_min_s__far_post float64	n_blocked_receivers float64	n_potential_receivers float64	blocking_score float64	blocked_threat_fraction float64	max_single_defender_blocking_score float64	sync_score_min float64	sync_score_mean float64	sync_score_high_quality_frac float64	obso_actual float64	obso_peak float64	obso_optimal float64	pausa_temporal float64	pausa_spatial float64	pausa_composite float64	space_created_m2 float64	space_denied_m2_opponent float64	elastic_frame_id float64	elastic_confidence float64	elastic_error_seconds float64	shape_graph_density_attacking float64	shape_graph_n_edges_attacking float64	shape_graph_mean_stability_attacking float64	shape_graph_density_defending float64	shape_graph_n_edges_defending float64	shape_graph_mean_stability_defending float64	ghost_gk_x float64	ghost_gk_y float64	ghost_gk_density_spread float64	structural_lbs float64	structural_sgm float64	structural_sdi float64	actor_reachable_area_m2 float64	off_ball_xt_team float64	off_ball_xt_opponent float64	off_ball_xt_diff float64	reachable_area_team float64	reachable_area_opponent float64	reachable_area_diff float64	xcross_attempt float64	xshot_occurrence float64	shot_crossing_y float64	shot_crossing_z float64	shot_speed float64	shot_time_to_goal_line float64	shot_on_target_derived bool	shot_crossing_source string	shot_crossing_confidence float64	shot_fit_n_frames float64	shot_fit_rmse float64	shot_fit_end_reason string	shot_z_profile string	xt_gk float64	xt_gk_possession float64	xt_gk_counter float64	xt_gk_direct float64	xt_gk_high_press float64	xt_gk_low_block float64	xt_gk_base float64	xt_gk_pev float64	xt_gk_rav float64	xt_gk_dzv float64	xt_gk_pressure float64	xt_gk_origin_source string	xt_gk_dest_source string	xt_gk_origin_confidence float64	xt_gk_completion_variant string	xt_gk_completion_source string	xt_gk_origin_x float64	xt_gk_origin_y float64	xt_gk_dest_x float64	xt_gk_dest_y float64	gk_completion float64	pitch_control_method string	ghost_gk_method string
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85c534d3d33fd3e2bf13e48b23a862f1	-2,610,804,279,666,496,000	-1,084,766,402,409,919,400	-7,219,013,638,704,294,000	1,376,290,947,922,071,300	49	idsse	1	170.812	pass	34.87	9.33	31.26	7.42	14,270	0.012	0.94	10	DFL-OBJ-0028FW	null	null	null	null	null	null	6.833363	0.34853	0	1	0.546958	0.53434	0	0	0.003955	0.5	0.58088	1	55.159524	51.400952	7.102381	30.477143	13.836667	4	false	0	0	null	null	0	false	0	null	38.386333	19.00019	754.926805	30.310476	40.391429	13.287227	10	44.553667	21.308905	673.297632	30.830952	36.600476	14.023406	10	0.070785	1.59553	-1.524745	0.438022	4.700461	2.843301	2.650913	2.902674	2.822283	2.944323	2.854409	6	6	0.747039	0.262426	0.532418	0.94	0.94	1	0.001199	0.001422	0.152638	0.842706	0.007852	0.006617	0	0	14,269	0.3897	0.052	0.355556	16	104.590118	0.377778	17	96.871877	82.615357	29.343849	380.959933	0	0.034887	3.822097	0	12.934489	202.79147	-189.85698	0	18.801843	-18.801843	0.005069	0.048036	null	null	null	null	null	<NA>	null	null	null	<NA>	<NA>	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	spearman	fft-cic
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6dbe49295ca6e479efaf84290ba28062	-2,610,804,279,666,496,000	-7,666,253,343,095,157,000	2,027,333,427,499,774,700	-975,248,052,755,103,500	206	idsse	1	935.713	freekick_short	64.22	34.6	96.21	17.57	33,393	-0.007	0.965	10	DFL-OBJ-002FZB	null	null	null	null	null	null	9.983314	0.263742	0	1	0.073148	0.069205	0	0	0	0.469323	0.44085	1	85.331786	85.169048	0.358571	28.430476	20.370476	4	true	5	null	null	null	null	null	null	null	78.596476	33.91781	607.261366	25.581429	36.365238	14.125356	10	81.936952	34.440143	361.454483	16.477619	33.573333	10.820616	10	3.411082	20.113844	-16.702762	0.208011	4.700461	1.438874	0.932269	1.502209	1.238824	1.558514	1.305908	5	10	2.839333	0.166907	0.673633	0.965	0.965	1	null	null	null	null	null	null	0	0	null	null	null	0.311111	14	120.513959	0.311111	14	119.022594	100.821625	34.000285	368.215408	null	null	null	4.700461	77.665275	118.549988	-40.884713	4.700461	9.400922	-4.700461	null	null	null	null	null	null	null	<NA>	null	null	null	<NA>	<NA>	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	spearman	fft-cic
981e4c3422cbcfd0fa3c449523a05555	-2,610,804,279,666,496,000	-1,084,766,402,409,919,400	3,320,493,879,544,644,000	1,376,290,947,922,071,300	213	idsse	1	952.898	pass	26.45	55.06	39.78	21.26	33,822	0.018	0.91	10	DFL-OBJ-0028FW	null	null	null	null	null	null	11.820657	0.923508	0	2	0.756727	0.720592	0	0	0.003912	0.5	0.417228	0	58.23381	54.647619	7.033333	22.134762	13.082857	4	false	1	4	5.106923	2.794385	3	true	1	around_line	41.763619	42.627429	1,345.050965	38.415238	57.309524	19.172014	10	50.041619	44.913238	555.782886	24.78	30.252857	12.329358	10	null	null	null	0.344854	9.400922	2.712129	2.527105	2.798904	2.709018	2.790416	2.704275	6	7	1.93271	0.328441	1.007827	0.91	0.91	1	0.027391	0.036293	0.097346	0.754731	0.281384	0.212369	0	0.000001	33,805	0.2613	0.698	0.333333	15	114.752856	0.333333	15	115.351578	83.10797	36.307422	381.321498	2	0.048316	0.102114	0	27.982823	310.335995	-282.353172	4.700461	9.400922	-4.700461	null	null	null	null	null	null	null	<NA>	null	null	null	<NA>	<NA>	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	spearman	fft-cic
9a72174b3d4acb31c3a1645838b040fd	-2,610,804,279,666,496,000	-1,084,766,402,409,919,400	3,320,493,879,544,644,000	1,376,290,947,922,071,300	217	idsse	1	963.306	pass	30.16	60.38	31.56	64.29	34,083	-0.014	0.93	10	DFL-OBJ-0028FW	null	null	null	null	null	null	11.412316	1.751795	0	0	0.637098	0.63626	0	0	0.005629	0.5	0.566109	1	61.898333	57.947619	6.061905	37.151429	15.563333	4	false	1	5	7.093671	3.356343	1	false	0	null	47.399048	32.917762	1,629.176446	38.364762	60.075238	20.542252	10	51.018	33.999571	955.706181	31.34619	41.935714	15.920043	10	0.098518	11.126062	-11.027543	0.368903	4.700461	2.523217	2.282116	2.576219	2.482049	2.653	2.544632	6	8	1.988734	0.24518	0.717402	0.93	0.93	1	0.024024	0.024432	0.151845	0.983302	0.158217	0.155575	0	0	34,079	0.2593	0.146	0.333333	15	113.437317	0.333333	15	112.53785	86.92916	34.321178	379.892275	0	0.160901	2.371626	4.700461	42.390151	231.669698	-189.279547	28.202765	9.400922	18.801843	0.007056	0.141731	null	null	null	null	null	<NA>	null	null	null	<NA>	<NA>	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	spearman	fft-cic
8a52613cdb55689e5917bfa2ad7bed8c	-2,610,804,279,666,496,000	-1,084,766,402,409,919,400	-7,280,670,893,001,932,000	1,376,290,947,922,071,300	218	idsse	1	966.305	pass	31.56	64.29	2.91	42.63	34,158	-0.015	0.925	10	DFL-OBJ-0028FW	null	null	null	null	null	null	5.326271	2.858409	0	0	1.113828	1.1122	19.830038	0	0.000076	1	0.727165	1	62.599881	58.380476	6.385714	31.79381	13.054762	4	false	1	6	6.353831	3.346085	4	true	1	around_line	47.406571	39.065952	1,562.30249	36.614762	58.352381	21.512888	10	49.90381	42.437238	1,067.663731	35.579048	41.408571	17.961334	10	0.210855	6.744877	-6.534021	0.411185	4.700461	2.471706	2.213893	2.511127	2.413643	2.607617	2.500854	5	6	1.962668	0.25571	0.875723	0.925	0.925	1	0.000049	0.000065	0.135367	0.755887	0.000361	0.000273	0	0	34,168	0.2592	0.415	0.333333	15	113.11665	0.333333	15	113.486903	87.480505	36.360447	380.436493	0	2.821015	18.462844	0	54.953638	162.36264	-107.409002	14.101382	23.502304	-9.400922	0.010831	0.068583	null	null	null	null	null	<NA>	null	null	null	<NA>	<NA>	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	spearman	fft-cic

End of preview. Expand in Data Studio

SPADL Action Context Features

Unified per-action context features for football matches from the luxury-lakehouse analytics platform. One row per SPADL action. Action-context is frames-required (lakehouse ADR-057): rows exist only for the 4 tracking providers and StatsBomb matches that carry 360 freeze-frames (SB360). Event-only matches (Wyscout, and StatsBomb without 360) are out of scope and produce no rows — their actions-level features live in the spadl-vaep-action-values dataset.

Provider Tiers

Tier	Providers	Feature Coverage
Tracking	IDSSE, Metrica, SkillCorner, GradientSports	Full tracking columns: pitch control, team shape, line-breaking, OBSO, PAUSA, gk_influence zones, xShotOccurrence, structural context, xCross, player influence, shot-goalmouth crossing geometry, GK pass-completion (`gk_completion_`), and the xT-GK goalkeeper distribution family (`xt_gk_`, five philosophy presets) — including the four resolved-coordinate audit columns `xt_gk_origin_x/y` + `xt_gk_dest_x/y` (the exact origin/destination the grid lookups used, incl. imputed goal-kick origins; LTR SPADL metres, `null` off-scope)
SB360	StatsBomb (with 360 freeze-frames)	Partial/sparse single-frame subset of the tracking columns — freeze-frames carry no velocity, so pitch control is `voronoi` and the velocity-dependent families (DAS, cover-shadows, off-ball, xCross, pre-shot GK position) stay `null`

Event-only matches are not part of this dataset (frames-required; ADR-057). GradientSports partitions are license-restricted: they publish to a private org-members-only companion repo (spadl-action-context-restricted) rather than this public dataset, per lakehouse ADR-049. The partition migrates here automatically once the license permits publication.

Pitch-control method provenance & SB360 coverage

The pitch_control_method column records which pitch-control model produced each row's pitch-control-derived metrics (OBSO, PAUSA, gk_influence/gk_closing_time_*): spearman (velocity-aware) on the tracking providers and voronoi (position-only) on SB360 freeze-frames (which carry no velocity). SB360 and tracking values for these columns are therefore produced by different estimators and are not directly comparable — segment on pitch_control_method.

The ghost_gk_method column records which ghost-GK KDE backend produced each row's ghost_gk_x/y/density_spread (one of scipy, vectorized, cpu-numba, fft, fft-cic). The default is fft-cic (a ~95% mode-exact fast approximation); a run may select an exact backend for higher accuracy, which yields different ghost_gk_* values. This column scopes only to ghost_gk_* and is independent of pitch_control_method — segment on ghost_gk_method before comparing ghost_gk_* across rows.

SB360 freeze-frame coverage is partial and sparse: each metric only populates the subset of actions whose freeze-frame contains the needed players, and xshot_occurrence in particular is non-null for only ~4% of SB360 actions (a non-random subsample). Do not compute naive provider-level averages over SB360 metrics — filter on non-null and account for the sampling.

Quick Start

Every row carries a data_source column. The dataset is split into one config per provider, so you can pull a single provider without downloading the rest:

from datasets import load_dataset

# All public providers at once (config "all" — the default):
ds = load_dataset("luxury-lakehouse/spadl-action-context")
df = ds["train"].to_pandas()
print(df["data_source"].value_counts())   # idsse, metrica, skillcorner

# Just one provider (downloads only that provider's parquet):
sc = load_dataset("luxury-lakehouse/spadl-action-context", "skillcorner")["train"].to_pandas()

Available configs: all (default), idsse, metrica, skillcorner. GradientSports lives in the private companion repo (spadl-action-context-restricted, config gradientsports).

Column Categories

Category	Example Columns
Identity	data_source, match_id, action_id, period_id, team_id, player_id
Game State	time_seconds, type_name, start_x, start_y, end_x, end_y
Frame Linkage	frame_id, timestamp_utc
GK Resolution	defending_gk_player_id_native (resolves the defending GK; the `gk_was_*` action-sequence flags are actions-level and moved to `spadl-vaep-action-values`, ADR-056)
GK Spatial	gk_x, gk_y, gk_distance_to_goal
Action Context	nearest_defender_distance, receiver_zone_density, defenders_in_triangle_to_goal
Actor Pre-Window	actor_arc_length_pre_window, actor_displacement_pre_window
Pressure	pressure_on_actor__andrienko_oval, __link_zones, __bekkers_pi
Pitch Control	pitch_control_at_target__spearman, __fernandez_bornn, __voronoi
Defensive Line	defensive_line_x, back_line_high_x, compactness_x, lateral_width
Off-Ball Context	line_break, n_attackers_behind_line, n_off_ball_runners_*
Ward Line-Breaking	line_break__ward, lines_broken__ward, line_breaking_type__ward
Team Shape	team_shape_{metric}_{attacking/defending} (14 cols)
DAS	das_team, das_opponent, das_diff
GK Influence	gk_pitch_control_share_weighted, gk_reachable_area_m2, gk_closing_time_*
Cover Shadows	n_blocked_receivers, blocking_score, blocked_threat_fraction
Sync Score	sync_score_min, sync_score_mean, sync_score_high_quality_frac
OBSO	obso_value, obso_total_threat
PAUSA	pausa_value, pausa_added_threat
Space Creation	space_created_m2, space_denied_m2_opponent (attacking LOO + rest-defense LOO; both m², >=0)
ELASTIC Sync	elastic_sync_score, elastic_compactness
Shape Graph	shape_graph_centrality, shape_graph_clustering
Ghost-GK	ghost_gk_x, ghost_gk_y, ghost_gk_density_spread (served = boosted-HGBR mean; density_spread = conditional-density dispersion)
Structural Pass	structural_lbs, structural_sgm, structural_sdi (Line Bypass Score / Space Gain / Disruption Index; Karakus & Arkadas 2026, arXiv:2603.28916; NaN for non-pass/cross)
Player Influence	actor_reachable_area_m2, off_ball_xt_{team,opponent,diff}, reachable_area_{team,opponent,diff} (Spearman 2018 + Singh 2018)
xCrossAttempt	xcross_attempt (P(cross attempt) for the in-possession team; Cao et al. 2025, arXiv:2505.11841; tracking-only — NaN on SB360)
xShotOccurrence	xshot_occurrence (P(shot attempted); Pipping-Gamón, Feng & Sabin 2026, arXiv:2512.00203)
Shot Goalmouth	shot_crossing_y, shot_crossing_z, shot_speed, shot_time_to_goal_line, shot_on_target_derived, shot_z_profile + provenance (shot_crossing_source/confidence, shot_fit_n_frames/rmse/end_reason) — ballistic shot-trajectory fit to the goal line (TF-48; Anzer & Bauer 2021; tracking shots only, post-contact)
Pitch-Control Provenance	pitch_control_method (spearman=tracking / voronoi=SB360)
Ghost-GK Backend Provenance	ghost_gk_method (scipy/vectorized/cpu-numba/fft/fft-cic)

Data Fields

All feature columns are float64 (nullable NaN) unless noted. Identity columns: data_source (string), match_id (string), action_id (int), period_id (int), time_seconds (float), team_id (string), player_id (string), type_name (string), start_x/start_y/end_x/end_y (float, SPADL 105x68).

Citation

@software{luxury_lakehouse,
  title  = {Luxury Lakehouse — Serverless Soccer Analytics Platform},
  url    = {https://github.com/karsten-s-nielsen/luxury-lakehouse}
}

License

CC-BY-NC-4.0 — see repository for details.

Downloads last month: 26

Papers for luxury-lakehouse/spadl-action-context

Structural Pass Analysis in Football: Learning Pass Archetypes and Tactical Impact from Spatio-Temporal Tracking Data

Paper • 2603.28916 • Published Mar 30

Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer

Paper • 2512.00203 • Published Jan 26

Framing Causal Questions in Sports Analytics: A Case Study of Crossing in Soccer

Paper • 2505.11841 • Published May 17, 2025