Datasets:

luxury-lakehouse
/

spadl-action-context

action_context_id string	match_key int64	team_key float64	player_key float64	defending_gk_player_key int64	action_id int64	data_source string	game_state 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	gk_was_distributing bool	gk_was_engaged bool	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_ball__spearman float64	pitch_control_at_ball__fernandez_bornn float64	pitch_control_at_ball__voronoi float64	defensive_line_x float64	back_line_high_x float64	compactness_x float64	lateral_width float64	max_lateral_gap float64	back_n_count int64	line_break bool	n_attackers_behind_line int64	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 int64	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 int64	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	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	gk_completion float64	pitch_control_method string	ghost_gk_method string
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6e85067adfa5c03b921c6676f3de58c6	-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	26	idsse	drawing	1	106.003	pass	29.56	53.86	4.51	39.82	12,650	0.003	0.985	10	DFL-OBJ-0028FW	false	false	null	null	null	null	null	null	5.49713	2.656163	0	1	1.08723	1.086895	18.886106	0	0.001702	0.183563	0.433378	0	61.301786	55.80381	8.127143	39.758095	16.00619	4	false	2	5	6.045542	3.362209	1	false	0	null	47.052762	34.445952	1,568.295864	35.319524	61.555714	20.382485	10	50.586667	36.268095	939.756301	32.702381	39.758095	15.91192	10	0.099396	1.579602	-1.480207	0.219946	9.400922	2.586528	2.331546	2.628459	2.530827	2.722802	2.615479	3	8	0.568141	0.147448	0.318981	0.985	0.985	1	0.000039	0.000044	0.168348	0.882451	0.000232	0.000205	0	0	12,665	0.3471	0.597	0.311111	14	120.689103	0.355556	16	101.062461	86.869292	34.640193	379.860415	0	2.358253	18.798118	0	18.109237	60.842533	-42.733296	9.400922	14.101382	-4.700461	0.002033	0.101613	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	spearman	fft-cic
41471ef2041e265fcc9d28da098666e9	-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	29	idsse	drawing	1	115.9	tackle	24.48	13.18	24.48	13.18	12,898	-0.02	0.9	10	DFL-OBJ-0028FW	false	false	null	null	null	null	null	null	3.255982	0.705701	1	0	0.550095	0.538231	9.163486	0	0.000955	0.030808	0.481288	0	53.249048	45.601905	12.285238	25.018571	12.66619	4	false	2	7	7.30297	3.652814	1	null	null	null	34.325286	27.608714	1,025.748676	37.010476	42.020952	17.74417	10	39.660048	27.201476	1,078.135156	37.863333	41.748095	17.606965	10	85.105785	6.684247	78.421538	0.275724	4.700461	2.830904	2.613226	2.875551	2.788584	2.947855	2.852854	2	7	0.691441	0.26281	0.524311	0.9	0.9	1	null	null	null	null	null	null	0	0	12,893	0.3337	0.18	0.333333	15	113.509882	0.333333	15	112.533519	83.130622	31.866545	380.904098	null	null	null	0	12.956777	53.650331	-40.693554	14.101382	9.400922	4.700461	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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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	drawing	1	966.305	pass	31.56	64.29	2.91	42.63	34,158	-0.015	0.925	10	DFL-OBJ-0028FW	false	false	null	null	null	null	null	null	5.326271	2.858409	0	0	1.113828	1.1122	19.830038	0	0.000076	0.966235	0.605622	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	0.239468	4.700461	2.471706	2.213893	2.511127	2.413643	2.607617	2.500854	5	6	0.688485	0.261218	0.285083	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	24.381399	44.171397	-19.789998	14.101382	23.502304	-9.400922	0.010831	0.068583	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	spearman	fft-cic
b0188aad042ec24885bb688999d2719e	-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	224	idsse	drawing	1	984.3895	dribble	62.34	45.13	76.13	37.44	34,610	-0.0105	0.9475	10	DFL-OBJ-002FZB	false	false	null	null	null	null	null	null	1.537644	2.747498	1	2	1.227526	1.210271	105.105887	0.504513	0.990417	0.5	0.420066	0	79.655952	76.321429	4.718095	35.712381	15.997619	4	false	1	6	4.910035	2.800639	2	null	null	null	65.036143	41.216429	1,170.459852	39.194762	44.713333	16.205831	10	68.628762	38.169571	774.879185	30.815714	35.712381	14.057891	10	3.280475	159.652071	-156.371596	0.078218	4.700461	1.517399	1.017926	1.577816	1.366007	1.69043	1.499861	2	4	0.051611	0.019219	0.051611	0.9475	0.9475	1	null	null	null	null	null	null	0	0	34,613	0.2942	0.1305	0.355556	16	104.479136	0.355556	16	108.7493	98.822207	35.431197	375.593664	null	null	null	0	5.692423	0.73476	4.957663	9.400922	9.400922	0	0.026874	0.145148	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	spearman	fft-cic
ea15afe2fab98d3fca4ac3ee39003813	-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	225	idsse	drawing	1	985.594	tackle	76.13	37.44	76.13	37.44	34,640	-0.006	0.97	10	DFL-OBJ-002FZB	false	false	null	null	null	null	null	null	2.72011	5.850649	1	1	2.340974	2.339996	51.960264	0.273831	0.803414	0.5	0.230477	0.472667	78.997381	78.052857	1.665714	29.964286	14.546667	4	false	1	3	7.195173	3.224985	1	null	null	null	66.165524	40.767667	1,052.11499	34.474762	46.160476	15.993139	10	69.857476	38.07419	632.888337	28.462857	29.964286	11.798477	10	4.237531	253.201344	-248.963814	0.032745	4.700461	1.408	0.937525	1.409452	1.168011	1.549823	1.34657	1	2	0.004463	0.001697	0.004463	0.97	0.97	1	null	null	null	null	null	null	0	0	34,663	0.3369	0.926	0.355556	16	107.734248	0.377778	17	99.529379	99.774598	34.700282	377.617126	null	null	null	4.700461	25.594831	1.07807	24.516761	14.101382	0	14.101382	0.069134	0.198067	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, with ~123 columns for tracking providers and ~5 for event-only providers. Covers all 6 data sources.

Provider Tiers

Tier	Providers	Feature Coverage
Event-only	StatsBomb, Wyscout	Identity + game state (~5 columns)
SB360	StatsBomb (with 360 freeze-frames)	Event-only + freeze-frame-derived context
Tracking	IDSSE, Metrica, SkillCorner, GradientSports	Full ~123 columns including pitch control, team shape, line-breaking, OBSO, PAUSA, gk_influence zones, xShotOccurrence, structural context, xCross, player influence, GK pass-completion (`gk_completion_`), and the xT-GK goalkeeper distribution family (`xt_gk_`, five philosophy presets)

GradientSports partitions are license-restricted: they are published 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, voronoi (position-only) on SB360 freeze-frames (which carry no velocity), and null on event-only rows. 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; null on event-only rows). 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, gk_was_distributing, gk_was_engaged
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_ball__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)
Pitch-Control Provenance	pitch_control_method (spearman=tracking / voronoi=SB360 / null=event-only)
Ghost-GK Backend Provenance	ghost_gk_method (scipy/vectorized/cpu-numba/fft/fft-cic; null=event-only)

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: 14

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