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This research applies machine learning techniques to spatio-temporal tracking data from the 2022 FIFA World Cup to classify football passes into four distinct structural archetypes. By developing interpretable metrics like the Line Bypass Score and Tactical Impact Value, the study offers a novel framework for analyzing tactical dynamics beyond simple outcome-based evaluations. The work demonstrates how advanced pattern recognition can extract meaningful insights from complex sports data, bridging computational methods with domain-specific analysis.
Structural Pass Analysis in Football: Learning Pass Archetypes and Tactical Impact from Spatio-Temporal Tracking Data
arXiv:2603.28916v2 Announce Type: replace Abstract: Football action-value approaches estimate scoring, possession, or other downstream outcome value; complementary descriptions can characterise how a pass relates to the opposition's spatial structure. We introduce an interpretable structural-passing framework using tracking and event information, with three descriptors: Line Bypass Score (LBS), a signed difference in opponents remaining goal-side between passer and receiver coordinates; Space Gain Metric (SGM), relative nearest-opponent distance at the receiver versus a floored passer reference; and Structural Disruption Index (SDI), aggregate differences in defensive-shape summaries between pass time and the nearest sampled state one second later. The metrics are transformed and standardised before combination in Tactical Impact Value (TIV), a fixed-weight structural composite. We apply the framework to 41,078 open-play pass events with identified passer and receiver from the 2022 FIFA World Cup. The selected K=4 partition provides an interpretable operational taxonomy of four profiles: Circulatory, Destabilising, Line-breaking, and Space-expanding. The profiles show different structural metric signatures, and the reported territorial-crossing and stored outcome indicators vary descriptively across profiles and TIV strata. Team, spatial, player, and passer--receiver analyses illustrate how the structural representation can be summarised at multiple levels. The findings position the framework as a complement to outcome-oriented action-value analysis, offering football analysts interpretable structural evidence to support tactical interpretation and guide further investigation.
Abstract:Football action-value approaches estimate scoring, possession, or other downstream outcome value; complementary descriptions can characterise how a pass relates to the opposition's spatial structure. We introduce an interpretable structural-passing framework using tracking and event information, with three descriptors: Line Bypass Score (LBS), a signed difference in opponents remaining goal-side between passer and receiver coordinates; Space Gain Metric (SGM), relative nearest-opponent distance at the receiver versus a floored passer reference; and Structural Disruption Index (SDI), aggregate differences in defensive-shape summaries between pass time and the nearest sampled state one second later. The metrics are transformed and standardised before combination in Tactical Impact Value (TIV), a fixed-weight structural composite. We apply the framework to 41,078 open-play pass events with identified passer and receiver from the 2022 FIFA World Cup. The selected K=4 partition provides an interpretable operational taxonomy of four profiles: Circulatory, Destabilising, Line-breaking, and Space-expanding. The profiles show different structural metric signatures, and the reported territorial-crossing and stored outcome indicators vary descriptively across profiles and TIV strata. Team, spatial, player, and passer--receiver analyses illustrate how the structural representation can be summarised at multiple levels. The findings position the framework as a complement to outcome-oriented action-value analysis, offering football analysts interpretable structural evidence to support tactical interpretation and guide further investigation.
Machine Learning (cs.LG); Computers and Society (cs.CY)
arXiv:2603.28916 [cs.LG]
(or arXiv:2603.28916v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2603.28916 Focus to learn more arXiv-issued DOI via DataCite
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