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

arXiv 2603.28916 ↗First posted 30 Mar 2026cs.LG cs.CYcited by 0

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Abstract (arXiv)

The increasing availability of spatio-temporal tracking data has created new opportunities for analysing tactical behaviour in football. However, many existing approaches evaluate passes primarily through outcome-based metrics such as scoring probability or possession value, providing limited insight into how passes influence the defensive organisation of the opponent. This paper introduces a structural framework for analysing football passes based on their interaction with defensive structure. Using synchronised tracking/event data, we derive three complementary structural metrics, Line Bypass Score, Space Gain Metric, and Structural Disruption Index, that quantify how passes alter the spatial configuration of defenders. These metrics are combined into a composite measure termed Tactical Impact Value (TIV), which captures the structural influence of individual passes. Using tracking and event data from the 2022 FIFA World Cup, we analyse structural passing behaviour across multiple tactical levels. Unsupervised clustering of structural features reveals four interpretable pass archetypes: circulatory, destabilising, line-breaking, and space-expanding passes. Empirical results show that passes with higher TIV are significantly more likely to lead to territorial progression, particularly entries into the final third and penalty box. Spatial, team-level analyses further reveal distinctive structural passing styles across teams, while player-level analysis highlights the role of build-up defenders as key drivers of structural progression. In addition, analysing passer-receiver interactions identifies structurally impactful passing partnerships that amplify tactical progression within teams. Overall, the proposed framework demonstrates how structural representations derived from tracking data can reveal interpretable tactical patterns in football.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 30 Mar 2026.

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References · 7

  1. 2018Actions Speak Louder Than Goals: Valuing Player Actions in Soccercited by 90
  2. 2020A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessionscited by 24
  3. 2023A Machine Learning Approach for Player and Position Adjusted Expected Goals in Football (Soccer)cited by 7
  4. 2014Classification of Passes in Football Matches using Spatiotemporal Datacited by 4
  5. 2023Bayes-xG: Player and Position Correction on Expected Goals (xG) using Bayesian Hierarchical Approachcited by 4
  6. 2025Through the Gaps: Uncovering Tactical Line-Breaking Passes with Clusteringcited by 2
  7. 2025What If They Took the Shot? A Hierarchical Bayesian Framework for Counterfactual Expected Goalscited by 2

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