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cs.LG updates on arXiv.org

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Intention Driven Identification of In-Possession Match Ph...
[Submitted on 8 Jun 2026 (v1), last revised 14 Jul 2026 (this ve · 2026-06-11 · via cs.LG updates on arXiv.org

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Abstract:Understanding tactical organisation in association football requires identifying in-possession match phases that are shaped by evolving tactical intentions rather than by spatial patterns alone. This study proposes an intention-driven framework for identifying phases from tracking data. Seven German Bundesliga matches recorded at 25 Hz with TRACAB were analysed. A hierarchical model was defined with three tactical intentions (Invade Opponent Space, Keep Possession, Scoring) and six phases (Build Up, Progression, Counter Attack, Maintenance, Sustained Threat, Finishing). A Temporal Graph Attention Network (T-GAN) combined frame-level player-interaction graphs, contextual features, and Transformer-based temporal modelling. Performance was evaluated using frame-level F1 and temporal Intersection over Union (tIoU). T-GAN achieved macro-average frame-level F1 scores of 0.87 at the intention level, 0.76 for invasion-related phases, and 0.79 for scoring phases. After filtering, mean class-wise tIoU increased from 0.44 to 0.67 for intentions and from 0.40 to 0.57 for phases, showing that sequence-level evaluation captured fragmentation and boundary errors missed by frame-level metrics. Model comparisons indicated that Transformer-based sequence modelling drove coherent segmentation, while graph-based relational modelling was most beneficial for Counter Attack. Misalignment analysis revealed common sequence level misalignments, mainly Build Up/Progression ambiguity, Build Up/Maintenance confusion, Counter Attack/Progression inconsistency, and shortened pre-shot Finishing segments. Overall, the framework translates tracking data into tactically interpretable phase representations for automated annotation, tactical analysis, and playing-style profiling.

Submission history

From: Yuesen Li [view email]
[v1] Mon, 8 Jun 2026 09:57:04 UTC (3,527 KB)
[v2] Wed, 10 Jun 2026 09:27:33 UTC (3,527 KB)
[v3] Tue, 14 Jul 2026 18:07:01 UTC (5,873 KB)