Intention Driven Identification of In-Possession Match Phases in Association Football through Temporal Graph Learning
arXiv:2606.09289v1 Announce Type: new Abstract: Understanding tactical organisation of association football, hereafter referred to as football, requires identifying distinct match phases. Yet in-possession phases are rarely directly observable and are shaped by evolving tactical intentions, rather than spatial patterns alone. This study proposes a data-driven framework for identifying in-possession match phases from spatiotemporal tracking data. Seven German Bundesliga matches recorded at 25 Hz with TRACAB were analysed. A hierarchical phase model was defined with three tactical intentions (In
This research is published as AI and machine learning techniques, specifically temporal graph learning, become sophisticated enough to analyze complex real-time sports data.
Advanced data-driven understanding of tactical dynamics in sports using AI could significantly impact coaching, player development, and strategic analysis workflows in professional sports.
The ability to automatically identify granular in-possession match phases based on evolving tactical intentions allows for more objective and detailed performance analysis than previously possible.
- · Professional sports teams (especially football)
- · Sports analytics companies
- · Sports data providers
- · AI/ML researchers in sports
- · Traditional, intuition-based sports analysts
This framework could lead to a new generation of AI-powered sports analytics tools for tactical decision-making.
The improved understanding of match phases could result in novel training methodologies and player recruitment strategies.
The application of temporal graph learning in sports might inspire similar analytical approaches in other complex, dynamic human-interaction systems.
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Read at arXiv cs.LG