Can Semantic Methods Enhance Team Sports Tactics? A Methodology for Football with Broader Applications

• URL: http://arxiv.org/abs/2601.00421v1
• Date: Thu, 01 Jan 2026 18:23:51 GMT
• Title: Can Semantic Methods Enhance Team Sports Tactics? A Methodology for Football with Broader Applications
• Authors: Alessio Di Rubbo, Mattia Neri, Remo Pareschi, Marco Pedroni, Roberto Valtancoli, Paolino Zica,
• Abstract summary: This paper explores how semantic-space reasoning can be extended to tactical decision-making in team sports.<n>A Python-based prototype demonstrates how these methods can generate interpretable, dynamically adaptive strategy recommendations.<n>The approach offers a generalizable framework for collective decision-making and performance optimization in team-based domains.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores how semantic-space reasoning, traditionally used in computational linguistics, can be extended to tactical decision-making in team sports. Building on the analogy between texts and teams -- where players act as words and collective play conveys meaning -- the proposed methodology models tactical configurations as compositional semantic structures. Each player is represented as a multidimensional vector integrating technical, physical, and psychological attributes; team profiles are aggregated through contextual weighting into a higher-level semantic representation. Within this shared vector space, tactical templates such as high press, counterattack, or possession build-up are encoded analogously to linguistic concepts. Their alignment with team profiles is evaluated using vector-distance metrics, enabling the computation of tactical ``fit'' and opponent-exploitation potential. A Python-based prototype demonstrates how these methods can generate interpretable, dynamically adaptive strategy recommendations, accompanied by fine-grained diagnostic insights at the attribute level. Beyond football, the approach offers a generalizable framework for collective decision-making and performance optimization in team-based domains -- ranging from basketball and hockey to cooperative robotics and human-AI coordination systems. The paper concludes by outlining future directions toward real-world data integration, predictive simulation, and hybrid human-machine tactical intelligence.

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