{"ID":2823512,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00421","arxiv_id":"2601.00421","title":"Can Semantic Methods Enhance Team Sports Tactics? A Methodology for Football with Broader Applications","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.","short_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 con...","url_abs":"https://arxiv.org/abs/2601.00421","url_pdf":"https://arxiv.org/pdf/2601.00421v2","authors":"[\"Alessio Di Rubbo\",\"Mattia Neri\",\"Remo Pareschi\",\"Marco Pedroni\",\"Roberto Valtancoli\",\"Paolino Zica\"]","published":"2026-01-01T18:23:51Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}