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.

翻译：本文探讨了传统上用于计算语言学的语义空间推理如何扩展到团队运动中的战术决策。基于文本与团队之间的类比——其中球员充当词语，集体配合传达意义——所提出的方法将战术配置建模为组合语义结构。每个球员被表示为整合技术、身体和心理属性的多维向量；团队特征通过上下文加权聚合成更高层次的语义表示。在这个共享向量空间中，诸如高位压迫、反击或控球组织等战术模板被编码为类似语言概念的结构。它们与团队特征的对齐通过向量距离度量进行评估，从而能够计算战术“契合度”和对手利用潜力。一个基于Python的原型展示了这些方法如何生成可解释、动态自适应的策略建议，并提供属性层面的细粒度诊断分析。除了足球领域，该方法为基于团队的领域——从篮球和曲棍球到协作机器人以及人机协调系统——中的集体决策和性能优化提供了一个可推广的框架。本文最后概述了未来研究方向，包括现实世界数据整合、预测性模拟以及混合人机战术智能。