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

TL;DR

The paper tackles the gap between quantitative football analytics and qualitative tactical reasoning by introducing a semantic-distance-based decision support system. It formalizes a shared 14-dimensional semantic space to encode both a team's contextual state and canonical tactical profiles, enabling real-time, interpretable tactic recommendations via distance minimization. The methodology includes a hierarchical context tree for data aggregation, explicit strategy-vector construction, dynamic weight adjustments, and an evaluation protocol using simulated scenarios and a pilot with real youth-match data. The work demonstrates that a generically trained, explainable vector framework can reproduce coherent tactical reasoning, adapt to context, and remain robust to perturbations, with clear paths toward real-time data integration, nonlinear metrics, and cross-domain extension. Overall, the approach offers a modular, interpretable, and potentially generalizable toolkit for connective decision-making in team-based settings beyond football.

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.

Figures (6)

• Figure 1: Context tree structure for two representative macro-attributes. Leaf nodes contain raw observables from match data; intermediate nodes aggregate by functional role; root nodes are the macro-attributes used in semantic distance computation. Edges represent weighted aggregation functions.
• Figure 2: System architecture of the tactical decision support prototype. Context signals are aggregated into 14 macro-attributes (team vector), matched to strategy templates via adapted semantic distance, and produce interpretable recommendations and diagnostics.
• Figure 3: Example of radar plot for the “Energetic and Balanced” scenario. The shaded blue area represents the team profile, while the orange outline indicates the ideal strategy vector.
• Figure 4: Sensitivity of adapted distance $d_{\text{adapt}}$ with respect to contextual weight $\lambda$ across the four scenarios. Smooth trends indicate stability in the optimal strategy selection.
• Figure 5: Relative importance of the five most influential macro-attributes across all simulations.