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 decisionmaking 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.
Modern football has undergone a radical transformation, evolving from a discipline grounded mainly in coaches' intuition and experience into one profoundly shaped by objective data analysis. The widespread adoption of advanced analytics systems, proprietary metrics such as expected goals (xG) and expected assists (xA), and the availability of detailed information on players' physical, technical, and tactical performance have enabled a quantitative understanding of phenomena once accessible only through human judgment [19].
In this data-driven landscape, tactical optimization-the ability to select and dynamically adjust playing strategies according to the team’s internal characteristics and the contingent match conditions-has become a decisive competitive factor. At elite levels, marginal advantages can determine the outcome of an entire season. Tactical effectiveness no longer depends solely on individual talent or preparation quality but also on the ability to interpret complex contexts, anticipate opponents’ actions, and adapt strategies in real time. However, traditional decision models based primarily on qualitative heuristics and experience reach their limits when faced with the high dimensionality and dynamism of modern play [14,23].
Despite significant progress in match analysis, a structural discontinuity persists between quantitative analytical tools-such as numerical performance indicators, spatial distributions, or xG-based predictive models-and qualitative contextual factors that critically influence collective performance. These include group cohesion, psychological resilience, team morale, residual energy, and the quality of tactical leadership [15,28]. Current decision support systems (DSS) tend to emphasize easily measurable variables (e.g., physical metrics) while neglecting intangible dimensions that often prove decisive in dynamic, high-pressure contexts. This gap results in: (i) loss of strategically relevant information; (ii) limited adaptability and personalization of recommendations; and (iii) persistent reliance on subjective intuition in crucial phases of play [21].
To address these challenges, the present study introduces a Decision Support System for Tactical Optimization grounded in an innovative semantic-distance methodology. The key idea is to represent, within a common vector space, both the team’s contextual configuration -aggregating technical, physical, psychological, and organizational attributes -and the ideal profiles of canonical tactical strategies (e.g., high pressing, counterattack, possession build-up). The system then recommends the most coherent strategy by minimizing the semantic distance (computed via cosine and Euclidean metrics) between these two sets of vectors. This approach enables transparent integration of quantitative data and expert knowledge, allowing dynamic adaptation to evolving match contexts (e.g., changes in collective energy or morale).
A distinctive feature of this research lies in the transfer of a general semantic methodology-conceived initially to bridge analytical frameworks and decision heuristics-to the particular tactical domain of football. In the reference paper Recommending Actionable Strategies [5], semantic distance was used to connect abstract structures (such as the 6C model) with historical heuristic systems (like the Thirty-Six Stratagems), demonstrating the potential to mediate between conceptual traditions.
The present work extends this paradigm to the operational level of tactical practice, replacing:
• general decision categories with 14 concrete macro-attributes capturing technical, psychological, and organizational dimensions of a team; and
• general heuristics with a structured repertoire of canonical football strategies (e.g., high pressing, counterattack, positional defense).
Thus, the contribution goes beyond replication, applying semantic modeling to the tactical decision-making process to produce contextual, interpretable, and immediately actionable recommendations for coaching staff. This transposition-from general semantic theory to applied sports intelligence-represents the main innovative contribution of the work, aligning with the interdisciplinary research frontier that integrates NLP, decision theory, and sports science.
The main contributions of this research can be summarized as follows:
Formalization of an Integrated Semantic Model for Football -Adaptation and extension of the Recommending Actionable Strategies methodology to the football domain, defining 14 multidimensional macro-attributes that synthesize a team’s complexity (including Offensive Strength, Psychological Resilience, and Tactical Cohesion).
Development of a Tactical Recommender Prototype -Implementation, in Python, of a recommendation engine capable of aggregating heterogeneous data and comparing them against predefined tactical models. The prototype includes dynamic weighting mechanisms to adjust recommendations in real tim
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