In recent years, the study of performance evaluation and tactical decision-making has been deeply transformed by the integration of Artificial Intelligence techniques in sports analytics. Because of the continuous player interactions that shape its inherently dynamic nature, football offers a particularly suitable context for evaluating such approaches. Indeed, this complexity requires analytical models capable of adapting to evolving tactical situations. Among different AI paradigms, Reinforcement Learning stands out as one of the most suitable approaches for dealing with such complexity. Without relying on explicit supervision, it enables agents to learn directly through interaction and to develop adaptive strategies that respond to evolving match contexts.