Creating testing environments to help progress AI research out of the lab and into the real world is immensely challenging. Given AI's long association with games, it is perhaps no surprise that sports presents an exciting opportunity, offering researchers a testbed in which an AI-enabled system can assist humans in making complex, real-time decisions in a multiagent environment with dozens of dynamic, interacting individuals. The rapid growth of sports data collection means we are in the midst of a remarkably important era for sports analytics. The availability of sports data is increasing in both quantity and granularity, transitioning from the days of aggregate high-level statistics and sabermetrics to more refined data such as event stream information (e.g., annotated passes or shots), high-fidelity player positional information, and on-body sensors. However, the field of sports analytics has only recently started to harness machine learning and AI for both understanding and advising human decision-makers in sports.

In comparison to some other sports, football has been rather late with starting to systematically collect large sets of data for scientific analytics purposes aiming to progress teams' gameplay. This is for several reasons, with the most prominent being that there are far less controllable settings of the game compared to other sports (large outdoor pitch, dynamic game, etc.), and also the dominant credo to rely mainly on human specialists with track records and experience in professional football. On these lines, Arrigo Sacchi, a successful Italian football coach and manager who never played professional football in his career, responded to criticism over his lack of experience with his famous quote when becoming a coach at Milan in 1987: "I never realised that to be a jockey you had to be a horse first." Football Analytics poses challenges that are well suited for a wide variety of AI techniques, coming from the intersection of 3 fields: computer vision, statistical learning and game theory (visualised in Figure 2). While these fields are individually useful for football analytics, their benefits become especially tangible when combined: players need to take sequential decision-making in the presence of other players (cooperative and adversarial) and as such game theory, a theory of interactive decision making, becomes highly relevant.