Game Plan: What AI can do for Football, and What Football can do for AI

Abstract

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).

Developing, evaluating and scaling learning agents in multi-agent environments

“…The availability of team sports data (e.g., in football or basketball) has been steadily increasing over recent years. Football, in particular, is a great test bed to study novel AI techniques with the potential to offer decision-makers advice in the form of an automated video-assistant coach (AVAC) [70]. Techniques from computer vision, statistical learning, and game theory all contribute to the understanding of the game.…”

“…The AVAC system sits at the intersection of these three research fields. In [70], we identify three frontiers: video predictive models, strategic video generation models, and interactive decision-making, and lay out a roadmap of specific research and engineering problems. The development of AI-powered sports analytics techniques offer great potential for decision-makers, broadcasters, players, and fans.…”

Integração entre a análise do desempenho e o ensino-aprendizagem nos esportes coletivos

“…Os registros de análises do desempenho nos esportes coletivos indicam extensa tradição de desenvolvimento metodológico (Eaves, 2015), favorecida pela crescente disponibilidade de dados e técnicas computacionais (Tuyls et al, 2021). A relevância científica do tema é atestada pelo volume crescente de pesquisas dedicadas ao assunto (Buldú et al, 2021).…”

Automatic event detection in football using tracking data

“…Event data for major football leagues and tournaments started to be collected by Opta Sports (now Statsperform) [12] at the end of the 20th century, and its widespread adoption has propelled the development of advanced football statistics for analytics, broadcast and sports-betting [54,41,53,42,49,34,19,32,39,21,23,52,24]. Nowadays, there are a number of commercial providers that manually collect event data for many different leagues [12,9,15,11], and event data from previous seasons is widely available.…”