Game Plan: What AI can do for Football, and What Football can do for AI
- URL: http://arxiv.org/abs/2011.09192v1
- Date: Wed, 18 Nov 2020 10:26:02 GMT
- Title: Game Plan: What AI can do for Football, and What Football can do for AI
- Authors: Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome
Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd
Steele, Pauline Luc, Adria Recasens, Alexandre Galashov, Gregory Thornton,
Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet
Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Perolat, Bart De
Vylder, Ali Eslami, Mark Rowland, Andrew Jaegle, Remi Munos, Trevor Back,
Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore
Graepel, Demis Hassabis
- Abstract summary: Predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision.
We illustrate that football analytics is 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.
- Score: 83.79507996785838
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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).
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