The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individuals sports, including baseball, basketball, and tennis . More recently, AItechniques have been applied to football, due to a huge increase in datacollection by professional teams, increased computational power, and advances in machine learning . The research challenges associated with predictive andprescriptive football analytics require new developments and progress at theintersection of statistical learning, game theory, and computer vision . We provide an overarching perspective highlighting how the combinationof these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for teams, spectators, and broadcasters in the years to come . We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual) to other industries (real or virtual) in other sports . Back to the page you came from: .

Author(s) : 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

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Keywords : football - ai - sports - learning - possibilities -

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