Game Plan: What AI can do for Football, and What Football can do for AI Article Swipe
Karl Tuyls
,
Shayegan Omidshafiei
,
Paul Müller
,
Zhe Wang
,
Jerome T. Connor
,
Daniel Hennes
,
Ian Graham
,
William Spearman
,
Tim Waskett
,
Dafydd Steele
,
Pauline Luc
,
Adrià Recasens
,
Alexandre Galashov
,
Gregory Thornton
,
Romuald Élie
,
Pablo Sprechmann
,
Pol Moreno
,
Kris Cao
,
Marta Garnelo
,
Praneet Dutta
,
Michal Vaľko
,
Nicolas Heess
,
Alex Bridgland
,
Julien Pérolat
,
Bart De Vylder
,
Ali Eslami
,
Mark Rowland
,
Andrew Jaegle
,
Rémi Munos
,
Trevor Back
,
Razia Ahamed
,
S. Miles Bouton
,
Nathalie Beauguerlange
,
Jackson Broshear
,
Thore Graepel
,
Demis Hassabis
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1613/jair.1.12505
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1613/jair.1.12505
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1613/jair.1.12505
- https://jair.org/index.php/jair/article/download/12505/26683
- OA Status
- diamond
- Cited By
- 10
- References
- 205
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3102441345
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3102441345Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1613/jair.1.12505Digital Object Identifier
- Title
-
Game Plan: What AI can do for Football, and What Football can do for AIWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-06Full publication date if available
- Authors
-
Karl Tuyls, Shayegan Omidshafiei, Paul Müller, Zhe Wang, Jerome T. Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adrià Recasens, Alexandre Galashov, Gregory Thornton, Romuald Élie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Vaľko, Nicolas Heess, Alex Bridgland, Julien Pérolat, Bart De Vylder, Ali Eslami, Mark Rowland, Andrew Jaegle, Rémi Munos, Trevor Back, Razia Ahamed, S. Miles Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis HassabisList of authors in order
- Landing page
-
https://doi.org/10.1613/jair.1.12505Publisher landing page
- PDF URL
-
https://jair.org/index.php/jair/article/download/12505/26683Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://jair.org/index.php/jair/article/download/12505/26683Direct OA link when available
- Concepts
-
Football, Computer science, Data science, Analytics, Big data, Field (mathematics), Artificial intelligence, Counterfactual thinking, Psychology, Operating system, Social psychology, Pure mathematics, Mathematics, Law, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1, 2023: 6, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
205Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.research | 76 |
| abstract_inverted_index.teams’ | 73 |
| abstract_inverted_index.analytics | 13, 84, 147 |
| abstract_inverted_index.baseball, | 22 |
| abstract_inverted_index.combining | 192 |
| abstract_inverted_index.exemplify | 185 |
| abstract_inverted_index.football, | 34 |
| abstract_inverted_index.including | 21, 196, 227 |
| abstract_inverted_index.increased | 46 |
| abstract_inverted_index.learning, | 53, 95 |
| abstract_inverted_index.microcosm | 121 |
| abstract_inverted_index.recently, | 27 |
| abstract_inverted_index.research, | 124 |
| abstract_inverted_index.virtual). | 236 |
| abstract_inverted_index.addressing | 59 |
| abstract_inverted_index.artificial | 4 |
| abstract_inverted_index.associated | 78 |
| abstract_inverted_index.behaviors. | 74 |
| abstract_inverted_index.challenges | 62, 77 |
| abstract_inverted_index.collection | 42 |
| abstract_inverted_index.downstream | 225 |
| abstract_inverted_index.envisioned | 224 |
| abstract_inverted_index.extensions | 230 |
| abstract_inverted_index.illustrate | 141 |
| abstract_inverted_index.individual | 19, 69 |
| abstract_inverted_index.players’ | 70 |
| abstract_inverted_index.predictive | 80, 203 |
| abstract_inverted_index.scientific | 61 |
| abstract_inverted_index.techniques | 29 |
| abstract_inverted_index.tremendous | 152 |
| abstract_inverted_index.attributes. | 219 |
| abstract_inverted_index.basketball, | 23 |
| abstract_inverted_index.combination | 112, 207 |
| abstract_inverted_index.coordinated | 72 |
| abstract_inverted_index.overarching | 107 |
| abstract_inverted_index.particular, | 117 |
| abstract_inverted_index.perspective | 108 |
| abstract_inverted_index.spectators, | 132 |
| abstract_inverted_index.statistical | 94, 215 |
| abstract_inverted_index.broadcasters | 134 |
| abstract_inverted_index.developments | 87 |
| abstract_inverted_index.highlighting | 109, 223 |
| abstract_inverted_index.illustrative | 197 |
| abstract_inverted_index.intelligence | 5 |
| abstract_inverted_index.intersection | 92 |
| abstract_inverted_index.prescriptive | 82 |
| abstract_inverted_index.professional | 44, 130 |
| abstract_inverted_index.computational | 47 |
| abstract_inverted_index.possibilities | 14, 228 |
| abstract_inverted_index.unprecedented | 12 |
| abstract_inverted_index.aforementioned | 194 |
| abstract_inverted_index.counterfactual | 200 |
| abstract_inverted_index.game-theoretic | 209 |
| abstract_inverted_index.state-of-the-art | 183 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 36 |
| citation_normalized_percentile.value | 0.89043392 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |