Artificial intelligence for team sports: a survey

Beal, Ryan; Norman, Timothy J.; Ramchurn, Sarvapali D.

doi:10.1017/s0269888919000225

Cited by 62 publications

(58 citation statements)

References 90 publications

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“…As multiple DFS teams can be entered into leagues, by having the uncertainties this would present an interesting portfolio optimisation problem where by entering a number of different teams at different risk levels we can maximise the chance of returning profits. Further challenges are discussed in (Beal, Norman, & Ramchurn, 2019).…”

Section: Discussionmentioning

confidence: 99%

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Optimising Daily Fantasy Sports Teams with Artificial Intelligence

Beal

Norman

Ramchurn

2020

International Journal of Computer Science in Sport

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This paper outlines a novel approach to optimising teams for Daily Fantasy Sports (DFS) contests. To this end, we propose a number of new models and algorithms to solve the team formation problems posed by DFS. Specifically, we focus on the National Football League (NFL) and predict the performance of real-world players to form the optimal fantasy team using mixed-integer programming. We test our solutions using real-world data-sets from across four seasons (2014-2017). We highlight the advantage that can be gained from using our machine-based methods and show that our solutions outperform existing benchmarks, turning a profit in up to 81.3% of DFS game-weeks over a season.

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Section: Discussionmentioning

confidence: 99%

“…Furthermore, we compare and contrast against human performance. Finally, (Beal, Norman, & Ramchurn, 2019) discusses applications of artificial intelligence in sports, including a section on fantasy sports. This paper also exposes DFS and American football as open research areas on the sports analytics field.…”

Section: Related Workmentioning

confidence: 99%

Optimising Daily Fantasy Sports Teams with Artificial Intelligence

Beal

Norman

Ramchurn

2020

International Journal of Computer Science in Sport

Self Cite

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“…Recent years have seen tremendous growing interest in sports analytics, not only from an economic and commercial perspective, but also from a purely scientific one, viz. the growing number of publications (Baumer & Zimbalist, 2014;Beal, Norman, & Ramchurn, 2019;Shih, 2017;Szymanski, 2020) and scientific events organized on the topic (e.g., CVSports International Workshop on Computer Vision in Sports at CVPR, 2020; Machine Learning and Data Mining for Sports Analytics, 2020; MIT Sloan Sports Analytics Conference, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…As evident in many different downstream domains that have benefited from applications of artificial intelligence (AI) and machine learning (ML), this is due to important technological advances in data collection and processing capabilities, progress in statistical and in particular deep learning, increased compute resources, and ever-growing economic activities associated with sports and culture (e.g., emergent consultancy ventures revolving around sports data collection and statistics, Beal et al, 2019;ChyronHego, 2020;InStat, 2020;Kuper & Szymanski, 2018;Opta, 2020;StatsBomb, 2020).…”

Section: Introductionmentioning

confidence: 99%

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

Tuyls

Omidshafiei²,

Müller³

et al. 2021

jair

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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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“…The concept of AI in team sports analytics is not new and has been considered in the context of match outcome prediction, tactical decision making, player investments, fantasy sports and injury prediction. 11 As in any specialised field, there are well-known approaches and techniques for analytics and visualisation that are already used by the community. 12,13 We propose that our solution starts with these.…”

Section: Introductionmentioning

confidence: 99%

Cognitive analysis in sports: Supporting match analysis and scouting through artificial intelligence

2021

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In elite sports, there is an opportunity to take advantage of rich and detailed datasets generated across multiple threads of the sporting business. Challenges currently exist due to time constraints to analyse the data, as well as the quantity and variety of data available to assess. Artificial Intelligence (AI) techniques can be a valuable asset in assisting decision makers in tackling such challenges, but deep AI skills are generally not held by those with rich experience in sporting domains. Here, we describe how certain commonly available AI services can be used to provide analytic assistance to sports experts in exploring, and gaining insights from, typical data sources. In particular, we focus on the use of Natural Language Processing and Conversational Interfaces to provide users with an intuitive and time-saving toolkit to explore their datasets and the conclusions arising from analytics performed on them. We show the benefit of presenting powerful AI and analytic techniques to domain experts, showing the potential for impact not only at the elite level of sports, where AI and analytic capabilities may be more available, but also at a more grass-roots level where there is generally little access to specialist resources. The work described in this paper was trialled with Leatherhead Football Club, a semi-professional team that, at the time, were based in the English 7th tier of football.

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Artificial intelligence for team sports: a survey

Cited by 62 publications

References 90 publications

Optimising Daily Fantasy Sports Teams with Artificial Intelligence

Optimising Daily Fantasy Sports Teams with Artificial Intelligence

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

Cognitive analysis in sports: Supporting match analysis and scouting through artificial intelligence

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