Match Point AI: A Novel AI Framework for Evaluating Data-Driven Tennis Strategies

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Authors

Research Organisations

External Research Organisations

  • Otto-von-Guericke University Magdeburg
  • Fraunhofer Institute for Transportation and Infrastructure Systems (IVI)
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Details

Original languageEnglish
Title of host publicationProceedings of the 2024 IEEE Conference on Games, CoG 2024
PublisherIEEE Computer Society
ISBN (electronic)9798350350678
ISBN (print)979-8-3503-5068-5
Publication statusPublished - 5 Aug 2024
Event6th Annual IEEE Conference on Games, CoG 2024 - Milan, Italy
Duration: 5 Aug 20248 Aug 2024

Publication series

NameIEEE Conference on Computatonal Intelligence and Games, CIG
ISSN (Print)2325-4270
ISSN (electronic)2325-4289

Abstract

Many works in the domain of artificial intelligence in games focus on board or video games due to the ease of reimplementing their mechanics [1], [2]. Decision-making problems in real-world sports share many similarities to such domains. Nevertheless, not many frameworks on sports games exist. In this paper, we present the tennis match simulation environment Match Point AI, in which different agents can compete against real-world data-driven bot strategies. Next to presenting the framework, we highlight its capabilities by illustrating, how MCTS can be used in Match Point AI to optimize the shot direction selection problem in tennis. While the framework will be extended in the future, first experiments already reveal that generated shot-by-shot data of simulated tennis matches show realistic characteristics when compared to real-world data. At the same time, reasonable shot placement strategies emerge, which share similarities to the ones found in real-world tennis matches.

Keywords

    Monte Carlo Tree Search, Sports Analysis, Tennis

ASJC Scopus subject areas

Cite this

Match Point AI: A Novel AI Framework for Evaluating Data-Driven Tennis Strategies. / Nübel, Carlo; Dockhorn, Alexander; Mostaghim, Sanaz.
Proceedings of the 2024 IEEE Conference on Games, CoG 2024. IEEE Computer Society, 2024. (IEEE Conference on Computatonal Intelligence and Games, CIG).

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Nübel, C, Dockhorn, A & Mostaghim, S 2024, Match Point AI: A Novel AI Framework for Evaluating Data-Driven Tennis Strategies. in Proceedings of the 2024 IEEE Conference on Games, CoG 2024. IEEE Conference on Computatonal Intelligence and Games, CIG, IEEE Computer Society, 6th Annual IEEE Conference on Games, CoG 2024, Milan, Italy, 5 Aug 2024. https://doi.org/10.48550/arXiv.2408.05960, https://doi.org/10.1109/CoG60054.2024.10645571
Nübel, C., Dockhorn, A., & Mostaghim, S. (2024). Match Point AI: A Novel AI Framework for Evaluating Data-Driven Tennis Strategies. In Proceedings of the 2024 IEEE Conference on Games, CoG 2024 (IEEE Conference on Computatonal Intelligence and Games, CIG). IEEE Computer Society. https://doi.org/10.48550/arXiv.2408.05960, https://doi.org/10.1109/CoG60054.2024.10645571
Nübel C, Dockhorn A, Mostaghim S. Match Point AI: A Novel AI Framework for Evaluating Data-Driven Tennis Strategies. In Proceedings of the 2024 IEEE Conference on Games, CoG 2024. IEEE Computer Society. 2024. (IEEE Conference on Computatonal Intelligence and Games, CIG). doi: 10.48550/arXiv.2408.05960, 10.1109/CoG60054.2024.10645571
Nübel, Carlo ; Dockhorn, Alexander ; Mostaghim, Sanaz. / Match Point AI : A Novel AI Framework for Evaluating Data-Driven Tennis Strategies. Proceedings of the 2024 IEEE Conference on Games, CoG 2024. IEEE Computer Society, 2024. (IEEE Conference on Computatonal Intelligence and Games, CIG).
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abstract = "Many works in the domain of artificial intelligence in games focus on board or video games due to the ease of reimplementing their mechanics [1], [2]. Decision-making problems in real-world sports share many similarities to such domains. Nevertheless, not many frameworks on sports games exist. In this paper, we present the tennis match simulation environment Match Point AI, in which different agents can compete against real-world data-driven bot strategies. Next to presenting the framework, we highlight its capabilities by illustrating, how MCTS can be used in Match Point AI to optimize the shot direction selection problem in tennis. While the framework will be extended in the future, first experiments already reveal that generated shot-by-shot data of simulated tennis matches show realistic characteristics when compared to real-world data. At the same time, reasonable shot placement strategies emerge, which share similarities to the ones found in real-world tennis matches.",
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