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  4. Match Point AI: A Novel AI Framework for Evaluating Data-Driven Tennis Strategies
 
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2024
Conference Paper
Title

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

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.
Author(s)
Nübel, Carlo
Otto-von-Guericke-Universität Magdeburg  
Dockhorn, Alexander
Leibniz Universität Hannover  
Mostaghim, Sanaz
Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI  
Mainwork
Proceedings of the 2024 IEEE Conference on Games (CoG)  
Conference
Conference on Games 2024  
Open Access
DOI
10.1109/CoG60054.2024.10645571
Additional full text version
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Language
English
Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI  
Keyword(s)
  • Tennis

  • sports analysis

  • Monte Carlo tree search

  • artificial intelligence

  • decision-making

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