@inproceedings{3e01a774b4be4bdd876e5b93dc1af76d,
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.",
keywords = "Monte Carlo Tree Search, Sports Analysis, Tennis",
author = "Carlo N{\"u}bel and Alexander Dockhorn and Sanaz Mostaghim",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 6th Annual IEEE Conference on Games, CoG 2024 ; Conference date: 05-08-2024 Through 08-08-2024",
year = "2024",
month = aug,
day = "5",
doi = "10.48550/arXiv.2408.05960",
language = "English",
isbn = "979-8-3503-5068-5",
series = "IEEE Conference on Computatonal Intelligence and Games, CIG",
publisher = "IEEE Computer Society",
booktitle = "Proceedings of the 2024 IEEE Conference on Games, CoG 2024",
address = "United States",
}