Match Point AI: A Novel AI Framework for Evaluating Data-Driven Tennis Strategies
This work addresses the problem of limited AI tools for sports analytics, specifically in tennis, by providing a novel simulation framework, though it is incremental as it builds on existing methods like MCTS for optimization.
The authors tackled the lack of AI frameworks for real-world sports by developing Match Point AI, a tennis match simulation environment that allows agents to compete against data-driven bot strategies, with initial experiments showing that simulated data exhibits realistic characteristics and generates reasonable shot placement strategies similar to real-world matches.
Many works in the domain of artificial intelligence in games focus on board or video games due to the ease of reimplementing their mechanics. 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 \textit{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.