Match Point AI: A Novel AI Framework for Evaluating Data-Driven Tennis Strategies
Carlo Nübel, Alexander Dockhorn, Sanaz Mostaghim
TL;DR
The paper addresses the need for AI frameworks to study decision-making in real-world sports by introducing Match Point AI, a tennis match simulation environment driven by historical data. It uses Monte Carlo Tree Search to optimize shot-direction decisions and evaluates data-driven bots against MCTS agents, finding that the simulated rally statistics and strategy patterns align with real-world observations. Key findings include comparable win-rate dynamics between simulated and real matches, and that UCT-based MCTS configurations often yield strong performance. The work provides a foundation for testing and evolving tennis strategies in a data-rich, controllable setting, with future improvements hinging on richer data such as velocity, position, and more shot types.
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. 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.