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

TL;DR

Match Point AI is a new simulation environment for evaluating data-driven tennis strategies, demonstrating realistic match data and strategic behaviors through Monte Carlo Tree Search optimization.

Contribution

The paper introduces Match Point AI, a novel tennis simulation framework that enables testing AI strategies against real-world data and showcases MCTS for shot selection optimization.

Findings

Simulated matches produce realistic shot data

Emergence of strategies similar to real-world tennis

Framework demonstrates potential for AI strategy evaluation

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.