Exploring Team Strategy Dynamics in Tennis Doubles Matches

In the dynamic and strategic environment of tennis doubles, understanding the multifaceted interactions between players is crucial for enhancing team performance. This paper introduces a novel analytical framework for tennis doubles, employing Markov Decision Processes (MDP) and probabilistic model checking (PMC) to model the intricate behaviors and interactions in doubles matches. Unlike existing studies predominantly focused on singles matches, our approach captures the complexities and uncertainties inherent in doubles, including larger state spaces and diverse action types. Through analyzing historical match data, we derive probability distributions, success rates, and transition probabilities, enabling the prediction of match outcomes and formulation of effective strategies. This work expands on previous singles match analytics, offering a comprehensive model that balances explainability and accuracy, suitable for both player and coach utilization. A preliminary evaluation using NCAA Division 1 tennis doubles matches validates our model and demonstrates its potential in strategic recommendation, thereby providing a significant step forward in the analytics of tennis doubles.