Modeling Tennis Matches Using Monte Carlo Simulations Incorporating Dynamic Parameters

Although it may seem to be one of the more unpredictable sports, tennis can be rather accurately modelled using the Monte Carlo method. This study aims to evaluate the accuracy of a Monte Carlo simulation that integrates dynamic tennis parameters in forecasting the outcome of a specific match. To predict the outcome of a tennis match, a conventional Monte Carlo simulation based on the identical and independent point distribution assumption requires only two parameters: the probabilities of both players winning a point on their own serve. A more sophisticated method proposed in this paper considers how fatigue affects a player’s performance and it analyses and implements the change in the probability of winning a service point after “breaking” an opponent’s service game. Calculating the relevant statistics required for player profiling was a critical step in this study. Following that, both previously mentioned variations of the Monte Carlo simulation were implemented to compare their performance. Finally, the method was tested on real-world tennis data.