What elements of the opening set influence the outcome of a tennis match? An in-depth analysis of Wimbledon data

Journal: IIMB Management Review 

Synopsis:  

Tennis as a sport has millions of fans, and the Wimbledon tournament has been a major attraction for many years. One of the key questions in tennis analytics revolves around the prediction of the outcome of a tennis match. It is influenced not only by player performance but also by match dynamics. Although tennis matches consist of consecutive sets and games, their impact on the overall result varies, highlighting an interesting area of research which is addressed in this paper.  

The authors emphasize the pivotal role of the opening set in determining match results with an initial exploratory discussion. Subsequently, they have proposed a model that delves into the elements of the first set, exploring their influence on match outcomes. In the model, the authors employ a penalized regression technique alongside binary regression models to analyse the impact of different game elements from the first set. Notably, their approach adeptly handles high-dimensional data, offering both explanatory power and superior predictive accuracy compared to benchmarks.  

Additionally, the proposed model enables real-time forecasting within matches, facilitating point-based and game-based predictions. Leveraging Wimbledon data from 2016 to 2021 for Men’s Singles and 2017 to 2021 for Women’s Singles, the authors demonstrate the versatility of their proposed model across various match scenarios. They illustrate the model’s applicability for within-game forecasting, scrutinizing its predictive efficacy following the completion of each point. The proposed approach of providing within-match forecast refines the model post each point to determine the necessary variables for predicting the match’s outcome, unveiling the precision and accuracy of the technique at each juncture of the game.  

Beyond match outcomes, the model provides coaches with insights to enhance players’ skills in serving or returning, thereby increasing the likelihood of securing the first set and, ultimately, the match. Furthermore, betting markets can leverage the proposed model’s predictive prowess to inform decisions and optimize profits during live tennis events.