Analysis of points outcome in ATP Grand Slam Tennis using big data and machine learning

TL;DR

This study applies machine learning models to predict tennis point outcomes using historical match data and player rankings, revealing key strategic factors, though with limited accuracy for top-ranked players.

Contribution

It introduces a machine learning approach to predict tennis points using public data, and interprets models to identify strategic factors influencing point outcomes.

Findings

Models outperform the baseline average in predicting points.

Prediction accuracy is limited for top-ranked players.

Model interpretability highlights important strategic factors.

Abstract

Tennis is one of the world's biggest and most popular sports. Multiple researchers have, with limited success, modeled the outcome of matches using probability modelling or machine learning approaches. The approach presented here predicts the outcomes of points in tennis matches. This is based on given a probability of winning a point, based on the prior history of matches, the current match, the player rankings and if the points are started with a first or second. The use of historical public data from the matches and the players' ranking has made this study possible. In addition, we interpret the models in order to reveal important strategic factors for winning points. The historical data are from the years 2016 to 2020 in the two Grand Slam tournaments, Wimbledon and US Open, resulting in a total of 709 matches. Different machine learning methods are applied for this work such as, e.g. logistic regression, Random forest, ADABoost, and XGBoost. These models are compared to a baseline model, namely a traditional statistics measure, in this case the average. An evaluation of the results showed that the models for points proved to be a fraction better than the average. However, with the applied public data and the information level of the data, the approach presented here is not optimal for predicting who wins when the opponents are on the same position on the ranking. This methodology is interesting with respect to examining which factors are important for the outcomes of who wins points in tennis matches. Other higher quality data sets exists from e.g. Hawk Eye, although these data sets are not available for the public.