Classic statistical and modern machine learning methods for modeling and prediction of major tennis tournaments

Abstract

The cumulative dissertation proposes a comprehensive approach to predicting outcomes in Grand Slam tennis tournaments, focusing on the probability that the first-named player will win. Our study incorporates several classical regression and machine learning models, evaluated using cross-validation and external validation through performance measures such as classification rate, predictive likelihood, and Brier score. Two specific aspects are examined in greater detail: non-linear effects and the inclusion of additional player and court-specific abilities. Moreover, we analyze the predictive potential of statistically enhanced covariates and apply procedures from the field of interpretable machine learning to make complex models more understandable. Our analyses show that in predicting Grand Slam tennis matches, while there are slight differences across various statistical and machine learning approaches, the specific forecasting strategy used plays an even more critical role. Additionally, the results confirm that enhanced variables contribute positively to model performance and provide deeper insights into predictors of match outcomes in sports analytics.