Predicting ATP Singles Matches using Machine Learning

Machine learning continues to grow in applications and its influence is felt across the world. This paper builds off the foundations of machine learning used for sports analysis and its specific implementations in tennis by attempting to predict the winner of ATP men’s singles tennis matches. Tennis provides a unique challenge due to the individual nature of singles and the varying career lengths, experiences, and backgrounds of players from around the globe. Related work has explored prediction with features such as rank differentials, physical characteristics, and past performance. This work expands on the studies by including raw player statistics and relevant environment features. State of the art models such as LightGBM and XGBoost, as well as a standard logistic regression are trained and evaluated against a dataset containing matches from 1991 to 2023. All models surpassed the baseline and each has their own strengths and weaknesses. Future work may involve expanding the feature space to include more robust features such as player profiles and ELO ratings, as well as utilizing deep neural networks to improve understanding of past player performance and better comprehend the context of a given match.