Toyan Ünal, Predicting Tennis Match Outcome: A Machine Learning Approach Using the SRP-CRISP-DM Framework
This thesis applies machine learning to predict outcomes of men’s singles tennis matches from 2009-2022, utilizing a standardized data mining framework, namely SRP-CRISP-DM, for replicable results. Employing six feature extraction techniques, three models, and two feature selection methods with time-based cross-validation and hyperparameter tuning, the Extreme Gradient Boosting model emerged as the top performer, scoring a Brier score of 0.1913 and an accuracy of 70.5%, with bookmakers' odds as the top predictive feature.
Date: 07.12.2023 Place: A-212