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Öğe Predicting the duration of professional tennis matches using MLR, CART, SVR and ANN techniques(Springer International Publishing Ag, 2024) Duen, Serdar; Peker, SerhatThis research aims to predict the duration of professional tennis matches by utilizing a dataset that includes player statistics, match characteristics and court attributes. Various machine learning techniques, such as multiple linear regression (MLR), classification and regression trees (CART), support vector regression (SVR) and artificial neural networks (ANN), are applied for this purpose. The study involves a comprehensive dataset spanning professional tournaments from 1993 to 2022. Separate predictive models were developed for tournaments played over 3 and 5 sets employing the corresponding ML techniques and their performances were compared. The findings revealed that the predictive models with MLR and SVR methods excel in best-of-3 set matches, while the ones with SVR and ANN exhibit superior performance for best-of-5 set matches. This research contributes valuable insights into the factors influencing match duration and aids in developing more effective predictive models for tennis events.
