Předpovídání výsledků tenisových zápasů. Jak důležitý je faktor různých povrchů?

Sklenička, Jan

This thesis focuses on predicting the outcomes of ATP tennis matches with the aim of investigating the effect of different tennis surfaces. We define nine surface variables based on two different clustering methods, and incorporate these variables into models that were estimated using logistic regression. Such approach allows us to robustly observe how the effect of specializing on a spe- cific surface translates into the match winning probability. The accuracy of the models ranges from 63% to 65%, with the models incorporating the effect of surface specialization displaying a superior predictive accuracy. Based on several evaluation metrics, the results confirm that surface specialization is a crucial variable when predicting tennis matches. We are also able to identify that the most accurate metric to measure surface specialization is the difference in normalized winning rate. Moreover, the analysis reveals that the specializa- tion on grass courts is the most important compared to clay or hard courts. Lastly, we formalize betting strategies based on the predicted probabilities from the models, and we are able to achieve positive out-of-sample return on invest- ment (ROI). Accounting for the effect of surface specialization increases ROI on the non-naive betting strategies. JEL Classification...

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