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Non-Linear Classification as a Tool for Predicting Tennis Matches
Hostačný, Jakub ; Baniar, Matúš (advisor) ; Krištoufek, Ladislav (referee)
Charles University Faculty of Social Sciences Institute of Economic Studies MASTER'S THESIS Non-Linear Classification as a Tool for Predicting Tennis Matches Author: Be. Jakub Hostacny Supervisor: RNDr. Matus Baniar Academic Year: 2017/2018 Abstract In this thesis, we examine the prediction accuracy and the betting performance of four machine learning algorithms applied to men tennis matches - penalized logistic regression, random forest, boosted trees, and artificial neural networks. To do so, we employ 40 310 ATP matches played during 1/2001-10/2016 and 342 input features. As for the prediction accuracy, our models outperform current state-of-art models for both non-grand-slam (69%) and grand slam matches (79%). Concerning the overall accuracy rate, all model specifications beat backing a better-ranked player, while the majority also surpasses backing a bookmaker's favourite. As far as the betting performance is concerned, we develop six profitable betting strategies for betting on favourites applied to non-grand-slam with ROI ranging from 0.8% to 6.5%. Also, we identify ten profitable betting strategies for betting on favourites applied to grand slam matches with ROI fluctuating between 0.7% and 9.3%. We beat both bench mark rules - backing a better-ranked player as well as backing a bookmaker's...
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Non-Linear Classification as a Tool for Predicting Tennis Matches
Hostačný, Jakub ; Baniar, Matúš (advisor) ; Krištoufek, Ladislav (referee)
Charles University Faculty of Social Sciences Institute of Economic Studies MASTER'S THESIS Non-Linear Classification as a Tool for Predicting Tennis Matches Author: Be. Jakub Hostacny Supervisor: RNDr. Matus Baniar Academic Year: 2017/2018 Abstract In this thesis, we examine the prediction accuracy and the betting performance of four machine learning algorithms applied to men tennis matches - penalized logistic regression, random forest, boosted trees, and artificial neural networks. To do so, we employ 40 310 ATP matches played during 1/2001-10/2016 and 342 input features. As for the prediction accuracy, our models outperform current state-of-art models for both non-grand-slam (69%) and grand slam matches (79%). Concerning the overall accuracy rate, all model specifications beat backing a better-ranked player, while the majority also surpasses backing a bookmaker's favourite. As far as the betting performance is concerned, we develop six profitable betting strategies for betting on favourites applied to non-grand-slam with ROI ranging from 0.8% to 6.5%. Also, we identify ten profitable betting strategies for betting on favourites applied to grand slam matches with ROI fluctuating between 0.7% and 9.3%. We beat both bench mark rules - backing a better-ranked player as well as backing a bookmaker's...
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