Work detail

Non-Linear Classification as a Tool for Predicting Tennis Matches

Author: Mgr. Jakub Hostačný
Year: 2018 - winter
Leaders: RNDr. Matúš Baniar
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 108
Awards and prizes:
Link: https://is.cuni.cz/webapps/zzp/detail/178854/
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 benchmark
rules - backing a better-ranked player as well as backing a bookmaker’s
favourite. Neural networks and random forest are the most optimal models
regarding the total profitability, while boosted trees yield the highest ROI. Besides,
we show that bet size based on the half-sized Kelly criterion outstrips
constant bet size for betting on favourites.

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