Predicting Success Probability in Professional Tennis Tournaments Using a Logistic Regression Model

Saurabh Srivastava ()
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Saurabh Srivastava: EXL Analytics

A chapter in Advances in Analytics and Applications, 2019, pp 59-65 from Springer

Abstract: Abstract With a global audience of over 1 billion, professional tennis is the most widely followed individual sports in the world. The present study attempts to model the probability of success for a tennis player in a men’s singles tournament of a given type (ATP 250, ATP 500, ATP Masters and Grand Slams) so as to enable his management team to take better decisions with respect to his calendar planning. The model in this study tries to arrive at the probability of success in a given category of the tournament by modelling the success of an athlete in that tournament (measured by his ability to reach the quarterfinals), using the logistic regression method. The scorecard that is built uses five variable categories to arrive at the probability of success, which can be used to rank order the tournaments in a given category for a player, and can be subsequently augmented through a linear programming method to help a player arrive at the most optimum selection of tournaments.

Keywords: Sports analytics; Logistic regression; Tennis (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-981-13-1208-3_6

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DOI: 10.1007/978-981-13-1208-3_6

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