Detection of Possible Match-fixing in Tennis Games

Abstract

This study seeks ways that can immediately detect match-fixing in a tennis match. We first explore the number of rallies observed in tennis matches of the ATP and WTA leagues to determine whether they follow the Benford’s law. We also artificially manipulate practice games to investigate whether the number of ralleys observed in manipulated matches also follows the Benford’s law. Experimental results demonstrate that the numbers collected from fixed games and the expected frequencies predicted by Benford’s Law are different. Based on the lessons learned, we develop a machine-learning-based model for detecting whether a given match is fixed or not. Our model shows a high accuracy in detecting fixed tennis matches, which has a great utility for fair tennis play.