Alternative methods of predicting competitive events: An application in horserace betting markets

Accurately estimating the winning probabilities of participants in competitive events, such as elections and sports events, represents a challenge to standard forecasting frameworks such as regression or classification. They are not designed for modeling the competitive element, whereby a specific participant's chance of success depends not only on his/her individual capabilities but also on those of his/her competitors. In this paper we consider this problem in the competitive context of horseracing and demonstrate how Breiman's (2001) random forest classifier can be adapted in order to predict race outcomes. Several empirical experiments are undertaken to demonstrate the features of the adapted random forest procedure and confirm its effectiveness as a forecasting model. Specifically, we demonstrate that predictions derived from the proposed model can be used to make substantial profits, and that these predictions outperform those from traditional statistical techniques.