[2L6-GS-3] Knowledge utilization and sharing

[2L6-GS-3-02] Optimal Horse Racing Betting Strategy Using Partially Recurrent Neural Networks

〇Kota Sugiyama¹, Haruka Yamasita¹ (1. Sophia University)

Keywords:Horse racing betting strategy, Partially Recurrent Neural Network, time-series data, Machine learning, Deep learning

In this study,we propose a model that appropriately predicts the probability that a horse will win and a method for betting on horses with the highest possible return based on the horse's odds information.Specifically, since horse racing data is a mixture of time series data and attribute data,we use a partially recurrent neural network,which can appropriately predict the odds of a horse winning,to predict the odds of a horse winning.Furthermore,we propose a method to search for the optimal betting strategy while minimizing the loss by not only predicting the winning rate but also betting based on the expected value of the odds.Finally,we simulate two types of betting,the conventional method and the proposed method,using actual horse racing data,where the winning probability is the probability of finishing in the top three positions.