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A HIERARCHICAL BAYESIAN ANALYSIS OF HORSE RACING

Noah Silverman

Abstract


Horse racing is the most popular sport in Hong Kong. Nowhere else in the world is such attention paid to the races and such large sums of money bet. It is literally a “national sport”. Popular literature has many stories about computerized “betting teams” winning fortunes by using statistical analysis.[1] Additionally, numerous academic papers have been published on the subject, implementing a variety of statistical methods. The academic justification for these papers is that a parimutuel game represents a study in decisions under uncertainty, efficiency of markets, and even investor psychology. A review of the available published literature has failed to find any Bayesian approach to this modeling challenge.

This study will attempt to predict the running speed of a horse in a given race. To that effect, the coefficients of a linear model are estimated using the Bayesian method of Markov Chain Monte Carlo. Two methods of computing the sampled posterior are used and their results compared. The Gibbs method assumes that all the coefficients are normally distributed, while the Metropolis method allows for their distribution to have an unknown shape. I will calculate and compare the predictive results of several models using these Bayesian Methods.


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References


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DOI: http://dx.doi.org/10.5750/jpm.v6i3.590

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