Approximating the Ordering Probabilities of Multi-entry Competitions by a Simple Method

Abstract

To predict the ordering probabilities of multi-entry competitions (e.g., horse races), Harville (1973) proposed a simple way of computing the ordering probabilities based on the simple winning probabilities. This simple model is implied by assuming that the underlying model (e.g., running times in horse racing) is the independent exponential or extreme-value distribution. Henery (1981) and Stern (1990) proposed to use normal and gamma distributions, respectively, for the running time. However, both the Henery and Stern models are too complicated to use in practice. Bacon-Shone et al. (1992b) have shown that the Henery and Stern models fit better than the Harville model for particular horse racing datasets. In this chapter, we first give a theoretical result for the limiting case that all the horses have the same abilities. This theoretical result motivates an approximation of ordering probabilities for the Henery and Stern models. We then show empirically that this approximation works well in practice.