Abstract
The 2016 Equibase data set of American Quarter Horse starts in North America was analyzed, with the purpose of ranking the sires of the racehorses. A speed z-score derived from the race times and distances was used as a racing performance measure. Mixed effects models were used on various subsets of the data based on race distance and sire offspring number. The sire categorical variable was considered as a random effect. Various statistical criteria were used to optimize the model. The constructed models were then varied in terms of the random and fixed effects included, and the conditional modes of the sire effects were extracted from these models. The benefit of the sire ranking that comes from this analysis is that it is controlled for track, jockey, trainer, weather, and several other variables that can impact speed. Sires are typically valued for high rankings for offspring earnings and winners. Yet a sire with a low stud fee may still produce offspring with a high ranking using our z-score model. The offspring of this bargain sire have the potential to produce fast offspring that could pay a dividend on a relatively low cost investment. The model sire ranking approach described in this paper is clearly bringing a new approach to the field of sire rankings.