Abstract
The popularity of the sport of auto racing is increasing rapidly, but its fans remain less interested in statistics than the fans of other sports. In this article, we propose a new class of models for permutations that closely resembles the behavior of auto racing results. We pose the model in a Bayesian hierarchical framework, which permits hierarchical specification and fully hierarchical estimation of interaction terms. We demonstrate the methodology using several rich datasets that consist of repeated rankings for a collection of drivers. Our models can potentially identify individuals racing in “minor league” divisions who have higher potential for competitive performance at higher levels. We also present evidence that one of the sport's more controversial figures, Jeff Gordon, is a statistically dominant figure.
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