The predictions that emerge from tournament theory have been tested in a number of sports-related settings. Since sporting events involving individuals (golf, tennis, running, auto racing) feature rank order tournaments with relatively large payoffs and easily observable outcomes, sports is a natural setting for such tests. In this paper, we test the predictions of tournament theory using a unique race-level data set from NASCAR. Most previous tests of tournament theory using NASCAR data used either season level data or race level data from a few seasons. Our empirical work uses race and driver level NASCAR data for 1114 races over the period 1975–2009. Our results support the predictions of tournament theory: the larger the spread in prizes paid in the race, measured by the standard deviation or interquartile range of prizes paid, the higher the average speed in the race. Our results account for the length of the track, number of entrants, number of caution flags, and unobservable year- and week-level heterogeneity.
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