As a textbook model of contingent markets, horse races are an attractive environment to study the attitudes towards risk of bettors. We innovate on the literature by explicitly considering heterogeneous bettors and allowing for very general risk preferences, including non-expected utility. We build on a standard single-crossing condition on preferences to derive testable implications; and we show how parimutuel data allow us to uniquely identify the distribution of preferences among the population of bettors. We then estimate the model on data from US races. Within the expected utility class, the most usual specifications (CARA and CRRA) fit the data very badly. Our results show evidence for both heterogeneity and nonlinear probability weighting.