The application of ranking probability models to racetrack betting

Hausch et al. (HZR) developed a betting system that demonstrated positive profilts at two racetracks. The system assumes running times are distributed exponentially, but other distributions for running times have been shown to produce a better fit using data from Hong Kong, the Meadowlands, and Japan. The better fit is at the cost of severely increased complexity in computing ranking probabilities, though. In response, Lo and Bacon-Shone proposed a simple model of computing ranking probabilities which closely approximates those based on the Henery and the Stern models and fits the data as well. This paper couples the Lo and Bacon-Shone model and the HZR system. For data sets from the United States and Hong Kong, the authors show improved profit over the HZR system at lower levels of risk using final betting data assuming zero computational costs. With data from Japan, the model shows little difference in profits from the HZR system.