Ranked events are pivotal in many important AI-applications such as QuestionAnswering and recommendations systems. This paper studies ranked events in the setting of harness racing.
For each horse there exists a probability distribution over its possible rankings. In the paper it is shown that a set of expected positions (and more generally, higher moments) for the horses induces this probability distribution.
The main contribution of the paper is a method which extracts this induced probability distribution from a set of expected positions. An algorithm isproposed where the extraction of the induced distribution is given by the estimated expectations. MATLAB code is provided for the methodology.
This approach gives freedom to model the horses in many different ways without the restrictions imposed by for instance logistic regression. To illustrate this point, we employ a neural network and ordinary ridge regression.
The method is applied to predicting the distribution of the finishing positions for horses in harness racing. It outperforms both multinomial logistic regression and the market odds.
The ease of use combined with fine results from the suggested approach constitutes a relevant addition to the increasingly important field of ranked events.
Harness racing; Competitive events; Ranked expectation; Ranking; Multinomial logistic regression; Machine Learning.