Adapting Least-Square Support Vector Regression Models to Forecast the Outcome of Horseraces

This paper introduces an improved approach for forecasting the outcome of horseraces. Building upon previous literature, a state-of-the-art modelling paradigm is developed which integrates least-square support vector regression and conditional logit procedures to predict horses' winning probabilities. In order to adapt the least-square support vector regression model to this task, some free parameters have to be determined within a model selection step. Traditionally, this is accomplished by assessing candidate settings in terms of mean-squared error between estimated and actual finishing positions. This paper proposes an augmented approach to organise model selection for horserace forecasting using the concept of ranking borrowed from internet search engine evaluation. In particular, it is shown that the performance of forecasting models can be improved significantly if parameter settings are chosen on the basis of their normalised discounted cumulative gain (i.e. their ability to accurately rank the first few finishers of a race), rather than according to general purpose performance indicators which weight the ability to predict the rank order finish position of all horses equally.