Identifying winners of competitive events: A SVM-based classification model for horserace prediction
The aim of much horserace modelling is to appraise the informational efficiency of betting markets. The prevailing approach involves forecasting the runners finish positions by means of discrete or continuous response regression models. However, theoretical considerations and empirical evidence suggest that the information contained within finish positions might be unreliable, especially among minor placings. To alleviate this problem, a classification-based modelling paradigm is proposed which relies only on data distinguishing winners and losers. To assess its effectiveness, an empirical experiment is conducted using data from a UK racetrack. The results demonstrate that the classification-based model compares favourably with state-of-the-art alternatives and confirm the reservations of relying on rank ordered finishing data. Simulations are conducted to further explore the origin of the models success by evaluating the marginal contribution of its constituent parts.
horseracing forecasting decision analysis finance support vector machines