Identifying winners of competitive events: A SVM-based classification model for horserace prediction

Lessmann, Stefan, Sung, M. and Johnson, Johnnie E.V. (2009) Identifying winners of competitive events: A SVM-based classification model for horserace prediction. European Journal of Operational Research, 196, (2), 569-577. (doi:10.1016/j.ejor.2008.03.018).


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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 model’s success by evaluating the marginal contribution of its constituent parts.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.ejor.2008.03.018
ISSNs: 0377-2217 (print)
Related URLs:
Keywords: forecasting, decision analysis, finance, horseracing, support vector machines
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
H Social Sciences > HA Statistics
Divisions : University Structure - Pre August 2011 > School of Management
ePrint ID: 51338
Accepted Date and Publication Date:
Date Deposited: 06 Jun 2008
Last Modified: 31 Mar 2016 12:29

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