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 model's success by evaluating the marginal contribution of its constituent parts.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Figlewski, Stephen, 1979. "Subjective Information and Market Efficiency in a Betting Market," Journal of Political Economy, University of Chicago Press, vol. 87(1), pages 75-88, February.
- Ruth N. Bolton & Randall G. Chapman, 2008.
"Searching For Positive Returns At The Track: A Multinomial Logit Model For Handicapping Horse Races,"
World Scientific Book Chapters,in: Efficiency Of Racetrack Betting Markets, chapter 17, pages 151-171
World Scientific Publishing Co. Pte. Ltd..
- Ruth N. Bolton & Randall G. Chapman, 1986. "Searching for Positive Returns at the Track: A Multinomial Logit Model for Handicapping Horse Races," Management Science, INFORMS, vol. 32(8), pages 1040-1060, August.
- Watson, Peter L. & Westin, Richard B., 1975. "Transferability of disaggregate mode choice models," Regional Science and Urban Economics, Elsevier, vol. 5(2), pages 227-249, May.
- Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
- Vaughan Williams, Leighton, 1999. "Information Efficiency in Betting Markets: A Survey," Bulletin of Economic Research, Wiley Blackwell, vol. 51(1), pages 1-30, January.
- Schnytzer, Adi & Shilony, Yuval, 1995. "Inside Information in a Betting Market," Economic Journal, Royal Economic Society, vol. 105(431), pages 963-971, July.
- Johnnie E. V. Johnson & Owen Jones & Leilei Tang, 2006. "Exploring Decision Makers' Use of Price Information in a Speculative Market," Management Science, INFORMS, vol. 52(6), pages 897-908, June.
- Raymond D. Sauer, 1998. "The Economics of Wagering Markets," Journal of Economic Literature, American Economic Association, vol. 36(4), pages 2021-2064, December.
- K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.
- Ming-Chien Sung & Johnnie E.V. Johnson, 2007. "Comparing the Effectiveness of One- and Two-step Conditional Logit Models for Predicting Outcomes in a Speculative Market," Journal of Prediction Markets, University of Buckingham Press, vol. 1(1), pages 43-59, February.
- Steven D. Levitt, 2004. "Why are gambling markets organised so differently from financial markets?," Economic Journal, Royal Economic Society, vol. 114(495), pages 223-246, 04.
- Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
- K. Coussement & D. Van den Poel, 2008. "Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques," Post-Print hal-00788096, HAL.
- Law, David & Peel, David A, 2002. "Insider Trading, Herding Behaviour and Market Plungers in the British Horse-Race Betting Market," Economica, London School of Economics and Political Science, vol. 69(274), pages 327-338, May. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:196:y:2009:i:2:p:569-577. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.