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Alternative methods of predicting competitive events: an application in horserace betting markets

Record type: Article

Accurately estimating the winning probabilities of participants in competitive events, such as elections and sports events, represents a challenge to standard forecasting frameworks such as regression or classification. They are not designed for modelling the competitive element, whereby a specific participant’s chance of success depends not only on his/her individual capabilities but also on those of his/her competitors. In this paper we consider this problem in the competitive context of horseracing and demonstrate how Breiman’s (2001) random forest classifier can be adapted in order to predict race outcomes. Several empirical experiments are undertaken which demonstrate the features of the adapted random forest procedure and confirm its effectiveness as a forecasting model. Specifically, we demonstrate that predictions derived from the proposed model can be used to make substantial profits, and that these predictions outperform those from traditional statistical techniques.

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Citation

Lessmann, Stefan, Sung, M. and Johnson, Johnnie E.V. (2010) Alternative methods of predicting competitive events: an application in horserace betting markets [in special issue: Sports Forecasting] International Journal of Forecasting, 26, (3), pp. 518-536. (doi:10.1016/j.ijforecast.2009.12.013).

More information

e-pub ahead of print date: 6 February 2010
Published date: June 2010
Keywords: probability forecasting, classification, random forest, sports forecasting

Identifiers

Local EPrints ID: 55125
URI: http://eprints.soton.ac.uk/id/eprint/55125
ISSN: 0169-2070
PURE UUID: c97e66c7-6701-464a-8a38-825ad69470d0

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Date deposited: 26 Aug 2008
Last modified: 17 Jul 2017 14:33

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Contributors

Author: Stefan Lessmann
Author: M. Sung

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