Alternative methods of predicting competitive events: an application in horserace betting markets
Alternative methods of predicting competitive events: an application in horserace betting markets
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.
probability forecasting, classification, random forest, sports forecasting
518-536
Lessmann, Stefan
3b9f8133-67bb-4bcc-9183-e1a5db294b01
Sung, M.
2114f823-bc7f-4306-a775-67aee413aa03
Johnson, Johnnie E.V.
6d9f1a51-38a8-4011-a792-bfc82040fac4
June 2010
Lessmann, Stefan
3b9f8133-67bb-4bcc-9183-e1a5db294b01
Sung, M.
2114f823-bc7f-4306-a775-67aee413aa03
Johnson, Johnnie E.V.
6d9f1a51-38a8-4011-a792-bfc82040fac4
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), .
(doi:10.1016/j.ijforecast.2009.12.013).
Abstract
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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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: 16 Mar 2024 03:39
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Author:
Stefan Lessmann
Author:
Johnnie E.V. Johnson
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