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

Identifying winners of competitive events: A SVM-based classification model for horserace prediction
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.
forecasting, decision analysis, finance, horseracing, support vector machines
0377-2217
569-577
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
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. (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).

Record type: Article

Abstract

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.

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More information

Published date: 2009
Keywords: forecasting, decision analysis, finance, horseracing, support vector machines
Organisations: Management

Identifiers

Local EPrints ID: 51338
URI: http://eprints.soton.ac.uk/id/eprint/51338
ISSN: 0377-2217
PURE UUID: 743c6c23-ca63-4415-a7b0-690cdd037e30
ORCID for M. Sung: ORCID iD orcid.org/0000-0002-2278-6185

Catalogue record

Date deposited: 06 Jun 2008
Last modified: 16 Mar 2024 03:39

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Contributors

Author: Stefan Lessmann
Author: M. Sung ORCID iD
Author: Johnnie E.V. Johnson

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