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

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  • Lessmann, Stefan
  • Sung, Ming-Chien
  • Johnson, Johnnie E.V.

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 modeling 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 to 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.

Suggested Citation

  • Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V., 2010. "Alternative methods of predicting competitive events: An application in horserace betting markets," International Journal of Forecasting, Elsevier, vol. 26(3), pages 518-536, July.
    • Handle: RePEc:eee:intfor:v:26:y::i:3:p:518-536
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    References listed on IDEAS

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    5. 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.
    6. Vaughan Williams,Leighton (ed.), 2005. "Information Efficiency in Financial and Betting Markets," Cambridge Books, Cambridge University Press, number 9780521816038.
    7. Stefan Lessmann & Ming-Chien Sung & Johnnie E.V. Johnson, 2007. "Adapting Least-Square Support Vector Regression Models to Forecast the Outcome of Horseraces," Journal of Prediction Markets, University of Buckingham Press, vol. 1(3), pages 169-187, December.
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    9. David Edelman, 2007. "Adapting support vector machine methods for horserace odds prediction," Annals of Operations Research, Springer, vol. 151(1), pages 325-336, April.
    10. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V., 2009. "Identifying winners of competitive events: A SVM-based classification model for horserace prediction," European Journal of Operational Research, Elsevier, vol. 196(2), pages 569-577, July.
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    Cited by:

    1. Ma, Tiejun & Tang, Leilei & McGroarty, Frank & Sung, Ming-Chien & Johnson, Johnnie E. V, 2016. "Time is money: Costing the impact of duration misperception in market prices," European Journal of Operational Research, Elsevier, vol. 255(2), pages 397-410.
    2. Christoph Schlembach & Sascha L. Schmidt & Dominik Schreyer & Linus Wunderlich, 2020. "Forecasting the Olympic medal distribution during a pandemic: a socio-economic machine learning model," Papers 2012.04378, arXiv.org, revised Jun 2021.
    3. Marie-Hélène Roy & Denis Larocque, 2012. "Robustness of random forests for regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 993-1006, December.
    4. Green, Lawrence & Sung, Ming-Chien & Ma, Tiejun & Johnson, Johnnie E. V., 2019. "To what extent can new web-based technology improve forecasts? Assessing the economic value of information derived from Virtual Globes and its rate of diffusion in a financial market," European Journal of Operational Research, Elsevier, vol. 278(1), pages 226-239.
    5. Steffen Q. Mueller, 2020. "Pre- and within-season attendance forecasting in Major League Baseball: a random forest approach," Applied Economics, Taylor & Francis Journals, vol. 52(41), pages 4512-4528, September.
    6. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
    7. Baboota, Rahul & Kaur, Harleen, 2019. "Predictive analysis and modelling football results using machine learning approach for English Premier League," International Journal of Forecasting, Elsevier, vol. 35(2), pages 741-755.
    8. S Lessmann & M-C Sung & J E V Johnson, 2011. "Towards a methodology for measuring the true degree of efficiency in a speculative market," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(12), pages 2120-2132, December.
    9. Schlembach, Christoph & Schmidt, Sascha L. & Schreyer, Dominik & Wunderlich, Linus, 2022. "Forecasting the Olympic medal distribution – A socioeconomic machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    10. Marc Garnica-Caparrós & Daniel Memmert & Fabian Wunderlich, 2022. "Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports," Information Systems and e-Business Management, Springer, vol. 20(3), pages 551-580, September.
    11. Wunderlich, Fabian & Memmert, Daniel, 2020. "Are betting returns a useful measure of accuracy in (sports) forecasting?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 713-722.
    12. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.
    13. Sung, Ming-Chien & McDonald, David C.J. & Johnson, Johnnie E.V. & Tai, Chung-Ching & Cheah, Eng-Tuck, 2019. "Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements," European Journal of Operational Research, Elsevier, vol. 272(1), pages 389-405.
    14. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    15. Hubáček, Ondřej & Šír, Gustav, 2023. "Beating the market with a bad predictive model," International Journal of Forecasting, Elsevier, vol. 39(2), pages 691-719.

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