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Searching for Positive Returns at the Track: A Multinomial Logit Model for Handicapping Horse Races

Author

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  • Ruth N. Bolton

    (Department of Marketing & Economic Analysis, Faculty of Business, University of Alberta, Edmonton, Alberta, Canada T6G 2R6)

  • Randall G. Chapman

    (Department of Marketing & Economic Analysis, Faculty of Business, University of Alberta, Edmonton, Alberta, Canada T6G 2R6)

Abstract

This paper investigates fundamental investment strategies to detect and exploit the public's systematic errors in horse race wager markets. A handicapping model is developed and applied to win-betting in the pari-mutuel system. A multinomial logit model of the horse racing process is posited and estimated on a data base of 200 races. A recently developed procedure for exploiting the information content of rank ordered choice sets is employed to obtain more efficient parameter estimates. The variables in this discrete choice probability model include horse and jockey characteristics, plus several race-specific features. Hold-out sampling procedures are employed to evaluate wagering strategies. A wagering strategy that involves unobtrusive bets, with a side constraint eliminating long-shot betting, appears to offer the promise of positive expected returns, even in the presence of the typically large track take encountered at Thoroughbred racing events.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormnsc:v:32:y:1986:i:8:p:1040-1060
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    File URL: http://dx.doi.org/10.1287/mnsc.32.8.1040
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    1. repec:pal:jorsoc:v:59:y:2008:i:10:d:10.1057_palgrave.jors.2602487 is not listed on IDEAS
    2. Lo Victor S & Bacon-Shone John, 2008. "Probability and Statistical Models for Racing," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(2), pages 1-14, April.
    3. Smith, Michael A. & Paton, David & Williams, Leighton Vaughan, 2009. "Do bookmakers possess superior skills to bettors in predicting outcomes?," Journal of Economic Behavior & Organization, Elsevier, vol. 71(2), pages 539-549, August.
    4. Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
      • Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, Research Program on Forecasting.
    5. 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.
    6. Kenneth L. Rhoda & Gerard T. Olson & Jack M. Rappaport, 1999. "Risk Preferences And Information Flows In Racetrack Betting Markets," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 22(3), pages 265-285, September.
    7. repec:eee:ecolet:v:156:y:2017:i:c:p:95-98 is not listed on IDEAS
    8. Vaughan Williams, Leighton, 1999. "Information Efficiency in Betting Markets: A Survey," Bulletin of Economic Research, Wiley Blackwell, vol. 51(1), pages 1-30, January.
    9. Vaughan Williams, Leighton & Stekler, Herman O., 2010. "Sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 445-447, July.
      • Herman O. Stekler, 2007. "Sports Forecasting," Working Papers 2007-001, The George Washington University, Department of Economics, Research Program on Forecasting, revised Jan 2007.
    10. 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.
    11. 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.
    12. Stefani Ray, 2011. "The Methodology of Officially Recognized International Sports Rating Systems," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-22, October.
    13. del Corral, Julio & Prieto-Rodríguez, Juan, 2010. "Are differences in ranks good predictors for Grand Slam tennis matches?," International Journal of Forecasting, Elsevier, vol. 26(3), pages 551-563, July.
    14. Strumbelj, E. & Sikonja, M. Robnik, 2010. "Online bookmakers' odds as forecasts: The case of European soccer leagues," International Journal of Forecasting, Elsevier, vol. 26(3), pages 482-488, July.
    15. Bernardo, Giovanni & Ruberti, Massimo & Verona, Roberto, 2015. "Testing semi-strong efficiency in a fixed odds betting market: Evidence from principal European football leagues," MPRA Paper 66414, University Library of Munich, Germany.
    16. Erhan Bayraktar & Alexander Munk, 2016. "High-Roller Impact: A Large Generalized Game Model of Parimutuel Wagering," Papers 1605.03653, arXiv.org, revised Mar 2017.
    17. 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.
    18. Rosenbloom, E. S., 2003. "A better probability model for the racetrack using Beyer speed numbers," Omega, Elsevier, vol. 31(5), pages 339-348, October.

    More about this item

    Keywords

    multinomial logit model; horse race wagering; stochastic utility model; ranked choice set data; discrete choice modeling;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • F65 - International Economics - - Economic Impacts of Globalization - - - Finance

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