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

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Author Info

  • 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.

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File URL: http://dx.doi.org/10.1287/mnsc.32.8.1040
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Bibliographic Info

Article provided by INFORMS in its journal Management Science.

Volume (Year): 32 (1986)
Issue (Month): 8 (August)
Pages: 1040-1060

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Handle: RePEc:inm:ormnsc:v:32:y:1986:i:8:p:1040-1060

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Related research

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

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Cited by:
  1. 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.
  2. 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.
  3. Vaughan Williams, Leighton, 1999. "Information Efficiency in Betting Markets: A Survey," Bulletin of Economic Research, Wiley Blackwell, vol. 51(1), pages 1-30, January.
  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.
  5. 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.
  6. 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.
  7. Rosenbloom, E. S., 2003. "A better probability model for the racetrack using Beyer speed numbers," Omega, Elsevier, vol. 31(5), pages 339-348, October.
  8. 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.
  9. 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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