Journal of Gambling Studies

, Volume 31, Issue 1, pp 73–89

Delusions of Expertise: The High Standard of Proof Needed to Demonstrate Skills at Horserace Handicapping

Authors

    • Institute for Health and Social Science ResearchCQUniversity
  • Matthew J. Rockloff
    • Institute for Health and Social Science ResearchCQUniversity
  • Alex Blaszcynski
    • School of PsychologyUniversity of Sydney
  • Clive Allcock
    • School of PsychologyUniversity of Sydney
  • Allen Windross
    • Balmoral Consultancy Services
Original Paper

DOI: 10.1007/s10899-013-9420-7

Cite this article as:
Browne, M., Rockloff, M.J., Blaszcynski, A. et al. J Gambl Stud (2015) 31: 73. doi:10.1007/s10899-013-9420-7

Abstract

Gamblers who participate in skill-oriented games (such as poker and sports-betting) are motivated to win over the long-term, and some monitor their betting outcomes to evaluate their performance and proficiency. In this study of Australian off-track horserace betting, we investigated which levels of sustained returns would be required to establish evidence of skill/expertise. We modelled a random strategy to simulate ‘naïve’ play, in which equal bets were placed on randomly selected horses using a representative sample of 211 weekend races. Results from a Monte Carlo simulation yielded a distribution of return-on-investments for varying number of bets (N), showing surprising volatility, even after a large number of repeated bets. After adjusting for the house advantage, a gambler would have to place over 10,000 bets in individual races with net returns exceeding 9 % to be reasonably considered an expert punter (α = .05). Moreover, a record of fewer bets would require even greater returns for demonstrating expertise. As such, validated expertise is likely to be rare among race bettors. We argue that the counter-intuitively high threshold for demonstrating expertise by tracking historical performance is likely to exacerbate known cognitive biases in self-evaluation of expertise.

Keywords

Horse racingExpertiseMonte Carlo simulationSelf-assessmentStatisticsPerformance-monitoring

Copyright information

© Springer Science+Business Media New York 2013