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Crowd performance in prediction of the World Cup 2014


  • O'Leary, Daniel E.


This paper investigates the performance of the Yahoo crowd and experts in predicting the outcomes of matches in the World Cup in 2014. The analysis finds that the Yahoo crowd was statistically significantly better at predicting outcomes of matches than experts and very similar in performance to established betting odds. In addition, this paper finds that there was a statistically significant difference between the Yahoo crowd and a different crowd's performances, for the same task, suggesting that characteristics of the “crowd matter.” Finally, this paper finds that different crowdsourcing approaches apparently provide different results. Accordingly, it is important to specify the particular crowdsourcing approach, rather than simply “crowdsource.”

Suggested Citation

  • O'Leary, Daniel E., 2017. "Crowd performance in prediction of the World Cup 2014," European Journal of Operational Research, Elsevier, vol. 260(2), pages 715-724.
  • Handle: RePEc:eee:ejores:v:260:y:2017:i:2:p:715-724
    DOI: 10.1016/j.ejor.2016.12.043

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    References listed on IDEAS

    1. Forrest, David & Simmons, Robert, 2000. "Forecasting sport: the behaviour and performance of football tipsters," International Journal of Forecasting, Elsevier, vol. 16(3), pages 317-331.
    2. Forrest, David & Goddard, John & Simmons, Robert, 2005. "Odds-setters as forecasters: The case of English football," International Journal of Forecasting, Elsevier, vol. 21(3), pages 551-564.
    3. Stefan M. Herzog & Ralph Hertwig, 2011. "The wisdom of ignorant crowds: Predicting sport outcomes by mere recognition," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(1), pages 58-72, February.
    4. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    5. Koning, Ruud H. & Koolhaas, Michael & Renes, Gusta & Ridder, Geert, 2003. "A simulation model for football championships," European Journal of Operational Research, Elsevier, vol. 148(2), pages 268-276, July.
    6. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    7. B. D. McCullough & Thomas McWilliams, 2010. "Baseball players with the initial “K” do not strike out more often," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 881-891.
    8. Dixon, Mark J. & Pope, Peter F., 2004. "The value of statistical forecasts in the UK association football betting market," International Journal of Forecasting, Elsevier, vol. 20(4), pages 697-711.
    9. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, vol. 21(2), pages 331-340.
    10. Howard Margolis, 1976. "A note on incompetence," Public Choice, Springer, vol. 26(1), pages 119-127, June.
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    Cited by:

    1. J. James Reade & Carl Singleton & Alasdair Brown, 2019. "Evaluating Strange Forecasts: The Curious Case of Football Match Scorelines," Economics & Management Discussion Papers em-dp2019-18, Henley Business School, Reading University.


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