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Finding profitable forecast combinations using probability scoring rules

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  • Grant, Andrew
  • Johnstone, David

Abstract

This study examines the success of bets on Australian Football League (AFL) matches made by identifying panels of highly proficient forecasters and betting on the basis of their pooled opinions. The data set is unusual, in that all forecasts are in the form of probabilities. Bets are made "on paper"Â against quoted market betting odds according to the (fractional) Kelly criterion. To identify expertise, individual forecasters are scored using conventional probability scoring rules, a "Kelly score"Â representing the forecaster's historical paper profits from Kelly-betting, and the more simplistic "categorical score"Â (number of misclassifications). Despite implicitly truncating all probabilities to either 0 or 1 before evaluation, and thus losing a lot of information, the categorical scoring rule appears to be a propitious way of ranking probability forecasters. Bootstrap significance tests indicate that this improvement is not attributable to chance.

Suggested Citation

  • Grant, Andrew & Johnstone, David, 2010. "Finding profitable forecast combinations using probability scoring rules," International Journal of Forecasting, Elsevier, vol. 26(3), pages 498-510, July.
    • Handle: RePEc:eee:intfor:v:26:y::i:3:p:498-510
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    Cited by:

    1. Rose D. Baker & Ian G. McHale, 2013. "Optimal Betting Under Parameter Uncertainty: Improving the Kelly Criterion," Decision Analysis, INFORMS, vol. 10(3), pages 189-199, September.
    2. David J Johnstone, 2023. "Capital budgeting and Kelly betting," Australian Journal of Management, Australian School of Business, vol. 48(3), pages 625-651, August.
    3. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
    4. D. J. Johnstone & S. Jones & V. R. R. Jose & M. Peat, 2013. "Measures of the economic value of probabilities of bankruptcy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 635-653, June.
    5. Baker, Rose D. & McHale, Ian G., 2013. "Forecasting exact scores in National Football League games," International Journal of Forecasting, Elsevier, vol. 29(1), pages 122-130.
    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. Adi Schnytzer, 2011. "The Prediction Market for the Australian Football League," Working Papers 2011-15, Bar-Ilan University, Department of Economics.

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