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Forecasting national team medal totals at the Summer Olympic Games

Author

Listed:
  • Forrest, David
  • Sanz, Ismael
  • Tena, J.D.

Abstract

The paper reports the results of an exercise to forecast national team medal totals at the Beijing Olympic Games, 2008. Forecasts were released to the media before the competitions commenced. The starting point was an established statistical model based on a regression analysis of medal totals in earlier Games, with past performance and GDP among the principal covariates. However, we based our own forecasts on a model with additional regressors, including a measure of public spending on recreation. This adaptation is shown to have improved the forecasting performance. We also made subjective, judgemental adjustments before releasing our final public forecasts, and we demonstrate that this led to a further increase in accuracy. These final forecasts were successful in predicting the principal changes in medal shares relative to the 2004 Games, namely the surge in medals for China and Great Britain and the substantial fall in medals for Russia.

Suggested Citation

  • Forrest, David & Sanz, Ismael & Tena, J.D., 2010. "Forecasting national team medal totals at the Summer Olympic Games," International Journal of Forecasting, Elsevier, vol. 26(3), pages 576-588, July.
  • Handle: RePEc:eee:intfor:v:26:y::i:3:p:576-588
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    References listed on IDEAS

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    1. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, pages 85-110.
    2. Sauer, Raymond D, et al, 1988. "Hold Your Bets: Another Look at the Efficiency of the Gambling Market for National Football League Games: Comment," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 206-213, February.
    3. Boulier, Bryan L. & Stekler, H. O., 2003. "Predicting the outcomes of National Football League games," International Journal of Forecasting, Elsevier, pages 257-270.
    4. Hon-Kwong Lui & Wing Suen, 2008. "Men, Money, And Medals: An Econometric Analysis Of The Olympic Games," Pacific Economic Review, Wiley Blackwell, vol. 13(1), pages 1-16, February.
    5. Andrew B. Bernard & Meghan R. Busse, 2004. "Who Wins the Olympic Games: Economic Resources and Medal Totals," The Review of Economics and Statistics, MIT Press, pages 413-417.
    6. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, pages 331-340.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Peter Dawson & Paul Downward & Terence C. Mills, 2014. "Olympic news and attitudes towards the Olympics: a compositional time-series analysis of how sentiment is affected by events," Journal of Applied Statistics, Taylor & Francis Journals, pages 1307-1314.
    2. Wladimir Andreff, 2012. "Is Hosting the Games Enough to Win? A predictive economic model of medal wins at 2014 Winter Olympics," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00794057, HAL.
    3. Peter Dawson & Paul Downward & Terence C. Mills, 2014. "Olympic news and attitudes towards the Olympics: a compositional time-series analysis of how sentiment is affected by events," Journal of Applied Statistics, Taylor & Francis Journals, pages 1307-1314.
    4. repec:ebl:ecbull:eb-17-00362 is not listed on IDEAS
    5. Wladimir Andreff, 2013. "Economic development as major determinant of Olympic medal wins: predicting performances of Russian and Chinese teams at Sochi Games," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00971788, HAL.
    6. David Forrest & Adams Ceballos & Ramón Flores & Ian G. McHale & Ismael Sanz & J.D. Tena, 2012. "Explaining and Forecasting National Team Medals Totals at the Summer Olympic Games," Chapters,in: International Handbook on the Economics of Mega Sporting Events, chapter 13 Edward Elgar Publishing.

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