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Forecasting national team medal totals at the Summer Olympic Games

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  • 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.

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  • 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, vol. 105(1), pages 85-110, November.
    2. Sauer, Raymond D & Brajer, Vic & Ferris, Stephen P & Marr, M Wayne, 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, vol. 19(2), 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, vol. 86(1), pages 413-417, February.
    6. 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.
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    Cited by:

    1. Johan Rewilak, 2021. "The (non) determinants of Olympic success," Journal of Sports Economics, , vol. 22(5), pages 546-570, June.
    2. David Forrest & J. D. Tena & Carlos Varela-Quintana, 2023. "The influence of schooling on performance in chess and at the Olympics," Empirical Economics, Springer, vol. 64(2), pages 959-982, February.
    3. Christoph Schlembach & Sascha L. Schmidt & Dominik Schreyer & Linus Wunderlich, 2020. "Forecasting the Olympic medal distribution during a pandemic: a socio-economic machine learning model," Papers 2012.04378, arXiv.org, revised Jun 2021.
    4. Carl Singleton & J. James Reade & Johan Rewilak & Dominik Schreyer, 2021. "How big is home advantage at the Olympic Games?," Economics Discussion Papers em-dp2021-13, Department of Economics, University of Reading.
    5. Martin Grancay & Tomas Dudas, 2018. "Olympic Medals, Economy, Geography and Politics from Sydney to Rio," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 22(2), pages 409-441, Spring.
    6. 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.
    7. Javier Otamendi & Luis M. Doncel, 2014. "Medal Shares in Winter Olympic Games by Sport: Socioeconomic Analysis After Vancouver 2010," Social Science Quarterly, Southwestern Social Science Association, vol. 95(2), pages 598-614, June.
    8. Pedro Garcia‐del‐Barrio & Carlos Gomez‐Gonzalez & José Manuel Sánchez‐Santos, 2020. "Popularity and Visibility Appraisals for Computing Olympic Medal Rankings," Social Science Quarterly, Southwestern Social Science Association, vol. 101(5), pages 2137-2157, September.
    9. 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: Wolfgang Maennig & Andrew Zimbalist (ed.), International Handbook on the Economics of Mega Sporting Events, chapter 13, Edward Elgar Publishing.
    10. Aaron Lowen & Robert O. Deaner & Erika Schmitt, 2016. "Guys and Gals Going for Gold," Journal of Sports Economics, , vol. 17(3), pages 260-285, April.
    11. Nicolas Scelles & Wladimir Andreff & Liliane Bonnal & Madeleine Andreff & Pascal Favard, 2020. "Forecasting National Medal Totals at the Summer Olympic Games Reconsidered," Social Science Quarterly, Southwestern Social Science Association, vol. 101(2), pages 697-711, March.
    12. 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, vol. 41(6), pages 1307-1314, June.
    13. Schlembach, Christoph & Schmidt, Sascha L. & Schreyer, Dominik & Wunderlich, Linus, 2022. "Forecasting the Olympic medal distribution – A socioeconomic machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    14. Eike Emrich & Freya Gassmann & Christian Pierdzioch, 2017. "Are Forfeitures of Olympic Medals Predictable? – A Test of the Efficiency of the International Anti-Doping System," Economics Bulletin, AccessEcon, vol. 37(3), pages 1620-1623.
    15. 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.
    16. Yun Hyeong Choi & Qingyuan Wei & Luyao Zhang & Seong-Jin Choi, 2022. "The Impact of Cultural Distance on Performance at the Summer Olympic Games," SAGE Open, , vol. 12(1), pages 21582440221, March.

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