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  • Congratulations to team GB, but why should we be so surprised? Olympic medal count can be predicted using logit regression models that include ‘home advantage’

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Congratulations to team GB, but why should we be so surprised? Olympic medal count can be predicted using logit regression models that include ‘home advantage’
  1. Alan M Nevill1,
  2. Nigel J Balmer2,
  3. Edward M Winter3
  1. 1School of Sport Performing Arts and Leisure, Research Institute of Healthcare Sciences, University of Wolverhampton, Wolverhampton, Walsall, UK
  2. 2University College London, London, UK
  3. 3The Centre for Sport and Exercise Science, Sheffield Hallam University, Sheffield, UKAMN, NJB and EMW contributed equally
  1. Correspondence to Professor Alan M Nevill, School of Sport Performing Arts and Leisure, Research Institute of Healthcare Sciences, University of Wolverhampton, Walsall WS1 3BD, UK; a.m.nevill{at}wlv.ac.uk

https://doi.org/10.1136/bjsports-2012-091719

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    For the vast majority, the London 2012 Olympic Games were a triumph and team Great Britain's (GB) successes in particular were remarkable. The 65 medals won exceeded most people's expectations. The accomplishments were testament to the achievements of athletes and their coaches, a source of delight and pride for spectators and an endorsement for those who provided scientific and medical support.

    But why should we have been so surprised? Over 3 years ago, Nevill et al1 predicted team GB would win 63 medals in London 2012. Using all cities/countries that had hosted the Olympic Games since the World War II, the authors modelled the number of medals awarded to competitors from each country as a binomial proportion (p) response variable (eg, in 2008 GB was awarded 47 medals out of a total of 958 available, ie, p=47/958=0.049) using a logit regression model. The logit model wasEmbedded Imagewhere the ‘Intercept’ parameters varied significantly between …

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    Footnotes

    • Competing interests None.

    • Provenance and peer review Not commissioned; externally peer reviewed.