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Determining the Best Track Performances of All Time Using a Conceptual Population Model for Athletics Records

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  • Stephenson Alec G.

    (CSIRO Mathematics, Informatics and Statistics, Clayton South, Victoria, Australia)

  • Tawn Jonathan A.

    (Department of Mathematics and Statistics, Lancaster University, UK)

Abstract

What is the best male and female athletics performance in history? We seek to answer this question for Olympic distance track events by simultaneously modelling race performances over all Olympic distances and all times. Our model uses techniques from a branch of statistics called extreme value theory, and incorporates information on improvements over time using an exponential trend in addition to a process which identifies the changing ability of the population of athletes across all distances. We conclude that the best male performance of all time is the 1968 world record of Lee Evans in the 400 m, and that the best female performance of all time is the current 1988 world record of Florence Griffith-Joyner in the 100 m. More generally, our approach provides a basis for deriving a ranking of track athletes over any distance and at any point over the last 100 years.

Suggested Citation

  • Stephenson Alec G. & Tawn Jonathan A., 2013. "Determining the Best Track Performances of All Time Using a Conceptual Population Model for Athletics Records," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 67-76, March.
    • Handle: RePEc:bpj:jqsprt:v:9:y:2013:i:1:p:67-76:n:7
    DOI: 10.1515/jqas-2012-0047
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    References listed on IDEAS

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    1. Samprit Chatterjee & Sangit Chatterjee, 1982. "New Lamps for Old: An Exploratory Analysis of Running Times in Olympic Games," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(1), pages 14-22, March.
    2. Cooley, Daniel & Nychka, Douglas & Naveau, Philippe, 2007. "Bayesian Spatial Modeling of Extreme Precipitation Return Levels," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 824-840, September.
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    Cited by:

    1. Griffin Jim E. & Hinoveanu Laurenţiu C. & Hopker James G., 2022. "Bayesian modelling of elite sporting performance with large databases," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 18(4), pages 253-268, December.
    2. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.

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