IDEAS home Printed from
   My bibliography  Save this article

Records in Athletics Through Extreme-Value Theory


  • Einmahl, John H. J.
  • Magnus, Jan R.


In this paper we shall be interested in two questions on extremes relating to world records in athletics.The first question is: what is the ultimate world record in a specific athletics event (such as the 100m for men or the high jump for women), given today's state of the art?Our second question is: how `good' is a current athletics world record?An answer to the second question will also enable us to compare the quality of world records in different athletics events. We shall consider these questions for each of twenty-eight events (fourteen for both men and women).We approach the two questions with the probability theory of extreme values and the corresponding statistical techniques.The statistical model is of nonparametric nature, but some `weak regularity' of the tail of the distribution function will be assumed.We will derive the limiting distribution of the estimated quality of a world record.While almost all attempts to predict an ultimate world record are based on the development of top performances over time, this will not be our method.Instead, we shall only use the top performances themselves.Our estimated ultimate world record tells us what, in principle, is possible now, given today's knowledge, material (shoes, suits, equipment), and drugs laws.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Einmahl, John H. J. & Magnus, Jan R., 2008. "Records in Athletics Through Extreme-Value Theory," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1382-1391.
  • Handle: RePEc:bes:jnlasa:v:103:i:484:y:2008:p:1382-1391

    Download full text from publisher

    File URL:
    File Function: full text
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    1. Einmahl, J. H.J. & Dekkers, A. L.M. & de Haan, L., 1989. "A moment estimator for the index of an extreme-value distribution," Other publications TiSEM 81970cb3-5b7a-4cad-9bf6-2, Tilburg University, School of Economics and Management.
    2. M. I. Barão & J. A. Tawn, 1999. "Extremal analysis of short series with outliers: sea-levels and athletics records," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(4), pages 469-487.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. M. Ivette Gomes & Armelle Guillou, 2015. "Extreme Value Theory and Statistics of Univariate Extremes: A Review," International Statistical Review, International Statistical Institute, vol. 83(2), pages 263-292, August.
    2. Cai, J., 2012. "Estimation concerning risk under extreme value conditions," Other publications TiSEM a92b089f-bc4c-41c2-b297-c, Tilburg University, School of Economics and Management.
    3. John H. J. Einmahl & Sander G. W. R. Smeets, 2011. "Ultimate 100‐m world records through extreme‐value theory," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 65(1), pages 32-42, February.
    4. Krajina, A., 2010. "An M-estimator of multivariate tail dependence," Other publications TiSEM 66518e07-db9a-4446-81be-c, Tilburg University, School of Economics and Management.
    5. Shaul Bar-Lev, 2008. "Point and confidence interval estimates for a global maximum via extreme value theory," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(12), pages 1371-1381.
    6. Wang, Bing Xing & Yu, Keming & Coolen, Frank P.A., 2015. "Interval estimation for proportional reversed hazard family based on lower record values," Statistics & Probability Letters, Elsevier, vol. 98(C), pages 115-122.
    7. de Valk, Cees, 2016. "A large deviations approach to the statistics of extreme events," Other publications TiSEM 117b3ba0-0e40-4277-b25e-d, Tilburg University, School of Economics and Management.

    More about this item

    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bes:jnlasa:v:103:i:484:y:2008:p:1382-1391. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.