IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

Exploring Competition Performance in Decathlon Using Semi-Parametric Latent Variable Models

Listed author(s):
  • Wimmer Valentin

    (Technische Universität München)

  • Fenske Nora

    (Ludwig-Maximilians-Universität München)

  • Pyrka Patricia

    (Technische Universität München)

  • Fahrmeir Ludwig

    (Ludwig-Maximilians-Universität München)

Registered author(s):

    In this paper, we explore competition performance in decathlon based on competition, training and personal data. Our data set comprises 3103 competition results from the decathlon world's best performance lists from 1998 to 2009. The aim of our analysis is to estimate latent factors describing the performance results andat the same timeto model effects of age, season, and year of the competition on the results. Thus, we apply a new statistical method, semi-parametric latent variable models (LVMs), which can be seen as a synthesis between classical factor analysis and semi-parametric regression. LVMs are especially well-suited for modeling decathlon data, because (i) they permit the assumption of latent factors and therefore take the correlation structure between the ten performance results into account, and (ii) they enable us to model (potentially non-linear) relationships between response variables and covariatescontrary to classical factor analysis. In our analysis, we apply LVMs with a semi-parametric predictor allowing for non-linear covariate effects on the latent factors. Thereby, we obtain well interpretable results: four latent factors standing for sprint, jumping, throwing, and endurance abilities, as well as interesting non-linear effects of age and season on these latent factors. We also compare our results from LVMs to those obtained from classical factor analysis.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Article provided by De Gruyter in its journal Journal of Quantitative Analysis in Sports.

    Volume (Year): 7 (2011)
    Issue (Month): 4 (October)
    Pages: 1-21

    in new window

    Handle: RePEc:bpj:jqsprt:v:7:y:2011:i:4:n:6
    Contact details of provider: Web page:

    Order Information: Web:

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    in new window

    1. Schomaker Michael & Heumann Christian, 2011. "Model Averaging in Factor Analysis: An Analysis of Olympic Decathlon Data," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(1), pages 1-15, January.
    2. Woolf Anne & Ansley Les & Bidgood Penelope, 2007. "Grouping of Decathlon Disciplines," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(4), pages 1-15, October.
    3. Pyrka Patricia & Wimmer Valentin & Fenske Nora & Fahrmeir Ludwig & Schwirtz Ansgar, 2011. "Factor Analysis in Performance Diagnostic Data of Competitive Ski Jumpers and Nordic Combined Athletes," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-22, July.
    4. Ludwig Fahrmeir & Alexander Raach, 2007. "A Bayesian Semiparametric Latent Variable Model for Mixed Responses," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 327-346, September.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:bpj:jqsprt:v:7:y:2011:i:4:n:6. 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: (Peter Golla)

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 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.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.