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A Bayesian approach to the use of athlete performance data within anti-doping

Montagna, Silvia, Hopker, James G. (2018) A Bayesian approach to the use of athlete performance data within anti-doping. Frontiers in Physiology, 9 (884). ISSN 1664-042X. (doi:10.3389/fphys.2018.00884) (KAR id:67380)

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The World Anti-doping Agency currently collates the results of all doping tests for

doping. Existing anti-doping strategies involve either the direct detection of use of

to their use. As the aim of any doping regime is to enhance athlete competitive

the fight against doping. In this regard, the identification of unexpected increases in

anti-doping testing programme. This study proposes a Bayesian framework for the

and limitations of such an approach. The Bayesian model was retrospectively applied to

the interindividual variability of intraindividual performance in order to create individualized

from athletes convicted for doping violations (3.69% of the sample) was used to assess

demonstrate the ability to detect performance differences (?1 m) between doped and

(i.e., doped vs. non-doped) with a high area under the curve (AUC = 0.97). However, the

non-doped athletes, misclassifying doped athletes as non-doped. This lack of sensitivity

and seasonality effects) potentially affecting performance into the framework. Further

and sensitivity.

Item Type: Article
DOI/Identification number: 10.3389/fphys.2018.00884
Subjects: R Medicine > RC Internal medicine > RC1200 Sports medicine
Divisions: Divisions > Division of Natural Sciences > School of Sport and Exercise Sciences
Depositing User: James Hopker
Date Deposited: 20 Jun 2018 12:47 UTC
Last Modified: 16 Feb 2021 13:55 UTC
Resource URI: (The current URI for this page, for reference purposes)
Montagna, Silvia:
Hopker, James G.:
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