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

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.