AUTHOR=Montagna Silvia , Hopker James TITLE=A Bayesian Approach for the Use of Athlete Performance Data Within Anti-doping JOURNAL=Frontiers in Physiology VOLUME=9 YEAR=2018 URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2018.00884 DOI=10.3389/fphys.2018.00884 ISSN=1664-042X ABSTRACT=

The World Anti-doping Agency currently collates the results of all doping tests for athletes involved in elite sporting competition with the aim of improving the fight against doping. Existing anti-doping strategies involve either the direct detection of use of banned substances, or abnormal variation in metabolites or biological markers related to their use. As the aim of any doping regime is to enhance athlete competitive performance, it is interesting to consider whether performance data could be used within the fight against doping. In this regard, the identification of unexpected increases in athlete performance could be used as a trigger for their closer scrutiny via a targeted anti-doping testing programme. This study proposes a Bayesian framework for the development of an “athlete performance passport” and documents some initial findings and limitations of such an approach. The Bayesian model was retrospectively applied to the competitive results of 1,115 shot put athletes from 1975 to 2016 in order establish the interindividual variability of intraindividual performance in order to create individualized career performance trajectories for a large number of presumed clean athletes. Data from athletes convicted for doping violations (3.69% of the sample) was used to assess the predictive performance of the Bayesian framework with a probit model. Results demonstrate the ability to detect performance differences (~1 m) between doped and presumed clean athletes, and achieves good predictive performance of doping status (i.e., doped vs. non-doped) with a high area under the curve (AUC = 0.97). However, the model prediction of doping status was driven by the correct classification of presume non-doped athletes, misclassifying doped athletes as non-doped. This lack of sensitivity is likely due to the need to accommodate additional longitudinal covariates (e.g., aging and seasonality effects) potentially affecting performance into the framework. Further research is needed in order to increase the framework structure and improve its accuracy and sensitivity.