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

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.