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
