En vigueur

Performance Monitoring for evaluating risk in anti-doping

Investigateur principal
J. Hopker
Pays
Royaume-Uni
Institution
University of Kent
Année approuvée
2020
Statut
En vigueur
Themes
Divers

Description du projet

Code: 20D03JH

The use of information technology within sport has significantly increased over recent years. The availability of data for longitudinal tracking of athletes over the course of their careers is critical to identify an individual’s performance progress. As such, a key component of this longitudinal monitoring is to be able to differentiate between “normal” increases in performance caused by maturation and training, from an “unnatural” improvement caused by doping. Our initial work demonstrates that it is possible to identify specific performance profiles characteristics for athletes with previous ADRVs, which appear to be consistent across the track and field disciplines we have investigated (100-800m, jumps & throws). However, there is a need to extend the current model and translate it into a useful tool for anti-doping authorities. Therefore, the purpose of this study is to refine and further develop our existing Bayesian modelling approach. Specifically, this project will enable us Page 2/8 to explore confounding factors for modeling performance data, and at the same time explore the effects of these confounders on the probability level needed to flag a suspicious individual, balancing false positives (clean athletes identified as doping) and false negatives (doping athletes which are not flagged). We will therefore develop a “risk” score for suspicious performance profiles. A key aspect of the method will be its impact on decisions made via the traditional Athlete Biological Passport (ABP). Working in conjunction with the AIU, we will retrospectively explore whether performance data adds value to decisions made by expert witnesses in historical passport cases. Using this approach, we will evaluate whether adding performance data to existing ABP data is more effective than current methods for identifying athletes who demonstrate may present adverse, or uncertain passport profiles findings.

Main findings

As the aim of any doping regime is to improve sporting performance, it has been suggested that analysis of athlete competitive results might be informative in identifying those at greater risk of doping. The aim of this research project was to investigate the utility of a statistical performance model to discriminate between athletes who have a previous anti-doping rule violation (ADRV) and those who do not.

We analysed performances of male and female 100 – 10,000m runners obtained from the World Athletics results database using a Bayesian spline model. Measures of unusual improvement in performance were quantified by comparing the yearly change athlete's performance (delta excess performance) to quantiles of performance (50%, 75% and 90%) in their age matched peers from the database population. Sudden or unexpected changes in an athlete's level of excess performance the exceed these quantiles might therefore be indicative of doping. The discriminative ability of these risk measures was investigated using the area under the ROC curve (AUC) with the highest values being observed using the 75% quantile (AUC = 0.78-0.80). To assess the specificity of the model using the 75% quantile at different age points we assessed the False Positive rate across different probability levels for delta excess performance. The true positive rate ranges between 0.20 and 0.67 across the ages due to the changes in the number of observed true positives (i.e. ADRVs) recorded at each age, and athletes within the database.

Further, we investigated the ability of delta excess performance to discriminate between athletes with and without adverse analytical findings (AAFs), adverse passport findings (APFs) and Anti-Doping Rule Violations (ADRVs). The 75% quantile for delta excess performance demonstrated AUC values of ~0.60 at age points with the highest numbers of APFs. However, by comparison, the model showed a better ability to discriminate the ADRV status of athletes by AUC values of ~0.65 to 0.75 at the same corresponding age points.

The findings of this project demonstrate the utility of performance monitoring to discriminate between athletes on the basis of their doping status. However, it is important to recognise that high levels of delta excess performance are not sufficient to prove an athlete is doping, and that information obtained from this type of analysis should be integrated with other data as part of a wider intelligence gathering approach to anti-doping.

Publication:

Hopker JG, Griffin, JE. Hinoveanu, LC. Saugy, J. Faiss, R. (2023) Competitive performance as a discriminator of doping status in elite athletes. Drug Testing and Analysis. 16:473-81. doi: 10.1002/dta.3563.