Hopker, James G;
Griffin, Jim E;
Hinoveanu, Laurentiu C;
Saugy, Jonas;
Faiss, Raphael;
(2023)
Competitive performance as a discriminator of doping status in elite athletes.
Drug Testing and Analysis
10.1002/dta.3563.
(In press).
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Abstract
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. This research study aimed 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 and 800 m runners obtained from the World Athletics database using a Bayesian spline model. Measures of unusual improvement in performance were quantified by comparing the yearly change in athlete's performance (delta excess performance) to quantiles of performance in their age-matched peers from the database population. The discriminative ability of these measures was investigated using the area under the ROC curve (AUC) with the 55%, 75% and 90% quantiles of the population performance. The highest AUC values across age were identified for the model with a 75% quantile (AUC = 0.78–0.80). The results of this study demonstrate that delta excess performance was able to discriminate between athletes with and without ADRVs and therefore could be used to assist in the risk stratification of athletes for anti-doping purposes.
Type: | Article |
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Title: | Competitive performance as a discriminator of doping status in elite athletes |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1002/dta.3563 |
Publisher version: | https://doi.org/10.1002/dta.3563 |
Language: | English |
Additional information: | Copyright © 2023 The Authors. Drug Testing and Analysis published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, https://creativecommons.org/licenses/by/4.0/, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Bayesian, biological passport, data analytics, modelling, risk stratification, sports, target testing |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10174910 |
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