Performance profiling as an intelligence-led approach to antidoping in sports.,Drug Testing and Analysis

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Performance profiling as an intelligence-led approach to antidoping in sports.
Drug Testing and Analysis ( IF 2.6 ) Pub Date : 2020-01-21 , DOI: 10.1002/dta.2748
James Hopker ₁ , Jim Griffin ₂ , James Brookhouse ₁ , John Peters ₃ , Yorck Olaf Schumacher ₄ , Sergei Iljukov _{5,

6}

Affiliation

The efficient use of testing resources is crucial in the fight against doping in sports. The athlete biological passport relies on the need to identify the right athletes to test, and the right time to test them. Here we present an approach to longitudinal tracking of athlete performance to provide an additional, more intelligence‐led approach to improve targeted antidoping testing. The performance results of athletes (male shot putters, male 100 m sprinters, and female 800 m runners) were obtained from a performance results database. Standardized performances, which adjust for average career performance, were calculated to determine the volatility in performance over an athlete's career. We then used a Bayesian spline model to statistically analyse changes within an athlete's standardized performance over the course of a career both for athletes who were presumed “clean” (not doped), and those previously convicted of doping offences. We used the model to investigate changes in the slope of each athlete's career performance trajectory and whether these changes can be linked to doping status. The model was able to identify differences in the standardized performance of clean and doped athletes, with the sign of the change able to provide some discrimination. Consistent patterns of standardized performance profile are seen across shot put, 100 m and 800 m for both the clean and doped athletes we investigated. This study demonstrates the potential for modeling athlete performance data to distinguish between the career trajectories of clean and doped athletes, and to enable the risk stratification of athletes on their risk of doping.

中文翻译：

性能剖析是一种以情报为主导的运动中反兴奋剂方法。

有效利用测试资源对于打击运动中的兴奋剂至关重要。运动员生物护照依赖于确定合适的运动员进行测试以及正确的时间对其进行测试的需求。在这里，我们提出了一种纵向跟踪运动员表现的方法，以提供另一种由情报主导的方法，以改进针对性的反兴奋剂测试。从成绩结果数据库中获得运动员（男子铅球运动员，男子100 m短跑运动员和女子800 m运动员）的成绩结果。计算根据平均职业表现进行调整的标准表现，以确定运动员职业生涯中表现的波动性。然后，我们使用贝叶斯样条模型对运动员运动过程中的变化进行统计分析。在职业生涯中的标准表现，既适用于被认为“干净”（未掺杂）的运动员，也适用于先前因犯有兴奋剂罪而被定罪的运动员。我们使用该模型调查了每位运动员的职业表现轨迹的斜率变化，以及这些变化是否可以与兴奋剂状态相关联。该模型能够识别出纯净和掺杂运动员标准成绩的差异，并且这种变化的迹象能够提供一些区别。在我们研究的干净运动员和掺杂运动员中，铅球在100 m和800 m上都能看到一致的标准化性能曲线。这项研究证明了对运动员成绩数据进行建模以区分干净和掺杂运动员的职业轨迹的潜力，

更新日期：2020-01-21

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