No abstract

The efficient use of testing resources is a key issue in the fight against doping. The longitudinal tracking of sporting performances to identify unusual improvements possibly caused by doping, so-called “athlete's performance passport” (APP) is a new concept to improve targeted anti-doping testing. In fact, unusual performances by an athlete would trigger a more thorough testing program. In the present case report, performance data is modeled using the critical power concept for a group of athletes based on their past performances. By these means, an athlete with unusual deviations from his predicted performances was identified. Subsequent target testing using blood testing and the athlete biological passport resulted in an anti-doping rule violation procedure and suspension of the athlete. This case demonstrates the feasibility of the APP approach where athlete's performance is monitored and might serve as an example for the practical implementation of the method.

Performance profiling is a new area of research that could potentially open new frontiers in the fight against doping. Even beyond exposing unnatural and pharmacology aided performances, there are other potential applications and benefits of performance modeling for the protection of the integrity of sports. The backbone of performance modeling in anti-doping is the individual tracking of performance through competition results or other metrics of sporting achievements. Since performance improvement is the primary goal of doping, it is expected that doping will affect competition results. Thus, individual tracking of performance could potentially expose suspicious cases that deserve more scrutiny from anti-doping officials and help to adjust targeted testing. On the other hand changes in performance levels could also be used to assess the efficiency of new anti-doping strategies. Another application of performance analysis is to develop unified classifications of athletes according to their level of performance. This classification has numerous practical meanings, but from anti-doping perspective it provides an opportunity to set exact criteria for athletes belonging to national and international testing pools and thus estimate the number of tests needed in different countries based on the number of athletes at ascertain performance level. At the moment, in the absence of unified and comprehensive criteria for national and international testing pools, there are no definitive regulations regarding exact doping test numbers needed. Thus, it creates inequality between nations and affects the credibility of the anti-doping system worldwide. Such classification would allow a more efficient use of anti-doping resources. Since doping is not the only threat to the integrity of sports, performance modeling can also help to reveal cases of other misbehavior in sports, like match fixing or result manipulation. In summary, performance modeling and its application to various fields is a new method to improve the efficiency of systems to safeguard the integrity of sports at different levels.

The purpose of this research was to evaluate the performances of female middle- and long-distance runners before and after the implementation of a new antidoping strategy (the Athlete Biological Passport [ABP]) in a country accused of systematic doping. A retrospective analysis of the results of Russian National Championships from 2008 to 2017 was performed. The 8 best female performances for the 800-m, 1500-m, 3000-m steeplechase, 5000-m, and 10,000-m events from the semifinals and finals were analyzed. The yearly number of athletes fulfilling standard qualifications for international competitions was also evaluated. Overall, numbers of athletes banned for doping in 2008–2017 were calculated. As a result, 4 events (800, 1500, 5000 [all P < .001], and 10,000 m [P < .01]) out of 5 showed statistically significant deterioration in the performances when comparing before and after the introduction of the ABP. The 3000-m steeplechase was the only event that did not show statistically significant change. The highest relative decrease in the number of runners who met standard qualification for international competition was for the 5000-m event (46%), followed by 1500-m (42%), 800-m (38%), 10,000-m (17%), and 3000-m steeplechase (1%). In conclusion, implementation of the ABP was followed by a significant reduction in the performance of female runners in a country accused of systematic doping. It can be reasonably speculated that more stringent antidoping testing, more specifically the introduction of the ABP, is a key reason for this reduction.

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.