| Literature DB >> 29312004 |
Sergei Iljukov1, Yorck O Schumacher2.
Abstract
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.Entities:
Keywords: analysis; competition results; match fixing; monitoring; passport; target testing
Year: 2017 PMID: 29312004 PMCID: PMC5743671 DOI: 10.3389/fphys.2017.01102
Source DB: PubMed Journal: Front Physiol ISSN: 1664-042X Impact factor: 4.566
Figure 1(A) Result of female discus throw season best performance (black squares) and the average of the Top 20 performances (open circles) for each year from 1960 to 2000 (modified from Schumacher and Pottgiesser, 2009). (B) Result of male 10,000 m seasons best performance (black squares) and the average of the Top 20 performances (open circles) for each year from 1975 to 2000 (modified from Schumacher and Pottgiesser, 2009).
Eight hundred meters times (min:sec,dec) for the Top 8 finishers at the national championships from 2008 to 2017.
| 02:01,61 | 02:01,79 | |||||||||
| 02:02,36 | 02:01,80 | |||||||||
| 02:02,55 | 02:01,93 | 02:01,45 | ||||||||
| 02:01,69 | 02:02,84 | 02:02,64 | 02:01,56 | |||||||
| 02:02,00 | 02:02,99 | 02:02,76 | 02:01,96 | |||||||
| 02:01,99 | 02:01,30 | 02:02,20 | 02:03,00 | 02:03,22 | 02:02,07 |
Times printed in bold are performances who fulfilled A qualification standards and times in italics those who fulfilled B qualification standards for major international competitions. The results presented in normal fonts did not meet the qualification criteria for any international event.
Figure 2Individual hammer throw results from May to September in the years from 2014 to 2017 for 24 athletes qualified for the 2016 Olympics Games Rio de Janerio. The arrows indicate two athletes with suspicious performance in 2015 the athletes in boxes are suspected of results manipulation in 2016. The circles indicates results obtained in 2014, triangles 2015, inversed triangles 2016 and squares 2017.