The team effect on doping in professional male road cycling (2005‐2016)

Abstract

This article questions organizations’ (clubs, teams, etc) responsibility in doping use from the case of anti‐doping rules violations (ADRVs) sanctioned by the Union Cycliste Internationale in professional cycling. We built a database with 271 caught riders among 10 551 professional riders employed from 2005 to 2016 in the three first world divisions. We developed a time‐discrete event history model with a multilevel perspective to consider if the ADRV is related to the characteristic of a rider's career path (level 1) and/or the team by which the rider is employed (level 2). Our results confirm two hypotheses: Beginning a career before 2005 or after the age of 22 increased the risk of being caught. Each additional year in the pack increased the risk, despite the fact that a sanctioned rider's career duration average is 7.8 years (3.9 for the others). These caught riders have experienced a more tumultuous career with team changes and an interrupted path. A 2.45 Median Odds Ratio led us to assert a team effect on ADRV. By a team residual effect calculation, we identify 17 teams with a significant effect within the 129 that experienced an ADRV. Our results allow us to emphasize that to understand doping we must take into account work and employment condition, as well as team's organization. This approach completes the dominant “methodological individualism” perspective that considers athletes as analytical units and provides guidelines to the anti‐doping bodies that focus their action on individuals.

Doping Prevalence in Competitive Sport: Evidence Synthesis with “Best Practice” Recommendations and Reporting Guidelines from the WADA Working Group on Doping Prevalence

“…Studies placed in SRP employed four different methods for determining prevalence, which were Direct Survey (DS) , Random Response Technique (RT) [102][103][104][105][106][107][108][109][110], Qualitative Interviews (QI) [111], and Network Scale Up (NS) [55]. Studies placed in SAP employed four different methods for determining prevalence, which were Testing Figures (TF) [112][113][114][115][116][117][118][119][120][121], Blood Profile (BP) [122][123][124], Anti-Doping Rule Violations (AD) [125][126][127], and Hair Sample (HS) [79]. One study [79] was found to use a method assigned to SRP and to SAP, thus it was included in both with its two assessment scores included independently in Electronic Supplementary Material Appendix S4.…”

“…Three studies employed anti-doping rule violations (ADRVs) as a method to examine doping behavior for a population of competitive athletes [125][126][127]. Examining ADRVs to determine doping prevalence offers several advantages.…”

“…Finally, researchers may also struggle to determine the population size from which the ADRVs emerged to establish doping prevalence. For example, Aubel et al [125] calculated the total number of ADRVs for professional cycling between 2005 to 2016 but did not determine the total number of professional cyclists for that period, which is necessary to establish the doping prevalence. In many cases, the number of athletes registered to an international sport federation or a national anti-doping organization may not be available to researchers and even with the information, researchers may struggle to determine with consistency the number of athletes actually controlled for ADRVs.…”

Annual banned‐substance review – Analytical approaches in human sports drug testing

“…The deliberate act of doping in sport has been shown to be fueled by various different factors, appellatively summarized as a dopogenic environment by Backhouse et al, ranging from (amongst others) the enormous overall demands of the modern sporting life that might exceed an athlete's personal resources to local level factors such as the athlete's team, coach, physicians, and peers as well as sport motivation . In addition, presumably or evidently inadvertent antidoping rule violations (ADRVs) have continued to be recorded, underlining the importance of education and information of the athletes’ support personnel and health care professionals .…”

Whose job is it anyway? A qualitative investigation into the influence of agents, race organisers, and sponsors on the risk of doping in elite distance running

“…Instead, it suggests systemic issues in the athletes' environment. The influence of various individuals within an athlete's environment on doping risk has been studied previously, from peers in team sports (Allen et al, 2017;Aubel et al, 2019;Mallia et al, 2016), to coaches (Barnes et al, 2022), parents (Erickson et al, 2017), physicians (Backhouse & McKenna, 2011), and other athlete support personnel (Barkoukis et al, 2019, Mazanov et al, 2014. The importance of the athlete environment on doping behaviour has long been recognised in academic research (Hauw, 2013;Hauw & Mohamed, 2015;Shelley et al, 2021;Petróczi et al, 2017;Petroczi et al, 2021).…”