A Transverse Doping Probability Passport: A Conceptual Illustration

Athlete doping rarely happens in isolation. So why do ADAs try to tackle the problem that way?


Consider this toy model of the social structure of a doping network. We may have individuals who may be Athletes, Trainers, Coaches, and Doctors. The individuals may instigate doping behavior, be reactive to doping behavior (dope if pressured, not dope if not pressured), be tolerant of doping, or not dope. They will also have varying numbers of connections to those who may be around them. In this example, it becomes visually obvious that targeting the instigators at the center of the doping networks is most likely to have the largest impact on the overall doping prevalence in the group.


Now consider who is actually in the WADA testing pool?


Consider who is missing from the testing pool?

Everyone else.


How does the doping network look from the perspective of current WADA testing?


In fairness to WADA, there is no illusion that majority of the dopers will be caught this way. Instead, the strategy is to take the dopers that are caught, and then punish them so harshly that it deters doping behavior within the group at large.

The issue that arises though is that severity of punishment must be supported by a proportional specificity of the doping test. In plain language, if you are going to end an athlete’s career, you better damn well be sure the test got it right. This system is inherently self-defeating cycle. The reason is that as you push the threshold toward better specificity you lose sensitivity. As you lose sensitivity you catch less dopers. As you catch less dopers you need to punish them even more severely to deter the group. As you punish them more severely, you better be even more damn well sure the test got it right. So you push the specificity even further and catch even less dopers and have to punish them even more severely (at some point I will code up a simple dynamical systems model to animate this example).


Does this deterrent model make any sense relative to how a doping network is most likely to work?


If we abandon the idea that testing must be biological, we can consider an alternative approach. In the case of occular motor deception detection http://converus.com/eyedetect-lie-detection/ , Converus offers a commercially available inexpensive product that is highly scalable with lab validation suggesting sensitivity and specificity over 80% https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3763937/ .

Now that testing is no longer limited by biological samples, how does that change the testing pool ?

Of course, the specificity here is not high enough to sanction any individual. But it is enough start to categories individuals into high or low credibility.


Next, we can take the now established Bayesian approach established by the bio-passport to use multiple indirect measures of individually low specificity to estimate the probability of doping to a high degree of specificity. However, unlike the current bio-passport which only considers longitudinal intra-individual data (results only for that athlete) we can expand the input data transversely across groups and testing methods.


Even if this system retained the high specificity harsh punishment approach, how likely would it be to fall into the self-defeating WADA negative feedback cycle?

I suggest that at least 2 main advantages would keep this approach from becoming just more of the same:

  1. The amount of data going in to athlete’s passports would be an order of magnitude higher improving sensitivity at the same level of specificity.
  2. Positive tests would more likely cluster within the center of the doping networks.

I suggest that the potential outcome would look far different to current testing.


Transverse Doping Probability Passport.


Current testing.