Sports journalists reported lies and then lie some more, just like dopers

Note: This is part 2 of a three-part response to an important article that appeared in the New York Times in August. See Part 1 here.

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Part 2 deals with a few disagreements I have with Tim Rohan, who is the author of the article.

The first sentence of Rohan's article took my breath away:

Doping experts have long known that drug tests catch only a tiny fraction of the athletes who use banned substances because athletes are constantly finding new drugs and techniques to evade detection.

It would be nice if he acknowledges the failure of sports journalists to inform the public of this piece of common knowledge ten or twenty years ago. If the false negative problem is "long known", why didn't the media report it?

Even when I was researching my first book five years ago, almost every story was about how athletes were wrongfully accused of doping, how only insufficiently talented athletes would need to dope, that a long string of negative tests threw doubt into the one positive result, how testers bungled the collection of samples thereby hurting an athlete's reputation, etc.

We also were led to believe that athletes were ingesting banned substances inadvertently because they took herbal supplements, that young, physically hyper-fit athletes had medical needs to patronize anti-aging clinics, that certain "good guys" were just curious, and somehow got ensnared on the very first and only time they tried steroids, that other "good guys" only took steroids when trying to recover from injuries, etc.

Some readers may recall the almost cultish defence of Floyd Landis after the Amercian cyclist won the Tour de France and then got stripped of the title. He made up some lame excuse which through the magic of sports journalism, developed into a kind of mass delusion, the evidence of which lives on at the Trust by Verify blog, long after Landis confessed, and took down the cycling profession while he sank. (See here for my previous coverage of the Landis circus.)

Shocker: all those stories were about "false positives".

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The cause-effect link in Rohan's quotation above is misleading. I won't repeat all of the arguments here--they are carefully laid out in Chapter 4 of Numbers Rule Your World (link)--but false negatives arise for a multitude of reasons. One reason for false negatives is the emergence of new drugs and techniques to evade detection. But the most important reason is that the test themselves are rigged so that it has high positive predictive value--meaning, a positive finding is highly trustworthy; and because of the inevitable tradeoff between false positives and false negatives in any diagnostic system, by making positive findings more valuable, the side effect is to make negative findings less valuable.

Such reporting perpetuates the myth of predictive models. Whenever the media reports on statistical methods used to predict things, it never ever tells us about the accuracy of such models. These reporters act as if predictions are 100% accurate, or close to it. But statistics is a science of uncertainty. Our society is overconfident about any kind of predictive models, not only for detecting dopers but also for detecting terrorists.

See Chapter 2 of Numbers Rule Your World, and Chapter 5 of Numbersense for more coverage of predictive models.

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The past year has finally seen a public recognition of the "false negative" problem in anti-doping testing, which I called attention to years ago. The downfall of Lance Armstrong and Alex Rodriguez, among others, confirms that elite athletes dope, and elite athletes can get tested hundreds of times and pass them all.

It would be appropriate if the sports journalist guild would issue a mea culpa: "Sorry readers, we have been duped."

For those who still haven't noticed, a drug test did not catch notorious superstar dopers Armstrong or Rodriguez.

 

Comments

[Ken]

Unfortunately it is pretty much impossible to catch someone who is using a synthetic form of something natural and has access to a laboratory. Take someone who is a good sprinter but has 2% less than the average in something that varies by plus or minus 5% in the population. They will just slowly raise the level artificially until they are 3-4% above the average. Maybe what we should do is make it the rule that you can dope as much as you like, but you must stay below certain levels.

[Kaiser]

If you set that threshold, then optimization theory will predict that everyone will now dope at that level. So it's a hard problem to solve.

In the book, I use the term "chemical positive" to describe positive findings assuming perfect testing conditions but like you pointed out, there are lots of reasons why a chemical positive is blocked.