Picking out peculiar performances with 10, 20, even 30 years of hindsight and data is easy compared to spotting them in real-time, says Berthelot, who is working on developing metrics that could be used without decades of data. Atypical performances, he says, stand out when compared to an athlete’s prior performances. Joyner, for instance, ran her fastest times in her late 20s and early 30s, an unusual trajectory for a sprinter, says Berthelot. Though Usain Bolt has also lowered the 100m world record time considerably in a short space of time, his race times have improved smoothly and, at 25, he sits at the typical peak of a sprinter’s career.
To make performance profiling more rigorous, sports scientists must determine a typical trajectory for each sport and each event by studying data from hundreds, even thousands of careers, says Berthelot. Sprinters tend to peak young and fall off quickly, while distance runners typically peak in their late 20s and early 30s and lose their speed less quickly. With this foundation and as much data as possible on an athlete’s past performances, computer models could put a probability on the likelihood that a performance was unusual enough to warrant closer drug screening, or whether it fits the trajectory of a exceptional career.
Undercover tactics
Such models would make predictions with less certainty for young athletes such as Ye, who do not have a long career’s worth of data points, but junior records could fill the gap, says Berthelot. Another challenge is to choose carefully the athletes and eras to which they calibrate such models. Career trajectories of doped athletes won’t do a good job discerning pharmacologically enhanced performances – garbage in, garbage out, as they say. And sports technology adds another problem to benchmarking performance profiling. Just as lighter bikes improved cycling times in the 1930s, full-body suits have been behind many of the performance gains in swimming in the 2000s, says Berthelot.
Proponents of performance profiling stress that such measures ought to be used to screen athletes, not discipline them. “That would be unfair,” says Tucker. “The final verdict is only ever going to be reached by testing. It has to be.”
Some endurance sports are already exploring this possibility. In December 2008 the governing body of biathlon, a gruelling winter sport that combines cross country skiing and target shooting, sentenced world champion Dmitry Yaroshenko to a two-year ban for taking EPO. Yaroshenko was flagged by a software programme that tracks blood physiology and performance, and out of competition testing turned up proof of EPO. Tucker also knows of one pro-cycling team that voluntarily reports power measurements to cycling authorities to show that its athletes are drug-free.
Critics may counter that institutionalised performance profiling will cast a pall over great races such as Ye’s, by looking for evidence of cheating. That needn’t be the case. Statistical profiling would be automatic and undercover, just as biological passport profiling already is. Tucker and other sports scientists expect that Ye and other Olympic gold medalists will draw closer scrutiny from doping authorities. But, in a connected age of super slo-mo replays, telemetry measurements and databases, shouldn’t such attention be based on statistics instead of hunches?
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