As a theorist, I can muse about empirical issues generally safe from the fear that I might seriously explore them. Next summer is another Olympic year of competition and commentary. Consider the 100M dash. Victory spells fame and fortune, second place historical obscurity.  Effort expended is real, and competitors can roughly see how their nearest rivals are doing in real time, although admittedly in a bit of a blur. To what extent then can we understand behavior in these races using auction theory? If we regress winning times on times of predecessors, is the second fastest time the best predictor, and does it obviate the power in the other order statistics? And is this more true in the 10,000M run, where events are less of a blur, or less true, because at some point the race is often a foregone conclusion?

Another prediction of auction theory is that the best  times should be more clustered in head to head race, for instance, than if we just asked runners to race alone, not knowing their rivals’ times, and then picked the fastest time.