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If you want to be a great sprint cyclist, you need to be able produce enormous bursts of power — so being big and muscular helps. If you want to be able cycle up mountains, on the other hand, you need great relative power — power divided by body weight — since every extra pound is deadweight that you have to haul upwards. But what about the middle ground? How does weight affect your performance in, say, a flat 40-km time trial?
Studies dating back to the 1980s have suggested that you need to use “allometric” scaling of weight to get the best prediction of performance in a 40-km time trial. Start by performing a graded peak power output (PPO) test, which is basically like a VO2max test, and PPO is the average power maintained for the last minute before you reach failure. Your PPO is a great way to predict how you’ll do in a 16-km time trial. Divide PPO by your body weight, and you have a great predictor of how you’ll do in a mountain race. And the interesting part: divide PPO by your weight to the power of 0.32 and you’ll have a great prediction of how you’ll do in a 40-km time trial.
This idea was first proposed by David Swain back in 1987, but hasn’t been tested much — which is why a new study just posted online at the British Journal of Sports Medicine, from Rob Lamberts and his colleagues at the University of Cape Town, put it to the test with 45 trained male cyclists. Here are some of the key results:
It’s pretty clear that the bottom graph (power divided by weight to the power of 0.32) provides a much better fit to the data than power (top) or power divided by weight (middle). So this is a useful piece of data for performance monitoring. But left unanswered is the question: why 0.32? Is this just an empirical number that happens to capture the tradeoffs between having more muscle and carrying more weight in exercise lasting about an hour? Or is there some physical or physiological explanation?