Endurance Analytics

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Sustainable Power is a moving target

The trouble with rider’s power output is it falls over time. Not in the course of a season, when we hope it increases due to training stimuli, but as a response to the duration of a ride. Whether we’re interested in short efforts, when sustainable power decreases as a function of anaerobic work capacity, or relatively longer ones, when fatigue is the problem, there is an inescapable reality that we become weaker as the clock ticks over. This reality can be visualised as a rider’s “Power Duration Curve”.

In some way stronger riders enjoy a double advantage. As well as going faster from the outset they also arrive at the finish line way before weaker riders, putting their effort on a very different section of the curve. Indeed pro level ironmen face a completely different race to the typical age grouper who will be out on course for hours more. What may be a tempo ride for someone can be a test of endurance for another. A layman might conclude that we should all ride full gas from the off, thus completing whatever course asap and aiming for some shorter (but higher) point on the curve. Unfortunately the realities of physiology quickly undermine such a strategy.

Completing any course as quickly as possible comes down to intelligent pacing. Even if we discount the opportunities presented by a computer optimised, variable power strategy, there is a problem to be solved. A rider needs to know the highest power output he can expect to be able to sustain for the duration of the course, to leave everything on the road and get the best ride out of himself. But what is his expected duration on course?! Only predictive analytics, performance modelling, can do a good job of revealing that.

There is a chicken and egg problem here. To estimate time on course we need to know (average) sustainable power, something to feed our model. But to identify sustainable power we need to know time on course! Thankfully computers are pretty good at solving these kind of problems…we can “iterate” a power value until we “solve” for a rider’s best sustainable power. Best means the number that leads to the fastest time on course, but rests no higher than the rider’s curve at that duration.

CPL Event Models have always solved for best sustainable power based on your inputs, but we haven’t done a great job of explaining this. That’s why, as of now, where you see our power prediction, you can click on “why this number?” to open an insightful chart. You’ll see two lines 1) Power Duration Curve… based on observed power inputs & 2) Power For Time on Course. Where these lines cross (and a rider’s fitness would have to be critically unsuited to the event if they don’t) is best sustainable power.

Best sustainable power is clearly a key number for any rider wanting to go as fast as possible, even if a variable power pacing strategy has been discounted. So of course we’d encourage everyone to use the free analytics here on CPL in uncovering that number. No rider can out-perform his fitness but the number comes with two guarantees. First, said rider will finish about as fast as he can, no guess work. And second, the suffering will end about as soon as it can do. Smart riders should like the sound of both.

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