Your power-duration curve, the best watts you can hold at every
duration from a sprint to your longest ride, is treated as a measurement
of what you can do. But it is really a best-of. And you can’t produce
your best across the full power-duration span in just a ride or two. So
how many rides of a decent duration would you need to get a solid
estimate of your ability?
We were building a durability study on GoldenCheetah field data and
wanted to know the effect of different observation windows on the MMP
(mean maximal power), that same best-watts-at-every-duration curve.
Working with our cleaned dataset, GCclean, we took 200,050 rides of 1.25
to 2.25 hours, across 1,854 riders and 3,966 rider-seasons. These are
only the rides that fall in that duration band, not the whole season,
about 51 per rider-season on average. For each season we recomputed the
power curve while shrinking the pool of rides we searched: all of them,
then 75%, 50%, on down to a single ride.
No surprise, the curve sags
Compared to searching all of those rides, a single ride will have an
MMP 39% lower at 15 seconds, and 22% at 20 to 30 minutes. The sprint end
of the curve will on average be further off the all-rides MMP than the
endurance end. In this dataset, the MMP estimate got much better at
about 15 rides and was solid by 25 or so. These are randomly sampled
rides so that number could likely be lower with a few intentional
efforts spread out over several rides.

Thinner data is also noisier
An MMP built from a couple of rides is not only lower, but jumpier as
well. The variability is higher at the short end, with a one-ride sprint
peak (Pmax) varying about 27%, while the endurance end varies by about
14% from ride to ride.

What to take from it
I think the main take home is the importance of throwing in some
dedicated efforts sprinkled across the duration of the curve on a
regular basis. Then when thinking about monitoring trends, 2 weeks is
going to be a bare minimum for looking for changes in trends. For
reliable data, month to month comparisons would be the way to go.
One caveat: these numbers are averages across about 4,000
rider-seasons, so treat the ride counts as a rule of thumb for the
group, not a guarantee for any one rider.
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