No, he isn't correct, as the model is not "fundamentally predisposed to underestimate power output."

OTOH, the use of OLS to fit the model will do so if the data set in question doesn't contain enough maximal efforts, as I have always emphasized (contrary to asgagd's patently-false statement). However, 1) that's not a limitation of the model, and 2) there are no viable alternatives. in particular, any sort of "envelope fit" results in biased parameter estimates.


The purposes are 1) to extract accurate (i.e., unbiased) and precise* parameter estimates reflective of the physiological characteristics that determine your performance ability, and 2) to smooth the mean maximal power curve to minimize noise/error in subsequent calculations (e.g., calculation of individually-based training levels).

OTOH, if all you want to do is predict someone's performance, I would suggest following the advice of whomever it was that originally opined "the best predictor of performance is performance itself."

*Note that to this day WKO4 remains the only program on the market that natively provides valid goodness-of-fit statistics. Other programs either leave you in the dark, or lie to you by providing R^2 values for non-linear curve fits (which is not a statistically valid approach).