awenborn wrote:
Andrew Coggan wrote:
OTOH, the use of OLS *will* do so if the dataset to which you fit the model doesn't contain enough maximal efforts...but 1) that's not a limitation of the *model*, and 2) there are no viable alternatives.I doesn't really need stating, but it's fundamentally impossible for a given dataset to have maximal efforts at all possible durations; any dataset will have some points of maximal effort and some (arguably lots more) points of sub-maximal effort. If all datapoints are given an equal weighting, as in the OLS method, then the PD curve will seemingly always underestimate your true capacity at a given duration.
The margin by which it underestimates will be dependent on the number of maximal efforts that you have at and around that given duration, and the practical significance of that margin of error may well be negligible. Regardless, given the inherently finite and transitory nature of the data being analysed here, is my original statement that it will always underestimate your true capacity not valid?
On your other points, notably the suitability of alternatives, I will happily defer to your substantially greater experience.
You are ignoring biological and technological variability. You are also conflating the map with the territory.
IOW, instead of thinking of your mean maximal power data as "clean" curve, you should think of it as a blurred/smudged line (with a width of +/- ~5%). Only if a sufficient number of points at critical durations* fall significantly below this zone or region will the model parameters be significantly biased. Furthermore, you need to recognize that just because a model predicts that you can do something, doesn't mean that you actually can. IOW, just because your actual data fall below (or above) the fitted curve doesn't necessarily mean that isn't a valid measure of your maximal performance at that duration.
Empirically, looking backwards 90 d is generally adequate to avoid issues in racing cyclists, at least throughout most of the year. OTOH, if, e.g., you become a strict trainer drone in winter, or are a triathlete or runner, it is less likely that you will spontaneously generate data robust enough to provide valid estimates of all of the parameters, and some formal "curve maintenance" testing may be required.
*Although all points used in the fitting exert some influence, some have more leverage than others. On average, for example, your maximal 47 s power has the greatest influence on the estimated FRC, but (obviously, I would think) has little to not impact on your estimated stamina.
Last edited by:
Andrew Coggan: Nov 8, 17 8:23