Andrew Coggan wrote:
There are alternatives to OLS, and I explored many of them when developing and fitting the model (note that the structure of the model and how it is fit are two different things). Unfortunately, none of these approaches are really valid solutions, as you end up"chasing noise" and routinely overestimating what someone can do. IOW, use of an "envelope fit", i.e., fitting only the extremes of the extremes, results in bias (this I have also demonstrated with large samples).Thanks for the reply. I, as many others do I'm sure, do genuinely appreciate you coming on here to explain, elaborate and defend your work from armchair critics like me. My post wasn't meant as a snipe as such, more of an observation!
That's an interesting (and somewhat disappointing) point regarding the possible bias of other fit methods. I guess, as you say, with a more comprehensive set of data the errors start to cancel each other out and this is clear just by looking at my own data over longer time-frames.
asgagd wrote:
Also, Andrew tries to fit the data, instead of trying to predict possible performance. Most people expect the second thing. I still don't truly understand the purpose of the first thing. But maybe my mind is to feeble to understand?That's is an interesting way to look at it. I think there's clearly merit and utility in both approaches, the former for analysing and comparing prior performances (e.g. comparing 2017 vs 2016 power profiles) and the latter for prescribing a training structure. How well each one is catered for I guess depends on the quality and diversity of your MMP data input.