Finally finished my piece on flexible NHL aging. I& #39;ll post a couple of important things in a thread here, but before any of that, here is the link to the piece. http://rpubs.com/cjtdevil/nhl_aging">https://rpubs.com/cjtdevil/...

The piece is in 3 parts. In part 1, I talk about the basics of aging including the standard delta method, why I prefer regression, and a commentary on the impact of "survivor bias"

There is very little math in this part, and is pretty generalizable.

In part 2, I explain the logic behind GAMs in this context, and show some preliminary results on what aging curves would look like if built off that foundational model --

gam( gar60 ~ player + s(age) )

This is also not TOO technical.

In part 3, I explain a potential method -- tensor-products -- for making aging curves flexible without losing the generalizability of the model:

gam( gar60 ~ player + te(age, decay_rate) )

This one is a little more technical, but also produces pretty pictures like this.

Thanks to @EvolvingWild, @jc_bradbury, @tangotiger, @IneffectiveMath, @903124S, and @garik16 all of whom I spoke with about this piece at some point, as well as @RK_Stimp, @iyer_prashanth, and @pflynnhockey who helped me edit.

I worked on this for about 2 months and I don& #39;t think it& #39;s perfect, but I& #39;ve been procrastinating for too long, so if you can make it through it, let me know what you think -- good or bad.