APBRmetrics

Given that you're spending a lot of time and thought on this anyway, why not spend some additional time to run your own RAPM? That'd give you the freedom to play around in e.g. regards to how many seasons to use, playoffs vs no playoffs, validation and test splits, among all kinds of other design de...

George - Leonard rates as best pair at +3.7... but sum of xRAPM is +7.1? Different degrees of regression or not playing well together or ? Pair RAPM is not influenced by other data. It's just RAPM ran with only pairs, on both offense and defense. It will correlate, but probably never equal the numb...

https://xrapm.com/ is now live and is being updated regularly For now its' main focus is impact metrics of various kinds. It also has shot charts, and other charts showing a player's development across standard BoxScore stats "Three Factor" RAPM is available, as is coach RAPM, 29-year RAPM...

In the 28-year rebounding version of this he's 2nd out of ~2,700 players in DReb% impact
https://docs.google.com/spreadsheets/d/ ... sp=sharing
Most players in his DReb% impact range are retired

So it would basically be RAPM but instead of points it would be effect on OREB%, TOV%, and that then right? I remember awhile back I feel I saw it on that nba rapm site before it all changed and everything, they did it in a way to get those things all add up to the RAPM number for the offensive or ...

Adjusted FtR is kind of pointless, so it's a good idea to combine eFG and FTs (including And1s) into a single factor

On the cutoff, I think clearly the answer is no. You want negative examples in your dataset just as much as you want positive ones. Normalization: Unless you meant some other kind of normalization, yes, you should probably be z-scoring X Three years seems standard says who but it still has some erro...

A lot of these questions depend on what your goal is. Since it doesn't appear that you're trying to build an SPM that best predicts future lineup performance, not all of these questions have a one-size-fits-all answer Averaging Stats: Is using a player's average stats across a period a promising app...

When you do a weighted average of JE's by possessions it averages to 0. I can't figure out why this is and would love any insights. If anyone has any insights on the differences i'd love to hear them I simply force it to be this way, ie I compute the possession weighed average and then subtract tha...

Is scaling appropriate? Done or not in popular versions? https://www.cryptbeam.com/rapm/ I'm gonna go ahead and say "inappropriate" The year-to-year swings of the penalization parameter (lambda) are actually extremely small. If you were to put a prior on your lambda (yes..) then it become...

In trying to come up with a replacement-level prior to correct for the fact that low-minute players are regressed towards 0, I ran RAPM with all players with less than 1000 possessions played over a 5-year period assigned to the same special player ID. This cumulative "replacement player"...

Wouldn't the intercept be interpreted as the expected pts/poss of a lineup of five players with 0 O-RAPM vs five players with 0 D-RAPM? Is that the wrong interpretation? That's not incorrect, but you can get practically the same effect from simply subtracting average pts/poss from your Y I only the...

https://github.com/rd11490/NBA_Tutorials/blob/master/rapm/rapm.py (relatedly, is this code considered good for RAPM calculation? Are there any needed adjustments, like are the lambda values good?). There are a couple of things in that implementation that I'm not a huge fan of, but it's not immediat...

I made the last image in a bit of a rush
Here's a better picture that's not just better centered, but also shows sample size

In-sample, it looks like this https://i.imgur.com/muj2r5X.png Seems fairly linear, with some noise. If I restrict to points that have larger sample size, R^2 increases. For the picture you see, it's 0.63 Could be a different story for out-of-sample, of course My hunch is that's it's still linear, as...