I'm responding to this post here, just to reduce clutter:
PedroKsBambino said:
To be clear, no one has suggested perfection is the goal, or the expectation. The concern expressed by two of us was specific, and likely material (though we can't be sure of course) if it hasn't been addressed. It may have been; you don't seem to know which is fine---I surely don't either.
A poster actually did suggest a lack of perfect as an issue ("That is a reasonable approach; however, it is far from a perfectly accurate one, too"), but given we all seem to be in agreement that it's not, I'm happy to move on.
What are your concerns specifically? I still honestly don't understand them. luckiestman's issues I can't really deal with, since he hasn't explained how it relates to something like this (he's posted interesting links, but I'm not smart enough to see how they relate here).
Your concerns I'm closer to grasping, but we seem to be talking past each other a bit, so maybe you can explain your concerns again?
1. Is your issue simply that performance is non-linear? (i.e. on good teams with plenty of outside shooting, Rajon Rondo is a +2 player, while on a bad team, he's neutral?). Is this what you were getting at with the "slow" player example? Phrased another way, "fit" is a real phenomenon, and players are not simply a collection of plusses and minuses.
If so, this is both obviously true and important, and no, xRAPM isn't doing much to deal with this issue. This isn't the sort of thing xRAPM can fudge - this would require a radical makeover of xRAPM. There are three parts of xRAPM that midly (but really only mildly) deal with this. 1) They use box score data - insofar as a player is putting up good/bad numbers, but the rest of his team is totally concealing their effect, this will be corrected for slightly; 2) There is data about height and position included (which hints at "fit"); 3) The regression takes into account that teams with a big lead tend to ease up, and that teams way behind tend to catch up - insofar as certain players are only coming in during certain game situations, that will be captured.
But generally, no, "fit" is not accounted for. If the Mavericks assembled Patrick Beverly, Kyle Korver, Andre Iguodala, David West, and Joakim Noah, that would rate as one of the best starting 5s in the NBA (maybe the best). But potentially, they'd never get a shot off, and lose every game 60-55 or some other bizarrely low score.
In practice, this isn't such a big problem. Teams take fit into account, and to my knowledge, so does everyone using xRAPM as a player assessment technique. Furthermore, even in the abstract, it's hard to know how much "fit" matters when the talent is there. That starting 5 above might be great for all I know because the talent is so good - they might win every game 70-60 alternatively in other words.
Either way, as I posted above, the proof is in the pudding. xRAPM techniques yield extremely accurate forward looking results. If this "fit" issue were especially important, this that would not be true. But no, "fit" is not dealt with.
2. You posted another issue about "we need to recognize that outliers (such as Faverani's bad-ness) will tend to get washed out to a larger degree than they probably should" - what did this mean? I thought you were referencing regression to the mean, but that seems not to have been the case. What did you mean?
What other questions do you have? I'm pretty familiar with just about everything in xRAPM (although I can't replicate it - I can only do RAPM, without the box score/height data), so I'm happy to help clear up some questions.