APBRmetrics

back2newbelf · Joined: 21 Jun 2005 Posts: 275

I can now easily update most files by the press of a button, so there might be almost daily updates from now on. Keep in mind that most ratings, especially the multiyear ones, don't change much from day to day. Time of update is now listed at the top of each ranking. The one year ranking will mostly be updated through the playoffs

I'll probably add another rating soon where I compute ratings of coaches on multiyear data (going back to 2002), then add those ratings to the computation of 1 year player ratings while leaving the coach ratings fixed. That might give a better estimation of players that play on super good/bad defensive teams. Of course, prediction performance of this method will have to be tested first.

Comments on the ratings:
-Thibodeau looks awesome, I didn't think it was possible to jump to the #1 spot after less than a full season
-Completely disagrees with the firing of O'Brian, but this method only measures the influence on the players being on the court. Did he have really weird substitution patterns?
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EvanZ · Joined: 22 Nov 2010 Posts: 303

Are you planning to do adjusted four factors any time soon?
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EvanZ · Joined: 22 Nov 2010 Posts: 303

Someone asked before, but are there standard errors somewhere? Even a ballpark would be nice for the 1-yr vs. 3.x-yr.
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back2newbelf · Joined: 21 Jun 2005 Posts: 275

Ilardi · Joined: 15 May 2008 Posts: 265 Location: Lawrence, KS

xkonk · Joined: 26 Jan 2011 Posts: 5

Just a few reasonable-sounding results from a google search:

http://stats.stackexchange.com/questions/2121/how-can-i-estimate-coefficient-standard-errors-when-using-ridge-regression

www.m-hikari.com/forth2/rashwanIJCMS9-12-2011.pdf (more math-y)

http://www.stat.purdue.edu/~xbw/courses/stat512/notes/topic5a.pdf

Across a few of the results, it seems like the main benefit of ridge regression is better predictive power, so the error of the predictors themselves aren't as critical as the error of the model overall. Response number two in the first link might be the most useful though: the errors may not have a real meaning at all in the context of ridge regression.

DSMok1 · Posted: Fri Mar 25, 2011 3:04 pm Post subject:

I agree with xkonk. I don't think the standard errors can be found that are meaningful. However, you could perhaps come up with some rough estimates based on out-of-sample predictions. Not sure how to do it, though.

The reason stderrs aren't going to be meaningful in ridge regression is this:

You're taking a highly correlated problem. You throw in a bunch of observations of 0 (this is the effect of ridge regression). Then if you get the standard errors of the regression, they'll be quite low using bootstrap or anything. The 0s cause that effect. But the 0s aren't actual data...in fact, they should have an "error" associated with them.
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Crow · Joined: 20 Jan 2009 Posts: 825

The Sloan research papers are available.

http://www.sloansportsconference.com/research-papers/2011-2/posters/

Anyone care to review and comment on them?

Piette used ridge regression and player pairs in the model. Omidiran used player pairs and boxscore stats in a single regression model I believe (the feasibility and desirability of which I have asked about before) but I don't think he used ridge regression.

Neither used multi-season to further reduce errors or made any other adjustments to my quick read beyond home-court.

Other things that might improve signal or reduce noise include: use of a performance "prior" or minutes or both, aging curves, consideration of the affects of rest & elevation, coaches, possession type and adjustment for clutch & garbage time.

Both cite improvement over their comparison base model. The changes are helpful but in the big picture might still be called modest, evolutionary ones and insufficient to win responsible high reliance on this model alone, though it could still be considered helpful as information from one tool among several with the weight still leaning, perhaps, towards direct boxscore based approaches.

Anyone here plan to incorporate either method for addressing player pairs into their APM version? Any reservations about how they did it?

Anyone plan to add more of the other adjustments listed above to try to reduce noise?

By the way, back2newbelf does the RAPM available at your site use your possession type sensitive approach? I assume it does, but want to be sure.

Ryan J. Parker · Joined: 23 Mar 2007 Posts: 711 Location: Raleigh, NC

Why can't you just use the closed form solution? Is it because of the way lambda is chosen?
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DSMok1 · Posted: Fri Mar 25, 2011 4:31 pm Post subject:

Ryan J. Parker · Joined: 23 Mar 2007 Posts: 711 Location: Raleigh, NC

http://www.stat.sc.edu/~hansont/stat704/Lecture18.pdf

Slide 17 is what you want I think.

Now you'll have to do a little work to connect all of those dots in the formula, but that looks like something you could compute.
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bbstats · Joined: 25 Apr 2010 Posts: 46

I want to see RAPM weighted (or adjusted) by possession leverage as Pomeroy has quantified! Someone should find the calculations for NBA games...

EDIT: Or even better - % increase or decrease in Win% as opposed to Points per 100.
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EvanZ · Joined: 22 Nov 2010 Posts: 303

DSMok1 · Posted: Wed Mar 30, 2011 4:40 pm Post subject:

Note that Jeremias Engelmann is back2newbelf, EvanZ... I wonder if any of the other researchers post here or at least read this forum?
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EvanZ · Joined: 22 Nov 2010 Posts: 303