Individual Player Value (“IPV”) is a Bayesian RAPM model, which uses a robust machine learning based SPM metric (“FORPM”) as a prior.

Our FORPM prior takes as inputs advanced boxscore statistics, demographic information on the players (height, age, wingspan, vertical, etc.), and a variety of interaction terms (such as Height*3PA/100 for floor spacing bigs). The choice of using a machine learning model (an ensemble consisting of random forest regressions and gradient boosting), rather than a traditional OLS regression, was made to allow us to throw the “kitchen sink” at the problem without worrying too much about overfitting the dataset, which is a common pitfall with standard regression techniques. As such, a properly specified machine learning model performs better out of sample than more plain vanilla regression-based models (especially with regard to defense).

We then calculate IPV by running the RAPM procedure, regressing to that FORPM prior. There is no previous season information used, to put it on par with other in-season metrics such as PER, WS, ASPM, or EZPM.

For those interested in a pure RAPM model (using previous year RAPM as prior), please visit our friends at http://www.gotbuckets.com, who now publish our RiRAPM values (updated weekly). They also calculate SwagR (a wins equivalent version of RAPM) using those values.

Tweets by @talkingpractice

Hi,

Great work, and thanks for sharing. Wondering if there is data available for players other than the 20 listed above. Looking for point guards, actually.

Hi, I’m just wondering if you’ll put the IPV back up? I’m curious, do you think the Rockets with Dwight will be real contenders? is it possible to see the ipv somewhere else?

We’ll likely put up some interesting/random IPV numbers from time to time during the season, but will probably not have time to regularly publish/update the entire list of player values.

Interesting. Last year, you used prior season data as well, right? That makes it more predictive. Will you be posting top 20 lists of that version? Is Lebron still on top there?

So sorry, but we somehow missed this until now. We did use prior season data last year, but decided to make it an in-season only metric this season. So IPV is simply “boxscore informed RAPM”, with as little overfitting as possible. We do track that other version internally, and Lebron is still first on that list as of today (Feb 8th).

Hi, thanks for posting updated RAPM, and not doing the box-score infused nonsense. Two things I wouldn’t mind seeing if it’s not too much trouble:

1. NPI RAPM just to get a sense of how heavily the priors influence the results.

2. Standard errors on all estimates. Always annoys me how no one that does RAPM shows these.

Another question about RAPM-do you or does anyone else weight the observations by minutes played? I’ve never seen a mention of this. Seems like it would go a long way to reduce the noise of NPI-APM.

Hi,

1, We might put up NPI at some point, but there doesn’t seem to be much demand for that at the moment from the analytics community, now that things like IPV, RiRAPM, xRAPM, etc are out there.

2, We have standard errors calculated via bootstrapping, but keep those internally.

3, If I’m understanding your question correctly, you’re asking if the final regression equation is weighted by the number of minutes (we use possessions) played by that 5v5 matchup. We do indeed do that (as does everyone else publishing numbers like this, I think).