Individual Player Value (IPV) is a Plus/Minus (RAPM) model, which uses a robust machine learning based statistical plus minus metric (FORPM) as a Bayesian prior.
Our FORPM prior takes as inputs advanced boxscore statistics and a variety of interaction terms, some involving demographic information on the players (such as Height*3PAR to capture the value of 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 standard 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.
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