Player Evaluation Metrics
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|Regularized Adjusted Plus Minus (RAPM)|
Author: Joe Sill
In "Regularized Adjusted Plus-Minus" (RAPM), the goal is to provide more accurate results by employing a special technique called "ridge regression" (a.k.a. regularization). It significantly reduces standard errors in Adjusted Plus-Minus (APM).
Conventional adjusted plus-minus is shown to do a poor job of predicting the outcome of future games, particularly when fit on less than one season of data. Adding regularization greatly improves accuracy, and some player ratings change dramatically. The enhancement with the RAPM is a Bayesian technique in which the data is combined with a priori beliefs regarding reasonable ranges for the parameters in order to produce more accurate models. That is what ridge regression (a.k.a. regularization) does.
Comments: RAPM is about twice as accurate as an APM using standard regression and using 3 years of data, where the weighting of past years of data and the reference player minutes cutoff has also been carefully optimized.