Posted by Neil Paine on July 10, 2009
I just realized I've been derelict in my linking duties recently, because I haven't thrown any love to the Basketball Geek, Mr. Ryan Parker, for some of his posts this week on multilevel modeling. Basically, MLM is a type of regression technique that you'd use in real-world situations where contextual effects occur on several levels (hence the name) and make it difficult to assume that the errors for each coefficient are uncorrelated. And basketball, as we know too well, is a game where performance is often heavily context-driven, so MLM is certainly a method that deserves more investigation as APBRmetrics becomes more and more sophisticated. This past week, Ryan used this type of random-effects model to predict 3-point shooting ability and offensive rebounding ability based on age and past performance. Essentially it's a really fancy way of regressing to the mean, but this method also has the potential to do a lot more than that because you can theoretically control for some of those pesky contextual effects that we analysts often run into when trying to unravel a game as complex as basketball.