Adjusted plus/minus has grown in popularity as an NBA player evaluation technique but remains controversial and can yield results which many basketball experts find counterintuitive. We present a framework for evaluating adjusted plus/minus and an enhancement to the technique which nearly doubles its accuracy. 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. Broader lessons for the sports analytics community regarding model evaluation and the use of Bayesian techniques are discussed.
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