Analyzing the effects of NBA head coaches

Abstract Evaluating the value-added of coaches in the NBA is a challenging task as the coaches with the best win/loss records often have the best players. This prompts a question of attribution: if two coaches had the same roster, which one would win? This paper attempts to answer this question by introducing a method for quantifying coaching effect in the NBA. We propose a method for isolating the effect of a coach’s in-game scheme on their team’s probability of winning a game while controlling for other factors, namely the relative strength of the two competing teams. To control for team strength, player performance metrics are aggregated into “Team-Adjusted VORP Difference” or ΔtVORP, meant to account for the difference in quality of on-court product between both teams. We model each coach’s win probability as a function of ΔtVORP using probit monotone Bayesian Additive Regression Trees. In comparing coaches’ win probability curves, we find some of the winningest coaches are close to average in terms of scheme, while other coaches are found to be truly great contributors to their teams.