A Hierarchical Linear Modeling Approach to Assessing NBA Player and Team Performance

With teams’ annual payrolls nearing $100 million and valuations for some teams exceeding $2 billion, the National Basketball Association is big business. That being the case, many pro-basketball general managers are turning to analytics to discover ways to improve organizational performance. The purpose of this paper was to highlight the results of an analytics-based assessment of both player and team performance, using data from the regular 2012-2013 NBA season. The analytical paradigm described in this paper consisted of a two-tiered hierarchical linear modeling design that combined a number of specific on-court and off-court factors. The analysis also introduced a relatively new sports performance metric—entropy, which can be used to measure the degree of disorder at both the player and the team level. The target variable was Hollinger’s Player Efficiency Rating. The results of the analysis revealed that a number of factors were statistically significant, including players’ ages, entropy, and compensation. NBA general managers can use this modeling approach to evaluate both trade and draft opportunities.