Finding Common Characteristics Among NBA Playoff and Championship Teams: A Machine Learning Approach

Abstract

In this paper, we employ machine learning techniques to analyze fifteen seasons of NBA regular season data from every team to determine the common characteristics among NBA playoff teams. Each team was characterized by 44 predictor variables and one binary response variable taking on a value of "TRUE" if a team had made the playoffs, and value of "FALSE" if a team had missed the playoffs. After fitting an initial classification tree to this problem, this tree was then pruned which decrease the test error rate. Further to this, a random forest of classification trees was grown which provided a very accurate model from which a variable importance plot was generated to determine which predictor variables had the greatest influence on the response variable. The result of this work was the conclusion that the most important factors in characterizing a team’s playoff eligibility are the opponent field goal percentage and the opponent points per game. This seems to suggest that defensive factors as opposed to offensive factors are the most important characteristics shared among NBA playoff teams.