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I address the difficult challenge of measuring the relative influence ofcompeting basketball game strategies, and I apply my analysis to playsresulting in three-point shots. I use a glut of SportVU player tracking datafrom over 600 NBA games to derive custom position-based features that capturetangible game strategies from game-play data, such as teamwork, playermatchups, and on-ball defender distances. Then, I demonstrate statisticalmethods for measuring the relative importance of any given basketball strategy.In doing so, I highlight the high importance of teamwork based strategies inaffecting three-point shot success. By coupling SportVU data with an advancedvariable importance algorithm I am able to extract meaningful results thatwould have been impossible to achieve even 3 years ago. Further, I demonstratehow player-tracking based features can be used to measure the three- pointshooting propensity of players, and I show how this measurement can identifyeffective shooters that are either highly-utilized or under-utilized.Altogether, my findings provide a substantial body of work for influencingbasketball strategy, and for measuring the effectiveness of basketball players.

An Investigation of Three-point Shooting through an Analysis of NBA Player Tracking Data