  I address the difficult challenge of measuring the relative influence of
competing basketball game strategies, and I apply my analysis to plays
resulting in three-point shots. I use a glut of SportVU player tracking data
from over 600 NBA games to derive custom position-based features that capture
tangible game strategies from game-play data, such as teamwork, player
matchups, and on-ball defender distances. Then, I demonstrate statistical
methods for measuring the relative importance of any given basketball strategy.
In doing so, I highlight the high importance of teamwork based strategies in
affecting three-point shot success. By coupling SportVU data with an advanced
variable importance algorithm I am able to extract meaningful results that
would have been impossible to achieve even 3 years ago. Further, I demonstrate
how player-tracking based features can be used to measure the three- point
shooting propensity of players, and I show how this measurement can identify
effective shooters that are either highly-utilized or under-utilized.
Altogether, my findings provide a substantial body of work for influencing
basketball strategy, and for measuring the effectiveness of basketball players.
