  The release of NBA player tracking data greatly enhances the granularity and
dimensionality of basketball statistics used to evaluate and compare player
performance. However, the high dimensionality of this new data source can be
troublesome as it demands more computational resources and reduces the ability
to easily interpret findings. Therefore, we must find a way to reduce the
dimensionality of the data while retaining the ability to differentiate and
compare player performance.
  In this paper, Principal Component Analysis (PCA) is used to identify four
principal components that account for 68% of the variation in player tracking
data from the 2013-2014 regular season and intuitive interpretations of these
new dimensions are developed by examining the statistics that influence them
the most. In this new high variance, low dimensional space, you can easily
compare statistical profiles across any or all of the principal component
dimensions to evaluate characteristics that make certain players and teams
similar or unique. A simple measure of similarity between two player or team
statistical profiles based on the four principal component scores is also
constructed. The Statistical Diversity Index (SDI) allows for quick and
intuitive comparisons using the entirety of the player tracking data. As new
statistics emerge, this framework is scalable as it can incorporate existing
and new data sources by reconstructing the principal component dimensions and
SDI for improved comparisons. Using principal component scores and SDI, several
use cases are presented for improved personnel management.
