A Scalable Framework for NBA Player and Team Comparisons Using Player Tracking Data

TL;DR

This paper introduces a scalable PCA-based framework for analyzing NBA player tracking data, enabling efficient comparison of players and teams by reducing data dimensionality and developing a similarity measure.

Contribution

The paper develops a PCA-based dimensionality reduction method and a similarity index (SDI) for NBA tracking data, facilitating intuitive comparisons and scalable analysis.

Findings

Identified four principal components explaining 68% of data variance.

Created the Statistical Diversity Index (SDI) for quick player and team comparisons.

Demonstrated use cases for personnel management using the framework.

Abstract

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.