Federación Bizkaina de Baloncesto

Observatorio de Baloncesto de Bizkaia

A scalable framework for NBA player and team comparisons using player tracking data


Player tracking data provides a platform for the creation of new basketball statistics that can dramatically improve the ability to evaluate and compare player performance. However, the increasing size of this new data source presents challenges in how to efficiently analyze the data and interpret findings. A scalable analytical framework is needed that can effectively reduce the dimensionality of the data while retaining the ability to compare player performance. In this paper, Principal Component Analysis (PCA) is used to identify four components accounting for 68% of the variation in player tracking data from the 2013-2014 regular season. The most influential statistics on these new dimensions are used to construct intuitive, practical interpretations. In this high variance, low dimensional space, comparisons across any or all of the principal components are possible to evaluate characteristics that make players and teams similar or unique. A simple measure of similarity between player or team statistical profiles based on the four principal components 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 principal component dimensions and SDI for improved comparisons. Using principal component scores and SDI, several use cases are presented for improved personnel management. Team principal component scores are used to quickly profile and evaluate team performance, more specifically how New York’s lack of ball movement negatively impacted success despite high average scoring efficiency as a team. SDI is used to identify players across the NBA with the most similar statistical performances to specific players. All-Star Tony Parker and shooting specialist Anthony Morrow are used as two examples and presented with in-depth comparisons to similar players using principal component scores and player tracking statistics. This approach can be used in salary negotiations, free agency acquisitions and trades, role player replacement, and more.

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Datos generales

Fecha publicación:
Bruce, Scott
Línea de investigación:
Filosofía del Juego
Tipo de documento:
Estudio académico

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