Characterizing the spatial structure of defensive skill in professional basketball
Alexander Franks, Andrew Miller, Luke Bornn, Kirk Goldsberry

TL;DR
This paper develops a novel quantitative approach using spatial, temporal, and statistical models to analyze and characterize defensive performance in professional basketball, addressing limitations of traditional metrics.
Contribution
It introduces a comprehensive framework combining spatial, temporal, and matrix factorization techniques to quantify defensive skills using player tracking data.
Findings
Supports some common views of defensive effectiveness
Challenges traditional defensive metrics
Provides new insights into defensive elements
Abstract
Although basketball is a dualistic sport, with all players competing on both offense and defense, almost all of the sport's conventional metrics are designed to summarize offensive play. As a result, player valuations are largely based on offensive performances and to a much lesser degree on defensive ones. Steals, blocks and defensive rebounds provide only a limited summary of defensive effectiveness, yet they persist because they summarize salient events that are easy to observe. Due to the inefficacy of traditional defensive statistics, the state of the art in defensive analytics remains qualitative, based on expert intuition and analysis that can be prone to human biases and imprecision. Fortunately, emerging optical player tracking systems have the potential to enable a richer quantitative characterization of basketball performance, particularly defensive performance.…
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