  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. Unfortunately, due
to computational and methodological complexities, that potential remains unmet.
This paper attempts to fill this void, combining spatial and spatio-temporal
processes, matrix factorization techniques and hierarchical regression models
with player tracking data to advance the state of defensive analytics in the
NBA. Our approach detects, characterizes and quantifies multiple aspects of
defensive play in basketball, supporting some common understandings of
defensive effectiveness, challenging others and opening up many new insights
into the defensive elements of basketball.
