NBA Court Realty

 

Dan Cervone, Moore-Sloan Data Science Fellow, New York University

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Abstract: Continuously throughout a basketball possession, offensive and defensive players control different regions of the basketball court. An efficient offense creates opportunities for ball control in high value regions (for the appropriate players), whereas good defense suppresses such opportunities. From this competitive dynamic alone, we can infer the implicit value of positioning and spacing among NBA players and teams using hierarchical, spatially regularized paired comparison models.
This allows us to quantify the NBA court “real estate market”, enabling new insights and metrics for both offense and defense. For instance, we can measure players’ off-ball impact on offense by calculating the value of the space freed up for their teammates to control. For analyzing defense, we can quantify how effectively different teams (and different lineups within teams) contain the offense within low-value regions of the court.

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