  During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with a
difficult decision when defending against the Cavaliers: "Do you double and
risk giving up easy shots, or stay at home and do the best you can?" It's a
tough call, but finding a good defensive strategy that effectively incorporates
doubling can make all the difference in the NBA. In this paper, we analyze
double teaming in the NBA, quantifying the trade-off between risk and reward.
Using player trajectory data pertaining to over 643,000 possessions, we
identified when the ball handler was double teamed. Given these data and the
corresponding outcome (i.e., was the defense successful), we used deep
reinforcement learning to estimate the quality of the defensive actions. We
present qualitative and quantitative results summarizing our learned defensive
strategy for defending. We show that our policy value estimates are predictive
of points per possession and win percentage. Overall, the proposed framework
represents a step toward a more comprehensive understanding of defensive
strategies in the NBA.
