The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA

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Abstract: During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with a difficult decision when defending against the Cleveland 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 whether the defense was 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. In particular, when double teaming Kyrie Irving on the 3 point line, the learned policy suggests leaving a man on the opposite wing open upon an attack from left, and leaving a man in the paint open upon an attack from the right. Based on data from past seasons, when doubling against the Cavs, we estimate that the Indiana Pacers and the Atlanta Hawks had the most room for improvement, while the Chicago Bulls and the Golden State Warriors were playing closest to the learned strategy. Overall, the proposed framework represents a step toward a more comprehensive understanding of defensive strategies in the NBA.

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