A Markov Approach to Untangling Intention Versus Execution in Tennis

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Doug Fearing, Stephanie Kovalchik, Tim Chan, Craig Fernandes


In this paper, we develop a novel modeling framework based on Markov reward processes and Markov decision processes to investigate how execution error (i.e., not hitting the ball exactly where a player intended) impacts a player's value function and strategy in tennis. We power our models with hundreds of millions of simulated tennis shots with 3D ball and 2D player tracking data. We find that optimal shot selection strategies in tennis become more conservative as execution error grows, and that having perfect execution with the empirical shot selection strategy is roughly equivalent to choosing one or two optimal shots with average execution error. We find that execution error on backhand shots is more costly than on forehand shots, and that optimal shot selection on a serve return is more valuable than on any other shot, over all values of execution error.