  We present a data-driven basketball set play simulation. Given an offensive
set play sketch, our method simulates potential scenarios that may occur in the
game. The simulation provides coaches and players with insights on how a given
set play can be executed. To achieve the goal, we train a conditional
adversarial network on NBA movement data to imitate the behaviors of how
players move around the court through two major components: a generator that
learns to generate natural player movements based on a latent noise and a user
sketched set play; and a discriminator that is used to evaluate the realism of
the basketball play. To improve the quality of simulation, we minimize 1.) a
dribbler loss to prevent the ball from drifting away from the dribbler; 2.) a
defender loss to prevent the dribbler from not being defended; 3.) a ball
passing loss to ensure the straightness of passing trajectories; and 4) an
acceleration loss to minimize unnecessary players' movements. To evaluate our
system, we objectively compared real and simulated basketball set plays.
Besides, a subjective test was conducted to judge whether a set play was real
or generated by our network. On average, the mean correct rates to the binary
tests were 56.17 \%. Experiment results and the evaluations demonstrated the
effectiveness of our system.
