Basketball Flow: Learning to Synthesize Realistic and Diverse Basketball GamePlays based on Strategy Sketches

In this study, we present BasketballFlow, a system designed to generate diverse basketball gameplays based on a pre-determined strategy sketch. A strategy sketch is a graphical representation that coaches use to outline their planned tactics, encompassing the projected routes of the ball and the offensive players. Despite the visual depiction of the offensive strategy, less experienced players might find it challenging to fully understand these tactics and often falter in their implementation due to interference from defensive players. Our system aims to remedy this by simulating different game scenarios that illustrate potential defensive maneuvers, thereby aiding these less experienced players in improving their success rate of tactical execution. BasketballFlow is composed of a variational generative adversarial network (VAEGAN) and a normalizing flow. The VAEGAN is tasked with producing highly accurate game scenarios, while the normalizing flow ensures a wide diversity in the simulated outcomes. Compared to other existing methods, BasketballFlow demonstrates superior proficiency in simulating a broad spectrum of gameplays while maintaining a lower Frèchet distance to real gameplays. The effectiveness of our BasketballFlow system is validated through our experimental results.