Koi Stephanos, Ghaith Husari, Brian Bennett, Matthew Harrison, Emma Stephanos
This paper introduces a novel approach to precision abstraction as it relates to the classification and analysis of play-actions in the NBA. We look specifically at dribble-hand-offs as observed from SportVU player tracking data and embed the raw coordinate data into hex map representations. We then outline the architecture for an automated pipeline capable of selecting action instances and clustering them into variants. These action variants can then be used to further differentiate players and strategies within the game context in which they are implemented.