LAUSR

Basketball is a game of simultaneous actions, and inter-player coordination is key for offensive success. One of the most challenging aspects in this regard is basket cutting on a teammate’s drive. The ability to make these cuts is considered to be an artistic skill, mastered by only a handful of players. This skill is also hard to assess, as there is no method to measure the players’ capability with respect to this quality–especially not automatically. Using SportVU data from the NBA, we created a mathematical model that identifies the openings in the defense which allow to perform a cut. Our model succeeds to generalize, as it detects these openings on average 139ms earlier than the actual cuts start and has an overall (balanced) accuracy of 0.818 on the test set. Having a tree-based gradient boosting classifier, we received a clear hierarchy of feature importance and were able to inspect the interactions between these attributes during action. This way, the model gives insights about the kind of defensive movements needed for a player to allow enough space to cut while in practical usage the analysis of the output can also help the coaching staff in designing play options and assessing player abilities. By paying more attention to the possible off ball movements during drives, offensive plays can become more versatile–benefiting the participants and the spectators alike.