CourtMotion: Learning Event-Driven Motion Representations from Skeletal Data for Basketball
This work addresses the challenge of anticipating basketball events for sports analytics by capturing nuanced motion patterns, though it is incremental as it builds on existing GNN and Transformer methods.
The paper tackled the problem of predicting basketball game events by learning event-driven motion representations from skeletal data, achieving a 35% reduction in trajectory prediction error compared to state-of-the-art position-based models and consistent performance gains across multiple analytics tasks.
This paper presents CourtMotion, a spatiotemporal modeling framework for analyzing and predicting game events and plays as they develop in professional basketball. Anticipating basketball events requires understanding both physical motion patterns and their semantic significance in the context of the game. Traditional approaches that use only player positions fail to capture crucial indicators such as body orientation, defensive stance, or shooting preparation motions. Our two-stage approach first processes skeletal tracking data through Graph Neural Networks to capture nuanced motion patterns, then employs a Transformer architecture with specialized attention mechanisms to model player interactions. We introduce event projection heads that explicitly connect player movements to basketball events like passes, shots, and steals, training the model to associate physical motion patterns with their tactical purposes. Experiments on NBA tracking data demonstrate significant improvements over position-only baselines: 35% reduction in trajectory prediction error compared to state-of-the-art position-based models and consistent performance gains across key basketball analytics tasks. The resulting pretrained model serves as a powerful foundation for multiple downstream tasks, with pick detection, shot taker identification, assist prediction, shot location classification, and shot type recognition demonstrating substantial improvements over existing methods.