# CourtMotion: Learning Event-Driven Motion Representations from Skeletal Data for Basketball

arXiv: https://arxiv.org/abs/2512.01478 · HTML: https://mlsanity.com/paper/2512.01478/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020)
- [Generating Multi-Agent Trajectories using Programmatic Weak Supervision](https://mlsanity.com/paper/1803.07612/) (2018)
- [Generative Modeling of Multimodal Multi-Human Behavior](https://mlsanity.com/paper/1803.02015/) (2018)
- [Generating Long-term Trajectories Using Deep Hierarchical Networks](https://mlsanity.com/paper/1706.07138/) (2017)
- [baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling](https://mlsanity.com/paper/2102.03291/) (2021)
- [baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents](https://mlsanity.com/paper/2104.11980/) (2021)
- [TranSPORTmer: A Holistic Approach to Trajectory Understanding in Multi-Agent Sports](https://mlsanity.com/paper/2410.17785/) (2024)
- [FootBots: A Transformer-based Architecture for Motion Prediction in Soccer](https://mlsanity.com/paper/2406.19852/) (2024)

## Abstract (arXiv)

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.