# Multi-Modal Trajectory Prediction of NBA Players

arXiv: https://arxiv.org/abs/2008.07870 · HTML: https://mlsanity.com/paper/2008.07870/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

## Built on by (most cited first, 19 of 19)

- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [Interaction Modeling with Multiplex Attention](https://mlsanity.com/paper/2208.10660/) (2022)
- [TranSPORTmer: A Holistic Approach to Trajectory Understanding in Multi-Agent Sports](https://mlsanity.com/paper/2410.17785/) (2024)
- [Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations](https://mlsanity.com/paper/2302.06569/) (2023)
- [FootBots: A Transformer-based Architecture for Motion Prediction in Soccer](https://mlsanity.com/paper/2406.19852/) (2024)
- [Time-series Imputation of Temporally-occluded Multiagent Trajectories](https://mlsanity.com/paper/2106.04219/) (2021)
- [Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS)](https://mlsanity.com/paper/2209.00451/) (2022)
- [Behavior-Inspired Neural Networks for Relational Inference](https://mlsanity.com/paper/2406.14746/) (2024)
- [Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2210.06274/) (2022)
- [About latent roles in forecasting players in team sports](https://mlsanity.com/paper/2304.08272/) (2023)
- [Neural Interaction Energy for Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2404.16579/) (2024)
- [Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review](https://mlsanity.com/paper/2506.14831/) (2025)
- [Rethinking Trajectory Prediction via "Team Game"](https://mlsanity.com/paper/2210.08793/) (2022)
- [Continuous football player tracking from discrete broadcast data](https://mlsanity.com/paper/2311.14642/) (2023)
- [A Multimodal Architecture for Endpoint Position Prediction in Team-based Multiplayer Games](https://mlsanity.com/paper/2507.20670/) (2025)
- [EnvShip: A Unified Framework for Context-Aware and Cross-Region Vessel Trajectory Forecasting](https://mlsanity.com/paper/2606.15240/) (2026)
- [Calisthenics Skills Temporal Video Segmentation](https://mlsanity.com/paper/2507.12245/) (2025)
- [Coherent Multi-Agent Trajectory Forecasting in Team Sports with CausalTraj](https://mlsanity.com/paper/2511.18248/) (2025)
- [DShuttleNet: A Badminton Doubles Dataset for Evaluating Team Coordination](https://mlsanity.com/paper/2610.14016/) (2026)

## Abstract (arXiv)

National Basketball Association (NBA) players are highly motivated and skilled experts that solve complex decision making problems at every time point during a game. As a step towards understanding how players make their decisions, we focus on their movement trajectories during games. We propose a method that captures the multi-modal behavior of players, where they might consider multiple trajectories and select the most advantageous one. The method is built on an LSTM-based architecture predicting multiple trajectories and their probabilities, trained by a multi-modal loss function that updates the best trajectories. Experiments on large, fine-grained NBA tracking data show that the proposed method outperforms the state-of-the-art. In addition, the results indicate that the approach generates more realistic trajectories and that it can learn individual playing styles of specific players.