# About latent roles in forecasting players in team sports

arXiv: https://arxiv.org/abs/2304.08272 · HTML: https://mlsanity.com/paper/2304.08272/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 15 of 15)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](https://mlsanity.com/paper/1511.05298/) (2015)
- [Fast Differentiable Sorting and Ranking](https://mlsanity.com/paper/2002.08871/) (2020)
- [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020)
- [Differentiable Ranks and Sorting using Optimal Transport](https://mlsanity.com/paper/1905.11885/) (2019)
- [Space-Time-Separable Graph Convolutional Network for Pose Forecasting](https://mlsanity.com/paper/2110.04573/) (2021)
- [Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty](https://mlsanity.com/paper/1711.09026/) (2017)
- [Generating Multi-Agent Trajectories using Programmatic Weak Supervision](https://mlsanity.com/paper/1803.07612/) (2018)
- [Stochastic Prediction of Multi-Agent Interactions from Partial Observations](https://mlsanity.com/paper/1902.09641/) (2019)
- [Generating Long-term Trajectories Using Deep Hierarchical Networks](https://mlsanity.com/paper/1706.07138/) (2017)
- [Multi-Modal Trajectory Prediction of NBA Players](https://mlsanity.com/paper/2008.07870/) (2020)
- [What Happened Next? Using Deep Learning to Value Defensive Actions in Football Event-Data](https://mlsanity.com/paper/2106.01786/) (2021)

## Built on by (most cited first, 2 of 2)

- [Staged Contact-Aware Global Human Motion Forecasting](https://mlsanity.com/paper/2309.08947/) (2023)
- [A Machine Learning Framework for Off Ball Defensive Role and Performance Evaluation in Football](https://mlsanity.com/paper/2601.00748/) (2026)

## Abstract (arXiv)

Forecasting players in sports has grown in popularity due to the potential for a tactical advantage and the applicability of such research to multi-agent interaction systems. Team sports contain a significant social component that influences interactions between teammates and opponents. However, it still needs to be fully exploited. In this work, we hypothesize that each participant has a specific function in each action and that role-based interaction is critical for predicting players' future moves. We create RolFor, a novel end-to-end model for Role-based Forecasting. RolFor uses a new module we developed called Ordering Neural Networks (OrderNN) to permute the order of the players such that each player is assigned to a latent role. The latent role is then modeled with a RoleGCN. Thanks to its graph representation, it provides a fully learnable adjacency matrix that captures the relationships between roles and is subsequently used to forecast the players' future trajectories. Extensive experiments on a challenging NBA basketball dataset back up the importance of roles and justify our goal of modeling them using optimizable models. When an oracle provides roles, the proposed RolFor compares favorably to the current state-of-the-art (it ranks first in terms of ADE and second in terms of FDE errors). However, training the end-to-end RolFor incurs the issues of differentiability of permutation methods, which we experimentally review. Finally, this work restates differentiable ranking as a difficult open problem and its great potential in conjunction with graph-based interaction models. Project is available at: https://www.pinlab.org/aboutlatentroles