Coherent Multi-Agent Trajectory Forecasting in Team Sports with CausalTraj

TL;DR

CausalTraj is a novel model for multi-agent trajectory forecasting in team sports that emphasizes joint probability and coherence, outperforming existing models on joint metrics and producing realistic gameplay simulations.

Contribution

It introduces CausalTraj, a temporally causal, likelihood-based model that improves joint trajectory prediction and coherence in multi-agent sports scenarios.

Findings

Achieves state-of-the-art results on joint metrics (minJADE, minJFDE).

Maintains competitive per-agent accuracy.

Produces qualitatively realistic gameplay evolutions.

Abstract

Jointly forecasting trajectories of multiple interacting agents is a core challenge in sports analytics and other domains involving complex group dynamics. Accurate prediction enables realistic simulation and strategic understanding of gameplay evolution. Most existing models are evaluated solely on per-agent accuracy metrics (minADE, minFDE), which assess each agent independently on its best-of-k prediction. However these metrics overlook whether the model learns which predicted trajectories can jointly form a plausible multi-agent future. Many state-of-the-art models are designed and optimized primarily based on these metrics. As a result, they may underperform on joint predictions and also fail to generate coherent, interpretable multi-agent scenarios in team sports. We propose CausalTraj, a temporally causal, likelihood-based model that is built to generate jointly probable multi-agent trajectory forecasts. To better assess collective modeling capability, we emphasize joint metrics (minJADE, minJFDE) that measure joint accuracy across agents within the best generated scenario sample. Evaluated on the NBA SportVU, Basketball-U, and Football-U datasets, CausalTraj achieves competitive per-agent accuracy and the best recorded results on joint metrics, while yielding qualitatively coherent and realistic gameplay evolutions.