Trajectory Imputation in Multi-Agent Sports with Derivative-Accumulating Self-Ensemble
This addresses the challenge of incomplete tracking data in team sports analytics, enabling more reliable downstream tasks like distance approximation and pass success probability estimation.
The paper tackles the problem of missing values in multi-agent sports trajectory data by proposing MIDAS, a framework that imputes trajectories with high accuracy and physical plausibility, significantly outperforming existing baselines in experiments on three sports datasets.
Multi-agent trajectory data collected from domains such as team sports often suffer from missing values due to various factors. While many imputation methods have been proposed for spatiotemporal data, they are not well-suited for multi-agent sports scenarios where player movements are highly dynamic and inter-agent interactions continuously evolve. To address these challenges, we propose MIDAS (Multi-agent Imputer with Derivative-Accumulating Self-ensemble), a framework that imputes multi-agent trajectories with high accuracy and physical plausibility. It jointly predicts positions, velocities, and accelerations through a Set Transformer-based neural network and generates alternative estimates by recursively accumulating predicted velocity and acceleration values. These predictions are then combined using a learnable weighted ensemble to produce final imputed trajectories. Experiments on three sports datasets demonstrate that MIDAS significantly outperforms existing baselines in both positional accuracy and physical plausibility. Lastly, we showcase use cases of MIDAS, such as approximating total distance and pass success probability, to highlight its applicability to practical downstream tasks that require complete tracking data.