Multiagent off-screen behavior prediction in football

Area of Science:

• Artificial Intelligence
• Computer Vision
• Robotics

Background:

• Multiagent systems present complex decision-making challenges due to environmental constraints and stochastic agent behaviors.
• Estimating agent behaviors, such as pedestrian prediction for autonomous vehicles, is crucial but often hindered by sporadic observations.
• In sports like football, occlusions in broadcast footage lead to partially observable player trajectories, complicating analysis.

Purpose of the Study:

• To develop a method for multiagent time-series imputation, specifically estimating missing player observations in partially observable football games.
• To leverage available past and future observations of visible agents to infer the states of unobserved (off-screen) agents.
• To apply this imputation method for downstream football analytics, particularly in scenarios requiring complete player data.

Main Methods:

• The Graph Imputer approach combines graph networks and variational autoencoders to learn a distribution of imputed trajectories.
• It utilizes both past and future observations of a subset of agents to estimate missing data for others.
• The method was evaluated on football match data, simulating partial observability using a camera module to mimic off-screen player estimation.

Main Results:

• The Graph Imputer successfully predicts the behaviors of partially observable, off-screen players in multiagent football settings.
• Quantitative experiments on football matches demonstrated superior performance compared to several state-of-the-art methods, including those specialized for football.
• The approach enabled downstream football analytics, such as pitch control estimation, under partial observability.

Conclusions:

• The Graph Imputer effectively addresses the challenge of multiagent time-series imputation in partially observable environments.
• This method significantly advances the ability to analyze and understand complex team sports dynamics even with incomplete observational data.
• The approach has practical implications for sports analytics, enhancing insights derived from real-world, often incomplete, game footage.