Wide Open Gazes: Quantifying Visual Exploratory Behavior in Soccer with Pose Enhanced Positional Data

2026-02-19

AI Summary

Traditional approaches to quantifying visual exploration behavior in football players suffer from positional bias, reliance on manual annotation, binary measurements, and limited predictive power. This work proposes a continuous, annotation-free metric of visual exploration applicable across all playing positions. By integrating pose estimation with spatiotemporal tracking data, we construct a velocity-dependent head-shoulder rotation-based field-of-view model that generates probabilistic two-dimensional visibility maps. These maps are fused with ball possession zones and pitch value surfaces to analyze visual behavior before and after receiving the ball. Using data from 32 matches of the 2024 Copa América, we demonstrate that the proposed metrics—such as the proportion of defensive areas visually sampled prior to reception—significantly predict subsequent gains in possession value following dribbling, while remaining compatible with mainstream football analytics frameworks.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Web data visualization

Abstract

Traditional approaches to measuring visual exploratory behavior in soccer rely on counting visual exploratory actions (VEAs) based on rapid head movements exceeding 125°/s, but this method suffer from player position bias (i.e., a focus on central midfielders), annotation challenges, binary measurement constraints (i.e., a player is scanning, or not), lack the power to predict relevant short-term in-game future success, and are incompatible with fundamental soccer analytics models such as pitch control. This research introduces a novel formulaic continuous stochastic vision layer to quantify players' visual perception from pose-enhanced spatiotemporal tracking. Our probabilistic field-of-view and occlusion models incorporate head and shoulder rotation angles to create speed-dependent vision maps for individual players in a two-dimensional top-down plane. We combine these vision maps with pitch control and pitch value surfaces to analyze the awaiting phase (when a player is awaiting the ball to arrive after a pass for a teammate) and their subsequent on-ball phase. We demonstrate that aggregated visual metrics - such as the percentage of defended area observed while awaiting a pass - are predictive of controlled pitch value gained at the end of dribbling actions using 32 games of synchronized pose-enhanced tracking data and on-ball event data from the 2024 Copa America. This methodology works regardless of player position, eliminates manual annotation requirements, and provides continuous measurements that seamlessly integrate into existing soccer analytics frameworks. To further support the integration with existing soccer analytics frameworks we open-source the tools required to make these calculations.

Problem

• visual exploratory behavior
• player position bias
• annotation challenges
• pitch control
• binary measurement

Innovation

• pose-enhanced tracking
• visual exploratory behavior
• stochastic vision layer
• pitch control integration
• continuous visual metrics