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

TL;DR
This paper introduces a continuous probabilistic model to quantify soccer players' visual perception using pose-enhanced tracking data, improving over traditional methods by reducing bias and enabling integration with existing analytics.
Contribution
It presents a novel stochastic vision layer that models players' visual fields from pose data, allowing continuous, bias-free measurement of visual exploratory behavior in soccer.
Findings
Visual metrics predict pitch control and value after passes.
Method works across all player positions without manual annotation.
Open-source tools facilitate integration into soccer analytics.
Abstract
Traditional approaches to measuring visual exploratory behavior in soccer rely on counting visual exploratory actions (VEAs) based on rapid head movements exceeding 125{\deg}/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…
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Taxonomy
TopicsSports Performance and Training · Sport Psychology and Performance · Video Analysis and Summarization
