Wide Open Gazes: Quantifying Visual Exploratory Behavior in Soccer with Pose Enhanced Positional Data
arXiv:2602.18519v1 Announce Type: new 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 maps with pitch control and pitch value surfaces to ana
arXiv:2602.18519v1 Announce Type: new 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 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.
Executive Summary
This article introduces a novel approach to measuring visual exploratory behavior in soccer using pose-enhanced spatiotemporal tracking. The method combines probabilistic field-of-view and occlusion models with pitch control and pitch value surfaces to analyze player visual perception. The research demonstrates that aggregated visual metrics are predictive of controlled pitch value gained at the end of dribbling actions, eliminating manual annotation requirements and providing continuous measurements. This methodology works regardless of player position and seamlessly integrates into existing soccer analytics frameworks. The research opens-source the tools required for these calculations, enhancing the potential for future research and practical applications. The study's findings have significant implications for the development of more effective soccer analytics models and the improvement of team performance.
Key Points
- ▸ The article introduces a novel formulaic continuous stochastic vision layer to quantify players' visual perception.
- ▸ The method combines probabilistic field-of-view and occlusion models with pitch control and pitch value surfaces.
- ▸ The research demonstrates that aggregated visual metrics are predictive of controlled pitch value gained at the end of dribbling actions.
Merits
Strength in Addressing Player Position Bias
The novel approach eliminates player position bias, focusing on an objective, data-driven analysis of visual exploratory behavior.
Demerits
Limited Generalizability to Other Sports
The study's findings may not be directly applicable to other sports with different rules, playing styles, or spatial requirements.
Expert Commentary
This article makes a significant contribution to the field of soccer analytics by introducing a novel approach to measuring visual exploratory behavior. The use of pose-enhanced spatiotemporal tracking and probabilistic field-of-view and occlusion models provides a more objective and data-driven analysis of player visual perception. The research demonstrates the predictive power of aggregated visual metrics, which can inform coaching strategies and player training programs. However, the study's findings may not be directly applicable to other sports, and the generalizability of the results to different playing styles or spatial requirements is limited. Nevertheless, the article provides a valuable framework for future research and practical applications in soccer analytics.
Recommendations
- ✓ Future studies should investigate the applicability of this methodology to other sports or domains with different spatial requirements or playing styles.
- ✓ The development of more effective soccer analytics models should prioritize the integration of visual exploratory behavior metrics, such as those introduced in this article, to enhance the accuracy and reliability of player performance evaluations.
