宽广的视线：利用姿态增强的位置数据量化足球中的视觉探索行为
Wide Open Gazes: Quantifying Visual Exploratory Behavior in Soccer with Pose Enhanced Positional Data
AI总结：
本研究提出了一种基于姿态增强数据的连续随机视觉层，用于量化足球中球员的视觉探索行为，通过概率视野模型和控球价值分析，预测控球价值并整合到现有足球分析框架中。
摘要：
传统测量足球中视觉探索行为的方法依赖于基于快速头部运动超过125°/s的视觉探索动作（VEAs）计数，但这种方法存在球员位置偏差（即专注于中中场球员）、标注挑战、二元测量限制（即球员在扫描或不扫描）、缺乏预测相关短期比赛未来成功的能力，以及与基本足球分析模型如控球率不兼容。本研究引入了一种新的公式化连续随机视觉层，以量化球员的视觉感知，利用姿态增强的空间时间追踪数据。我们的概率视野和遮挡模型结合头部和肩部旋转角度，以创建速度依赖性的视觉地图，用于二维俯视平面中单个球员的视觉感知。我们将这些视觉地图与控球率和控球价值表面相结合，分析球员在接球后等待阶段（即球员在传球后等待球到达队友时）以及随后的持球阶段。我们证明，汇总的视觉指标——如等待传球时观察到的防守区域百分比——能够预测控球价值在滑动动作结束时的获得情况，使用32场比赛的同步姿态增强追踪数据和持球事件数据，来自2024年南美杯。该方法无论球员位置如何都能工作，消除了手动标注需求，并提供了连续的测量结果，无缝融入现有的足球分析框架。为了进一步支持与现有足球分析框架的集成，我们开源了所需的工具，以使这些计算成为可能。
英文摘要：
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.
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