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Using Optical Tracking System Data to Measure Team Synergic Behavior: Synchronization of Player-Ball-Goal Angles in a Football Match

机译:使用光学跟踪系统数据来测量团队协同行为:足球比赛中玩家球门的角度同步

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摘要

The ecological dynamics approach to interpersonal relationships provides theoretical support to the use of kinematic data, obtained with sensor-based systems, in which players of a team are linked mainly by information from the performance environment. Our goal was to capture the properties of synergic behavior in football, using spatiotemporal data from one match of the 2018 FIFA WORLD CUP RUSSIA, to explore the application of player-ball-goal angles in cluster phase analysis. Linear mixed effects models were used to test the statistical significance of different effects, such as: team, half(-time), role and pitch zones. Results showed that the cluster phase values (synchronization) for the home team, had a 3.812×10−2±0.536×10−2 increase with respect to the away team (X2(41)=259.8, p<0.001) and that changing the role from with ball to without ball increased synchronization by 16.715×10−2±0.283×10−2 (X2(41)=12227.0, p<0.001). The interaction between effects was also significant. The player-team relative phase, the player-ball-goal angles relative frequency and the team configurations, showed that variations of synchronization might indicate critical performance changes (ball possession changes, goals scored, etc.). This study captured the ongoing player-environment link and the properties of team synergic behavior, supporting the use of sensor-based data computations in the development of relevant indicators for tactical analysis in sports.
机译:人际关系的生态动态方法为使用基于传感器的系统而获得的运动数据提供了理论支持,其中一支团队的玩家主要通过性能环境中的信息链接。我们的目标是捕捉足球中协同行为的性质,从2018年国际足联世界杯俄罗斯的一场比赛中使用时空数据,探讨球员球门的应用在聚类阶段分析中的应用。线性混合效果模型用于测试不同效果的统计显着性,例如:团队,一半( - 达特),角色和俯仰区域。结果表明,家庭团队的集群相位值(同步)有3.812×10-2±0.536×10-2增加,相对于散发队伍(x2(41)= 259.8,p <0.001)和变化从球到没有球的角色增加了16.715×10-2±0.283×10-2的同步(x2(41)= 12227.0,p <0.001)。效果之间的相互作用也显着。玩家团队的相对阶段,玩家球门的角度角度相对频率和团队配置,表明同步的变化可能表明关键性能变化(球占有变化,目标等目标等)。本研究捕获了正在进行的玩家环境链接和团队协同行为的属性,支持在体育中的战术分析的相关指标中使用基于传感器的数据计算。

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