Centre d'Études des Transformations des Activités Physiques et Sportives UR 3832, UFR STAPS, University of Rouen Normandie, 76000 Rouen, France.
Mitochondria, Oxidative Stress and Muscular Protection Laboratory (EA 3072), Faculty of Medicine, University of Strasbourg, 67081 Strasbourg, France.
Sensors (Basel). 2022 Jul 29;22(15):5692. doi: 10.3390/s22155692.
In handball, the way the team organizes itself in defense can greatly impact the player's activity and displacement during the play, therefore impacting the match demands. This paper aims (1) to develop an automatic tool to detect and classify the defensive organization of the team based on the local positioning system data and check its classification quality, and (2) to quantify the match demands per defensive organization, i.e., defining a somehow cost of specific defensive organizations. For this study, LPS positional data (X and Y location) of players from a team in the Spanish League were analyzed during 25 games. The algorithm quantified the physical demands of the game (distance stand, walk, jog, run and sprint) broken down by player role and by specific defensive organizations, which were automatically detected from the raw data. Results show that the different attacking and defending phases of a game can be automatically detected with high accuracy, the defensive organization can be classified between 1-5, 0-6, 2-4, and 3-3. Interestingly, due to the highly adaptive nature of handball, differences were found between what was the intended defensive organization at a start of a phase and the actual organization that can be observed during the full defensive phase, which consequently impacts the physical demands of the game. From there, quantifying for each player role the cost of each specific defensive organization is the first step into optimizing the use of the players in the team and their recovery time, but also at the team level, it allows to balance the cost (i.e., physical demand) and the benefit (i.e., the outcome of the defensive phase) of each type of defensive organization.
在手球中，球队在防守中的组织方式会极大地影响球员在比赛中的活动和位移，从而影响比赛需求。本文旨在：(1)开发一种自动工具，根据本地定位系统数据检测和分类球队的防守组织，并检查其分类质量；(2)量化每种防守组织的比赛需求，即定义特定防守组织的某种成本。为此，本研究分析了西班牙联赛中一支球队的 25 场比赛中球员的 LPS 位置数据（X 和 Y 位置）。该算法量化了比赛的体能需求（按球员角色和特定防守组织划分的距离站立、行走、慢跑、跑步和冲刺），这些需求是从原始数据中自动检测到的。结果表明，比赛的不同攻防阶段可以以高精度自动检测，防守组织可以分为 1-5、0-6、2-4 和 3-3 级。有趣的是，由于手球高度适应的性质，在一个阶段开始时的预期防守组织和在整个防守阶段中实际观察到的组织之间存在差异，这会对比赛的体能需求产生影响。由此，为每个球员角色量化每种特定防守组织的成本是优化球队中球员使用和恢复时间的第一步，而且在团队层面上，它允许平衡每种防守组织的成本（即体能需求）和效益（即防守阶段的结果）。
