揭露团队运动中的速度曲线异常:数据处理和解释的实用指南
Rui Marcelino1,2,3, Hugo Silva1,2
1Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, ELITE Research Community, Maia, Portugal.
Frontiers in sports and active living
|March 9, 2026
概括
团队运动中的峰值匹配速度 (PMS) 可能会因碰撞而扭曲. 本研究提出了一种方法来识别和消除这些异常,提高运动员表现监测和训练决策的准确性.
科学领域:
- 运动科学 运动科学 运动科学
- 生物力学 生物力学
- 绩效分析 绩效分析
背景情况:
- 团队体育运动中的高速排位监测通常使用峰值匹配速度 (PMS).
- 在训练处方,伤害预防和绩效分析方面,PMS至关重要.
- 像球这样的异常事件可以扭曲PMS,损害运动员监测的准确性.
研究的目的:
- 提出一种实用的策略,用于识别和解决运动员监测中的速度-时间模式异常.
- 通过删除文物来提高运动员监测过程的可靠性.
- 提高反映真实球员身体能力的衍生指标的准确性.
主要方法:
- 系统地绘制加速时间和速度时间曲线在一起.
- 将加速轴与峰值匹配速度 (PMS) 调整对齐,以快速检测不切实际的模式.
- 识别和排除诸如极端加速或减速在最大速度附近的工件.
主要成果:
- 拟议的方法允许快速检测不切实际的速度-时间模式.
- 排除文物确保衍生指标更准确地反映了玩家的真实身体能力.
- 该策略可以适应各种团队体育运动,并增强次最大冲刺的解释.
结论:
- 这种低成本,广泛适用的方法加强了运动员跟踪输出的可靠性.
- 通过确保数据完整性,保护绩效分析和培训决策.
- 通过解决速度时间数据中的工件来提高运动员监控的准确性.
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