篮球运动监测:定性的探索性方法

B. Serrano, M. Comer, Geoff Puls
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引用次数: 0

摘要

随着体育技术的必然提高,体育的竞争力也在不断提高。体育技术是一个涵盖生物识别、可穿戴技术和数据分析的保护伞。体育技术的核心是一个近亲,体育分析。如果体育技术是从表现角度衡量运动员体验的总称,那么体育分析就是清理、处理和操纵这些变量以产生有意义的输出的过程。可以收集的变量数量很多,但通常可以分为内部和外部度量。内部指标旨在衡量运动员对训练负荷的反应(HR, HRV,激素,睡眠模式)。外部指标旨在衡量运动员进行了多少训练(实时运动员跟踪、反向跳跃、等距大腿中部拉)。内部和外部度量标准的收集方式不断发展,这取决于组织的时间、资源和人员分配。这就是运动科学家和运动表现人员之间的合作可以聚集在一起,覆盖变量并找到关系的地方。没有正确的方法来分析数据,因为每个组织都有自己的目标。例如,数据可用于减轻伤害、评估准备情况,或仅用于建立基线值。可以使用的一些常见统计技术包括相关性、回归和主成分分析(PCA)。然而,执行这些计算可能是复杂的,一些组织可能缺乏从业者,这可能限制他们使用数据的能力。在看到这种差距后,作者提出了一种定性的方法来监测篮球的工作量,但可以应用于大多数运动。本文的目的是使运动绩效工作人员能够学习和实施定性运动监测方法的基本知识。
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Athletic Monitoring in Basketball: A Qualitative Exploratory Approach
The competitiveness of sport continues to increase with the corollary increase in sport technology. Sport technology is an umbrella that encompasses biometrics, wearable technology, and data analytics. Central to sport technology is a close relative, sport analytics. If sport technology is the general term for measuring what the athlete is experiencing from a performance standpoint, then sport analytics is the process of cleaning, processing, and manipulating these variables into meaningful output. The number of variables that can be collected are extensive but can be generally categorized into internal and external metrics. Internal metrics seek to measure how the athlete responds to training load (HR, HRV, Hormones, Sleep patterns). External metrics seek to measure how much work the athlete is performing (Live player tracking, Countermovement Jump, Isometric Mid-Thigh Pull). Internal and external metrics are continually evolving in the manner they are collected which depends on the time, resources, and staff allocation of an organization. This is where the collaboration between sport scientists and sport performance staff can come together to overlay variables and find relationships. There is no correct way to analyze data because each organization will have their own goals. For example, data could be used for injury mitigation, readiness assessment, or simply to establish baseline values. Some common statistical techniques that can be used include correlation, regression, and principal component analysis (PCA). However, performing these calculations may be complex and some organizations may lack a practitioner which can limit their ability to use data. In seeing this gap, the authors propose a qualitative approach to workload monitoring made for basketball but can be applied to most sports. The purpose of this paper is to empower sports performance staffs to learn and implement the basics of qualitative athletic monitoring approach.
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