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职业足球运动员的关键负荷指标和负荷变异性:一项完整赛季的研究。

Key load indicators and load variability in professional soccer players: a full season study.

机构信息

Health Research Centre, University of Almería, Almería, Spain.

Laboratoire de Biologie de l'exercice Pour la Performance et la Santé,Université d'Evry, IRBA, Université Paris Saclay, Evry, France.

出版信息

Res Sports Med. 2023 May-Jun;31(3):201-213. doi: 10.1080/15438627.2021.1954517. Epub 2021 Jul 14.

Abstract

The aims of this study were to 1) determine the key load indicators in professional soccer through principal component analysis (PCA); and 2) analyse the load variability of each training and match day within the microcycle considering the principal components. Data from 111 load variables were collected using tracking systems in both training and match days (MD). The results showed that 7 variables, which belonged to the first two components of the PCA, explained 80.3% of total variance. Specifically, these variables were Metabolic power, total of steps, Fourier transform (FFT) duration, deceleration distance covered (2-3 m/s), total of running actions (12-18 km/h; 21-24 km/h), and distance covered (6-12 km/h). Regarding the analysis of the load variability of each training and match day within the microcycle, the lowest load variability was observed in -1MD. Also, a great load variability in +1MD with significant differences compared to -5MD (<0.001; =0.49) and -4MD (=0.01; =0.26) was found. This study suggests the use of the PCA in the context of team sports to reduce the large number of variables, which are daily managed by strength and conditioning coaches, in addition to the analysis of load variability of each training and match day within the microcycle.

摘要

本研究的目的是

1)通过主成分分析(PCA)确定职业足球的关键负荷指标;2)考虑主要成分,分析微周期内每个训练和比赛日的负荷变化。使用跟踪系统在训练和比赛日(MD)中收集了 111 个负荷变量的数据。结果表明,前两个 PCA 成分中的 7 个变量解释了总方差的 80.3%。具体而言,这些变量分别为代谢功率、总步数、傅里叶变换(FFT)持续时间、2-3 米/秒的减速距离、12-18 公里/小时(21-24 公里/小时)的总跑步动作和 6-12 公里/小时的跑步距离。关于微周期内每个训练和比赛日的负荷变化分析,-1MD 观察到的负荷变化最小。此外,在+1MD 中发现了很大的负荷变化,与-5MD(<0.001;=0.49)和-4MD(=0.01;=0.26)相比存在显著差异。本研究表明,在团队运动的背景下使用 PCA 可以减少力量和体能教练日常管理的大量变量,同时还可以分析微周期内每个训练和比赛日的负荷变化。

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