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专业足球运动员训练和比赛中身体需求的预测模型。

Predictive modelling of the physical demands during training and competition in professional soccer players.

机构信息

Faculty of Sport Sciences, Universidad Europea de Madrid, Spain.

School Computer Science, Department of Information Technologies and Systems, University of Castilla-la Mancha, Spain.

出版信息

J Sci Med Sport. 2020 Jun;23(6):603-608. doi: 10.1016/j.jsams.2019.12.008. Epub 2019 Dec 17.

Abstract

OBJECTIVES

The present study aimed to predict the cut-off point-values that best differentiate the physical demands of training and competition tasks including friendly matches (FM), small sided games (SSG), large sided games (LSG), mini-goal games (MG) and ball circuit-training (CT) in professional soccer players.

DESIGN

Experimental randomized controlled trial.

METHODS

Fourteen professional players participated in all tasks with the CT, SSG and MG consisting of 8 repetitions of 4-min game play, interspersed by 2-min of active recovery. The training data were compared to the first 32-min of the LSG and two competitive FM per player. All movement patterns from walking to sprint running were recorded using 10Hz GPS devices while player perception of exertion was recorded via a visual analogue scale, post-task. Decision tree induction was applied to the dataset to assess the cut-off point-values from four training drills (SSG, LSG, MG, and CT) and FM for every parameter combination.

RESULTS

Distance covered during jogging (2.3-3.3m/s; >436m), number of decelerations (≤730.5) and accelerations (≤663), and maximum velocity reached (>5.48m/s) characterized the physical demands during competition (FM) with great variability amongst training drills.

CONCLUSION

The use of these novel, cut-off points may aid coaches in the design and use of training drills to accurately prepare athletes for soccer competition.

摘要

目的

本研究旨在预测最佳的分界值,以区分职业足球运动员的训练和比赛任务(包括友谊赛[FM]、小场比赛[SSG]、大场比赛[LSG]、迷你球门比赛[MG]和球循环训练[CT])的身体需求。

设计

实验性随机对照试验。

方法

14 名职业球员参加了所有任务,其中 CT、SSG 和 MG 由 8 次 4 分钟的比赛组成,中间穿插 2 分钟的主动恢复。将训练数据与 LSG 的前 32 分钟和每名球员的两场竞争性 FM 进行比较。使用 10Hz GPS 设备记录所有从步行到冲刺跑的运动模式,球员在赛后通过视觉模拟量表记录用力程度。应用决策树归纳法对数据集进行分析,以评估来自四个训练练习(SSG、LSG、MG 和 CT)和 FM 的每个参数组合的分界值。

结果

慢跑(2.3-3.3m/s;>436m)、减速(≤730.5 次)和加速(≤663 次)以及最大速度(>5.48m/s)的距离覆盖了比赛(FM)中的身体需求,而训练练习之间的差异很大。

结论

使用这些新的分界值可以帮助教练设计和使用训练练习,以便更准确地为足球比赛做准备。

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