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运动表现参数可预测表现出色的男足球队。一项针对职业足球的多赛季研究。

Locomotor performance parameters as predictors of high-performing male soccer teams. A multiple-season study on professional soccer.

机构信息

Faculty of Physical Education, Gdańsk University of Physical Education and Sport, Gdańsk, Poland.

Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu, Kuala Nerus, Malaysia.

出版信息

Sci Rep. 2024 Nov 18;14(1):28547. doi: 10.1038/s41598-024-80181-z.

DOI:10.1038/s41598-024-80181-z
PMID:39558131
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11574320/
Abstract

This study aims to explore the interplay between locomotor demands and goal differentials to better understand their combined influence on overall success. Spanning three competitive seasons within the male Turkish Super League, this study analyzed all participating teams across 124 matches. Locomotor demands, including total distance (m) covered (TD), distances covered (m) at different speed thresholds (0.21-2.0 m/s; 2.01-4.0 m/s; 4.01-5.5 m/s; and 5.5-7.7 m/s), and the number of accelerations in range of 5.5-7.0 m/s (n), were quantified using an optical tracking system. Subsequently, regression models were employed to predict the total points earned by all teams over the three seasons. The logistic regression model, tailored to predict team categorization as high-points earners (HPE) or low-points earners (LPE) based on locomotor variables, exhibited a mean accuracy of 74%. Notably, total distance covered, running speed intervals between 4.4 and 5.5 m/s, and the number of accelerations in range of 5.5-7.0 m/s emerged as significant predictors of team success. Our findings highlight the pivotal role of running speed (4.01-5.5 m/s), number of accelerations, and total distance in predicting success for high-performing teams. Coaches can leverage these insights to refine training programs, thereby optimizing team performance, and fostering success in competitive environments.

摘要

本研究旨在探讨运动需求和目标差异之间的相互作用,以更好地理解它们对整体成功的综合影响。本研究在土耳其超级联赛的三个竞技赛季中,分析了所有参赛球队的 124 场比赛。运动需求包括总跑动距离(m)(TD)、不同速度阈值(0.21-2.0 m/s;2.01-4.0 m/s;4.01-5.5 m/s;和 5.5-7.7 m/s)的跑动距离(m),以及 5.5-7.0 m/s 范围内的加速度次数(n),使用光学跟踪系统进行量化。随后,采用回归模型预测所有球队在三个赛季中获得的总积分。定制的逻辑回归模型用于预测球队的分类,即高积分球队(HPE)或低积分球队(LPE),基于运动变量,模型的平均准确率为 74%。值得注意的是,总跑动距离、4.4-5.5 m/s 之间的跑动速度区间以及 5.5-7.0 m/s 范围内的加速度次数是球队成功的重要预测指标。我们的研究结果强调了跑动速度(4.01-5.5 m/s)、加速度次数和总跑动距离在预测高绩效球队成功方面的关键作用。教练可以利用这些见解来改进训练计划,从而优化球队表现,并在竞争环境中取得成功。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69e0/11574320/99829ffb4cb4/41598_2024_80181_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69e0/11574320/f6e3225a3e13/41598_2024_80181_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69e0/11574320/2c32388831e6/41598_2024_80181_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69e0/11574320/99829ffb4cb4/41598_2024_80181_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69e0/11574320/f6e3225a3e13/41598_2024_80181_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69e0/11574320/2c32388831e6/41598_2024_80181_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69e0/11574320/99829ffb4cb4/41598_2024_80181_Fig3_HTML.jpg

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