基本运动技能对预测青少年基层足球技术技能的重要性:一种机器学习方法

IF 1.5 4区 教育学 Q3 HOSPITALITY, LEISURE, SPORT & TOURISM
Michael J. Duncan, Emma L. J. Eyre, Neil Clarke, Abdul Hamid, Yanguo Jing
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引用次数: 0

摘要

本研究使用机器学习方法确定了基层青少年足球运动员足球技术技能的贡献者。采用根特大学(University of Ghent)运球测试对162名经常参加基层足球运动的7 ~ 14岁(mean±SD = 10.5±2.1)岁的男孩进行了人体测量和成熟偏移(从年龄到峰值高度速度(APHV)的时间)、基本运动技能(FMS)、感知身体能力、身体健康和足球技术技能的评估。教练们根据球员的年龄对他们的整体足球技术进行了评分。进行了统计分析,使用机器学习模型从其他变量预测技术技能。采用5倍交叉验证的逐步递归特征消除方法来消除表现最差的特征,并在此过程中评估L1和L2正则化。五种模型(线性、脊状、套索、随机森林和增强树)随后以启发式方法使用一小部分合适的算法,在合理的时间框架内达到合理的精度水平,进行预测,并将其与测试集进行比较,以了解模型的预测能力。机器学习分析的结果表明,FMS总分(0到50)是预测技术足球技能的最重要特征,其次是教练对儿童技能的年龄、比赛经验年数和APHV的评分。使用随机森林,预测踢草根足球的男孩的技术技能有99%的准确率,FMS是最重要的贡献者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Importance of fundamental movement skills to predict technical skills in youth grassroots soccer: A machine learning approach
This study determined the contributors to soccer technical skills in grassroots youth soccer players using a machine learning approach. One hundred and sixty-two boys aged 7 to 14 (mean ± SD = 10.5 ± 2.1) years, who were regularly engaged in grassroots soccer undertook assessments of anthropometry and maturity offset (the time from age at peak height velocity (APHV)), fundamental movement skills (FMS), perceived physical competence, and physical fitness and technical soccer skill using the University of Ghent dribbling test. Coaches rated player's overall soccer skills for their age. Statistical analysis was undertaken, using machine learning models to predict technical skills from the other variables. A stepwise recursive feature elimination with a 5-fold cross-validation method was used to eliminate the worst-performing features and both L1 and L2 regularisation were evaluated during the process. Five models (linear, ridge, lasso, random forest, and boosted trees) were then used in a heuristic approach using a small subset of suitable algorithms to achieve a reasonable level of accuracy within a reasonable time frame to make predictions and compare them to a test set to understand the predictive capabilities of the models. Results from the machine learning analysis indicated that the total FMS score (0 to 50) was the most important feature in predicting technical soccer skills followed by coach rating of child skills for their age, years of playing experience and APHV. Using a random forest, technical skills could be predicted with 99% accuracy in boys who play grassroots soccer, with FMS being the most important contributor.
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来源期刊
CiteScore
3.50
自引率
15.80%
发文量
208
期刊介绍: The International Journal of Sports Science & Coaching is a peer-reviewed, international, academic/professional journal, which aims to bridge the gap between coaching and sports science. The journal will integrate theory and practice in sports science, promote critical reflection of coaching practice, and evaluate commonly accepted beliefs about coaching effectiveness and performance enhancement. Open learning systems will be promoted in which: (a) sports science is made accessible to coaches, translating knowledge into working practice; and (b) the challenges faced by coaches are communicated to sports scientists. The vision of the journal is to support the development of a community in which: (i) sports scientists and coaches respect and learn from each other as they assist athletes to acquire skills by training safely and effectively, thereby enhancing their performance, maximizing their enjoyment of the sporting experience and facilitating character development; and (ii) scientific research is embraced in the quest to uncover, understand and develop the processes involved in sports coaching and elite performance.
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