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数据挖掘用于田径运动员和跑步者的心理分析

Cristina Sanz-Fernández¹, José Luis Pastrana Brincones², Julen Castellano³

¹Department of Education Sciences, Universidad de La Rioja, Logroño, Spain.

Frontiers in psychology

|February 14, 2025

概括

这项研究使用数据挖掘来根据动机和自信等心理因素创建独特的运动员个人资料,揭示了针对性训练干预措施的关键差异.

科学领域:

运动心理学体育心理学
在田径运动中数据挖掘.
绩效分析绩效分析

背景情况:

在体育运动中,心理因素至关重要.
有限的研究将数据挖掘应用于运动员概况.
了解运动员心理学有助于表现.

研究的目的:

分析运动员的动机,自信,流动和心理技能.
使用聚类技术构建差异化的运动资料.
在这些个人资料中识别不同的行为模式.

主要方法:

数据挖掘和集群分析应用于心理数据.
使用的经过验证的尺度:体育动机量表 (SMS),TEOSQ,CACD,FDS-2,IPED.
样本:470名参与者 (年龄14-70岁).

主要成果:

确定了与特定运动员个人资料相对应的独特行为模式.
根据性别,参与模式 (在线/面对面) 和运动员类型 (联合/跑步者) 观察到显著差异.
在不同体育类别和学科中观察到的变化.

结论:

运动员心理分析对于提高表现至关重要.
结果支持量身定制的心理培训计划.
调查结果可以指导干预措施,以满足特定群体的需求.

关键词:

聚类集群是指聚类的聚类.数据挖掘是数据挖掘的一个方法.混合方法是一种混合方法.专业领域的专业领域.赛道和田野运动会

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