|本期目录/Table of Contents|

[1]练光祥.基于大数据分析的网球运动员训练优化策略[J].体育科学研究,2025,(3):83-88.
　LIAN Guangxiang.The Optimization of Technical Training Strategy for Tennis Players Based on Big Data Analysis[J].sports science research,2025,(3):83-88.
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基于大数据分析的网球运动员训练优化策略(PDF)
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《体育科学研究》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年3期
页码:
83-88
栏目:
体育教育学
出版日期:
2025-05-28

文章信息/Info

Title:
The Optimization of Technical Training Strategy for Tennis Players Based on Big Data Analysis
作者:
练光祥
（集美大学体育学院，福建 厦门 361021）
Author(s):
LIAN Guangxiang
（Physical Education Institute of Jimei University,Xiamen 361021,China）
关键词:
大数据; 网球技术训练; 训练优化; 数据挖掘; 个性化训练
Keywords:
big data; tennis skill training; training optimization; data mining; personalized training
分类号:
-
DOI:
-
文献标志码:
A
摘要:
为了探索基于大数据分析的网球运动员训练优化策略，研究通过传感器和高速摄像技术收集了5名国家二级网球运动员的训练与比赛数据，利用Hadoop和Spark框架进行存储与计算，结合数据挖掘算法分析，发现大数据分析显著提升了运动员的技术训练效果，包括发球技术、正反手击球稳定性、网前战术等。研究结果显示，ATH002发球技术提升23.4%，ATH004反手技术和心理耐压能力分别提升21.9%和25.7%。同时，精细化训练计划减少了运动伤病。研究构建的数据驱动训练框架包括计划、行动、评估、调整4个环节，验证了大数据分析的有效性。未来研究将优化数据收集与处理，扩大样本量，探索跨学科数据分析模型。
Abstract:
This study explores the application of big data analysis in tennis technical training.Data from five national second-level tennis players were collected using sensors and high-speed cameras,processed with Hadoop and Spark frameworks,and analyzed with data mining algorithms.Results show that big data analysis significantly enhanced training effectiveness,improving serve technique,stroke stability,and net play tactics.Athlete ATH002 saw a 23.4% improvement in serve technique,while ATH004’s backhand technique and mental resilience increased by 21.9% and 25.7%,respectively.Refined training plans reduced injuries.The proposed data-driven training framework,encompassing planning,action,assessment,and adjustment,validates big data analysis’s effectiveness.Future research will optimize data collection,expand sample sizes,and explore interdisciplinary analysis models.

参考文献/References:

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备注/Memo

备注/Memo:
更新日期/Last Update: 2025-06-17