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    男排运动员大力跳发球技术量化诊断干预模型与效果验证

    A Quantitative Diagnosis-Intervention Model for the Jump Serve Technique of Male Volleyball Players and Its Effect Verification

    • 摘要:
      目的 构建“发球效能诊断—球速测量与分析—技术诊断与优化”的大力跳发球定量诊断与精准干预路径,为我国男排大力跳发球科学化训练提供标准化路径。
      方法 选取2022—2023年全国男排超级联赛前3名队伍的比赛发球技术效果(3 841次发球)为统计分析对象,同时以上海男排13名优秀运动员为干预研究对象,采用Data Volley 4统计技术效果数据、用雷达测速仪测定发球球速,用三维动捕或视频反馈的方式,分析反馈大力跳发球运动学数据。通过实施8周干预训练等方法构建量化诊断干预模式,并进行效果验证。
      结果 上海男排大力跳发球使用占比(63.21%)具备一定优势,但得分率7.30%、100 km/h以上的高球速占比落后北京男排;助跑步幅不合理及冲跳远度不足是制约发球球速提升的重要影响因素。13名运动员干预后平均球速从97.15 km/h提升至104.38 km/h,提升幅度7.44%,配对t检验显示差异极显著(t=6.20,P<0.01),效应量(Cohen's d=1.72)为大效应水平。
      结论 构建的“发球效能诊断—球速测量与分析—技术诊断与优化”闭环诊断干预模式,可实现大力跳发球技术的定量化、可视化诊断与精准干预,对提升运动员跳发球球速攻击性具有积极作用。

       

      Abstract: Objective: To construct a quantitative diagnosis and precise intervention model for jump serve, namely "serve efficacy diagnosis–serve speed measurement and analysis–technical diagnosis and optimization", to provide a standardized framework for the scientific training of jump serve in Chinese male volleyball teams. Methods: Serving technical effects (3 841 serves) from the top three teams of 2022-2023 Chinese Men's Volleyball League were statistically analyzed, with 13 elite athletes of the Shanghai Men's Volleyball Team as intervention participants. Data Volley 4 was adopted to collect technical performance statistics. Serve velocity was measured using a radar speed gun, and kinematic data of power jump serves were analyzed through 3D motion capture or video feedback. An 8-week intervention training was carried out to develop a quantitative diagnosis-intervention model and to verify its efficacy. Results: The Shanghai Men's Volleyball Team demonstrated a certain advantage with a jump serve usage rate of 63.21%, but its scoring rate (7.30%) and the proportion of high-speed serves above 100 km/h lagged behind those of the Beijing Men's Volleyball Team. Unreasonable approach stride length and insufficient jump distance were identified as critical factors restricting the improvement of serve speed. After the intervention, the average serving speed of the 13 athletes increased from 97.15 km/h to 104.38 km/h, representing an improvement of 7.44%. The paired t-test revealed an extremely significant difference (t=6.20, P<0.01), with a large effect size (Cohen's d=1.72). Conclusion: The established closed-loop diagnosis-intervention model of "serve efficacy diagnosis – serve speed measurement and analysis – technical diagnosis and optimization" enables quantitative and visualized diagnosis as well as precise intervention of jump serve technique, and exerts a positive effect on enhancing the aggressiveness of athletes' jump serve.

       

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