John L Grace, Meghan E Hancock, Madison L Malone, Bahman Adlou, Jerad J Kosek, Hannah R Houde, Christopher M Wilburn, Wendi H Weimar
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Regression analyses examined the components in relation to normalized jump height and rookie Approximate Value (AV) using an alpha level of 0.05.</p><p><strong>Results: </strong>After analysis, only the overall model for normalized jump height was statistically significant (R^2^ = 0.69, p = 0.002).</p><p><strong>Discussion: </strong>While no single variable predicted jump height, distinct strategies were evident between the top and bottom 25% performers based on component correlations. The regression model approached significance in predicting rookie AV (R^2^ = 0.94, p = 0.052), with notable components like heel pauses for skilled positions and greater knee flexion for linemen. By creating models that can predict jump height or AV, variables can be identified that can be used to improve one's jump height or, in the case of AV, that can be used to predict which draft prospects will perform better in the NFL.</p>","PeriodicalId":51644,"journal":{"name":"Open Access Journal of Sports Medicine","volume":"15 ","pages":"197-208"},"PeriodicalIF":1.3000,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11624661/pdf/","citationCount":"0","resultStr":"{\"title\":\"Video Analysis of Elite American Football Athletes During Vertical Jump.\",\"authors\":\"John L Grace, Meghan E Hancock, Madison L Malone, Bahman Adlou, Jerad J Kosek, Hannah R Houde, Christopher M Wilburn, Wendi H Weimar\",\"doi\":\"10.2147/OAJSM.S481805\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>The National Football League (NFL) combine tests the athleticism of prospects competing for the draft. The vertical jump is included to test lower extremity power, yet the components which lead to the greatest performance remain elusive. Therefore, this study aimed to utilize a sample of elite athletes to analyze vertical jump components associated with increased performance and the relationship between vertical jump performance and rookie-year success.</p><p><strong>Methods: </strong>Videos of 50 NFL prospects performing the vertical jump task were analyzed for various countermovement jump components. Regression analyses examined the components in relation to normalized jump height and rookie Approximate Value (AV) using an alpha level of 0.05.</p><p><strong>Results: </strong>After analysis, only the overall model for normalized jump height was statistically significant (R^2^ = 0.69, p = 0.002).</p><p><strong>Discussion: </strong>While no single variable predicted jump height, distinct strategies were evident between the top and bottom 25% performers based on component correlations. The regression model approached significance in predicting rookie AV (R^2^ = 0.94, p = 0.052), with notable components like heel pauses for skilled positions and greater knee flexion for linemen. 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引用次数: 0
摘要
简介:国家橄榄球联盟(NFL)结合测试的运动能力的前景竞争选秀。垂直跳跃包括测试下肢力量,但导致最大性能的组件仍然难以捉摸。因此,本研究旨在利用精英运动员样本来分析垂直起跳与提高成绩相关的成分,以及垂直起跳成绩与新秀赛季成功之间的关系。方法:对50名NFL预演运动员垂直起跳的录像进行分析,分析其各种反动作起跳成分。回归分析使用0.05的alpha水平检验了归一化跳高和新秀近似值(AV)的相关成分。结果:经分析,只有整体模型对归一化跳跃高度有统计学意义(R^2^ = 0.69, p = 0.002)。讨论:虽然没有单一变量预测跳跃高度,但基于组件相关性,在前25%和后25%的表演者之间存在明显的不同策略。回归模型在预测新秀AV方面接近显著性(R^2^ = 0.94, p = 0.052),其中技术位置的脚跟停顿和锋线队员的膝关节弯曲是显著的组成部分。通过创建可以预测跳高或AV的模型,可以确定变量,这些变量可以用来提高一个人的跳高,或者,在AV的情况下,可以用来预测哪个选秀前景将在NFL表现更好。
Video Analysis of Elite American Football Athletes During Vertical Jump.
Introduction: The National Football League (NFL) combine tests the athleticism of prospects competing for the draft. The vertical jump is included to test lower extremity power, yet the components which lead to the greatest performance remain elusive. Therefore, this study aimed to utilize a sample of elite athletes to analyze vertical jump components associated with increased performance and the relationship between vertical jump performance and rookie-year success.
Methods: Videos of 50 NFL prospects performing the vertical jump task were analyzed for various countermovement jump components. Regression analyses examined the components in relation to normalized jump height and rookie Approximate Value (AV) using an alpha level of 0.05.
Results: After analysis, only the overall model for normalized jump height was statistically significant (R^2^ = 0.69, p = 0.002).
Discussion: While no single variable predicted jump height, distinct strategies were evident between the top and bottom 25% performers based on component correlations. The regression model approached significance in predicting rookie AV (R^2^ = 0.94, p = 0.052), with notable components like heel pauses for skilled positions and greater knee flexion for linemen. By creating models that can predict jump height or AV, variables can be identified that can be used to improve one's jump height or, in the case of AV, that can be used to predict which draft prospects will perform better in the NFL.