Grace John L, Hancock Meghan E, Malone Madison L, Adlou Bahman, Kosek Jerad J, Houde Hannah R, Wilburn Christopher M, Weimar Wendi H
School of Kinesiology, Auburn University, Auburn, AL, USA.
Open Access J Sports Med. 2024 Dec 3;15:197-208. doi: 10.2147/OAJSM.S481805. eCollection 2024.
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.
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.
After analysis, only the overall model for normalized jump height was statistically significant (R^2^ = 0.69, p = 0.002).
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.
美国国家橄榄球联盟(NFL)联合试训会测试参加选秀的球员的运动能力。垂直纵跳测试用于评估下肢力量,但能带来最佳成绩的相关因素仍不明确。因此,本研究旨在利用精英运动员样本,分析与成绩提高相关的垂直纵跳组成部分,以及垂直纵跳成绩与新秀赛季表现之间的关系。
分析了50名参加NFL选秀的球员进行垂直纵跳任务的视频,以获取各种反向纵跳组成部分的数据。回归分析使用0.05的显著性水平,研究了与标准化跳高技术和新秀近似值(AV)相关的组成部分。
分析后发现,只有标准化跳高技术的整体模型具有统计学意义(R² = 0.69,p = 0.002)。
虽然没有单一变量能预测跳高技术,但根据组成部分的相关性,表现最好和最差的25%的球员之间存在明显不同的策略。回归模型在预测新秀AV方面接近显著性水平(R² = 0.94,p = 0.052),对于技术型位置球员而言,足跟停顿等组成部分很重要,而对于前锋球员而言,更大的膝关节屈曲角度很重要。通过创建能够预测跳高技术或AV的模型,可以识别出可用于提高个人跳高技术的变量,或者在AV的情况下,可用于预测哪些选秀球员在NFL中表现会更好的变量。