Performance Services, Canadian Sport Institute Pacific, Victoria, British Columbia, Canada.
Exercise Science, Physical & Health Education, University of Victoria, Victoria, British Columbia, Canada.
J Strength Cond Res. 2024 Aug 1;38(8):1433-1439. doi: 10.1519/JSC.0000000000004799.
Agar-Newman, DJ, MacRae, F, Tsai, M-C, and Klimstra, M. Predicting sprint performance from the vertical and horizontal jumps in National Football League Combine athletes. J Strength Cond Res 38(8): 1433-1439, 2024-Identifying fast athletes is an important part of the National Football League (NFL) Combine. However, not all athletes partake in the 36.58-m sprint, and relying on this single test may miss potentially fast athletes. Therefore, the purpose of this study was to determine whether sprinting times can be predicted using simple anthropometric and jumping measures. Data from the NFL Combine between the years 1999-2020 inclusive were used (n = 4,149). Subjects had a mean (±SD) height = 1.87 ± 0.07 m and body mass = 111.96 ± 20.78 kg. The cross-validation technique was used, partitioning the data into a training set (n = 2,071) to develop regression models to predict time over the 9.14-, 9.14- to 18.29-, 18.29- to 36.58-m, and 36.58-m segments using vertical jump, broad jump, height, and mass as the independent variables. The models were then evaluated against a test set (n = 2,070) for agreement. Statistically significant (p < 0.01) models were determined for 9.14-m time (adjusted R2 = 0.76, SEE = 0.05 seconds), 9.14- to 18.29-m time (adjusted R2 = 0.74, SEE = 0.04 seconds), 18.29- to 36.59-m time (adjusted R2 = 0.79, SEE = 0.07 seconds), and 36.58-m time (adjusted R2 = 0.84, SEE = 0.12 seconds). When evaluated against the test set, the models showed biases of -0.05, -0.04, -0.02, and -0.02 seconds and root-mean-square error of 0.07, 0.05, 0.07, and 0.12 seconds for the 9.14-, 9.14- to 18.29-, 18.29- to 36.58-m, and 36.58-m segments, respectively. However, 5-6% of the predictions lay outside of the limits of agreement. This study provides 4 formulae that can be used to predict sprint performance when the 36.58-m sprint test is not performed, and practitioners can use these equations to determine training areas of opportunity when working with athletes preparing for the NFL Combine.
阿加-纽曼、DJ、麦克雷、蔡美慈和克里姆斯特对美国国家橄榄球联盟综合体能测试中垂直跳和水平跳与短跑成绩的关系进行了预测。J 力量与体能研究 38(8):1433-1439, 2024. - 识别短跑运动员是美国国家橄榄球联盟(NFL)综合体能测试的重要组成部分。然而,并非所有运动员都参加 36.58 米短跑测试,仅依赖这一项测试可能会错过有潜力的短跑运动员。因此,本研究旨在确定是否可以使用简单的人体测量和跳跃测量来预测短跑成绩。本研究使用了 1999 年至 2020 年期间美国国家橄榄球联盟综合体能测试的数据(n = 4149)。受试者的平均(±SD)身高= 1.87 ± 0.07 米,体重= 111.96 ± 20.78 千克。使用交叉验证技术,将数据分为训练集(n = 2071)和测试集(n = 2070),使用垂直跳、跳远、身高和体重作为自变量,建立预测 9.14 米、9.14 米至 18.29 米、18.29 米至 36.58 米和 36.58 米各分段时间的回归模型。然后,将模型应用于测试集进行验证。确定了具有统计学意义(p < 0.01)的 9.14 米时间模型(调整后的 R2 = 0.76,SEE = 0.05 秒)、9.14 米至 18.29 米时间模型(调整后的 R2 = 0.74,SEE = 0.04 秒)、18.29 米至 36.59 米时间模型(调整后的 R2 = 0.79,SEE = 0.07 秒)和 36.58 米时间模型(调整后的 R2 = 0.84,SEE = 0.12 秒)。将模型应用于测试集时,各模型的偏差分别为-0.05、-0.04、-0.02 和-0.02 秒,9.14 米、9.14 米至 18.29 米、18.29 米至 36.58 米和 36.58 米各分段的均方根误差分别为 0.07、0.05、0.07 和 0.12 秒。然而,约 5%-6%的预测结果超出了可接受范围。本研究提供了 4 个公式,可在不进行 36.58 米短跑测试的情况下预测短跑成绩,从业人员可以使用这些公式在与准备参加美国国家橄榄球联盟综合体能测试的运动员一起工作时,确定训练机会领域。