Feng Zeyu, Lochhead Liam, Kohn Jordan N, Appelbaum L Gregory
Human Performance Optimization Laboratory, Department of Psychiatry, University of California, San Diego, USA.
Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, USA.
Sports Biomech. 2025 Jun;24(6):1529-1547. doi: 10.1080/14763141.2023.2298959. Epub 2024 Jan 8.
This study examined the relationship between psychomotor abilities and baseball performance by analysing data from 379 athletes who participated in the USA Baseball, Prospect Development Pipeline (PDP). Hit and pitch metrics were generated during practice sessions using the Rapsodo System. Data were compared through exploratory factor analysis and hierarchical regression. Factor analysis grouped batter's PDP evaluations into four latent variables accounting for 63% of variance. Pitcher performance grouped into three factors accounting for 51% of variance. Regression on batter data revealed a significant demographic/anthropometric base model with height, weight, and age that accounted for 58% of the batted ball speed (R = 0.581). Player position explained 2% of the variance (R = 0.604), and PDP evaluation scores contributed an additional 3% (R = 0.631). Regression of pitcher data showed a significant base demographic/anthropometric model accounting for 36% of fastball pitch speeds (R = 0.363), with the PDP evaluation scores adding 6% additional variance (R = 0.424). Uniformly, assessments of lower body strength added the greatest predictive information. Hand grip strength did not correlate with pitch metrics. While demographics/anthropometrics are major contributors to batted and pitched ball speed, position and psychomotor variables add statistically significant contributions and may be of practical value for player selection.
本研究通过分析来自379名参加美国棒球协会前景发展管道(PDP)的运动员的数据,考察了心理运动能力与棒球表现之间的关系。在训练期间使用Rapsodo系统生成击球和投球指标。通过探索性因素分析和层次回归对数据进行比较。因素分析将击球手的PDP评估分为四个潜在变量,解释了63%的方差。投手表现分为三个因素,解释了51%的方差。对击球手数据的回归显示,一个由身高、体重和年龄组成的显著人口统计学/人体测量学基础模型,该模型解释了58%的击球速度(R = 0.581)。球员位置解释了2%的方差(R = 0.604),PDP评估分数又贡献了3%(R = 0.631)。对投手数据的回归显示,一个显著的基础人口统计学/人体测量学模型解释了36%的快球投球速度(R = 0.363),PDP评估分数又增加了6%的方差(R = 0.424)。一致地,对下肢力量的评估增加了最大的预测信息。握力与投球指标无关。虽然人口统计学/人体测量学是击球和投球速度的主要贡献因素,但位置和心理运动变量也增加了具有统计学意义的贡献,并且可能对球员选拔具有实际价值。