Handgrip strength in elite youth football: potential for performance prediction and the moderating effects of age and maturation

Abstract

Handgrip strength (HGS) is a simple and reliable indicator of general muscular strength, yet its relevance in elite youth football remains insufficiently understood. This study examined the utility of HGS as a practical indicator of athletic performance in this population, focusing on its associations with sport-specific motor abilities and the moderating influence of age and biological maturation. A total of 221 elite male youth football players aged 11-19 years completed a standardized performance test battery that included HGS (via dynamometer), dynamic balance (Star Excursion Balance Test), vertical jumps (Counter Movement Jump, Abalakov Jump, Heading Jump), horizontal jumps (Broad Jump, Single-Leg Hop for Distance), and sprints (10 m and 30 m). Pearson correlation coefficients were used to assess associations between HGS and motor performance outcomes, while linear regression models tested the moderating effects of age and maturity offset. HGS was strongly associated with jumping (r = 0.69-0.75 for vertical; r = 0.73-0.75 for horizontal) and sprinting performance (r = -0.62 to -0.73) and showed small but significant associations with dynamic balance (r = -0.29; all p < .001). Regression analyses confirmed significant main effects of HGS on jumping (β = 0.31-0.60) and sprinting (β = -0.23 to -0.33), moderated by both age and maturation status. No significant effects were observed for balance. The combination of HGS and age accounted for up to 67% of the variance in sprinting and up to 61% in jumping. These findings demonstrate that HGS is a robust and practical predictor of sprinting and jumping performance, especially when combined with age. This makes HGS a valuable, resource-efficient tool for performance diagnostics and talent development in elite and youth football, especially in settings where extensive testing is impractical.

Keywords: biological maturation; functional testing; hand grip dynamometer; lower limb power; performance testing; sprint speed; youth athletes.

Figure 1

Linear regression analysis examining the relationship between handgrip strength and (A) dynamic balance (star excursion balance test—limb symmetry Index), (B) vertical jumps (countermovement jump, Abalakov jump, heading jump), (C) horizontal jumps (broad jump, single-Leg Hop for distance), and (D) sprint performance (10 m, 30 m), with age included as a moderating variable. All variables are z-standardized. Handgrip strength (x-axis) and performance outcomes (y-axis) are plotted at three levels of age: one standard deviation (SD) below the mean (−1 SD, red line), at the mean (blue line), and one SD above the mean (+1 SD, green line). Number of participants: n = 221.

Figure 2

Linear regression analysis examining the relationship between handgrip strength and (A) dynamic balance (star excursion balance test—limb symmetry Index), (B) vertical jumps (countermovement jump, abalakov jump, heading jump), (C) horizontal jumps (broad jump, single-Leg Hop for distance), and (D) sprint performance (10 m, 30 m), with maturity offset (MO) included as a moderating variable. All variables are z-standardized. Handgrip strength (x-axis) and performance outcomes (y-axis) are plotted at three levels of MO: one standard deviation (SD) below the mean (−1 SD, red line), at the mean (blue line), and one SD above the mean (+1 SD, green line). Number of participants: n = 221.