Morphological and Performance Biomechanics Profiles of Draft Preparation American-Style Football Players

Background/Objectives: Using advanced methodologies may enhance athlete profiling. This study profiled morphological and laboratory-derived performance biomechanics by position of American-style football players training for the draft. Methods: Fifty-five players were categorized into three groups: Big (e.g., lineman; n = 17), Big–skill (e.g., tight end; n = 11), and Skill (e.g., receiver; n = 27). Body fat (BF%), lean body mass (LBM), and total body mass were measured using a bioelectrical impedance device. Running ground reaction force (GRF) and ground contact time (GCT) were obtained using an instrumented treadmill synchronized with a motion capture system. Dual uniaxial force plates captured countermovement jump height (CMJ-JH), normalized peak power (CMJ-NPP), and reactive strength. Asymmetry was calculated for running force, GCT, and CMJ eccentric and concentric impulse (IMP). MANOVA determined between-group differences, and radar plots for morphological and performance characteristics were created using Z-scores. Results: There was a between-group difference (F(26,80) = 5.70, p < 0.001; Wilk’s Λ = 0.123, partial η2 = 0.649). Fisher’s least squares difference post hoc analyses showed that participants in the Skill group had greater JH, CMJ-NPP, reactive strength, and running GRF values versus Big players but not Big–skill players (p < 0.05). Big athletes had greater BF%, LBM, total body mass, and GCT values than Skill and Big–skill athletes (p < 0.05). Big–skill players had greater GCT asymmetry than Skill and Big players (p < 0.05). Asymmetries in running forces, CMJ eccentric, and concentric IMP were not different (p > 0.05). Morphological and performance biomechanics differences are pronounced between Skill and Big players. Big–skill players possess characteristics from both groups. Laboratory-derived metrics offer precise values of running and jumping force strategies and body composition that can aid sports science researchers and practitioners in refining draft trainee profiles.

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Publication Details

Journal
Biomechanics
Published
2024-11-10
DOI
https://doi.org/10.3390/biomechanics4040049
Citations
2
Primary Topic
Sports Performance and Training
Type
article
Field-Weighted Citation Impact
0.96
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article

Morphological and Performance Biomechanics Profiles of Draft Preparation American-Style Football Players

Ahmad Nasri, Pete Bommarito, Monique Butcher-Mokha, Maria Berrocales et al.
2 citations
Biomechanics
Sports Performance and Training
0.96
article

Morphological and Performance Biomechanics Profiles of Draft Preparation American-Style Football Players

Ahmad Nasri, Pete Bommarito, Monique Butcher-Mokha, Maria Berrocales, Jack Stensland, Easa Taha, Joseph Petruzzelli, Aidan Rohman, Andrew I. Schafer, Talia Thompson
article en
2 citations

Abstract

Background/Objectives: Using advanced methodologies may enhance athlete profiling. This study profiled morphological and laboratory-derived performance biomechanics by position of American-style football players training for the draft. Methods: Fifty-five players were categorized into three groups: Big (e.g., lineman; n = 17), Big–skill (e.g., tight end; n = 11), and Skill (e.g., receiver; n = 27). Body fat (BF%), lean body mass (LBM), and total body mass were measured using a bioelectrical impedance device. Running ground reaction force (GRF) and ground contact time (GCT) were obtained using an instrumented treadmill synchronized with a motion capture system. Dual uniaxial force plates captured countermovement jump height (CMJ-JH), normalized peak power (CMJ-NPP), and reactive strength. Asymmetry was calculated for running force, GCT, and CMJ eccentric and concentric impulse (IMP). MANOVA determined between-group differences, and radar plots for morphological and performance characteristics were created using Z-scores. Results: There was a between-group difference (F(26,80) = 5.70, p < 0.001; Wilk’s Λ = 0.123, partial η2 = 0.649). Fisher’s least squares difference post hoc analyses showed that participants in the Skill group had greater JH, CMJ-NPP, reactive strength, and running GRF values versus Big players but not Big–skill players (p < 0.05). Big athletes had greater BF%, LBM, total body mass, and GCT values than Skill and Big–skill athletes (p < 0.05). Big–skill players had greater GCT asymmetry than Skill and Big players (p < 0.05). Asymmetries in running forces, CMJ eccentric, and concentric IMP were not different (p > 0.05). Morphological and performance biomechanics differences are pronounced between Skill and Big players. Big–skill players possess characteristics from both groups. Laboratory-derived metrics offer precise values of running and jumping force strategies and body composition that can aid sports science researchers and practitioners in refining draft trainee profiles.

BiomechanicsVol. 4(4)
Nova Southeastern University (US), South University (US)
Openalex Percentile: Top 26%
Sports Performance and Training
0.96
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