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Different Movement Strategies in the Countermovement Jump Amongst a Large Cohort of NBA Players
Previous research has demonstrated large amounts of inter-subject variability in downward (unweighting & braking) phase strategies in the countermovement jump (CMJ). The purpose of this study was to characterize downward phase strategies and associated temporal, kinematic and kinetic CMJ variabl...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7504515/ https://www.ncbi.nlm.nih.gov/pubmed/32887399 http://dx.doi.org/10.3390/ijerph17176394 |
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author | Rauch, Jacob Leidersdorf, Eric Reeves, Trent Borkan, Leah Elliott, Marcus Ugrinowitsch, Carlos |
author_facet | Rauch, Jacob Leidersdorf, Eric Reeves, Trent Borkan, Leah Elliott, Marcus Ugrinowitsch, Carlos |
author_sort | Rauch, Jacob |
collection | PubMed |
description | Previous research has demonstrated large amounts of inter-subject variability in downward (unweighting & braking) phase strategies in the countermovement jump (CMJ). The purpose of this study was to characterize downward phase strategies and associated temporal, kinematic and kinetic CMJ variables. One hundred and seventy-eight NBA (National Basketball Association) players (23.6 ± 3.7 years, 200.3 ± 8.0 cm; 99.4 ± 11.7 kg; CMJ height 68.7 ± 7.4 cm) performed three maximal CMJs. Force plate and 3D motion capture data were integrated to obtain kinematic and kinetic outputs. Afterwards, athletes were split into clusters based on downward phase characteristics (k-means cluster analysis). Lower limb joint angular displacement (i.e., delta flexion) explained the highest portion of point variability (89.3%), and three clusters were recommended (Ball Hall Index). Delta flexion was significantly different between clusters and players were characterized as “stiff flexors”, “hyper flexors”, or “hip flexors”. There were no significant differences in jump height between clusters (p > 0.05). Multiple regression analyses indicated that most of the jumping height variance was explained by the same four variables, (i.e., sum concentric relative force, knee extension velocity, knee extension acceleration, and height) regardless of the cluster (p < 0.05). However, each cluster had its own unique set of secondary predictor variables. |
format | Online Article Text |
id | pubmed-7504515 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75045152020-09-24 Different Movement Strategies in the Countermovement Jump Amongst a Large Cohort of NBA Players Rauch, Jacob Leidersdorf, Eric Reeves, Trent Borkan, Leah Elliott, Marcus Ugrinowitsch, Carlos Int J Environ Res Public Health Article Previous research has demonstrated large amounts of inter-subject variability in downward (unweighting & braking) phase strategies in the countermovement jump (CMJ). The purpose of this study was to characterize downward phase strategies and associated temporal, kinematic and kinetic CMJ variables. One hundred and seventy-eight NBA (National Basketball Association) players (23.6 ± 3.7 years, 200.3 ± 8.0 cm; 99.4 ± 11.7 kg; CMJ height 68.7 ± 7.4 cm) performed three maximal CMJs. Force plate and 3D motion capture data were integrated to obtain kinematic and kinetic outputs. Afterwards, athletes were split into clusters based on downward phase characteristics (k-means cluster analysis). Lower limb joint angular displacement (i.e., delta flexion) explained the highest portion of point variability (89.3%), and three clusters were recommended (Ball Hall Index). Delta flexion was significantly different between clusters and players were characterized as “stiff flexors”, “hyper flexors”, or “hip flexors”. There were no significant differences in jump height between clusters (p > 0.05). Multiple regression analyses indicated that most of the jumping height variance was explained by the same four variables, (i.e., sum concentric relative force, knee extension velocity, knee extension acceleration, and height) regardless of the cluster (p < 0.05). However, each cluster had its own unique set of secondary predictor variables. MDPI 2020-09-02 2020-09 /pmc/articles/PMC7504515/ /pubmed/32887399 http://dx.doi.org/10.3390/ijerph17176394 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Rauch, Jacob Leidersdorf, Eric Reeves, Trent Borkan, Leah Elliott, Marcus Ugrinowitsch, Carlos Different Movement Strategies in the Countermovement Jump Amongst a Large Cohort of NBA Players |
title | Different Movement Strategies in the Countermovement Jump Amongst a Large Cohort of NBA Players |
title_full | Different Movement Strategies in the Countermovement Jump Amongst a Large Cohort of NBA Players |
title_fullStr | Different Movement Strategies in the Countermovement Jump Amongst a Large Cohort of NBA Players |
title_full_unstemmed | Different Movement Strategies in the Countermovement Jump Amongst a Large Cohort of NBA Players |
title_short | Different Movement Strategies in the Countermovement Jump Amongst a Large Cohort of NBA Players |
title_sort | different movement strategies in the countermovement jump amongst a large cohort of nba players |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7504515/ https://www.ncbi.nlm.nih.gov/pubmed/32887399 http://dx.doi.org/10.3390/ijerph17176394 |
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