Literature DB >> 30058950

Application of the principal component waveform analysis to identify improvements in vertical jump performance.

Pablo Floría1, Alberto Sánchez-Sixto2, Andrew J Harrison3.   

Abstract

The purpose of this study was to determine the effects of training on the force-, velocity-, and displacement-time curves using principal component analysis (PCA) to examine the pre to post intervention changes. Thirty-four trained women basketball players were randomly divided into training and control groups. The training intervention consisted of full squats combined with repeated jumps. The effects of the intervention were analysed before and after the training period of 6 weeks by comparing the principal component scores. The magnitude of differences within-/between-group were calculated and expressed as standardised differences. After the intervention period, clear changes in principal components were observed in the training group compared to the control group. These were related to the execution of a vertical jump with a faster and deeper countermovement that was stopped with greater force. This resulted in greater force from the start of the upward movement phase which was maintained for a longer time. This increase in force throughout a greater range of motion increased the take-off velocity and consequently jumping height.

Entities:  

Keywords:  Ground reaction forces; evaluation; training

Mesh:

Year:  2018        PMID: 30058950     DOI: 10.1080/02640414.2018.1504602

Source DB:  PubMed          Journal:  J Sports Sci        ISSN: 0264-0414            Impact factor:   3.337


  3 in total

1.  A Systematic Review of Methods and Criteria Standard Proposal for the Use of Principal Component Analysis in Team's Sports Science.

Authors:  Daniel Rojas-Valverde; José Pino-Ortega; Carlos D Gómez-Carmona; Markel Rico-González
Journal:  Int J Environ Res Public Health       Date:  2020-11-24       Impact factor: 3.390

2.  Determining jumping performance from a single body-worn accelerometer using machine learning.

Authors:  Mark G E White; Neil E Bezodis; Jonathon Neville; Huw Summers; Paul Rees
Journal:  PLoS One       Date:  2022-02-10       Impact factor: 3.240

Review 3.  Training Design, Performance Analysis, and Talent Identification-A Systematic Review about the Most Relevant Variables through the Principal Component Analysis in Soccer, Basketball, and Rugby.

Authors:  José Pino-Ortega; Daniel Rojas-Valverde; Carlos D Gómez-Carmona; Markel Rico-González
Journal:  Int J Environ Res Public Health       Date:  2021-03-05       Impact factor: 3.390

  3 in total

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