Force-time curve variables of countermovement jump as predictors of volleyball spike jump height

2020 ◽  
Vol 50 (4) ◽  
pp. 470-476
Author(s):  
Javad Sarvestan ◽  
Zdeněk Svoboda ◽  
João Gustavo de Oliveira Claudino
Keyword(s):  
Jump Height ◽  
Countermovement Jump ◽  
Time Curve ◽  
Force Time

scholarly journals Stretch-Shortening Cycle in Countermovement Jump: Exclusive Review of Force-Time Curve Variables in Eccentric and Concentric Phases

2017 ◽  
Author(s):  
Mahdi Cheraghi ◽  
Javad Sarvestan ◽  
Masoud Sebyani ◽  
Elham Shirzad
Keyword(s):  
Peak Power ◽  
Vertical Jump ◽  
Average Power ◽  
Peak Force ◽  
Jump Height ◽  
Time Curve ◽  
Jump Performance ◽  
Volleyball Players ◽  
Time Variables ◽  
Force Time

The importance of vertical jump in sport fields and rehabilitation is widely recognized. Furthermore, Force-Time variables of vertical jump are factors affecting jumping height. Exclusive review of each of this variables, in eccentric and concentric phases, can lead to a specific focus on them during jumping exercises. So, the aims of his study were to a) reviewing the relationship between force-time curve variables of eccentric and concentric phases with jump height and b) description of this variables in Iran national youth volleyball players society. This is an observational study. 12 elite volleyball player (Male, Iran national youth volleyball players, 17±0.7 years) have participated in this study. Correlation between Force-Time variables - included peak force (PF), relative peak force (RPP), peak power (PP), average power (AP), relative peak power (RPP), and Modified Reactive Strength Index (MRSI) - in eccentric and concentric phases and ultimate jump height has been studied. Results showed that the average power (r=0.7) and relative peak force (r=0.75) of concentric phase and MRSI (r=0.83) have significant correlation with ultimate jump height (JH). Relative peak power and average power of concentric phase can massively effect Jump Height in sports like volleyball, which vertical jump is an integral part of them. Focus on both of these factors, which has been studied in this research, in training programs, can improve athlete jump performance significantly.

scholarly journals Relationships between force-time curve variables and jump height during countermovement jumps in young elite volleyball players

Acta Gymnica ◽  
2018 ◽  
Vol 48 (1) ◽  
pp. 9-14 ◽  
Author(s):  
Javad Sarvestan ◽  
Mahdi Cheraghi ◽  
Masoud Sebyani ◽  
Elham Shirzad ◽  
Zdenek Svoboda
Keyword(s):  
Jump Height ◽  
Time Curve ◽  
Volleyball Players ◽  
Force Time

scholarly journals Understanding the Key Phases of the Countermovement Jump Force-Time Curve

2018 ◽  
pp. 1 ◽  
Author(s):  
John J. McMahon ◽  
Timothy J. Suchomel ◽  
Jason P. Lake ◽  
Paul Comfort
Keyword(s):  
Countermovement Jump ◽  
Time Curve ◽  
Force Time

scholarly journals Surfing the Waves of the CMJ; Are There between-Sport Differences in the Waveform Data?

Sports ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 168 ◽  
Author(s):  
James Parker ◽  
Lina Lundgren
Keyword(s):  
Principal Component ◽  
Force Platform ◽  
Jump Height ◽  
Time Curve ◽  
Waveform Data ◽  
Additional Information ◽  
Mass Displacement ◽  
Force Time ◽  
High Level ◽  
The Waves

The ability to analyse countermovement jump (CMJ) waveform data using statistical methods, like principal component analysis, can provide additional information regarding the different phases of the CMJ, compared to jump height or peak power alone. The aim of this study was to investigate the between-sport force-time curve differences in the CMJ. Eighteen high level golfers (male = 10, female = 8) and eighteen high level surfers (male = 10, female = 8) performed three separate countermovement jumps on a force platform. Time series of data from the force platform was normalized to body weight and each repetition was then normalized to 0–100 percent. Principal component analyses (PCA) were performed on force waveforms and the first six PCs explained 35% of the variance in force parameters. The main features of the movement cycles were characterized by magnitude (PC1 and PC5), waveform (PC2 and PC4), and phase shift features (PC3). Surf athletes differ in their CMJ technique and show a greater negative centre of mass displacement when compared to golfers (PC1), although these differences are not necessarily associated with greater jump height. Principal component 5 demonstrated the largest correlation with jump height (R2 = 0.52). Further studies are recommended in this area, to reveal which features of the CMJ that relate to jumping performance, and sport specific adaptations.

scholarly journals The Effect of Training with Weightlifting Catching or Pulling Derivatives on Squat Jump and Countermovement Jump Force–Time Adaptations

2020 ◽  
Vol 5 (2) ◽  
pp. 28
Author(s):  
Timothy J. Suchomel ◽  
Shana M. McKeever ◽  
John J. McMahon ◽  
Paul Comfort
Keyword(s):  
Training Program ◽  
Vertical Jump ◽  
Group Differences ◽  
Specific Training ◽  
Countermovement Jump ◽  
Time Curve ◽  
Squat Jump ◽  
Specific Loading ◽  
Force Time

The purpose of this study was to examine the changes in squat jump (SJ) and countermovement jump (CMJ) force–time curve characteristics following 10 weeks of training with either load-matched weightlifting catching (CATCH) or pulling derivatives (PULL) or pulling derivatives that included force- and velocity-specific loading (OL). Twenty-five resistance-trained men were randomly assigned to the CATCH, PULL, or OL groups. Participants completed a 10 week, group-specific training program. SJ and CMJ height, propulsion mean force, and propulsion time were compared at baseline and after 3, 7, and 10 weeks. In addition, time-normalized SJ and CMJ force–time curves were compared between baseline and after 10 weeks. No between-group differences were present for any of the examined variables, and only trivial to small changes existed within each group. The greatest improvements in SJ and CMJ height were produced by the OL and PULL groups, respectively, while only trivial changes were present for the CATCH group. These changes were underpinned by greater propulsion forces and reduced propulsion times. The OL group displayed significantly greater relative force during the SJ and CMJ compared to the PULL and CATCH groups, respectively. Training with weightlifting pulling derivatives may produce greater vertical jump adaptations compared to training with catching derivatives.

Relationship Between Strength Characteristics and Unweighted and Weighted Vertical Jump Height

2009 ◽  
Vol 4 (4) ◽  
pp. 461-473 ◽  
Author(s):  
Jenna M. Kraska ◽  
Michael W. Ramsey ◽  
G. Gregory Haff ◽  
Nate Fethke ◽  
William A. Sands ◽  
...  
Keyword(s):  
Body Mass ◽  
Isometric Force ◽  
Vertical Jump ◽  
Force Plate ◽  
Collegiate Athletes ◽  
Practical Method ◽  
Relative Strength ◽  
Jump Height ◽  
Time Curve ◽  
Force Time

Purpose:To investigate the relationship between maximum strength and differences in jump height during weighted and unweighted (body weight) static (SJ) and countermovement jumps (CMJ).Methods:Sixty-three collegiate athletes (mean ± SD; age= 19.9 ± 1.3 y; body mass = 72.9 ± 19.6 kg; height = 172.8 ± 7.7 cm) performed two trials of the SJ and CMJ with 0 kg and 20 kg on a force plate; and two trials of mid-thigh isometric clean pulls in a custom rack over a force plate (1000-Hz sampling). Jump height (JH) was calculated from fight time. Force-time curve analyses determined the following: isometric peak force (IPF), isometric force (IF) at 50, 90, and 250 ms, and isometric rates of force development (IRFD). Absolute and allometric scaled forces, [absolute force/(body mass0.67)], were used in correlations.Results:IPF, IRFD, F50a, F50, F90, and F250 showed moderate/strong correlations with SJ and CMJ height percent decrease from 0 to 20 kg. IPFa and F250a showed weak/moderate correlations with percent height decrease. Comparing strongest (n = 6) to weakest (n = 6): t tests revealed that stronger athletes (IPFa) performed superior to weaker athletes.Conclusion:Data indicate the ability to produce higher peak and instantaneous forces and IRFD is related to JH and to smaller differences between weighted and unweighted jump heights. Stronger athletes jump higher and show smaller decrements in JH with load. A weighted jump may be a practical method of assessing relative strength levels.

Using Random Forest Regression to Determine Influential Force-Time Metrics for Countermovement Jump Height

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Justin J. Merrigan ◽  
Jason D. Stone ◽  
John P. Wagle ◽  
W. G. Hornsby ◽  
Jad Ramadan ◽  
...  
Keyword(s):  
Random Forest ◽  
Jump Height ◽  
Countermovement Jump ◽  
Random Forest Regression ◽  
Force Time

scholarly journals Within-Subject Consistency of Unimodal and Bimodal Force Application during the Countermovement Jump

Sports ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 143 ◽  
Author(s):  
Jason Lake ◽  
John McMahon
Keyword(s):  
Curve Shape ◽  
Countermovement Jump ◽  
Time Data ◽  
Time Curve ◽  
Subject Agreement ◽  
Group Averaging ◽  
Force Time ◽  
Force Data ◽  
And Training ◽  
Shape Data

Countermovement jump (CMJ) force data are often time-normalized so researchers and practitioners can study the effect that sex, training status, and training intervention have on CMJ strategy: the so-called force–time curve shape. Data are often collected on an individual basis and then averaged across interest-groups. However, little is known about the agreement of the CMJ force–time curve shape within-subject, and this formed the aim of this study. Fifteen men performed 10 CMJs on in-ground force plates. The resulting force–time curves were plotted, with their shape categorized as exhibiting either a single peak (unimodal) or a double peak (bimodal). Percentage-agreement and the kappa-coefficient were used to assess within-subject agreement. Over two and three trials, 13% demonstrated a unimodal shape, 67% exhibited a bimodal shape, and 20% were inconsistent. When five trials were considered, the unimodal shape was not demonstrated consistently; 67% demonstrated a bimodal shape, and 33% were inconsistent. Over 10 trials, none demonstrated a unimodal shape, 60% demonstrated a bimodal shape, and 40% were inconsistent. The results of this study suggest that researchers and practitioners should ensure within-subject consistency before group averaging CMJ force–time data, to avoid errors.

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