The effects of fatigue on CMjs

Prepared by Justin Wolfe, Yann Lacoste and Pat NolanThe effects of fatigue on Counter Movement jump squats Hypothesis

Running the Test

Data Collection Methods

Results and Data Analysis

Sources of Error

Increasing Accuracy of Testing

Table of contentsFatigue does indeed affect jump height, and considering that fatigue is brought on by a minute of continuous jumps, we believe that the closer to the minute mark the test subject gets, the more fatigued he will become and subsequently, the lower his jump height will be.

HypothesisJustin/Pat3Two jumpers

One set each of 1:00min max countermovement jump squats

Recording using an IPhone 4S

Both jumpers filmed in the P.IS.E. GymRunning the test

Test subject A

Video of CMJSKinovea Data collection methods

We put up a timer, filmed it sideways6Measurement of Max Knee Angle

Measured Take Off Time and Landing Time for each jump to determine Flight Time

Jump Height formula =-(-9.81*flight time^2)/8

AND statement to determine if Knee Angle between 75-115Analyzing dataResults tablesJustin Wolfe- Jumper AJump #Max Knee AngleTake Off TimeLanding TimeFlight Time (sec)Jump Height (m)Angle between 75-1151651,041,640,60,44FALSE2672,342,910,570,40FALSE3663,614,180,570,40FALSE4614,885,470,590,43FALSE5666,26,770,570,40FALSE6587,528,080,560,38FALSE7578,829,390,570,40FALSE85910,1210,710,590,43FALSE96311,4412,010,570,40FALSE106112,813,320,520,33FALSE116114,1214,680,560,38FALSE126115,4616,010,550,37FALSE136216,7917,370,580,41FALSE147218,2618,790,530,34FALSE156819,5320,050,520,33FALSE167320,8621,40,540,36FALSE176222,1722,710,540,36FALSE186923,5324,050,520,33FALSE196324,8925,40,510,32FALSE206426,2726,790,520,33FALSE216627,6628,160,50,31FALSE226429,0429,570,530,34FALSE236430,4430,960,520,33FALSE246531,7932,330,540,36FALSE256633,1133,620,510,32FALSE266034,3834,910,530,34FALSE276335,7336,230,50,31FALSE285837,0737,540,470,27FALSE296438,3838,880,50,31FALSE306439,740,20,50,31FALSE316641,0741,570,50,31FALSE327142,4142,890,480,28FALSE337543,7844,270,490,29TRUE346745,1945,660,470,27FALSE356846,5246,990,470,27FALSE367347,9548,410,460,26FALSE377549,2949,720,430,27TRUE389150,9751,410,440,24TRUE397352,4352,90,470,27FALSE406553,954,380,480,28FALSE417055,3355,780,450,25FALSE427556,7357,220,490,29TRUE436658,1558,620,470,27FALSE446959,6260,060,440,24FALSEYann Lacoste - Jumper BJump #Max Knee Angle Take Off TimeLanding TimeFlight Time (sec)Jump Height (m)Knee Angle Between 75-1151731,632,20,570,40FALSE26933,530,530,34FALSE3514,374,870,50,31FALSE4655,636,170,540,36FALSE5596,947,440,50,31FALSE6678,278,770,50,31FALSE7629,5710,040,470,27FALSE86010,8711,370,50,31FALSE94612,2412,740,50,31FALSE105813,6414,110,470,27FALSE114615,1115,580,470,27FALSE125016,4116,880,470,27FALSE135717,8518,280,430,23FALSE145019,1819,650,470,27FALSE154820,5220,950,430,23FALSE166921,8222,250,430,23FALSE176723,2523,690,440,24FALSE186824,4924,920,430,23FALSE196925,7926,190,40,20FALSE206727,0927,560,470,27FALSE215528,4628,860,40,20FALSE225829,8330,260,430,23FALSE235031,1631,530,370,17FALSE244932,5332,90,370,17FALSE254133,8634,260,40,20FALSE265635,135,470,370,17FALSE275136,3736,70,330,13FALSE2810637,9738,30,330,13TRUE297639,239,60,40,20TRUE306440,4440,80,360,16FALSE317541,7742,070,30,11TRUE3210242,9143,270,360,16TRUE338744,4144,570,160,03TRUE348045,5845,910,330,13TRUE358346,7847,110,330,13TRUE367548,0848,440,360,16TRUE379049,3549,680,330,13TRUE385050,6150,950,340,14FALSE397951,9252,220,30,11TRUE407653,1853,480,30,11TRUE418354,4554,750,30,11TRUE429355,9956,250,260,08TRUE4310357,2957,620,330,13TRUE44 jumps for Jumper A, and 43 jumps for Jumper B

Confirmed Hypothesis

Jumper A (Justin) average of the first 10 jumps = 0.40m average of the last 10 jumps = 0.26 meters

Jumper B (Yann) average for the first 10 jumps = 0.32m Average of the last 10 jumps = 0.12mresults

Video evidence

Justins performance analysisyanns performance analysisJump Height Analysis of Jumper BA value of 1 means that a linear equation describes the relationship betweenXandYperfectly, with all data points lying on aline for whichYincreases asXincreases

Strong correlation in between 0.5 to 1.0

Correlation coefficientJumper B: R = 0,86561

Jumper A: R = 0,85553

is a measure of thecorrelation(linear dependence) between two variablesXandY, giving a value between +1 and 1 inclusive.

Pearson's correlation coefficient between two variables is defined asmuch tworandom variableschange together

A value of 1 implies that a linear equation describes the relationship betweenXandYperfectly, with all data points lying on alinefor whichYincreases asXincreases

Strong0.5 to 1.0 = we have strong correlation!

13Jump height a vs bThe plot line connecting all the (fixed) averages is the moving average.the first element of the moving average is obtained by taking the average of the initial fixed subset of the number series. Then the subset is modified by "shifting forward"; that is, excluding the first number of the series and including the next number following the original subset in the series

creating a series ofaveragesof different subsets of the full data set.

14Frequency of distributionJump HeightFrequency0.2-0.29130.3-0.39210.4-0.499Jump HeightFrequency0.0-0.920.1-0.19170.2-0.29150.3-0.3970.4-0.491Flight time analysis (Jumper A)Max Knee angle vs. Jump numberResults cont.

Jumper A ResultsJumper B ResultsFree-hand filmingSlight changes from jumper A to jumper B

Distance from jumper to cameraman

Difficult to measure sideways data

Cheating the resultsMeasuring by flight time

Sources of potential error

Using different form, subjects could increase their recorded jump height by simply raising their legs, but not actually increasing their vertical

19Use higher quality cameraEx. High-Speed camera (Quarter of a million frames per second)Better video editing softwareGive more precise numbers with more accurate analysisUse joint markersHelp in assuring measurements are accurate

Increasing accuracy

We have found that fatigue does indeed impact jump height. Hypothesis confirmed

Squat jumps for a minute straight are incredibly painful Conclusion