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Is % of body fat a better predictor of sprint time than BMI?

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Luke Green

on 2 March 2015

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Transcript of Is % of body fat a better predictor of sprint time than BMI?

Is % of Body Fat a Better Predictor
of Sprint Time than BMI?

Luke, Dan, Tom & Ross
Background and Rationale
Key Literature
Strengths & Limitations
Background & Rationale
Key Literature
Total of 18 subjects (Male=11, Female= 7)
Mean Age- 19.89±1.02
Mean Height- 1.76±0.10
Mean Weight- 77.66±12.72
Mean Body Mass Index (BMI)- 24.88± 2.84
Mean % Body fat- 20.62± 7.69
Method Cont...
Methods Cont...
Results Cont...
Results Cont...
Strengths & Limitations
Thank You
Your Time

Subjects height was measured on a Portable Stadiometer (Seca, Leicester Height Meter, Germany) and % of body fat and weight on the Body Composition Analyzer scales (Tanita, SC-330ST, Netherlands).

These values were used to calculate subjects BMI.
A runway was measured with a tape measure (Fiberglass Measuring Tape 30m, San Tyau, SF type, Taiwan) at 30m and timing gates (Brower, Test Center Timing System, USA) were set up at the start and finish line.
Table to show Male and Female mean values
Subjects used a 3-point start and commenced their sprint on their own accord once the timing gates had been started. Three sprints were conducted by each subject and then an average taken from these values. Subjects had a 3 minute rest (Chaouachi et al., 2009) of passive recovery to allow for full ATP regeneration to then complete another maximal sprint. Subjects walked back to the start line and waited for the next sprint. Sprint times were repeated to ensure reliability.
Procedure Continued
Statistical Analysis
The Data was formatted using Microsoft Excel 2010. IBM SPSS 21 was used to perform statistical tests within the study. The data file was tested for normality and then the appropriate statistical test was used. The value of p<0.05 was accepted as statistically significant.
Normality tests were run on the data and from the table below you can see that none of the variables came back as significant, therefore we can assume that they are normally distributed.
Results Continued
Sample Size- only 18 subjects, and uneven distribution of males and females.
Instrumentation- Tanita weighing scales only measures the % of body fat within the legs.
Literature- literature is on elite sport rather than normal populations.
Validity- % of body fat used could not of been a valid measure of the variable due to measuring equipment.
Linear and Multiple regression tests were run.
BMI came back as not significant, p=.378, where as % of Body Fat was significant with p<0.05.
BMI is not significant (p=.378) in linear regression, as well as not significant in multiple regression (p=0.083). % of Body Fat was only significant predictor p<0.05. Model summary shows that % Body Fat can account for 60.8% of subject Average Sprint Time.
Using the equation generated by regression value, the next table gives the predicted Sprint time values based on the subjects %body fat value.
Average Sprint Time= 3.73 + (0.066 X %BF)
Actual Vs Predicted
Average time difference between
all values is 0.31s.
Maximum time difference is +0.76
minimum time difference is +0.03
Under and Overestimation's
from predicted equation
The results show that 60.8% of sprint speed is accounted for by % of Body fat. This is a significant finding, this was because of the p value of p<0.05. this disputes the findings of Chaouachi et al. (2009).
BMI was found not to be significant, p>0.05. This also disputes the findings of Watts, Coleman & Nevill (2012) as they found BMI to be a predictor of sprint time.
The results of this study may differ from the 2012 study as they used 100m sprints whereas this study used 30m and the 2009 study used 30m sprints.
Also the study from Watts, used world-class athletes which is differs from this study consisting of sports science students of various competitive levels.
Morphological factors such as BMI and % of Body Fat have been used to assess sprint time.
Study- Coh, MIlanovic & Kampmiller (2001) found having a lower % body fat resulted in better sprint time. Had similar BMI values to this study.
Study- Sedeaud et al. (2014) found that BMI was a predictor of sprint time. BMI was useful in determining athletic event athlete should compete in.
1- Coaches monitoring player performance throughout the season, rather than sprint testing.
2- Rugby players are encouraged to gain weight. Will this influence sprint time.
This study found that % of Body Fat contributed to 30m sprint time with an accountable percentage of 60.8%. This has answered the research question that suggested that % of Body Fat was better a predictor of sprint time that BMI.

Background and Rationale
Key Literature
Strengths & Limitations
Watts Vs.
100m -
5, 10 & 30m
n= 569 (not clear on whether same subject),
%BF -
World-class sprinters -
Elite handball players
BMI was found to influence sprint time -
% of Body Fat was found to not be a predictor of sprint time

Sedeaud, A., Marc, A., Marck, A., Dor, F., Schipman, J., Dorsey, M., Haida, A., Berthelot, G. & Toussaint, JF. (2014) BMI, a Performance Parameter for Speed Improvement. PLoS ONE. 9 (2) e90183. doi:10.1371/journal.pone.0090183.
Coh, M., Milanovic, D. & Kampmiller, T. (2001) Morphological and Kinematic Characteristics of Elite Sprinters. Collegium Antropologicum. 2 (25), pp605-610.
Watts, A.S>, Coleman, I. & Nevill, A. (2012) The changing shape characteristics associated with success in world-class sprinters. Journal of Sports Science. 30 (11), pp1085-1095.
Chaouachi, A., Brughelli, M., Levin, G., Boudhina, N. B. B., Cronin, J. & Chamari, K. (2009) Anthropometric, physiological and performance characteristics of elite team-handball players. b Journal of Sports Sciences. 27 (2) pp 151-157.

Future Research
Results from this study could initiate research in the future into assessing muscle mass as another variable in predicting sprint time.
Does increased muscle mass in upper and lower leg improve sprint time?
An increased sample would also be encouraged within future research.
Link to Rationale
As 60.8% of sprint time is accounted for by % of body fat, this can be used by coaches throughout the rugby season in order to monitor players sprint time. This will enable coaches to not have to alter their training programme's to fit in sprint training.
Due to % of body fat being a significant predictor, this means that Rugby players can be encouraged to gain weight as long as it is Fat Free Mass and maintain sprint time.
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