Is there a relation between BMI and hours playing sport?
Introduction
Many physical characteristics and sporting activities are attributed to the length of the body. There are great argument about the relationship of the body length and the sporting ability. However, it cannot be denied that the length of the body have a relationship with the physical performance of the in the sporting activities. According to Norton, Olds, Olive and Craig (1996), there are many sports where height is a key determinant of success. On the other hand, shortness in stature is particularly advantageous in acceleration and changing direction (ATP Tour, 1995).
Anthropometric data showed that the shortest athletes were those at the very short events like 100M or less. Shorter legs will generally have a lower moment of inertia or resistance to movement, than longer ones and so is beneficial in very short events (Payne and Isaacs, 2005).
Many researchers have devoted on the topic about the relationship between body segment measurements and motor performance of children. As a result, it is believed that child’s high center of gravity sometimes makes them difficult to come to a fast, complete stop when the activity involves a fast forward or backward movement (Kinnunen, D. A., Colon, G., Espinoza, D., Overby, L. Y., & Lewis, D. K. (2001). Besides, the leg length is also believed to have influence in motor performance. Dintiman and Ward (2003) pointed out that leg length is one of the three factors that can account for individual differences in sprinting speed. The greater shoulder width and arm length could be an advantage in throwing tasks (Loovis, E. M., & Butterfield, S. A. 2003).
In this project, we are interested in determining if there is any relationship between the BMI and the hours playing sport. The hours playing sport is measured by the performance of the player in the field. The focus of this study will include nine variables, one dependent and eight independent variables. The study will be conducted at Mosley hill athletic club and the runners who are involved in the athletic competition. The study will also review statistics from the data collected from the Mosley hill athletic club and the runners (Steigelman, & Gwen (1981). The regression analysis between the independent variables and the dependent variables will be conducted to investigate if there is any relationship.
Hypothesis
- There exist no significant relationship between the height and the BMI
There exist no significant relationship between the height and the BMI
- There exist no significant relationship between the age of the athlete and the performance of the athletes.
There exist a significant relationship between the age of athlete and the performance of the athletes.
- There exist no significant relationship between the eight of the athlete and the BMI
There exist a significant relationship between the height of athlete and the BMI
Methods
The data will be obtained from Mossley Hill Athletic club. A regression analysis of these data will be conducted to investigate if there is any relationship between the independents variables and the dependent variable. A correlation coefficient analysis will also be conducted to investigate the significance of the model. Different scatter plots will be applied to show the relationships of the variables and also boxplots will be used to check on the normality of the data.
Regression Analysis: BMI versus gender
The regression equation is
BMI = 23.8 + 0.161 gender
Predictor Coef SE Coef T P
Constant 23.831 1.309 18.20 0.000
gender 0.1613 0.8981 0.18 0.858
S = 2.74991 R-Sq = 0.1% R-Sq(adj) = 0.0%
Source DF SS MS F P
Regression 1 0.244 0.244 0.03 0.858
Residual Error 38 287.356 7.562
Total 39 287.600
In this study a correlation analysis was conducted to investigate the relationship between the two variables. The output
Pearson correlation of gender and BMI = 0.185
P-Value = 0.014
Table1: descriptive analysis of all the variables and BMI
Descriptive Statistics: metres, Kg, gender, Years, cm, cm_1, BMI
Variable N N* Mean SE Mean StDev Q1 Median Q3
metres 40 0 7.66 4.15 26.25 1.65 1.73 1.78
Kg 40 0 71.05 1.51 9.56 62.00 70.00 77.75
gender 40 0 1.3750 0.0775 0.4903 1.0000 1.0000 2.0000
Years 40 0 47.45 2.77 17.52 28.75 47.50 63.75
cm 40 0 82.97 1.81 11.43 72.25 83.00 93.75
cm_1 40 0 104.15 1.31 8.28 99.00 102.00 109.75
BMI 40 0 24.052 0.429 2.716 22.125 24.150 25.900
The table above shows the descriptive statistic of the various variables that are associated with the athletes’ data. The average height of these forth athlete is 7.66 meters and their median is 1.73. According to the weight variables, the average weight is 71.05 and their median is 70.0. The mode is also clearly indicated for each variable. From the output the regression analysis is not significant and thus there is no relationship between the BMI and the height is centimeter.
The regression equation is
BMI = 24.3 - 0.0057 Years
Predictor Coef SE Coef T P
Constant 24.322 1.269 19.17 0.000
Years -0.00569 0.02512 -0.23 0.822
S = 2.74922 R-Sq = 0.1% R-Sq(adj) = 0.0%
Source DF SS MS F P
Regression 1 0.388 0.388 0.05 0.822
Residual Error 38 287.212 7.558
Total 39 287.600
The scatter plots
Figure 1: scatter plot between the BMI and the height is centimeters
Figure 2: the scatterplot between the BMI and the years of experience in athletics
There is a negative relationship between the age and the performance of the athletes.
Figure3: scatterplot between the BMI and the weight of the athlete in kg
There is positive relationship between the weight of the athlete and their performance.
Figure 4: The histogram of BMI
Figure 5: The box plot for the variables
Figure 6: From the box plot above the data is not normally distributed.
Discussion and conclusion
References
ATP Tour (1995). ATP 1995 player guide. Ponte Vedra Beach, Florida: ATP Tour.
Dintiman, G., & Ward, R. (2003). Sport speed. (3rd ed.). Champaign, IL: Human Kinetics.
Loovis, E. M., & Butterfield, S. A. (2003). Relationship of hand length to catching performance
Kinnunen, D. A., Colon, G., Espinoza, D., Overby, L. Y., & Lewis, D. K. (2001).
Anthropometric correlates of basketball free-throw shootings by young girls. Perceptual
& Motor Skills, 93(1), 105-108.
Steigelman, & Gwen (1981). The role of motor performance in the social status of preschool
children. Ph.D. dissertation, University of Oregon, United States -Oregon. Retrieved
8209680).
Norton, K., Olds, T., Olive, S., & Craig, N. (1996). Anthropometry and sports performance. In
K. Norton, & T. Olds (Eds.), Anthropometrica: A textbook of body measurement for
sports and health courses (pp. 287-352). Sydney, Australia: UNSW Press.
Payne, V. G., & Isaacs, L. D. (2005). Human motor development: A lifespan approach. (6th ed.).
Boston, IL: McGraw-Hill
Appendix
The data on the athlete and their relative characteristics
