Anthropometric and Body Composition Characteristics as Predictors of Ball Speed Performance in Cricket for Pace Bowlers: Multiple Regression Approach

Authors

  • Sapna Mandoli Department of Exercise Physiology, Lakshmibai National Institute of Physical Education, Gwalior, Madhya Pradesh, India
  • Deepak Sharma Department of Exercise Physiology, Lakshmibai National Institute of Physical Education, Gwalior, Madhya Pradesh, India
  • Hem Chandra Joshi Department of Sports Biomechanics, Lakshmibai National Institute of Physical Education, NERC, Guwahati, Assam, India

DOI:

https://doi.org/10.21276/apjhs.2022.9.2.11

Keywords:

Adjusted R square, Multiple regression model, Total arm length

Abstract

Aim and Objective: The study aimed to investigate the relationship of selected anthropometric and body compositional variables to the releasing speed of the ball in medium pace bowlers. Materials and Methods: Thirty senior’s women board players (BCCI) were selected for the study (mean age 23.4 ± 2.03, mean height 152.1 ± 3.44, and mean weight 52.4 ± 4.21). Sliding caliper and anthropometric rode were used for anthropometrical measurements, and for the body compositional measurement, bio-electrical impedance was used and the releasing speed of the ball was assessed using velocity speed radar gun. Results: Among all the selected variables, the model identified three variables which were muscle mass, total arm length, and fat and the regression model explains 97.6% of the total variability in the muscle mass, total arm length, and fat in relation to ball release speed of the pace bowlers and which is quite good. Conclusion: The regression equation which was developed by the model was as follow: Speed of the ball (performance of pace bowlers) = –94.767+5.15×(Muscle Mass)+1.488×(Total Arm Length)+0.220×(Fat).

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Published

2022-04-01

How to Cite

Sapna Mandoli, Deepak Sharma, & Hem Chandra Joshi. (2022). Anthropometric and Body Composition Characteristics as Predictors of Ball Speed Performance in Cricket for Pace Bowlers: Multiple Regression Approach. Asian Pacific Journal of Health Sciences, 9(2), 44–47. https://doi.org/10.21276/apjhs.2022.9.2.11