Article in HTML

Author(s): Mithilesh Kumar Singh, Arti Dhankhar, Rakesh Kumar Patel, Rajeev Choudhary

Email(s): Email ID Not Available

Address: Research Scholar, School of Studies in Physical Education, Pt. Ravishankar Shukla University, Raipur (C.G.)
Associate Professor, Ramjas College, University of Delhi
Lecturer, DIET, Shahjahanpur (U.P.)
Professor in Physical Education, Pt. Ravishankar Shukla University, Raipur (C.G.)
*Corresponding author: artidhankhar@ramjas.du.ac.in

Published In:   Volume - 28,      Issue - 2,     Year - 2022


Cite this article:
Singh, Dhankhar, Patel and Choudhary (2022). Prophesy of Spiker Performance on the basis of selected Anthropometric Characteristics. Journal of Ravishankar University (Part-A: SOCIAL-SCIENCE), 28(2), pp. 41-50.



Prophesy of Spiker Performance on the basis of selected Anthropometric Characteristics

Mithilesh Kumar Singh1, Arti Dhankhar2,*, Rakesh Kumar Patel3, Rajeev Choudhary4

 1 Research Scholar, School of Studies in Physical Education, Pt. Ravishankar Shukla University, Raipur (C.G.)

2Associate Professor, Ramjas College, University of Delhi

3Lecturer, DIET, Shahjahanpur (U.P.)

4Professor in Physical Education, Pt. Ravishankar Shukla University, Raipur (C.G.)


*Corresponding author: artidhankhar@ramjas.du.ac.in


 


Abstract

Objective: The study was conducted with an objective to prophesy the spiker’s performance on the basis of anthropometric characteristics.

Variables: In the study, Spiker’s Performance was selected as dependent variable (DV) and selected anthropometric characteristics  i.e. SH (Spiker’s Height), SW (Spiker’s Weight), SAL (Spiker’s Arm Length), SFAL (Spiker’s Fore Arm Length, SUAL (Spiker’s Upper Arm Length), SUAC (Spiker’s Upper Arm Circumference), SWC (Spiker’s waist Circumference), SHC (Spiker’s Hip Circumference), SLL (Spiker’s Leg Length), SLLL (Spiker’s Lower Leg Length), STC (Spiker’s Thigh Circumference) and SCC (Spiker’s Calf Circumference) were observed independent variables (IV).

Subjects: For the purpose of the present study, 75 spikers were selected as subjects from interuniversity level volleyball tournament organized in India.

Statistical Analysis: To find out relationship between Dependent Variable (Spiker’s Performance) and Independent Variables (selected Anthropometric Characteristics), product moment correlation and multiple correlations were applied. For the prophecy of Dependent Variable (Spiker’s Performance) on the basis of Independent Variables (selected Anthropometric Characteristics), multiple regression equation was applied.

Conclusions: For the prophecy of Dependent Variable (Spiker’s Performance) on the basis of Independent Variables (selected Anthropometric Characteristics) two regression models are established. Established regression models are: (1) Spiker’s Performance = -35.586 +.667 X Spiker’s Arm Length and (2) Spiker’s Performance = -23.512 +.458 X Spiker’s Arm Length + .210 X Spiker’s Upper Arm Circumference.

 

Keywords - Spiker’s Performance and Anthropometric Characteristic

 Introduction

Volleyball is a team game in which two teams of six players are separated by a net. Each team tries to score points by grounding a ball on the other team’s court under organized rules. It has been a part of the official program of the summer Olympic games since 1964. In India it was in the year 1952 that the first national championship was held at Chennai (Uppal, A. K., & Satyanarayana, V.). Anthropometric has been used for identification and understanding human physical difference as an early tool of physical anthropology. Anthropometry is derived from a Greek word anthropos which means “human” and metron which means “measure”. Anthropometry involves the systematic measurement of physical properties of human body. A French mathematician was the first known person to use the term “anthropometric or anthropometry,” the measurement of man dates back to ancient civilizations and is the oldest form of measurement. It was of great interest in ancient India and later in Egypt where study was undertaken to find one part or component of the body that would predict or become common measurement of all body parts. In Egypt, for example the length of the middle finger was considered a common measure of all body proportions. For instance, 5 finger length to knee, 10 to the pubic arch and 8 to the length of the arm reach. The Greeks were experts in body proportions. Not only in the game of volleyball, but in all the games, there is significant contribution of anthropometry. In different games, players play at different playing positions with different specialty, as a results different anthropometric measurements are required at different positions, due to differential nature of requirement.

 Objective of the study

The objective of the study was to prophesy spiker’s performance on the basis of anthropometric characteristics.

 Variables of the study

For the purpose of the study, Spiker’s Performance was selected as dependent variable (DV) and selected anthropometric characteristics  i.e. SH (Spiker’s Height), SW (Spiker’s Weight), SAL (Spiker’s Arm Length), SFAL (Spiker’s Fore Arm Length, SUAL (Spiker’s Upper Arm Length), SUAC (Spiker’s Upper Arm Circumference), SWC (Spiker’s waist Circumference), SHC (Spiker’s Hip Circumference), SLL (Spiker’s Leg Length), SLLL (Spiker’s Lower Leg Length), STC (Spiker’s Thigh Circumference) and SCC (Spiker’s Calf Circumference) were observed independent variables (IV).

 

1.     Subjects of the study

The study included the subjects who participated in Inter-university level Volleyball Tournament organized under the banner of Association of Indian Universities in India. A total of 75 male spikers were purposively selected for the study. The age of the subjects ranged from 18 - 28 Years.

2.     Statistical Analysis:

To find out relationship between Dependent Variable (Spiker’s Performance) and Independent Variables (selected Anthropometric Characteristics), product moment correlation and multiple correlations were applied. For the prophesy of Dependent Variable (Spiker’s Performance) on the basis of Independent Variables (selected Anthropometric Characteristics), multiple regression equation was applied.

 

 Results and Findings:

Table – 1: Table showing Pearson Correlation Coefficient and Significance (1-tailed) showing the relationship between Spiker’s Performance and selected Anthropometric Characteristics

 

 

SP

SH

SW

SAL

SFAL

SUAL

SUAC

SWC

SHC

SLL

SLLL

STC

SCC

Pearson Correlation Coefficient

SP

1.000

.659

.567

.666

.553

.646

.622

.595

.548

.582

.617

.552

.614

SH

.659

1.000

.906

.889

.817

.769

.903

.912

.834

.918

.943

.865

.858

SW

.567

.906

1.000

.800

.740

.687

.908

.826

.757

.854

.839

.809

.789

SAL

.666

.889

.800

1.00

.912

.875

.756

.881

.798

.835

.824

.874

.884

SFAL

.553

.817

.740

.912

1.000

.598

.704

.779

.717

.847

.743

.842

.860

SUAL

.646

.769

.687

.875

.598

1.000

.644

.799

.709

.628

.731

.711

.710

SUAC

.622

.903

.908

.756

.704

.644

1.000

.789

.735

.804

.851

.731

.732

SWC

.595

.912

.826

.881

.779

.799

.789

1.00

.899

.844

.882

.867

.840

SHC

.548

.834

.757

.798

.717

.709

.735

.899

1.000

.793

.822

.789

.809

SLL

.582

.918

.854

.835

.847

.628

.804

.844

.793

1.00

.838

.847

.881

SLLL

.617

.943

.839

.824

.743

.731

.851

.882

.822

.838

1.000

.813

.793

STC

.552

.865

.809

.874

.842

.711

.731

.867

.789

.847

.813

1.000

.887

SCC

.614

.858

.789

.884

.860

.710

.732

.840

.809

.881

.793

.887

1.000

Significance (1-tailed)

SP

.

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

SH

.000

.

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

SW

.000

.000

.

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

SAL

.000

.000

.000

.

.000

.000

.000

.000

.000

.000

.000

.000

.000

SFAL

.000

.000

.000

.000

.

.000

.000

.000

.000

.000

.000

.000

.000

SUAL

.000

.000

.000

.000

.000

.

.000

.000

.000

.000

.000

.000

.000

SUAC

.000

.000

.000

.000

.000

.000

.

.000

.000

.000

.000

.000

.000

SWC

.000

.000

.000

.000

.000

.000

.000

.

.000

.000

.000

.000

.000

SHC

.000

.000

.000

.000

.000

.000

.000

.000

.

.000

.000

.000

.000

SLL

.000

.000

.000

.000

.000

.000

.000

.000

.000

.

.000

.000

.000

SLLL

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.

.000

.000

STC

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.

.000

SCC

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.

 

Table- 1 shows Pearson Correlation Coefficient and Significance (1- tailed) in relation to Spiker’s Performance and selected Anthropometric Characteristics. Significant relationship was found between SP and SH (r = .659); SP and SW (r = .567); SP and SAL (r = .666); SP and SFAL (r = .553); SP and SUAL (r = .646); SP and SUAC (r = .622); SP and SWC (r = .595); SP and SHC (r = .548); SP and SLL (r = .582); SP and SLLL (r = .617); SP and STC (r = .552); SP and SCC (r = .614); SH and SW (r = .906); SH and SAL (r = .889); SH and SFAL (r = .817); SH and SUAL (r = .769); SH and SUAC (r = .903); SH and SWC (r = .912); SH and SHC (r = .834); SH and SLL (r = .918); SH and SLLL (r = .943); SH and STC (r = .552); SH and SCC (r = .858); SW and SAL (r = .800); SW and SFAL (r = .740); SW and SUAL (r = .687); SW and SUAC (r = .908); SW and SWC (r = .826); SW and SHC (r = .757); SW and SLL (r = .854); SW and SLLL (r = .839); SW and STC (r = .809); SW and SCC (r = .789); SAL and SFAL (r = .912); SAL and SUAL (r = .875); SAL and SUAC (r = .756); SAL and SWC (r = .881); SAL and SHC (r = .798); SAL and SLL (r = .835); SAL and SLLL (r = .824); SAL and STC (r = .874); SAL and SCC (r = .884); SFAL and SUAL (r = .598); SFAL and SUAC (r = .704); SFAL and SWC (r = .779); SFAL and SHC (r = .717); SFAL and SLL (r = .847); SFAL and SLLL (r = .743); SFAL and STC (r = .842); SFAL and SCC (r = .860); SUAL and SUAC (r = .644); SUAL and SWC (r = .799); SUAL and SHC (r = .709); SUAL and SLL (r = .628); SUAL and SLLL (r = .731); SUAL and STC (r = .711); SUAL and SCC (r = .710); SUAC and SWC (r = .789); SUAC and SHC (r = .735); SUAC and SLL (r = .804); SUAC and SLLL (r = .851); SUAC and STC (r = 731); SUAC and SCC (r = .732); SWC and SHC (r = .899); SWC and SLL (r = .844); SWC and SLLL (r = .882); SWC and STC (r = .867); SWC and SCC (r = .840); SHC and SLL (r = .793); SHC and SLLL (r = .822); SHC and STC (r = .789); SHC and SCC (r = .809); SLL and SLLL (r = .838); SLL and STC (r = .847); SLL and SCC (r = .881); SLLL and STC (r = .813); SLLL and SCC (r = .793) and STC and SCC (r = .887) prospectively.

 

Table- 2: Table showing residual statistics in relation to the establishment of Models for the prophesy of Spiker’s Performance on the basis of selected Anthropometric Characteristics


Minimum

Maximum

Mean

Standard Deviation

Predicted Value

16.8593

24.4574

20.5067

2.06141

Residual

-5.73647

5.63883

.00000

2.16324

Standardized Predicted Value

-1.769

1.917

.000

1.000

Standardized Residual

-2.616

2.571

.000

.986

a. Dependent Variable: SP

 

Table- 2 shows residual statistics in relation to the establishment of Models for the prognostication of Spiker’s Performance on the basis of selected Anthropometric Characteristics. In this table value of standardized residual shows the outliers of the residual. In assumption of application of multiple regression, there should not be any outliers of the residuals and the minimum value of standardized residuals should not be less -3 and the maximum value should not be above +3. In this table, the minimum value of standardized residual is -2.616 and the maximum value of standardized residual is 2.571. This proved that the standardized residuals lie in the expected range from -3 to +3. It is concluded that there is no outliers in the residuals.

 

Figure- 1: Figure showing Mean, Standard Deviation and normal curve of residuals in relation to the establishment of Models for the prophesy of Spiker’s Performance on the basis of selected Anthropometric Characteristics

 

Figure- 1 shows Mean, Standard Deviation and normal curve of residuals in relation to the establishment of Models for the prognostication of Spiker’s Performance on the basis of selected Anthropometric Characteristics. As per another assumption of application of multiple regression, the residuals should be normally distributed with mean 0 and Standard Deviation 1. The figure shows the normality of residuals, mean is almost 0 and Standard Deviation is 0.986 (near to one), so the assumption, that residuals should be normally distributed with mean 0 and Standard Deviation 1is also fulfilled.  

 

Figure- 2: Figure showing Normal P-P Plot in relation to the establishment of Models for the prophesy of Spiker’s Performance on the basis of selected Anthropometric Characteristics

 

Figure 2 shows the Normal P-P Plot in relation to the establishment of Models for the prophesy of Spiker’s Performance on the basis of selected Anthropometric Characteristics. In this figure, a standard line is generated. In case of the normality of residuals, all the scores should be scattered near to this line. In this figure all the scores are scattered near to this line, this proved that residuals are normally distributed.

 

Figure- 3: Figure showing Constant Variance of residuals in relation to the establishment of Models for the prophesy of Spiker’s Performance on the basis of selected Anthropometric Characteristics

 

Figure shows Constant Variance of residuals in relation to the establishment of Models for the prophesy of Spiker’s Performance on the basis of selected Anthropometric Characteristics. The figure shows no clear-cut pattern, this shows that there is constant variance of residuals. This proves this assumption also fulfilled.

 

Table- 3: Table showing Model Summary in relation to the establishment of Models for the prophesy of Spiker’s Performance on the basis of selected Anthropometric Characteristics

 

Established Models

R- Value

R Square- Value

Adjusted R Square Value

Standard  Error of the Estimate Value

Durbin - Watson Value

1

.666a

.443

.436

2.24462

 

1.556

2

.690b

.476

.461

2.19308

a. Predictors: (Constant), SAL

 

b. Predictors: (Constant), SAL, SUAC

c. Dependent Variable: SP

 

Table- 3 shows the Model Summary in relation to the establishment of Models for the prophesy of Spiker’s Performance on the basis of selected Anthropometric Characteristics. For the purpose, two models are established. First model is established on the basis of Spiker’s Arm Length and second model is established by Indian Spiker’s Arm Length and Spiker’s Upper Arm Circumference. This table also shows Durbin - Watson Value. This value is shows that there is no strong positive and no strong negative relationship. This value away from 0 and 4 and the expected value is near to 2. So this assumption also is fulfilled.

 

Findings related to model- 1: In case of model 1, the R- value of .666 shows the coefficient of correlation between Spiker’s Performance and Spiker’s Arm Length. The value of R Square .443 shows that 44% Spiker’s Performance is explained by Spiker’s Arm Length.

 

Findings related to model- 2: In case of model 2, the R- value of .690 shows the coefficient of correlation between Spiker’s Performance and Spiker’s Arm Length & Spiker’s Upper Arm Circumference. The value of Adjusted R Square .461 shows that 46% Spiker’s Performance is explained by Spiker’s Arm Length & Spiker’s Upper Arm Circumference.

 

Table 4: Table showing results of Analysis of Variance in relation to the establishment of Models for the prophesy of Spiker’s Performance on the basis of selected Anthropometric Characteristics

 

Models

Sum of Squares value

Degree of freedom

Mean Square value

F-