Prophesy of Spiker Performance on the basis of selected Anthropometric Characteristics

Mithilesh Kumar Singh¹, Arti Dhankhar^2,*, 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.)

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

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

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

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

Table- 4 shows 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. The result of analysis of variance talks about the utility of established regression models. If the value of established model is significant in that case, the established model has a utility. In both models, the F value of 58.144 and 32.690 are found significant. This proved that the established models are useful and the result may be generalized.

Table- 5: Table showing results of related to establishment of Models for the prophesy of Spiker’s Performance on the basis of selected Anthropometric Characteristics
 

Table- 5 shows results of related to establishment of Models for the prophesy of Spiker’s Performance on the basis of selected Anthropometric Characteristics.

Findings related to model- 1:

In case of model one, the value of constant is found -35.586 and B- value of Spiker’s Arm Length is found .667.

            Spiker’s Performance = -35.586 +.667 X Spiker’s Arm Length

Findings related to model- 2:

In case of model two, the value of constant is found -23.512 and B- value of Spiker’s Arm Length is found .458 & the value of Spiker’s Upper Arm Circumference is found .210.

Spiker’s Performance = -23.512 +.458 X Spiker’s Arm Length + .210 X Spiker’s Upper Arm Circumference

Table- 6: Table showing details of excluded variables in relation to the establishment of Models for the prognostication of Spiker’s Performance on the basis of selected Anthropometric Characteristics

Table- 6 shows details of excluded variables in relation to the establishment of Models for the prognostication of Spiker’s Performance on the basis of selected Anthropometric Characteristics. In model one, the excluded variables are SH, SW, SFAL, SUAL, SUAC, SWC, SHC, SLL, SLLL, STC, SCC and in second model the excluded variables are SH, SW, SFAL, SUAL, SWC, SHC, SLL, SLLL, STC and SCC.

 References

1. Best, J.W. (1963). Research in education. U.S.A.: Prentice Hall.
2. Chan, Y. H. (2003). Biostatistics 101: Data presentation. Singapore medicine journal, 44 (6), 280-285.
3. Chan, Y. H. (2003). Biostatistics 104: correlational Analysis. Singapore medicine journal, 44 (12): 614-619.
4. Choudhary, R., Patel, R. K., Chaudhary, S. D., & Singh, M. K. (2018). Differential Impact of Participation in Handball & Kabaddi on Selected. International Journal of Physical Education and Sports, 3(3), 41-44.
5. Clark, H. H., & Clark, D. H. (1975). Research process in physical education. Englewood cliffs, New Jersey: Prentice Hall, Inc.
6. Field, A. (2009). Discovering statistics using SPSS. London: SAGE Publications Ltd.
7. Gay, L.R. (2000). Educational research. U.S.A: Prentice Hall.
8. Gupta, S. L. & Gupta, H. (2011). SPSS for researchers. New Delhi: international Book House Pvt. Ltd.
9. Landau, S. & Everitt, B. S. (2004). A handbook of statistical analysis using SPSS. New York: Chapman & Hall/CRC Press LLC.
10. Patel, R. K., & Choudhary, R. (2015). Comparison of reaction ability among different playing positions of handball players. National journal of Physical Education and Sports Science, 2(1), 80-82.
11. Patel, R. K., & Choudhary, R. (2016). Balance ability possessed by handball players pertaining to different playing positions. International journal of Applied Research, 2(4), 481-483.
12. Patel, R. K., & Choudhary, R. (2016). Estimation of Right Shooter performance in Handball on the basis of Coordinative abilities. Physical Education Yogic & Allied Sciences, 428-434.
13. Patel, R. K., & Choudhary, R. (2016). Prediction of Right Winger Performance in Handball on the Basis of Selected Co-ordinative Abilities. International Journal of Physical Education and Applied Exercise Sciences, 2(1), 106-112.
14. Patel, R. K., & Choudhary, R. (2017). Prediction of Goal Keeper Performance on the Basis of Coordinative Abilities. International Journal of Physical Education and Applied Exercise Sciences, 2(2), 16-22.
15. Patel, R. K., Choudhary, R., Singh, M. K., & Dalal, P. (2017). Estimation of Left Shooter’s Performance in Handball on the Basis of Coordinative Abilities. National Journal of Physical Education and Sports Sciences, 3(2), 34-37.
16. Singh, M. K, and Choudhary, R. (2021). Prognostication of Setter Performance on the basis of Selected Anthropometric Characteristics. International Research Journal of Sports Glimpses. V (II), 143-147
17. Singh, M. K, Patel, R. K. and Choudhary, R. (2021). Prediction of Libero’s Performance on the basis of Selected Anthropometric Characteristics. Journal of Ravishankar University. 27 (1), 89-98
18. Uppal, A. K., & Satyanarayana, V. (2020). Physical education UGC-NET, KVS, NVS, DSSSB, TGT, PTI, PGT, SCERT- TET. Delhi: Sports Publication, page- 845.
19. Verma, J. P. (2000). A text book on sports statistics. Gwalior: Venus Publications.