Anthropometric Measurements, Physiological and Biomotoric Test to

Identify Talented Basketball Athletes

Ritoh Pardomuan

, Toho Cholik Mutohir

, and Nining Widyah Kusnanik

Department of Physical Education, STKIP PGRI Jombang, Jalan Pattimura, Jombang, Jawa Timur, Indonesia

Department of Sport Science, Universitas Negeri Surabaya, Jalan Ketintang, Surabaya, Jawa Timur, Indonesia

ritohpardomuan@stkipjb.ac.id

Keywords: Anthropometric Measurements, Physiological Test, Biomotoric Test, Talented Basketball Athletes.

Abstract: This research was aimed at developing an equation model and software to identify talented basketball

athletes. To this end, a quantitative research and development approach was employed. The research

subjects were 145 student basketball players aged 10-12 in Surabaya. Data were analyzed using factor and

discriminant analysis. This research resulted in the following equation: D = - 3.420 + (- 0.22 Body Height)

+ (- 0.031 Sitting Height) + (- 0.020 Arm Span) + (- 0.153 Right Leg Length) + (0.204 Left Leg Length) +

(- 0.111 Palm Length) + (0.247 Back Muscular Flexibility) + (- 0.007 Illinois Agility Run) + (0,067 Right

Leg Vertical Jump) + (0.071 Left Leg Vertical Jump) + (0,011 Double Leg Vertical Jump) + (- 0.60 20-

Meter Sprint) + (0.044 Multistage Fitness Test) + (0.009 Ball Throwing). It was concluded that the

developed equation and software could be used to identify talented basketball players.

1 INTRODUCTION

Talent identification is a structured and systematic

effort to identify potential sports talents. The

development of talent identification tools has been a

fundamental to the projection of athletes’ potentials

(Vaeyens, Roel et al., 2009; Smith, David. J. 2003;

Wolstencroft, Elaine., 2002). Talent identification is

necessary in sports including basketball. Basketball

is a fast, dynamic, and high-intensity game with a

constantly changing tempo between attacking and

defending. In addition, basketball also requires pace,

acceleration, explosive moves, and

anaerobic/aerobic energy to do high-intensity

activities like rebounding, passing, jumping,

shooting, fast-breaking, and performing a high-speed

play (Ahmed, 2013; Alemdaroğlu, 2012; Erculj,

2010). Basketball players are required to possess the

following attributes: strength, speed, agility,

accuracy, vertical jump, anaerobic capacity, aerobic

capacity, and power to perform in the highest level.

Sports researchers, practitioners, and coaches

carefully develop instruments to identify and select

potential athletes (Abbott & Collins 2002).

Talent identification model has been developed

in many countries such as Australia, China, Japan,

Scotland, and Germany (Abbott & Collins, 2002;

Aussie Sport, 1993). Research on talented athlete

identification model has been previously conducted

by focusing on certain sports like paddling,

volleyball, lawn tennis, football, and basketball

(Chahal, 2013; William & Reilly, 2000; Hoare,

2000). This research was aimed at developing an

anthropometric measurement instrument,

physiological test, biomotoric test, equation model

and software to identify talented basketball athletes.

The success of sporting development is a result of a

long-term, programmed, organized, structured, and

measurable coaching process. It also heavily relies

on the quality of sports talents’ potentials. No matter

how good the training program or the coaches are,

things will not work out if the athletes have poor

potentials.

2 METHODS

This research and development used a quantitative

method.

2.1 Sample / Participants

The research population were student basketball

players aged 10-12 in Surabaya. 55 samples were

taken in Phase 2 using a purposive sampling

technique, and 90 samples were taken in Phase 3.

Thus, the total number samples were 145.

Pardomuan, R., Mutohir, T. and Kusnanik, N.

Anthropometric Measurements, Physiological and Biomotoric Test to Identify Talented Basketball Athletes.

In Proceedings of the 2nd International Conference on Sports Science, Health and Physical Education (ICSSHPE 2017) - Volume 1, pages 129-133

ISBN: 978-989-758-317-9

129

2.2 The tested variables

Obviously, variables in this research were

anthropometric measurements, biometric testing and

physiological testing.

2.2.1 Anthropometric measurement

An anthropometric measurement is a process to

assess the size of certain parts of body anatomically.

Athlete anthropometry has been a focus of many

studies. In basketball, it is not only a performance

predictor, but also a determinant factor in the

selection process. A player’s physical size will

determine where he will play in a team (Alejandro,

et al., 2015; Drinkwater, 2008). The anthropometric

measurement in this research included: body height,

sitting height, body weight, arm span, right/left leg

length, palm length, and reaching height.

2.2.2 Physiological testing

A physiological test is a systematic and objective

procedure to assess the function of body organs and

functional relationship between the organs in

question. In this research, the physiological test

measured basketball players’ aerobic and anaerobic

skills. In basketball, anaerobic skills are crucial to do

defensive-offensive transitions, shooting, jumping,

blocking, passing, layup, and other technical skills

(Araujo, et al., 2013, Çetin & Muratli, 2013; Köklü,

et al., 2011).

Aerobic endurance is body capacity to resist

fatigue caused by persistent aerobic burden; i.e.,

some relatively long physical activities with low to

moderate intensity. Maximum aerobic capacity

(VO2max) is the best indicator of maximum aerobic

power in basketball (Köklü, et al., 2011; Chaouachi,

2019). The physiological test in this study included:

20-meter sprint, 30-meter sprint, and multistage

fitness test.

2.2.3 Biomotoric testing

A biomotoric test is a systematic and objective

procedure to assess movement skills. Muscular

strength and muscular explosive power are two

important factors to determine movement mobility

independently. Physical abilities like leg and arm

muscular strength are very important in sports

(Tschopp, 2011; Erculj, 2010). The biomotoric test

in this research included: push up, sit up, back

muscular flexibility, standing board jump, Illinois

agility run, single leg vertical jump, double leg

vertical jump, and ball throwing.

2.3 Experimental procedure

This research was carried out in three phases: Phase

1 was designing selected test instrument, phase 2

trying out selected test instrument, and phase 3

implementing selected test instrument.

2.4 Statistical analysis

Data were analyzed using factor and discriminant

analysis. The research was carried out in 2016 to

2017. The data used in this study consisted of

primary data and secondary data, including: number

of pipeline orders, number of coats required for a

pipe, number of coatings required for all pipes, lead

time (raw material ordering time), ordering cost and

storage cost incurred for the project, organizations

and projects structure, data and events from the

internet and journals.

This research uses quantitative approach with

descriptive research type. Especially descriptive

comparative analysis is used to compare three lot

sizing techniques in MRP; Lot for Lot, EOQ, and

POQ. The analysis starts with MRP step which

include: 1) creating a Master Production Schedule,

2) creating a product structure or Bills of Materials,

3) collecting lead time data of raw material ordering,

4) preparing a Gross Requirements Plan, 5) Make a

Net Requirements Plan, 6) determine the ordering

time of goods (Planned Order Release) with lot

sizing method, 7) determine the right lot sizing

method (Heizer, 2014).

Determining the right lot sizing method will

result in a minimum total inventory cost.

Determination of this method is done by comparing

total inventory cost based on company calculation

with total cost obtained through calculation by lot

sizing method. Lot sizing methods used in this

research are Lot for Lot, Economic Order Quantity

(EOQ), and Periodic Order Quantity. The software

for data analysis use Production and Operation

(POM) for Windows ver. 3 (build 18).

3 RESULTS AND DISCUSSION

At the first phase, 21 instruments were developed;

i.e., anthropometric measurements including body

height, sitting height, body weight, arm span,

right/left leg length, palm length, and reaching

height measurement; physiological test including 20-

meter sprint, 30-meter sprint, and multistage fitness

test; biomotoric test including push up, sit up, back

muscular flexibility, standing board jump, Illinois

ICSSHPE 2017 - 2nd International Conference on Sports Science, Health and Physical Education

130

agility run, single leg vertical jump, double leg

vertical jump, and ball throwing. The second phase

was trying out the selected test instrument by

identifying the discriminant variable. The object

identification was aimed at identifying research

variables to be used to test the discrepancies

between groups. The variables in questions were

body height, sitting height. arm span, right leg

length, left leg length, palm length, back muscular

flexibility, Illinois agility run, single leg vertical

jump, standing board jump, double leg vertical

jump, 20-meter sprint, multistage fitness test, 30-

meter sprint, and ball throwing. The third phase was

implementing the selected test instrument by

identifying selected discriminant variables. The

variables in questions were body height, sitting

height. arm span, right leg length, left leg length,

palm length, back muscular flexibility, Illinois

agility run, single leg vertical jump, standing board

jump, double leg vertical jump, 20-meter sprint,

multistage fitness test, 30-meter sprint, and ball

throwing.

The category of a variable in a discriminant

analysis can be explained by the used discriminant

variables. Table 1 presents the canonical correlation

score. The canonical correlation score can be said

good if it is >0.50 or 50%. Table 1 shows that the

category of basketball and non-basketball can be

explained by the variables body height, sitting

height, arm span, right leg length, left leg length,

palm length, back muscular flexibility, Illinois

agility run, right leg vertical jump, left leg vertical

jump, double leg vertical jump, 20-meter sprint,

multistage fitness test, and ball throwing. The

canonical correlation score was 0.612 or 61.2%. The

other 38.8% could be explained by other variables.

The difference in the average of discriminant

variables of basketball and non-basketball group can

be identified by looking at the Wilks's lambda

adjusted by the chi-square score. This difference can

be seen in the p-value (sig) in Table 2.

Table 1: Eigenvalues.

Function

Eigenvalue

% of

Variance

Cumulative

Canonical

Correlation

.600a

100.0

.612

a. First 1 canonical discriminant functions were used in the

analysis

Table 2: Wilks's Lambda.

Test of

Function(s)

Wilks's

Lambda

Chi-

square

Sig.

.625

38.059

.001

It was revealed that the average scores of the

independent variables (body height, sitting height,

arm span, right leg length, left leg length, palm length,

back muscular flexibility, Illinois agility run, right leg

vertical jump, left leg vertical jump, double leg

vertical jump, 20-meter sprint, multistage fitness test,

and ball throwing) of both basketball and non-

basketball athletes were collectively different. Once it

was determined that the used variables could become

the discriminant variables, it was necessary to find out

the difference between each one of these discriminant

variables of both categories.

Table 3: Canonical discriminant function coefficients.

Variables

Function

Height

-.022

Sitting Height

-.031

Arm Span

-.020

Right Leg Length

-.153

Left leg Length

.204

Palm Length

-.111

Back Muscular Flexibility

.247

Illinois Agility Run

-.007

Right Leg Vertical Jump

.067

Left Leg Vertical Jump

.071

Double Leg Vertical Jump

.011

20-m Sprint

-.060

Multistage Fitness Test

.044

Ball Throwing

.009

(Constant)

-3.420

Unstandardized coefficients

The average score of back muscular flexibility

was 0.247. Since it was the highest coefficient, it

could be used to predict the difference between

basketball and non-basketball athletes’ potentials.

Since the average score of the discriminant variables

used to differentiate potentials among basketball

group sometimes were similar so that it was

necessary to identify the size of the samples that

really belonged to the basketball category and that of

those that really belonged to the non-basketball

category.

Anthropometric Measurements, Physiological and Biomotoric Test to Identify Talented Basketball Athletes

131

7 out 25 basketball groups belonged to the non-

basketball category since the ratio of the average

score of their discriminant variables were closer to

the non-basketball category. 12 out 25 non-

basketball groups belonged to the basketball

category since the ratio of the average score of their

discriminant variables were closer to the basketball

category.

Table 4: Classification results

b,c.

Category

Predicted Group

Membership

Total

Basketball

Non-

Basketball

Original

Count

Basketball

Non-

Basketball

Ungrouped

cases

Basketball

72.0

28.0

100.0

Non-

Basketball

18.5

81.5

100.0

Ungrouped

cases

36.4

63.6

100.0

Cross-

validated

Count

Basketball

Non-

Basketball

56.0

44.0

100.0

Non-

Basketball

24.6

75.4

100.0

a. Cross validation is done only for those cases in the

analysis. In cross validation, each case is classified by

the functions derived from all cases other than that

case.

b. 78,9% of original grouped cases correctly classified.

c. 70,0% of cross-validated grouped cases correctly

classified.

Based on the discriminant equation, it can be

seen that back muscular flexibility was the most

dominant variable so that it could be used to predict

one’s potentials in basketball. Back muscular

flexibility is one of the biomotoric attributes every

basketball player must have. Flexibility refers to the

range of movement in a joint or series of joints.

Natural joint movements rely on the existing

tendons, ligaments, and muscle fibers. Flexibility is

particularly useful to avoid a muscle injury because

in basketball games the tempo is constantly

changing and basketball games require pace,

acceleration, and explosive moves (Ahmed, 2013).

The developed software in this study was a

statistics computer program. The result of data

analysis, which was the discriminant equation

model, was encoded into computer programming

language to facilitate sports researchers,

practitioners, and coaches in identifying basketball

talents. The developed form design was then

programmed in Microsoft Access. The developed

software was named TIBA (Talent Identification

Basketball Athletes). This research resulted in the

following equation: D = - 3.420 + (- 0.22 Body

Height) + (- 0.031 Sitting Height) + (- 0.020 Arm

Span) + (- 0.153 Right Leg Length) + (0.204 Left

Leg Length) + (- 0.111 Palm Length) + (0.247 Back

Muscular Flexibility) + (- 0.007 Illinois Agility Run)

+ (0.067 Right Leg Vertical Jump) + (0.071 Left

Leg Vertical Jump) + (0.011 Double Leg Vertical

Jump) + (- 0.060 20 Meter-Sprint) + (0.044

Multistage Fitness Test) + (0.009 Ball Throwing).

4 CONCLUSIONS

It was concluded that the developed equation model

and software could be used to identify talented

basketball athletes. Instruments to identify talented

basketball athletes were anthropometric

measurement (body height, sitting height, arm span,

right leg length, left leg length, and palm length)

physiological test (20-m sprint and multistage fitness

test), and biomotoric test (back muscular flexibility,

Illinois agility run, right leg vertical jump, left leg

vertical jump, double leg vertical jump, and ball

throwing).

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