A model for talent identification in cricket based on owa operator

International Journal of Information Technology & Management Information System (IJITMIS),
ISSN 0976 – 6405(Print), ISSN 0976 – 6413(Online) Volume 4, Issue 2, May - August (2013), © IAEME
40
A MODEL FOR TALENT IDENTIFICATION IN CRICKET BASED ON
OWA OPERATOR
1
Gulfam Ahamad, 2
S. Kazim Naqvi , 3
M.M. Sufyan Beg
1
FTK-Centre for Information Technology, Jamia Millia Islamia, New Delhi – 110025, India.
2
FTK-Centre for Information Technology, Jamia Millia Islamia, New Delhi – 110025, India.
3
Department of Computer Engineering, Jamia Millia Islamia, New Delhi – 110025, India.
ABSTRACT
Talent identification in sports is a challenging and significant task which is considered
highly subjective. However, several attempts have been made in the past to reduce the
subjectivity in this task. In this paper we have reviewed several talent models which have been
proposed in the past. We have also presented a brief summary of each of these models
focusing on their modus-operandi. The paper also identifies essential parameters for talent
assessment in cricket. A model based on Ordered Weighted Averaging Aggregation (OWA)
operator has also been proposed. The paper also presents an example demonstrating the
application of the proposed algorithm on sample data.
Keywords: Talent identification, OWA, Linguistic Quantifier, Talent Classifier.
1.0 INTRODUCTION
The ability to perform well in sports may vary amongst individuals. A person may be
exceptionally good at one sport whiles he may only be average at others. It is interesting to
study what determines the ability of a person to excel or not excel in a given sport. This ability
is commonly referred to as talent. Talent refers to the skills that someone has naturally to do
something that is hard. A number of authors [20], [19], [14] have defined talent as an
increasable natural endowment of a superior quality of a person.
Talent identification is a process to identify the ability of superior quality. It is a
complex multifaceted, multidimensional and multi-stage process [5] [11] [17] [28]. Many
earlier studies [19] [12] [29] have characterized the talent by a number of factors viz. Health,
Motor, Functional, Morphological, Physiological, Anthropometric, Psychological, Social,
Cultural, Game Intelligence, Technical/ Tactical Abilities and Genetics. Although, no
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ISSN 0976 – 6405(Print)
ISSN 0976 – 6413(Online)
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consensus seems to have emerged on the completeness of the above parameters which are
believed to contribute towards talent in an individual.
Talent identification is another area with significant importance for individuals and
sports organizations. Correct and timely identification of sports talent can build careers and
bring glory to the nations. On the other side, persistence on incorrectly chosen sports will
invariably lead to wastage of time and resources. To identify talent in sports a number of
studies have been made in the past [8] [18] [29] [1] [21] [28] [9] [6] [6] [5] [26] [27] [26] [3]
[2] [13]. During Our literature survey, we did not come across any study for Talent
Identification in Cricket.
In this paper, we have proposed a model based on OWA Operator to assess the talent
of a cricket enthusiast. In section 1.0 of the paper, we summarize the frequently cited studies
in Talent Identification. The studies summarized in section 1.1 also conclude that no Talent
Identification Model has been developed for Cricket. In section 2.0, we present the parameters
which can be effectively used for building talent identification model for cricket. In section
3.0, we discuss OWA operator. Applications of OWA operator for talent identification in
cricket has been discussed in section 4.0. In Section 5.0, we present the conclusions.
1.1 Talent Identification Models in Sports
Timely talent identification in sports is very significant and challenging task. To
identify talent in sports, various computer based talent identification models have been
proposed. These models could potentially play a very significant role in lives of sports
enthusiast and sports organizations. Some of these are listed below [7]:
• An individual can make timely decision to pursue a sport of his interest or not.
• Provide a cost effective way for assessing talent levels without assistance of coaches.
• They can help in increasing the confidence levels in athletes.
• Online talent models can even be more potent as they provide unparalleled reach. They
can be accessed anytime anywhere by anyone.
• Online Talent Identification models can also help sports authorities in getting
indications on talent distribution in various geographical regions within the country.
Such model can help in separating extraordinarily talented athletes from average.
• They can also address the challenge of inadequate number of coaches available in the
country.
All Talent Identification models leverage physical characteristics of athletes which
they deem significant for the sport. Most of the models have applied statistical techniques
including variance [8], standard deviation, t-test, regression [3] [27] [6] [29], ANOVA [9],
MANOVA [26] [4] [21], Few models have also attempted to use Fuzzy Logic and Expert
Systems. Also majority of the models have been developed for Track & Field sports [1] [18]
[8], a few of them have been developed for soccer [21] [24] [28], hockey [4], water polo [6],
baseball [3] [2], handball [26] and table-tennis [13]. Here we present a brief summary of 15 of
such models.
In [8], a model was proposed to enable identification of talent in track and field sports
in Iran. The model chose the age range of 6-12 years. The basic parameters employed by the
model are motor ability, anthropometric, psychological, physiological, sociological and
cultural characteristics of prospective athlete. The model used statistical variance on the
identified parameters.

42
A model for 14 identified sports (athletics sprint jump, martial arts of kicking type,
martial arts of pulling and pushing type, football, tennis, handball, volleyball, water polo,
rowing, swimming, athletics long distance running, basketball, athletic throwing and
gymnastics) was proposed [18]. The model was applied on the athletes in the age range of 6-
18 years. The model uses characteristics which include: motor skills, morphological and
functional and it uses expert system and fuzzy logic membership technique to help rate talent
of a person in a particular sport.
In [29], Model was presented an application for selection of sports talent using
statistical regression equation with computer programming is proposed for athletic jump
without any age criteria. Authors have claimed that their approach can also be applied to other
sports as well.
In [1], a model was proposed for the age range of 11-15 years with the characteristics
of sports interactive task, physiological capacities, motor capacities and biometrics qualities. It
was implemented on Scottish children with the help of statistical techniques. The model was
meant to determine talent of children in 12-identified sports (high jump, long jump, sprinting,
hurdling, karate, triathlon, shot-put, skiing (DH), curling, hockey, tennis and squash)
In [21], a model was proposed for the age range of 15-16 years with anthropometric,
physiological, psychological and soccer specific characteristics. It used Multivariate Analysis
of Variance (MANOVA) technique to distinguish between elite and sub-elite groups on the
basis of performance on test items.
In [28], a model was proposed to determine the relationship between physical and
performance characteristics for the age of 13-16 years. The authors applied MANCOVA
technique to the data to identify the most important physical parameters for soccer.
In [9], a model predicted talent of players in age range of 18-years. The model used
ANOVA statistical methods on the physical characteristics of player which include body
height, weight, skeletal age, choice reaction time, stepping speed and stepping endurance.
A study [6] was meant to identify and develop talent in water polo sports. It was
proposed for the age range of 14-15 years with the characteristics of motor ability related to
water activity, physical ability and evaluation of game intelligence. The authors applied
statistical analysis unpaired t-test ANOVA on the data to assess the talent.
This research [5] demonstrated application of MANCOVA to understand the
relationship between multidimensional performance characteristics and level of performance
in talented youth hockey players. The model was implemented for the age range of 13.2-14.2
years and considered the anthropometric, physiological, psychological, technical and tactical
characteristics.
In the [16], a model was meant to detect talent in handball sports. The model used
MANCOVA on morphological, physical fitness, anthropometric, hand ball specific motor
skills and maturation characteristics. The model was applied on group of 14-16 years.
In [27] a model used essential characteristics of team sport. The model applied regression and
canonical analysis techniques.
A method in [28] is proposed for selecting players in team sports game with the help of
standard deviation and expert system.
In [3], a model was proposed for Croatia. The model uses characteristics which include
potential, morphological, technical knowledge and coordination abilities with the help of
statistical techniques t-test, z-test, and correlation.
This was proposed for different age groups with the help of graphical statistical
techniques. The model used characteristics which included special tactical and technical skills
for basketball [2].

43
In [13], a model was developed for children in the age range of 6-8 years with the help
of t-test with standard deviation. The model uses characteristics including height, weight, skin
fold, deep bend for flexibility, polygon for coordination, bent arm hang on horizontal bar for
strength test, sit up test for trunk strength, standing jump for explosive strength, 60 meters
sprint for speed and 600 meters run for endurance.
Based on our survey of published literature we can infer that no model for identifying of talent
in cricket has been reported as on date. Further, to develop TID model for cricket we need to
identify parameters which can be measured and thus can produce data for analysis. In the next
section we identify such parameters which may play important role in identifying talent in
Cricket.
2.0 PARAMETERS FOR TALENT IDENTIFICATION IN CRICKET
The Talent Identification report [25] summarizes the talent parameters for cricket. The
parameters are based on physical/motor, anthropometric and cognitive characteristics. To
quantify these parameters various tests have been identified in [7].The identified
characteristics and its parameters are listed in Table1 and Parameters with tests are listed in
Table2.
Talent Characteristics Talent Parameter
Physical/ Motor Ability
Tests
Speed, Agility, Flexibility, Balance
Static/ Dynamic, Endurance, Upper
Body Strength, Lower Body
Strength, Fatigue Index, Shoulder
Flexibility, Bowler Accuracy, Under
Arm Throw Accuracy, Under Arm
Throw Accuracy, Catching Ability,
Ground Fielding Ability
Cognitive Ability Tests Self Motivation, Reaction Time,
Hand Eye Coordination, Creativity,
Decision Making, Self Control &
Self Monitoring, Integrity and Work
Ethic, Willingness, Concentration
and Focus, Stress
Anthropometric Tests Body Mass Index, Vo2max
Table 1: Talent requirements for cricket in terms of various parameters with characteristics

44
Parameters Tests
Speed Speed Test (T1)
Agility Illions Agility Test (T2)
Endurance Step Up and Down Test (T3)
Stress Stress Test Quiz (T4)
Self Motivation Self Motivation Test Quiz (T5)
Upper Body Strength Push Up Test (T6)
Lower Body Power Hop Run Test (T7)
Reaction Ruler Catching Test (T8)
Flexibility Sit and Reach Test (T9)
Fatigue Index RAST (Running Based Anaerobic Sprit) Test (T10)
Bowler Accuracy Bowler Accuracy Test (T11)
Through Catching Accuracy Through Catching Accuracy Test (T12)
Under Arm Through
Accuracy
Under Arm Through Accuracy Test (T13)
Catching Ability Catching Ability Test (T14)
Ground Fielding Assessment of Clean Pick Ups. (T15)
VO2 Max Maximum Oxygen Up Taken Test (T16)
Body Mass Index Weight/ Height2
(T17)
Hand Eye Coordination Catching and Throwing the Ball in Cyclic Order with
Hands (T18)
Creativity Creativity Test Quiz (T19)
Decision Making Decision Making Ability Test Quiz (T20)
Self Control and Self
Monitoring
Self Control and Self Monitoring Test Quiz (T21)
Will Power Will Power Test Quiz (T22)
Self Confidence Self Confidence Test Quiz (T23)
Integrity and Work Ethic Integrity and Work Ethic Ability Test Quiz (T24)
Shoulder Flexibility Shoulder Flexibility Test Based On Physical Action (T25)
Balance Beam Test for Balance (T26)
Balance in Static Form Balance Test Based on Physical Action (T27)
Concentration and Focus Concentration and Focus Skill Test Quiz (T28)
Table 2: Talent requirements for cricket in terms of various tests and parameters.
Various quantitative tests (given in table1) have been identified in [7] which can help
in measuring each one of the above characteristics. Given the ability to quantify the
characteristics for talent assessment in Cricket, a talent identification model can be build. In
section 4.0 we first introduce Ordered Weighted Averaging/Aggregation operator, which we
apply to the problem of Talent Identification in section 4.0.

45
3.0 ORDERED WEIGHTED AVERAGING AGGREGATION (OWA) OPERATOR
The OWA operator was introduced by [22] to provide a means of aggregation, which
unifies in one operator the conjunctive and disjunctive behavior. It provides a parameterized
family of aggregation operators including many of the well-known operators like maximum,
minimum, k-order statistics, median and arithmetic mean. For n different scores nxxx ,...,, 21 ,
the aggregation of these scores may be done using the OWA operator as follows.
OWA ( nxxx ,...,, 21 ) = ∑=
n
i
ii yw
1
where iy is the ith
largest score from amongst nxxx ...,, 21 . The weights are all non-negative
iwi,∀ ≥0, and 





=∑=
1
1
n
i
iw . We note that the arithmetic mean function may be obtained using
the OWA operator, if ∀i,
n
wi
1
= . Similarly, the OWA operator would yield the maximum
function with iw =1 and iw =0 for all i≠ 1. The minimum function may be obtained from the
OWA operator when nw =1 and iw = 0 for all i ≠ n.
In fact, it has been shown [22] that the aggregation done by the OWA operator is always
between the maximum and minimum. To find the values of the weights iw , we need to make
use of the relative fuzzy linguistic quantifiers, explained as follows.
3.1 Relative Fuzzy Linguistic Quantifier
A relative quantifier, Q: [0, 1] → [0, 1], satisfies:
Q(0) = 0,
[ ]1,0∈∃r such that Q(r) = 1.
In addition, it is non-decreasing if it has the following property:
[ ]1,0, ∈∀ ba , if a> b, then Q(a) ≥ Q(b).
The membership function of a relative quantifier can be represented as shown in [1]:







−
−
=
1
0
)(
ab
ar
rQ
if
if
if
br
bra
ar
>
≤≤
<
, …………..[1]
where [ ]1,0,, ∈rba and )/()( miQrQ =
[22] Computes the weights wi of the OWA aggregation from the function Q describing the
quantifier. In the case of relative quantifier, with m criteria [23],
,,....,2,1)),/)1(()/( mimiQmiQwi =−−= with Q(0) = 0.

46
3.2 Normalized Adequacy Coefficient
The adequacy coefficient [15] is an index used for calculating the differences between
two elements, or two sets, or two fuzzy sets, etc. The adequacy coefficient is very similar to
the hamming’s distance but with some differences. It makes it more complete in a lot of
decision making problems, especially, when we cannot accept that one set (Y) is higher than
the other (X). The similarity between two sets in the adequacy coefficient is calculated with
( ))1(1 yx +−∧ or the complement( ))(0 yx −∨ . For two sets },...,{ 1 nxxX = and
},...,{ 1 nyyY = , the weighted adequacy coefficient can be defined as follows.
Definition: According to [15], A weighted adequacy coefficient of dimension-n is a mapping
WAC: [ ] [ ] [ ]1,01,01,0 →×
nn
that has an associated weighting vector W of dimension n with
∑=
=
n
j
jw
1
1 and [ ]1,0∈jw , such that:
WAC [ ])1(1),...,,(
1
,11 ii
n
i
inn yxwyxyx +−∧= ∑=
,
where ix and iy are the ith
arguments of the sets X and Y, respectively.
Note that if nwi /1= , for all i, then, the weighted adequacy coefficient becomes the
normalized adequacy coefficient (NAC).
NAC [ ])1(1
1
),...,,(
1
,11 ii
n
i
nn yx
n
yxyx +−∧= ∑=
,
4.0 APPLICATION OF OWA TO TALENT IDENTIFICATION PROBLEM IN
CRICKET
As discussed in section 2, the talent identification is a difficult task. Attempts have
been made to identify parameters to assess talent in Cricket. These parameters were
summarized in section 3. To build a cricket talent assessment model, we collected responses
from cricket experts for the identified 28-tests. The experts were requested to categorize the
result range of each of the 28-tests to judge the suitability of the result value for each of the
five talent classes, viz. Extraordinary Talented [EOT], Very Much Talented [VMT], Much
Talented [MT], Moderately Talented [MDT], and Not Talented [NT]. A sample question to
record assessment of an expert is shown in figure1:
Please fill your rating for the "Speed Test" for the five categories of talent classes.
Run 35 meters in a straight line and record the time in seconds.
[<4.80 sec ] [4.80- 5.09 sec] [5.10-5.29sec] [5.30- 5.60 sec ] [ >5.60 sec ]
Extraordinary Talented
Very Much Talented
Much Talented
Moderately Talented
Not Talented
Figure1: A sample question from questionnaire.

47
It may be observed that expert is expected to record his assessment for each of the 5-
talent classes by choosing an interval. For each of the chosen interval we record its average
value as the assessment vet[i] of the expert. All such assessments for each talent class-i are
recorded in an Expert Assessment ][iEA matrix as follows:
2821 ... ttt
][iEA =
ne
e
e
.
.
.
2
1




















][...][][
......
......
......
][...][][
][...][][
21
22212
12111
iviviv
iviviv
iviviv
mnnn
m
m
tetete
tetete
tetete
………. (2)
.281, ≤≤∀ jj jt is a test aimed at quantifying the physical ability.
where [ ]4,3,2,1,0∈i
4
3
2
1
0
=
=
=
=
=
i
i
i
i
i
for
for
for
for
for
NT
MDT
MT
VMT
EOT
Test Definition: Each test it is a 5x5 matrix comprising of rows representing Talent Classes
(TC) and columns representing permissible responses. Each response is recorded in form of
(a, b) where a is the lowest permissible value and b is the highest permissible value for each
class and each test. Thus,
].28,1[)Re,( ∈∀= isponseTCTCRti
Where, TCR means Talent Class Response Matrix.
The data for all the values obtained in above 5-matrices [eqn. 2] were normalized using min-
max transformation so that each value now falls in [0, 1]. Min-max normalization maps a
value v of an attribute A to v′ in the range of [new_minA, new_maxA] by computing.
( ) AnewAnewAnew
AA
Av
v min_min_max_
minmax
min
' +−
−
−
= .
The five normalized matrices ][iEA now comprise the Knowledge Base for our Model. A
broad architecture for the Talent Identification Model is shown in figure 2. Any cricket

48
enthusiast can provide the outcome result values for himself for each of the 28-tests. The
model then uses the Talent Classifier described in section 4.2 to classify the talent of person in
any one of the five possible talent classes.
.
Figure 2: Architecture of Cricket Talent Identification Model
4.1 Algorithm for Talent Identification
The algorithm for talent identification is given below:
Algorithm to compute weights (wi)
begin
for each talent class ]4,0[];[ ∈jjTC .
for each test ]28,1[, ∈iti
for each expert ],0[);( nkkE ∈ where n = total no. of experts
;/)( nkkr =
for each expert ],0[);( nkkE ∈
begin
if akr <)(
0)( =kQR
else if )({ kra ≤ }b≤
)/())(()( abakrkQR −−=
else
})( bkr >
Talent
Classifier (TC)
Normative Data for Talent (NDFT)
1o 2o . . . . . io . . . . . . 28o
Knowledge Base
EOT
VMT
MT
MDT
NT
Cricket Enthusiast

49
1)( =kQR
end;
if )0( <>k
);1()(],[ −−= kQRkQRikw
End Loop k;
End Loop j;
End Loop i;
//For the Talent Class ]4,0[];[ ∈jjTC and test ]28,1[, ∈iti calculate OWA [j,i] as follows:
for each talent class ]4,0[];[ ∈jjTC .
for each test ]28,1[, ∈iti
if a < b then
);]),[(d_assign(arrange_an=y(k) ascendingjEAitΠ
else
);]),[(d_assign(arrange_an=y(k) descendingjEAitΠ
end for k;
sum: = 0;
sum += w(k,i)*y(k);
OWA(j, i) = sum;
End for j;
End for i;
End;
4.2 Classification of Talent
To classify the talent of a person, we first record his/ her test records, oi for each of the
28-tests in a 28x1 NDTF (Normative Data for Talent) matrix as follows:
)............,()( 2821 ooovNDFT =
We now calculate the normalized adequacy coefficient (NAC) between NDFT and OWA(j,i)
where 4..0∈j and 28..1∈i . The classifier now classifies the talent based on the
28..1,4..0))),,(,(( ∈∈ ijijOWANDFTNACMax . For example, if the maximum value is
obtained for j=3, then Talent class is identified as “Much Talented”.
4.3 Experiments and Results
We now demonstrate the application of algorithm on the ith
test viz. the speed test and
the jth
talent class, viz. “Extra Ordinary Talent (EOT)”. Firstly, we normalize the rough range
for the identified test for the five talent classes. The sample normalized values are shown in
figure 3. We assume that assessments of 33 experts (E1, E2, E3,….., E33) are available and
recorded in the matrix Experts Assessment (EA) as described in equation 2.

50
Figure3: Relative Fuzzy Linguistic Quantifiers
For the jth
talent class, with the sample normalized interval a= 0, b= 0.465 we now
calculate weights using algorithm described in section 4.1. This is also depicted in table 3
below:
Table3: Calculation of Weights
i= 0,1,2,…..33 0 1 2 … 33
i/33 = r 0/33= 0 1/33
=0.03
2/33
=0.06
… 33/33
=1
)()/( rQniQ =







−
−
=
1
0
)(
ab
ar
rQ
if
if
if
br
bra
ar
>
≤≤
<
0 0.06 0.13 … 1
)
1
()(
n
i
Q
n
i
Qwi
−
−=
Not
Required
0.06-0
= 0.06
0.13-0.06
=0.07
… 1-1
=0.000
0
1
1
EOT VMT MT MDT NT
0.0-0.465 0.46-0.515 0.52-0.549 0.55-0.60 0.601-1.0
x

51
Table4: Weights for Extra Ordinary talented (EOT)
Table5: Experts opinion for Extra Ordinary talented (EOT)

52
Now the ordered weighted averaging aggregation (OWA) operator is used to aggregate
the experts opinions for EOT class.
OWA(0,1) = ∑=
33
1i
[0.06, 0.07, 0.06,……….. 0.00 ]. Ascending or Descending [15] [0.23, 0.22,
0.33, ….… , 0.23) = 0.89.
Similarly, values are aggregated for all the tests. These values are depicted in Table 6.
The normative data for talent (NDFT) of cricket enthusiast for all tests is depicted in Table7.
Now we find the Normalized Adequacy Coefficients using the equation (3) for NDFT of
cricket enthusiast with all five talent class.
Table6: Aggregated values of all talent classes for 28 tests
Table7: Assessed results of cricket enthusiast against 28 tests
Normalized Adequacy Coefficient value for Extraordinary Talent (EOT):
NAC [ ] )3....(..........)1(1
1
),...,,(
1
,11 ii
n
i
nn yx
n
yxyx +−∧= ∑=
)].85.09.01(1............)54.023.01(1)489.0219.01(1[
28
1
)85.0,9.0,......,28.0,041.0,54.0,23.0,489.0,219.0()(
+−∧+++−∧++−∧=
=− NACEnthusiastVMTNAC
8860714.0]81.24[
28
1
]95.0............11[
28
1
]95.01.........31.1127.11[
28
1
==
+++=
∧++∧+∧
Similarly, the values for other classes are calculated and shown below.
NAC(VMT-Enthusiast)= 0.9209286
NAC(MT-Enthusiast)= 0.8971786
NAC(MDT-Enthusiast)= 0.8983571
NAC(NT-Enthusiast)= 0.8206071

53
Now, Max Operator is used to find the maximum value among the above given normalized
adequacy coefficients.
MAX (NAC(EOT-Enthusiast), NAC(VMT-Enthusiast), NAC(MT-Enthusiast), NAC(MDT-
Enthusiast), NAC(NT-Enthusiast))
= MAX(0.8860714, 0.9209286,0.8971786, 0.8983571, 0.8206071) = 0.9209286
Since the maximum Max(NAC(TC[j], NDFT)) ∀ j ∈ (0,4) is achieved against VMT. We
identify the talent of enthusiast as “Very Much talented”.
5.0 CONCLUSION
This paper reviewed several talent identification models in sports which have been
proposed in research papers. The study revealed that no talent identification model has yet
been proposed for cricket. To develop such a model we identified 28-parameters and
corresponding tests to quantify the talent of an enthusiast against these parameters. We then
build a database of experts’ opinion for classification of talent into five categories based on the
results of the identified tests. An algorithm using Ordered Weighted Averaging Aggregation
(OWA) operator was proposed for aggregation of opinions whose results can be easily used
for classification of talent in cricket. The paper also presents an experiment with results to
demonstrate the application of the model.
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