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  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   INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & MANAGEMENT INFORMATION SYSTEM (IJITMIS) ISSN 0976 – 6405(Print) ISSN 0976 – 6413(Online ) Volume 4, Issue 2, May - August (2013), pp. 40-55 © IAEME: Journal Impact Factor (2013): 5.2372 (Calculated by GISI)   IJITMIS © I A E M E  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   41  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.  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   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].  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   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  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   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/ Height 2 (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 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.
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