OWA Based Model for Talent Selection in Cricket

Ahamad, Gulfam; Naqvi, S. Kazim; Beg, M. M. Sufyan

doi:10.1007/978-3-319-03674-8_22

Cited by 8 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ahamad et al [31] proposed an ordered weighted averaging aggregation (OWA) operator based cricket TiD model. This model is the first cricket TiD of its kind [14]. The end results of this model when compared with the manual TiD system proved to be justifiable and promising.…”

Section: Literature Reviewmentioning

confidence: 85%

See 1 more Smart Citation

Optimal feature selection for cricket talent identification

2023

IJATEE

View full text Add to dashboard Cite

Cricket talent identification (TiD) is a methodical process that aims to find the young athletes possessing a potential to excel in the cricket sport at an early age. The sports scientists have identified a set of twenty-eight parameters that determine the cricket TiD. In order to realize the objective of computational efficiency by reducing the feature set, we perform an optimal feature selection for cricket TiD using nine different feature selection techniques Viz. mutual information, information gain ratio, correlation, chi square, univariate root mean square error (RMSE), receiver operating characteristic (ROC) with decision tree classifier, reliefF, boruta and oneR. The individual results obtained from the feature selection techniques are provided along with the individual ranking. We aggregate the results using two different rank aggregation techniques namely average ranking aggregation and majority vote ranking aggregation. The aggregation resultsshow a significant agreement between the two schemes. Fourteen out of twenty-eight features are selected using a threshold of 0.52-the value selected on recommendation of four different domain experts. 71.4% of the selected features are sport-centric and only 28.6% of the selected features are from the cognitive ability category. To the best of our knowledge, this is first such attempt to identify the talent in cricket using this methodology.

show abstract

Section: Literature Reviewmentioning

confidence: 85%

“…In cricket, the TiD programs are relatively new [13], although the field is emerging. To the best of our knowledge, [14] developed the first TiD model for cricket. Ahamad et al [15] have identified 28 attributes that are sufficient to predict the potential talent of young enthusiasts.…”

Section: Introductionmentioning

confidence: 99%

Optimal feature selection for cricket talent identification

2023

IJATEE

View full text Add to dashboard Cite

show abstract

“…In Sub Asia it is celebrated like a religion. Ahamad et al [ 56 ] proposes a talent identification model for cricket. The authors have identified twenty-eight [28] parameters that determine the talent of an enthusiast shown in Table 1 .…”

Section: Case Study Of Cricket Talent Identificationmentioning

confidence: 99%

An IoT/FOG based framework for sports talent identification in COVID-19 like situations

Khan

Ahamad

Naseem

2022

Int. j. inf. tecnol.

Self Cite

View full text Add to dashboard Cite

COVID-19 crippled all the domains of our society. The inevitable lockdowns and social distancing procedures have hit the process of traditional sports talent identification (TiD) severely. This will interrupt the career-excellency of athletes and will also affect the future talent in the years to come. We explore the effect of COVID-19 on sports talent identification and propose an IoT/Fog-based framework for theTiD process during COVID-19 and COVID–like situations. Our proposed novel six-layer model facilitates the sports talent identification remotely using the various latest Information and Communication Technologies like IoT, fog and cloud computing. All the stakeholders like experts, coaches, players, institutes etc. are taken into consideration. The framework is mobile, widely accessible, scalable, cost-effective, secure, platform/location independent and fast. A brief case study of cricket talent identification using the proposed framework is also provided.

show abstract

“…Rashid et al (2014) introduced generalized interval-valued trapezoidal fuzzy TOPSIS for robot selection. Ahamad et al (2013Ahamad et al ( , 2014Ahamad et al ( , 2015Ahamad et al ( , 2016 presented ranking order of cricket players team using the ordered weighted averaging aggregation by considering anthropometric, physical and cognitive skills and capabilities of the players. Rashid (2019, 2020) presented generalized fuzzy bestworst MCDM extensions for selection problems.…”

Section: Introductionmentioning

confidence: 99%

Best–worst method for robot selection

Asif

Rashid

2020

Soft Comput

View full text Add to dashboard Cite

For different applications, there are different robots having capabilities and specifications accordingly. For a particular application and industrial requirement, proper and suitable selection of robot is a difficult task. Numerous robot selection methods are available. Considering the research works on industrial robot selection, group best-worst method is employed in this paper for the proper selection of robots. Weighing the decision makers by considering their past experience is an important factor considered for expert and reliable selection of robot. Objective weights to describe the importance of the attributes along with the decision maker subjective preferences to describe the weights of the attribute are considered. Two problems are discussed for a detailed description and results are compared with the well-known group analytical hierarchy process method. The results show that due to lower minimum violation and lower total deviation, the proposed method performs better. Keywords Group best-worst method (G-BWM) Á Group analytic hierarchy process (G-AHP) Á Multiple attribute group decision making Á Subjective weights Á Objective weights Á integrated weights Communicated by V. Loia.

show abstract

OWA Based Model for Talent Selection in Cricket

Cited by 8 publications

References 21 publications

Optimal feature selection for cricket talent identification

Optimal feature selection for cricket talent identification

An IoT/FOG based framework for sports talent identification in COVID-19 like situations

Best–worst method for robot selection

Contact Info

Product

Resources

About