OWA Based Model for Talent Selection in Cricket

  • Gulfam Ahamad
  • S. Kazim Naqvi
  • M. M. Sufyan Beg
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 312)

Abstract

Talent selection in cricket is a task which is usually carried out by coaches and senior players. The method relies on instincts or natural abilities of the selectors for talent assessment and selection. However, it suffers with subjectivity, personal biasness and external influences. In country such as India where more than 1-million players play cricket daily, talent selection problem becomes significant. In this paper, we propose a model which can rank players in order of their talent. The model can potentially help reduce the implicit problems of manual talent selection system. The model assesses the cricketing talent of individual players based on the quantitative outcome of the identified parametric tests for assessing players’ physical/motor, anthropometric and cognitive skills and capabilities with respect to cricket. The Ordered weighted averaging aggregation (OWA) operator with Relative Fuzzy Linguistic Quantifier (RFLQ) is used to measure the weights and aggregate players’ talent values. The model is applied to the Jamia Millia Islamia’s (JMI) University Cricket team and results have been summarized.

Keywords

Talent selection in Cricket OWA RFLQ Model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Omkar, S.N., Verma, R.: Cricket Team selection using Genetic Algorithm. In: International Congress on Sports Dynamics (ICSD 2003), Melbourne, Australia, September 1-3 (2003)Google Scholar
  2. 2.
    Merigó, J.M., Gil-Lafuente, A.M.: Decision making in sport management based on OWA operator. International Journal of Expert System with Application 38, 10408–10413 (2011)CrossRefGoogle Scholar
  3. 3.
    Merigo, J.M., Casanovas, M.: Decision-making with distance measures and induced aggregation operators. International Journal of Computers & Industrial Engineering 60, 66–76 (2011)CrossRefGoogle Scholar
  4. 4.
    Casanovas, M., Merigo, J.M.: Fuzzy aggregation operators in decision making with Dempster-Shafer. International Journal of Expert System with Application 39, 7138–7149 (2012)CrossRefGoogle Scholar
  5. 5.
    Merigo, J.M.: Fuzzy decision making with immediate probabilities. International Journal of Computers & Industrial Engineering 58, 651–657 (2010)CrossRefGoogle Scholar
  6. 6.
    Rao, C., Peng, J.: Fuzzy Group Decision Making Model Based On Credibility Theory and Gray Relative Degree. International Journal of Information Technology & Decision Making 8(3), 515–527 (2009)CrossRefMATHGoogle Scholar
  7. 7.
    Merigo, J.M., Gil-Lafuente, A.M.: Fuzzy induced generalized aggregation operators and its application in multi-person decision making. International Journal of Expert System with Application 38, 9761–9772 (2011)CrossRefGoogle Scholar
  8. 8.
    Merigo, J.M., et al.: Group decision making with distance measures and probabilistic information. International Journal of Knowledge-Based system 40, 81–87 (2013)CrossRefGoogle Scholar
  9. 9.
    Merigo, J.M., Gil-Lafuente, A.M.: Induced 2-tuple linguistic generalized aggregation operators and their application in decision-making. International Journal of Information Science (2013), http://dx.doi.org/10.1016/j.ins.2013.02.039
  10. 10.
    Merigo, J.M., Casanovas, M.: Induced aggregation operators in the Euclidean distance and its application in financial decision making. International Journal of Expert System with Application 38, 7603–7608 (2011)CrossRefGoogle Scholar
  11. 11.
    Merigo, J.M., Casanovas, M.: Induced and uncertain heavy OWA operators. International Journal of Computers & Industrial Engineering 60, 106–116 (2011)CrossRefGoogle Scholar
  12. 12.
    Cabrerizo, F.J., et al.: A Consensus Model for Group Decision Making Problems with Unbalanced Fuzzy Linguistic Information. International Journal of Information Technology & Decision Making 8(1), 109–131 (2009)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Merigo, J.M., Gil-Lafuente, A.M.: New decision-making techniques and their application in the selection of financial product. International Journal of Information Science 180, 2085–2094 (2010)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Merigo, J.M.: Probabilities in the OWA operator. International Journal of Expert System with Application 39, 11456–11467 (2012)CrossRefGoogle Scholar
  15. 15.
    Zeng, S., et al.: The uncertain probabilistic OWA distance operator and its application in group decision making. International Journal Of Applied Mathematical Modeling 37, 6266–6275 (2013)CrossRefGoogle Scholar
  16. 16.
    Zhou, L.-G., et al.: Uncertain generalized aggregation operators. International Journal of Expert System with Application 39, 1105–1117 (2012)CrossRefGoogle Scholar
  17. 17.
    Merigo, J.M., et al.: Uncertain induced aggregation operators and its application in tourism management. International Journal of Expert System with Application 39, 869–880 (2012)CrossRefGoogle Scholar
  18. 18.
    Lager, R.R.: On ordered weighted averaging aggregation operators in multimedia decision making. IEEE Trans. Systems Man Cyber Net. 18, 183–190 (1988)CrossRefGoogle Scholar
  19. 19.
    Chang, S.-L., et al.: Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycle. International Journal of Production Economics 100(2), 348–359 (2006)CrossRefGoogle Scholar
  20. 20.
    Ahamad, G., Naqvi, S.K., Sufyan Beg, M.M.: A Studyof Talent Identification Models in Sportsand Parameters for Talent Identification in Cricket. In: International Conference on Physical Education and Sports Science, Department of Physical Health Sports Education, Aligarh, India, Ref.No.ICPESS/OP/160, November 16-18 (2012)Google Scholar
  21. 21.
  22. 22.
    Elferink-Gemser, M.T., Visscher, C., Lemmink, K., et al.: Relation between multidimensional performance characteristics and level of performance in talented youth field hockey players. Journal of Sports Sciences 22(11-12), 1053–1063 (2004)CrossRefGoogle Scholar
  23. 23.
    Vrljic, K., Mallett Clifford, J.: Coaching knowledge in identifying football talent. International Journal of Coaching Science (2008)Google Scholar
  24. 24.
    Karasilshchikov, O.: Talent Recognition and Development- Elaborating on a Principle Model. International Journal of Developmental Sport Management 1(1), 16150 (2011)Google Scholar
  25. 25.
    Vaeyens, R., Malina, R.M., Janssens, M.: A Multidisciplinary Selection Model for Youth Soccer: The Ghent Youth Soccer Project. British Journal of Sports Medicine 40, 928–934 (2006)CrossRefGoogle Scholar
  26. 26.
    Peltola, 1992. Williams & Reilly, 2000, Talent Identification in British Judo, p. 216, www.bath.ac.uk/sports/judoresearch/Full%20texts/Talent%20Identification%20in%20British%20%20Judo.pdf
  27. 27.
    Pearson, D.T., Naughton, G.A., Torode, M.: Predictability of physiological testing and the role of maturation in talent identification for adolescent team sports. Journal of Science and Medical in Sport 9(4), 277–287 (2006)CrossRefGoogle Scholar
  28. 28.
    Mallillin, T.R., Josephine Joy Reyes, B., et al.: Sports Talent Identification in 1st and 2nd year UST High School Students. Philippine Journal of Allied Health Sciences 2(1), 41–42 (2007)Google Scholar
  29. 29.
    Talent Identification Report Explanatory Notes, http://static.ecb.co.uk/files/talent-id-report-explanatory-notes-1403.doc
  30. 30.
    Ahamad, G., Naqvi, S.K., Sufyan Beg, M.M.: A Model for Talent Identification in Cricket Based on OWA Operator. International Journal of Information Technology and Management Information System 4(2), 40–55 (2013) ISSN Print: 0976-6405, ISSN Online 0976-6413Google Scholar
  31. 31.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gulfam Ahamad
    • 1
  • S. Kazim Naqvi
    • 1
  • M. M. Sufyan Beg
    • 2
  1. 1.FTK-Centre for Information TechnologyJamia Millia IslamiaNew DelhiIndia
  2. 2.Department of Computer EngineeringJamia Millia IslamiaNew DelhiIndia

Personalised recommendations