A Methodology for Selection Starting Line-Up of Football Players in Qatar World Cup 2022



Abstract Views
664

Downloads
354

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted Dec 25, 2022
Published Apr 14, 2023
10.24018/ejsport.2023.2.2.56

Abstract viewed = 664 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

The process of player selection in multi-player sports is a complex multi-criteria problem. In Football, the role of a coach is selecting players as the starting line-up that is formed subjectively without regard to various criteria. The decision of the coach can impact team performance. This paper proposes a new model for selection starting line-up integrating criteria in FIFA World Cup 2022.The aim is to use MOORA method in football player selection process. We test the efficiency of the model using France National football team and we demonstrate how the methodology can facilitate decision making by selecting players.

Keywords: decision making football game MOORA starting line-up

References

  1. Ahmed Z, Akerib DS,Arrenberg S, Bailey CN, Balakishiyeva D &Baudis L. (2016). Results from a low- energy analysis of the CDMS II germanium data. Phys. Rev. Lett, 106 (13), 131302.
     Google Scholar
  2. Ahmed F, Jindal A& Deb K. (2013). Multi-objective optimization and decision-making approaches to cricket team selection. Appl. Soft Comput, 13 (1) 402–414.
     Google Scholar
  3. Al- Shboul R, Syed T, Memon J & Khan F. 2017. Automated player selection for sports teams using competitive neural networks. International Journal of Advanced Computer Science and Applications, 8(8). Article 8. Available at: http://www.academia.edu/34895719/ Automated-Player- Selection-for- Sports-Team-using-Competitive-Neural-Networks.
     Google Scholar
  4. Anwar K. (2023). Neutrosophic MCDM approach for performance evaluation and recommendation of best players in sports league. International Journal of Neutrosophic Science, 2690–6805. ISSN (Print). 2692–6148. Doi: https://doi.org/10.54216/IJNS.200111.
     Google Scholar
  5. Chang.C. (2016). Based on analytic hierarchy process model of big data analysis of the nba schedule impact on team ranking. Journal of Computational and theoretical Nanoscience, 13.12 (2016), 10001–10005.
     Google Scholar
  6. Data Baseof france national football (2022). https://www.footballdatabase.eu/fr/club/equipe/347- france/2022.
     Google Scholar
  7. Gökgöz F, Yalçın I., (2019). An integrated approach to the world cup teams using entropy-based ARAS and SAW methods. 25th ISTANBUL Int’l Conference on Literature, Languages, Humanities & Social Sciences (ILLHSS-19).
     Google Scholar
  8. Jarvandi A, Sarkani S &Mazzuchi T. (2013). Modeling team compatibility factors using a semi-Markov decision process: A data- driven approach to player selection in soccer. Journal of Quantitative Analysis in Sports, 9(4), 347–366. Scopus. Available at: http://doi.org/10.1515/jqas- 2012-0054.
     Google Scholar
  9. Khatrouch I, Belhouchet H, Boujelbene Y & El Mahamedi A. (2022). Multi criteria decision making approach for football team selection. South Asian Research Journal of Business and Management, 4(3), May–Jun-2022.
     Google Scholar
  10. Liu Y, Li Z, & Yang H. (2016). Volleyball athlete’s selection index weight based on analytic hierarchy process consistency check. Journal Of Computational and Theoretical Nanoscience, 13. (12), 9979–9982.
     Google Scholar
  11. MacMahon C, Bailey A, Croser M&Weissensteiner J. (2019). Exploring the skill of recruiting in the Australian Football League. International Journal of Sports Science & Coachin, 14(1),72–81.
     Google Scholar