IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i14p2369-d857266.html
   My bibliography  Save this article

Developing a Multicriteria Decision-Making Model Based on a Three-Layer Virtual Internet of Things Algorithm Model to Rank Players’ Value

Author

Listed:
  • Che-Wei Chang

    (Department of Recreational & Graduate Institute of Recreational Sport Management, National Taiwan University of Sport, No. 16, Sec. 1, Shuang-Shih Road, Taichung 404, Taiwan)

Abstract

This paper proposes a multicriteria decision-making model based on a three-layer virtual internet of things (IoT) algorithm to automatically track and evaluate professional football players’ performance over the Internet. The three layers were respectively related to (1) automated data reading, (2) the players’ comprehensive grey relational degree calculation, and (3) the players’ classification. The methodology was applied in the context of the COVID-19 pandemic to investigate the performance of the top 10 defenders (according to The Sun, an internationally renowned sports website) in the European leagues, participating in the knockout phase of the 2019–20 UEFA Champions League. The results indicate that Virgil van Dijk of Liverpool FC was the best defender, followed by Harry Maguire of Manchester United, and Sergio Ramos of Real Madrid in the second and third positions, respectively. However, this ranking contradicted that of The Sun’s, which ranked these defenders in the seventh, tenth, and eighth positions, respectively. These results can help club management, coaches, and teams negotiate price positioning and future contract renewals or player transfers.

Suggested Citation

  • Che-Wei Chang, 2022. "Developing a Multicriteria Decision-Making Model Based on a Three-Layer Virtual Internet of Things Algorithm Model to Rank Players’ Value," Mathematics, MDPI, vol. 10(14), pages 1-19, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2369-:d:857266
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/14/2369/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/14/2369/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thomas Sexton & Herbert Lewis, 2003. "Two-Stage DEA: An Application to Major League Baseball," Journal of Productivity Analysis, Springer, vol. 19(2), pages 227-249, April.
    2. G. Villa & S. Lozano, 2018. "Dynamic Network DEA approach to basketball games efficiency," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(11), pages 1738-1750, November.
    3. Ceren Oral, 2016. "Financial Performance Evaluation of Sport Clubs Traded in Borsa Istanbul by Using Grey Relational Analysis," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(5), pages 293-299, May.
    4. Amar Oukil & Srikrishna Madhumohan Govindaluri, 2017. "A systematic approach for ranking football players within an integrated DEA‐OWA framework," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 38(8), pages 1125-1136, December.
    5. Torben Tiedemann & Tammo Francksen & Uwe Latacz-Lohmann, 2011. "Assessing the performance of German Bundesliga football players: a non-parametric metafrontier approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 19(4), pages 571-587, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chih-Hai Yang & Hsuan-Yu Lin & Chiang-Ping Chen, 2014. "Measuring the efficiency of NBA teams: additive efficiency decomposition in two-stage DEA," Annals of Operations Research, Springer, vol. 217(1), pages 565-589, June.
    2. Fabíola Zambom-Ferraresi & Belén Iráizoz & Fernando Lera-López, 2019. "Are football managers as efficient as coaches? Performance analysis with and inputs in the Premier league," Applied Economics, Taylor & Francis Journals, vol. 51(3), pages 303-314, January.
    3. Ana Pérez-González & Pablo Carlos & Elisa Alén, 2022. "An analysis of the efficiency of football clubs in the Spanish First Division through a two-stage relational network DEA model: a simulation study," Operational Research, Springer, vol. 22(3), pages 3089-3112, July.
    4. Villa, G. & Lozano, S., 2016. "Assessing the scoring efficiency of a football match," European Journal of Operational Research, Elsevier, vol. 255(2), pages 559-569.
    5. Amar Oukil & Srikrishna Madhumohan Govindaluri, 2017. "A systematic approach for ranking football players within an integrated DEA‐OWA framework," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 38(8), pages 1125-1136, December.
    6. Hadi Ghafoorian & NikIntan Norhan & Mohammed Ndaliman Abubakar & Fazel Mohammadi Nodeh, 2013. "Efficiency Considering Credit Risk in Banking Industry, Using Two-stage DEA," Journal of Social and Development Sciences, AMH International, vol. 4(8), pages 356-360.
    7. Dina A. M. Miragaia & João J. M. Ferreira & Cédric T. Vieira, 2024. "Efficiency of Non-profit Organisations: a DEA Analysis in Support of Strategic Decision-Making," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 3239-3265, March.
    8. Pelloneová Natalie, 2023. "Evaluating Hockey Players Using Andersen and Petersen's Super-Efficiency Model: Who is the Best Czech Hockey Player in the NHL?," Polish Journal of Sport and Tourism, Sciendo, vol. 30(3), pages 23-28, September.
    9. Hirofumi Fukuyama & William L. Weber, 2017. "Japanese Bank Productivity, 2007–2012: A Dynamic Network Approach," Pacific Economic Review, Wiley Blackwell, vol. 22(4), pages 649-676, October.
    10. Afsharian, Mohsen & Ahn, Heinz & Harms, Sören Guntram, 2019. "Performance comparison of management groups under centralised management," European Journal of Operational Research, Elsevier, vol. 278(3), pages 845-854.
    11. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    12. Nikos Chatzistamoulou & Kounetas Kostas & Antonakis Theodor, 2022. "Salary Cap, Organizational Gap, and Catch-up in the Performance of NBA Teams: A Two-Stage DEA Model Under Heterogeneity," Journal of Sports Economics, , vol. 23(2), pages 123-155, February.
    13. Sombat Singkharat & Aree Wiboonpongse & Yaovarate Chaovanapoonphol, 2012. "Efficiency of improved peeled longan drying technology in Thailand: A metafrontier approach," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 1(3), pages 19-32, September.
    14. Li, Yongjun & Lei, Xiyang & Dai, Qianzhi & Liang, Liang, 2015. "Performance evaluation of participating nations at the 2012 London Summer Olympics by a two-stage data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 243(3), pages 964-973.
    15. Jean-Baptiste Vilain, 2018. "Three essays in applied economics [Trois essais en économie appliquée]," SciencePo Working papers Main tel-03419493, HAL.
    16. Fiona Carmichael & Giambattista Rossi & Denis Thomas, 2017. "Production, Efficiency, and Corruption in Italian Serie A Football," Journal of Sports Economics, , vol. 18(1), pages 34-57, January.
    17. Fan, Jing-Li & Zhang, Hao & Zhang, Xian, 2020. "Unified efficiency measurement of coal-fired power plants in China considering group heterogeneity and technological gaps," Energy Economics, Elsevier, vol. 88(C).
    18. An‐Pang Wang & Che‐Wei Chang & Juin‐Ming Tsai & Shiu‐Wan Hung, 2021. "A performance evaluation of Major League Baseball teams: An integrated social network and data envelopment analysis," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(6), pages 1421-1434, September.
    19. Lu, Wen-Min & Liu, John S. & Kweh, Qian Long & Wang, Chung-Wei, 2016. "Exploring the benchmarks of the Taiwanese investment trust corporations: Management and investment efficiency perspectives," European Journal of Operational Research, Elsevier, vol. 248(2), pages 607-618.
    20. AGRELL, Per J. & HATAMI-MARBINI, Adel, 2013. "Frontier-based performance analysis models for supply chain management: state of the art and research directions," LIDAM Reprints CORE 2555, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2369-:d:857266. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.