IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i6p926-d1361372.html
   My bibliography  Save this article

Predicting Fan Attendance at Mega Sports Events—A Machine Learning Approach: A Case Study of the FIFA World Cup Qatar 2022

Author

Listed:
  • Ahmad Al-Buenain

    (Mechanical and Industrial Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar)

  • Mohamed Haouari

    (Mechanical and Industrial Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar)

  • Jithu Reji Jacob

    (Computer Science and Engineering Department, College of Science and Technology, Cochin University, Kalamassery 682022, India)

Abstract

Mega sports events generate significant media coverage and have a considerable economic impact on the host cities. Organizing such events is a complex task that requires extensive planning. The success of these events hinges on the attendees’ satisfaction. Therefore, accurately predicting the number of fans from each country is essential for the organizers to optimize planning and ensure a positive experience. This study aims to introduce a new application for machine learning in order to accurately predict the number of attendees. The model is developed using attendance data from the FIFA World Cup (FWC) Russia 2018 to forecast the FWC Qatar 2022 attendance. Stochastic gradient descent (SGD) was found to be the top-performing algorithm, achieving an R 2 metric of 0.633 in an Auto-Sklearn experiment that considered a total of 2523 models. After a thorough analysis of the result, it was found that team qualification has the highest impact on attendance. Other factors such as distance, number of expatriates in the host country, and socio-geopolitical factors have a considerable influence on visitor counts. Although the model produces good results, with ML it is always recommended to have more data inputs. Therefore, using previous tournament data has the potential to increase the accuracy of the results.

Suggested Citation

  • Ahmad Al-Buenain & Mohamed Haouari & Jithu Reji Jacob, 2024. "Predicting Fan Attendance at Mega Sports Events—A Machine Learning Approach: A Case Study of the FIFA World Cup Qatar 2022," Mathematics, MDPI, vol. 12(6), pages 1-25, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:6:p:926-:d:1361372
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/6/926/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/6/926/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jin Li, 2017. "Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what?," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-16, August.
    2. Stephen P. Ferris & Sulgi Koo & Kwangwoo Park & David T. Yi, 2022. "The Effects of Hosting Mega Sporting Events on Local Stock Markets and Sustainable Growth," Sustainability, MDPI, vol. 15(1), pages 1-15, 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. Rufino, Marta M. & Albouy, Camille & Brind'Amour, Anik, 2021. "Which spatial interpolators I should use? A case study applying to marine species," Ecological Modelling, Elsevier, vol. 449(C).
    2. Fort, Hugo, 2020. "Making quantitative predictions on the yield of a species immersed in a multispecies community: The focal species method," Ecological Modelling, Elsevier, vol. 430(C).
    3. Siwei Li & Jingjing An & Yaqiu Li & Xiagu Zhu & Dongdong Zhao & Lixian Wang & Yonghui Sun & Yuanzhao Yang & Changhao Bi & Xueli Zhang & Meng Wang, 2022. "Automated high-throughput genome editing platform with an AI learning in situ prediction model," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    4. Ritabrata Roy & Mrinmoy Majumder, 2022. "Assessment of water quality trends in Deepor Beel, Assam, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(12), pages 14327-14347, December.
    5. Shinyoung Kwag & Daegi Hahm & Minkyu Kim & Seunghyun Eem, 2020. "Development of a Probabilistic Seismic Performance Assessment Model of Slope Using Machine Learning Methods," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
    6. Fort, Hugo, 2018. "On predicting species yields in multispecies communities: Quantifying the accuracy of the linear Lotka-Volterra generalized model," Ecological Modelling, Elsevier, vol. 387(C), pages 154-162.
    7. Daniel S. Maynard & Lalasia Bialic-Murphy & Constantin M. Zohner & Colin Averill & Johan Hoogen & Haozhi Ma & Lidong Mo & Gabriel Reuben Smith & Alicia T. R. Acosta & Isabelle Aubin & Erika Berenguer , 2022. "Global relationships in tree functional traits," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

    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:12:y:2024:i:6:p:926-:d:1361372. 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.