Predicting Fan Attendance at Mega Sports Events—A Machine Learning Approach: A Case Study of the FIFA World Cup Qatar 2022
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/6/926 |
| _version_ | 1797240175004221440 |
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| author | Ahmad Al-Buenain Mohamed Haouari Jithu Reji Jacob |
| author_facet | Ahmad Al-Buenain Mohamed Haouari Jithu Reji Jacob |
| author_sort | Ahmad Al-Buenain |
| collection | DOAJ |
| description | 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<sup>2</sup> 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. |
| first_indexed | 2024-04-24T18:03:15Z |
| format | Article |
| id | doaj.art-f8a5c6ff9c18460099dae000ab0602f1 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-04-24T18:03:15Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-f8a5c6ff9c18460099dae000ab0602f12024-03-27T13:53:20ZengMDPI AGMathematics2227-73902024-03-0112692610.3390/math12060926Predicting Fan Attendance at Mega Sports Events—A Machine Learning Approach: A Case Study of the FIFA World Cup Qatar 2022Ahmad Al-Buenain0Mohamed Haouari1Jithu Reji Jacob2Mechanical and Industrial Engineering Department, College of Engineering, Qatar University, Doha 2713, QatarMechanical and Industrial Engineering Department, College of Engineering, Qatar University, Doha 2713, QatarComputer Science and Engineering Department, College of Science and Technology, Cochin University, Kalamassery 682022, IndiaMega 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<sup>2</sup> 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.https://www.mdpi.com/2227-7390/12/6/926mega sports eventsFIFA World Cupmachine learningattendee predictionstochastic gradient descent |
| spellingShingle | Ahmad Al-Buenain Mohamed Haouari Jithu Reji Jacob Predicting Fan Attendance at Mega Sports Events—A Machine Learning Approach: A Case Study of the FIFA World Cup Qatar 2022 Mathematics mega sports events FIFA World Cup machine learning attendee prediction stochastic gradient descent |
| title | Predicting Fan Attendance at Mega Sports Events—A Machine Learning Approach: A Case Study of the FIFA World Cup Qatar 2022 |
| title_full | Predicting Fan Attendance at Mega Sports Events—A Machine Learning Approach: A Case Study of the FIFA World Cup Qatar 2022 |
| title_fullStr | Predicting Fan Attendance at Mega Sports Events—A Machine Learning Approach: A Case Study of the FIFA World Cup Qatar 2022 |
| title_full_unstemmed | Predicting Fan Attendance at Mega Sports Events—A Machine Learning Approach: A Case Study of the FIFA World Cup Qatar 2022 |
| title_short | Predicting Fan Attendance at Mega Sports Events—A Machine Learning Approach: A Case Study of the FIFA World Cup Qatar 2022 |
| title_sort | predicting fan attendance at mega sports events a machine learning approach a case study of the fifa world cup qatar 2022 |
| topic | mega sports events FIFA World Cup machine learning attendee prediction stochastic gradient descent |
| url | https://www.mdpi.com/2227-7390/12/6/926 |
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