dc.contributor.author | Şahin, Mehmet | |
dc.contributor.author | Uçar, Murat | |
dc.date.accessioned | 2022-12-12T08:48:01Z | |
dc.date.available | 2022-12-12T08:48:01Z | |
dc.date.issued | 2022 | en_US |
dc.identifier.citation | Şahin, M., Uçar, M. (2022). Prediction of sports attendance: A comparative analysis. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 236 (2), pp. 106-123.
https://doi.org/10.1177/17543371209831 | en_US |
dc.identifier.uri | https://doi.org/10.1177/17543371209831 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12508/2421 | |
dc.description.abstract | In this study, a comparative analysis for predicting sports attendance demand is presented based on econometric, artificial intelligence, and machine learning methodologies. Data from more than 20,000 games from three major leagues, namely the National Basketball Association (NBA), National Football League (NFL), and Major League Baseball (MLB), were used for training and testing the approaches. The relevant literature was examined to determine the most useful variables as potential regressors in forecasting. To reveal the most effective approach, three scenarios containing seven cases were constructed. In the first scenario, each league was evaluated separately. In the second scenario, the three possible combinations of league pairings were evaluated, while in the third scenario, all three leagues were evaluated together. The performance evaluations of the results suggest that one of the machine learning methods, Gradient Boosting, outperformed the other methods used. However, the Artificial Neural Network, deep Convolutional Neural Network, and Decision Trees also provided productive and competitive predictions for sports games. Based on the results, the predictions for the NBA and NFL leagues are more satisfactory than the predictions of the MLB, which may be caused by the structure of the MLB. The results of the sensitivity analysis indicate that the performance of the home team is the most influential factor for all three leagues. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | SAGE Publications | en_US |
dc.relation.isversionof | 10.1177/17543371209831 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Forecasting | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Sports attendance | en_US |
dc.subject | Sports economics | en_US |
dc.subject.classification | Competitive Balance | |
dc.subject.classification | Football | |
dc.subject.classification | Major League Baseball | |
dc.subject.other | Adaptive boosting | |
dc.subject.other | Baseball | |
dc.subject.other | Basketball | |
dc.subject.other | Convolutional neural networks | |
dc.subject.other | Decision trees | |
dc.subject.other | Deep neural networks | |
dc.subject.other | Machine learning | |
dc.subject.other | Sensitivity analysis | |
dc.subject.other | Comparative analysis | |
dc.subject.other | Effective approaches | |
dc.subject.other | Gradient boosting | |
dc.subject.other | Influential factors | |
dc.subject.other | Machine learning methods | |
dc.subject.other | National basketball associations | |
dc.subject.other | Training and testing | |
dc.subject.other | Forecasting | |
dc.title | Prediction of sports attendance: A comparative analysis | en_US |
dc.type | article | en_US |
dc.relation.journal | Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology | en_US |
dc.contributor.department | Mühendislik ve Doğa Bilimleri Fakültesi -- Endüstri Mühendisliği Bölümü | en_US |
dc.contributor.department | İşletme ve Yönetim Bilimleri Fakültesi -- Yönetim Bilişim Sistemleri Bölümü | |
dc.identifier.volume | 236 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.startpage | 106 | en_US |
dc.identifier.endpage | 123 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.contributor.isteauthor | Şahin, Mehmet | |
dc.contributor.isteauthor | Uçar, Murat | |
dc.relation.index | Scopus | en_US |