Predict the Value of Football Players Using FIFA Video Game Data and Machine Learning Techniques
Yazarlar (2)
Mustafa A. Al-Asadı
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Selçuk Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | IEEE Access (Q2) | ||
| Dergi ISSN | 2169-3536 Dergi Bilgileri (2022) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2022 |
| Cilt / Sayı / Sayfa | 10 / 1 / 22631–22645 | DOI | 10.1109/ACCESS.2022.3154767 |
| Makale Linki | https://ieeexplore.ieee.org/abstract/document/9721908 | ||
| UAK Araştırma Alanları |
Yapay Zeka
|
||
| Özet |
| Football is a popular sport; however, it is a big business as well. From a managerial perspective, the important decisions that team managers make —Concerning player transfers, issues related to player valuation, especially the determination of transfer fees and market values, are of major concern. Market values can be understood as estimates of transfer fees— prices that could be paid for a player on the football market. Therefore, market values play an important role in transfer negotiations. The market has traditionally been estimated by football experts. However, expert judgments are inaccurate and not transparent. Data analytics may thus provide a sound alternative or a complementary approach to experts-based estimations of market value. In this study, we propose an objective quantitative method to determine football players’ market values. The method is based on the application of machine learning … |
| Anahtar Kelimeler |
| FIFA video game data | football analytics | machine learning | Player value prediction | regression |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 48 |
| Scopus | 85 |
| Google Scholar | 149 |