دانشگاه الزهرا

  اخبار و اعلانات

 آمار

تعداد نشریات25
تعداد شماره‌ها967
تعداد مقالات8,010
تعداد مشاهده مقاله13,573,913
تعداد دریافت فایل اصل مقاله9,616,772
Salimi, Mehdi, Tayebi, Mohsen, Labbaf, Amir Hossein. (1402). Estimating Iranian Professional Football Players' Prices A Neural Networks Approach. جلوه هنر, 3(3), 13-28. doi: 10.22051/sbj.2023.42806.1069
Mehdi Salimi; Mohsen Tayebi; Amir Hossein Labbaf. "Estimating Iranian Professional Football Players' Prices A Neural Networks Approach". جلوه هنر, 3, 3, 1402, 13-28. doi: 10.22051/sbj.2023.42806.1069
Salimi, Mehdi, Tayebi, Mohsen, Labbaf, Amir Hossein. (1402). 'Estimating Iranian Professional Football Players' Prices A Neural Networks Approach', جلوه هنر, 3(3), pp. 13-28. doi: 10.22051/sbj.2023.42806.1069
Salimi, Mehdi, Tayebi, Mohsen, Labbaf, Amir Hossein. Estimating Iranian Professional Football Players' Prices A Neural Networks Approach. جلوه هنر, 1402; 3(3): 13-28. doi: 10.22051/sbj.2023.42806.1069

Estimating Iranian Professional Football Players' Prices A Neural Networks Approach

Sports Business Journal
مقاله 1، دوره 3، شماره 3 - شماره پیاپی 7، آذر 2023، صفحه 13-28    اصل مقاله (1.27 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22051/sbj.2023.42806.1069
نویسندگان
Mehdi Salimi* 1؛ Mohsen Tayebi2؛ Amir Hossein Labbaf3
1Associated Professor in Sport Management, Sport sciences Faculty, University of Isfahan, Isfahan, Iran.
2PhD in Sport Management, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran.
3MSc in Sport Management, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran.
چکیده
Purpose: The study aimed to estimate the price of Iranian professional football league players.
Methodology: The research method was mixed exploratory designs, qualitative and quantitative methods. The research participants in the qualitative section included managers, club coaches, and experts familiar with the football players' market. Therefore, fourteen participants were selected by snowball until we reached saturation. The research tool in the qualitative section included in-depth interviews, so the reliability of which the re-test method was 81%. In the quantitative section, the statistical population had all the football players in the Iranian Football Professional League during 2016-2020, and random sampling was done. So, 863 players were selected to use their data for analysis. The quantitative methods were also collected from valid sites and the Iranian Football League Organization. The model was also designed through radial neural networks using software SPSS and R.
Findings: Qualitative section showed that the player's performance, personal characteristics and abilities, club characteristics, and bubble-creating factors are influential in determining the price of football players. In the quantitative section, a model with three hidden layers was designed, which had a nominal error rate in predicting the price of players.
Originality: Today, one of the main problems in the field of transfer in football leagues is the small number of appropriate criteria for pricing players. This study aims to estimate the price of professional football players using artificial neural networks.
کلیدواژه‌ها
Football Clubs؛ Players Market؛ Players' Value؛ Pricing؛ Players Transfer
عنوان مقاله [English]
برآورد قیمت بازیکنان حرفه‌ای فوتبال ایران برمبنای استفاده از شبکه‌های عصبی
نویسندگان [English]
مهدی سلیمی1؛ محسن طیبی2؛ امیرحسین لباف3
1دانشیار مدیریت ورزشی، دانشکده علوم ورزشی، دانشگاه اصفهان، اصفهان، ایران.
2دکتری گروه مدیریت ورزشی، دانشکده علوم ورزشی، دانشگاه اصفهان، اصفهان، ایران.
3کارشناس ارشد مدیریت ورزشی، دانشکده علوم ورزشی، دانشگاه اصفهان، اصفهان، ایران.
چکیده [English]
هدف: هدف از انجام این پژوهش برآورد قیمت بازیکنان لیگ برتر فوتبال ایران بود.
روش: روش تحقیق، آمیخته اکتشافی و ترکیبی از روش‌های کیفی و کمی بود. جامعه آماری پژوهش در بخش کیفی شامل مدیران، مربیان باشگاه‌ها و کارشناسان آشنا به حوزه بازار بازیکنان  فوتبال بودند که چهارده نفر از آنها به‌روش گلوله برفی تا رسیدن به اشباع نظری انتخاب شدند. ابزار تحقیق در بخش کیفی شامل مصاحبه عمیق بود که پایایی آن با روش باز‌آزمایی 81 درصد محاسبه شد. در بخش کمی، جامعه آماری کلیه بازیکنان لیگ حرفه‌ای فوتبال ایران طی سال‌های 99-1395 بودند که با روش نمونه‌گیری در دسترس، ۸۶۳ بازیکن انتخاب شدند. داده‌ها از سایت‌های معتبر و سازمان لیگ فوتبال ایران جمع آوری شد. همچنین این مدل از طریق شبکه‌های عصبی شعاعی با استفاده از نرم‌افزار SPSS و R طراحی شد.
یافته‌ها: بخش کیفی نشان داد که عملکرد بازیکن، ویژگی‌ها و توانایی‌های فردی، ویژگی‌های باشگاهی و عوامل حباب‌ساز در تعیین قیمت بازیکنان فوتبال مؤثر است. در قسمت کمی مدلی با سه لایه مخفی طراحی شد که کمترین میزان خطا را در پیش بینی قیمت بازیکنان داشت.
اصالت و ابتکار مقاله: امروزه یکی از مشکلات اساسی در زمینه نقل و انتقالات در لیگ های فوتبال کم بودن معیارهای مناسب برای قیمت گذاری بازیکنان است. هدف از این مطالعه برآورد قیمت بازیکنان حرفه ای فوتبال با استفاده از شبکه‌های عصبی مصنوعی است.
کلیدواژه‌ها [English]
ارزش بازیکنان, باشگاه‌های فوتبال, بازار بازیکنان, قیمت گذاری, نقل‌و‌انتقال بازیکنان
مراجع
  1. Abdi, S., Zangi Abadi, M., & Talebpour, M. (2016). Determination of the Role of Effective Factors in Valuation of Players in Iranian Premier Football League. Human Resource Management in Sports, 3(2), 121-136. https://doi.org/10.22044/shm.2016.831
  2. Amir, E., & Livne, G. (2005). Accounting, Valuation and Duration of Football Player Contracts. Journal of Business Finance & Accounting, 32(3-4), 549-586. https://doi.org/10.1111/j.0306-686X.2005.00604.x
  3. Brandes, L., & Franck, E. (2012). Social preferences or personal career concerns? Field evidence on positive and negative reciprocity in the workplace. Journal of Economic Psychology, 33(5), 925-939. https://doi.org/10.1016/j.joep.2012.05.001
  4. Bryson, A., Frick, B., & Simmons, R. (2009). The Returns to Scarce Talent: Footedness and Player Remuneration in European Soccer. Centre for Economic Performance, LSE, CEP Discussion Papers, 14(6), 606-628. https://doi.org/10.1177/1527002511435118
  5. Dey, P., Banerjee, A., Ghosh, D., & Mondal, A. (2014). AHP-neural network based player price estimation in IPL. International Journal of Hybrid Information Technology (IJHIT-SERSC), 7(3), 15-24. https://doi.org/10.14257/ijhit.2014.7.3.03
  6. Felipe, J. L., Fernandez-Luna, A., Burillo, P., de la Riva, L., Sánchez-Sánchez, J., & García-Unanue, J. (2020). Money Talks: Team Variables and Player Positions that Most Influence the Market Value of Professional Male Footballers in Europe. Sustainability, 12(3709), 3709. https://doi.org/10.3390/su12093709
  7. Franck, E., & Nüesch, S. (2011). The effect of wage dispersion on team outcome and the way team outcome is produced. Applied Economics, 43(23), 3037-3049. https://doi.org/10.1080/00036840903427224
  8. Frick, B. (2007). The Football Players' Labor Market: Empirical Evidence from the Major European Leagues. Scottish Journal of Political Economy, 54(3), 422-446. https://doi.org/10.1111/j.1467-9485.2007.00423.x
  9. Fry, T., Galanos, G., & Posso, A. (2014). Let's Get Messi? Top-Scorer Productivity in the European Champions League. Scottish Journal of Political Economy, 61(3), 261-279. https://doi.org/10.1111/sjpe.12044
  10. Ganjkhanloo, A., Memari, Z., & Khabiri, M. (2021). Marketing Strategies to Developing the Iranian Sports Industry. Sports Business Journal, 1(2), 95-113. https://doi.org/10.22051/sbj.2022.38999.1020
  11. Garcia-del-Barrio, P., & Pujol, F. (2007). Hidden Monopsony Rents in Winner-take-all Markets — Sport and Economic Contribution of Spanish Soccer Players. Managerial and Decision Economics, 28(1), 57-70. https://doi.org/10.1002/mde.1313
  12. Gerrard, B., & Dobson, S. (2000). Testing for monopoly rents in the market for playing talent – Evidence from English professional football. Journal of Economic Studies, 27(3), 142-164. https://doi.org/10.1108/01443580010326049
  13. He, M., Cachucho, R., & Knobbe, A. (2015). Football player's performance and market value. In Proceedings of the 2nd workshop of sports analytics. Paper presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases,  https://ceur-ws.org/Vol-1970/paper-11.pdf
  14. Herm, S., Callsen-Bracker, H.-M., & Kreis, H. (2014). When the crowd evaluates soccer players’ market values: Accuracy and evaluation attributes of an online community. Sport Management Review, 17(4), 484-492. https://doi.org/10.1016/j.smr.2013.12.006
  15. Izadyar, M., Memari, Z., & Mousavi, M.-H. (2016). Pricing Equation for Iranian Premier League Football Players. Journal of Economic Research (Tahghighat- E- Eghtesadi), 51(1), 25-40. https://doi.org/10.22059/jte.2016.57595
  16. Keefer, Q. (2015). The Sunk-Cost Fallacy in the National Football League: Salary Cap Value and Playing Time. Journal of Sports Economics, 18(3), 282-297. https://doi.org/10.1177/1527002515574515
  17. Klobučník, M., Plešivčák, M., & Vrábeľ, M. (2019). Football clubs’ sports performance in the context of their market value and GDP in the European Union regions. Bulletin of Geography. Socio-economic Series, 45(45), 59-74. https://doi.org/10.2478/bog-2019-0024
  18. Lehmann, E., & Schulze, G. (2008). What Does it Take to be a Star? – The Role of Performance and the Media for German Soccer Players. Applied Economics Quarterly (formerly: Konjunkturpolitik), 54(1), 59-70. https://www.econstor.eu/handle/10419/47902
  19. Lucifora, C., & Simmons, R. (2003). Superstar Effects in Sport: Evidence From Italian Soccer. Journal of Sports Economics, 4(1), 35-55. https://doi.org/10.1177/1527002502239657
  20. Majewski, S. (2021). Football players' brand as a factor in performance rights valuation. Journal of Physical Education and Sport, 21(4), 1751-1760. https://doi.org/10.7752/jpes.2021.04222
  21. Memari, Z., Esmaeili, M., & Jafari, M. (2023). How Could a Football Player Transfer Business be More Successful? A Model-Based on Game Theory Approach. Sports Business Journal, 3(2), 13-26. https://doi.org/10.22051/sbj.2023.42401.1067
  22. Memari, Z., Hoda, K., & Safaie, A. (2020). The Valuation of Football Players with Data Mining Technique (Case Study: Esteghlal Club). Sport management journal, 12(3), 735-757. https://doi.org/10.22059/jsm.2019.262922.2128
  23. Metelski, A., & Kornakov, K. (2021). Effect of lockdown owing to COVID-19 on players' match statistics in Bundesliga. Journal of Physical Education and Sport, 21(1), 110-114. https://doi.org/10.7752/jpes.2021.01015
  24. Müller, O., Simons, A., & Weinmann, M. (2017). Beyond crowd judgments: Data-driven estimation of market value in association football. European Journal of Operational Research, 263(2), 611-624. https://doi.org/10.1016/j.ejor.2017.05.005
  25. Pawlowski, T., Breuer, C., & Hovemann, A. (2010). Top Clubs' Performance and the Competitive Situation in European Domestic Football Competitions. Journal of Sports Economics, 11(2), 186-202. https://doi.org/10.1177/1527002510363100
  26. Poli, R. (2005). The football player’s trade as global commodity chain. Transnational networks from Africa to Europe.
  27. Roşca, V. (2012). The Financial Contribution of International Footballer Trading to the Romanian Football League and to the National Economy. Theoretical and Applied Economics, 4(569), 145-166. https://openaccess.library.uitm.edu.my/Record/doaj-5c981d1c653049b9a19bf521fdd0213c
  28. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747. https://arxiv.org/abs/1609.04747
  29. Ruijg, J., & van Ophem, H. (2015). Determinants of football transfers. Applied Economics Letters, 22(1), 12-19. https://doi.org/10.1080/13504851.2014.892192
  30. Salimi, M., & Tayebi, M. (2018). Management Information Systems in Sport Organizations. Academic Jihad of Isfahan Branch.
  31. Sarlab, R., Alipour Nadinluoi, Z., & Mahmoudi, N. (2022). Study on the Marketing Mix of the Iranian Football Industry. Sports Business Journal, 2(1), 13-25. https://doi.org/10.22051/sbj.2022.39725.1026
  32. Soltanhoseini, M., Razavi, S. M. J., & Salimi, M. (2017). Identifying and Prioritizing Barriers to the Privatization of Soccer Industry in Iran Using Multi-Criteria Analysis and Copeland's Approach. Sport Management Studies, 9(41), 15-36. https://doi.org/10.22089/smrj.2017.913
  33. Tayebi, M., Soltan Hoseini, M., Salimi, M., & Lenjannezhadian, S. (2022). Comparison of Linear Regression and Artificial Neural Network Methods for Estimating the Price of Iranian Professional Football Players. Sport Management Studies, 14(71), 117-154. https://doi.org/10.22089/smrj.2020.8238.2824
  34. Tunaru, R., & Viney, H. (2010). Valuations of Soccer Players from Statistical Performance Data. Journal of Quantitative Analysis in Sports, 6(2), 10-10. https://doi.org/10.2202/1559-0410.1238
  35. Yaldo, L., & Shamir, L. (2017). Computational Estimation of Football Player Wages. International Journal of Computer Science in Sport, 16(1), 18-38. https://doi.org/10.1515/ijcss-2017-0002
  36. Yu, Y., & Liu, F. (2019). Effective Neural Network Training With a New Weighting Mechanism-Based Optimization Algorithm. IEEE Access, 7, 72403-72410. https://doi.org/10.1109/ACCESS.2019.2919987
  37. Zhu, F., Lakhani, K. R., Schmidt, S. L., & Herman, K. (2015). TSG Hoffenheim: football in the age of analytics. Harvard Business School Case, 616(10). https://www.hbs.edu/faculty/Pages/item.aspx?num=49569
آمار
تعداد مشاهده مقاله: 334
تعداد دریافت فایل اصل مقاله: 304


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب