نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری مدیریت ورزشی دانشگاه اصفهان، اصفهان، ایران
2 دانشیار مدیریت ورزشی، دانشکده تربیت بدنی و علوم ورزشی، دانشگاه اصفهان، اصفهان، ایران
3 استادیار مدیریت ورزشی، دانشگاه اصفهان، اصفهان، ایران
4 استادیار بیومکانیک ورزشی، دانشکده تربیت بدنی و علوم ورزشی، دانشگاه اصفهان، اصفهان، ایران
چکیده
هدف: هدف از انجام این تحقیق تخمین قیمت بازیکنان لیگ حرفه‌ای فوتبال ایران بود.
روش‌شناسی: روش انجام تحقیق با استفاده از طرح های آمیخته اکتشافی بود که تلفیقی از روش‌های کیفی و کمی می‌باشد. جامعه‌ی آماری تحقیق در بخش کیفی شامل مدیران، مربیان باشگاه‌ها و کارشناسان خبره و آشنا با حوزه خرید و فروش بازیکنان بودند که چهارده نفر تا رسیدن به نقطه اشباع، به روش گلوله برفی انتخاب شدند. در بخش کمی نیز جامعه آماری شامل کلیه‌ی فوتبالیست‌های حاضر در لیگ حرفه ای فوتبال خلیج فارس ایران در سال های 1396-1398 بودند. ابزار تحقیق در روش کیفی شامل مصاحبه عمیق بود که پایایی آن از طریق روش بازآزمون 81 درصد محاسبه شد. داده‌های مورد نیاز برای تجزیه و تحلیل روش های کمی نیز از سایت‌های معتبر و سازمان لیگ فوتبال ایران جمع‌آوری شدند. همچنین طراحی مدل از طریق شبکه‌های عصبی شعاعی با بهره‌گیری از نرم افزار R انجام پذیرفت.
یافته‌ها: یافتههای پژوهش در بخش کیفی نشان داد که عملکرد بازیکن، ویژگیهای شخصی، توانایی‌های آن‌ها، ویژگیهای باشگاه و عوامل ایجاد کننده حباب، در تعیین قیمت بازیکنان فوتبال مؤثر است. همچنین در بخش کمی مدلی با سه لایه‌ی پنهان طراحی شد که کمترین میزان خطا را در پیشبینی قیمت بازیکنان داشت.
نتیجه‌گیری: در مجموع طراحی این مدل موجب آگاهی مسئولین خرید و فروش باشگاه‌های فوتبال خواهند شد تا بتوانند بازیکنان با استعداد و دارای هزینه نقل و انتقال مناسب را با حداقل هزینه و سطح بالایی از قابلیت اطمینان جذب نمایند.
کلیدواژه‌ها
عنوان مقاله [English]
Estimate of prices of professional Iranian football players using neural networks
نویسندگان [English]
- Mohsen Tayebi 1
- Mohamad Soltan Hoseini 2
- Mehdi Salimi 3
- Shahram Lenjannejadian 4
1 Mohsen Tayebi, Ph.D. Student in Sport Management, University of Isfahan, Isfahan, Iran
2 Associate Professor in Sport Management, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran.
3 Assistant Professor in Sport Management, University of Isfahan, Isfahan, Iran
4 Assistant Professor in Sport Biomechanics, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran.
چکیده [English]
Objective: The purpose of conducting this study was to estimate pricesof Iranian professional football league players.
Methodology: The research method was mixed exploratory designs, which is a combination of qualitative and quantitative methods. The statistical population of the research in the qualitative section included managers, club coaches and experts who were familiar with the field of buying and selling players, and fourteen of them were selected by snowball method until they reached saturation point. In the quantitative section, the statistical population included all the football players present in the Iranian Persian Gulf Football Professional League during 2016-2019. The research tool of qualitative method included in-depth interviews, the reliability of which was calculated 81% through re-test method. The data needed to analyze 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 R.
Results: The study results of the qualitative section showed that the player's performance, personal characteristics and abilities, club characteristics and bubble-creating factors are effective on determining the price of football players. Also, in the quantitative section, a model with two hidden layers was designed, which had the least error rate in predicting the price of players.
Conclusion: In general, the design of this model will make the officials of buying and selling of football clubs aware so that they can attract talented and cost-effective players with the minimum cost and high level of reliability.
کلیدواژه‌ها [English]
- Pricing
- Player Value
- Artificial Intelligence
- Amir, E., & Livne, G. (2005). Accounting, Valuation and Duration of Football Player Contracts. Journal of Business Finance & Accounting, 32(3&4), 549-586.
- Bazargan, A. (2015). Introduction to Qualitative and Mixed Research Methods, Common Approaches in Behavioral Sciences. Tehran, Iran: Didar Publication. [Persian]
- 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.
- Brandes, L., Franck, E., & Nüesch, S. (2008). Local heroes and superstars: an empiri- cal analysis of star attraction in German soccer. Journal of Sports Economics, 9(3), 226-286.
- Bryson, A., Frick, B., & Simmons, R. (2012). The returns to scarce talent: footedness and player remuneration in European soccer. Journal of Sports Economics, 14(6), 606-628.
- Carmichael, F., Forrest, D., & Simmons, R. (1999). The labor market in association football: who gets transferred and for how much? Bulletin of Economic Research, 51(2), 125-150.
- Dey, P. k., Banerjee, A., Ghosh, D., N., & Mondal, A., Ch. (2014). AHP-Neural Network Based Player Price Estimation in IPL. International Journal of Hybrid Information Technology, 7(3), 15-24.
- 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.
- Frick, B. (2007). The football players’ labor market: empirical evidence from the ma- jor European leagues. Scottish Journal of Political Economy, 54(3), 422-446.
- Frick, B. (2011). Performance, salaries, and contract length: empirical evidence from German soccer. International Journal of Sport Finance, 6(2), 87-118.
- Fry, T. R. L., 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.
- 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.
- 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.
- 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 (ECML PKDD), Retrieved March 14, 2017, from https://dtai.cs. kuleuven.be/events/MLSA15/papers/mlsa15_ submission _ 8.pdf.
- 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.
- 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. [Persian]
- Keefer, Q. A. W. (2017). The sunk-cost fallacy in the national football league. Journal of Sports Economics, 18(3), 282-297.
- KeLin Du, M., & Swamy, N. S. (2013). Neural Networks and Statistical Learning. Springer Science & Business Media.
- Kiefer, S. (2014). The impact of the Euro 2012 on popularity and market value of football players. International Journal of Sport Finance, 9(2), 95-110.
- Lantz, B. (2013). Machine Learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications. Birmingham- Mumbai: Packt Publishing.
- Lee, M., Pitts, B., & Quartman, J. (2019). Research Methods in Sport Management. (H. Asadi & A. A. Asefi, Trans. 2 ed.). Tehran: University of Tehran Press.
- Lehmann, E. E., & Schulze, G. G. (2008). What does it take to be a star? The role of performance and the media for German soccer players. Applied Economics Quarterly, 54(1), 59-70.
- Lucifora, C., & Simmons, R. (2003). Superstar effects in sport: evidence from Italian soccer. Journal of Sports Economics, 4(1), 35-55.
- Maier, H. R., Jain, A., Dandy, G. C., & Sudheer, K. P. (2010). Methods used for the development of neural networks for the prediction of water resorce variables in river system: current status and future directions. Environ Softw, 25(8), 891-909.
- Medcalfe, S. (2008). English league transfer prices: is there a racial dimension? A re-examination with new data. Applied Economics Letters, 15(11), 865-867.
- 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, 611-624. doi:10.1016/j.ejor.2017.05.005.
- Polti, R. (2005). The football players’ trade as a global commodity chain. Transactional networks from Africa to Europe. The Workshop on Social Networks of Traders and Managers in Africa.
- Razavi, S., & tolson, B. A. (2011). A new formulation for feed forward neural networks. Neural Netw IEEE Trans, 22(10), 1855-1598.
- Rosca, 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.
- Ruijg, J., & van Ophem, H. (2014). Determinants of football transfers. In. Department of Economics & Econometrics: Amsterdam School of Economics.
- Schmeh, K. (2005). Titel, Tore, Transaktionen: Ein Blick hinter die Kulissen des Football- Business. Heidelberg: Redline Wirtschaft.
- Seddon, P. B. (2001). IT Evaluation Revisited: Plus a Change. Proceedings of Eight European. Paper presented at the Conference on Information Technology (ECITE), Oxford, United Kingdom.
- Soltan Hosseini, M., Zebardast, M. A., Nasr Esfahani, D., Amoo Zadeh, Z., & S., H. Z. (2017). Principles of Sports Marketing. Esfahan. Isfahan, Iran: Sana Gostar Publishing. [Persian]
- Tunaru, R. S., & Viney, H. P. (2010). Valuations of Soccer Players from Statistical Performance Data. Journal of Quantitative Analysis in Sports, 6(2), 1-21. doi:10.2202/1559-0410.1238
- Tunaru, R., Clark, E., & Viney, H. (2005). An option pricing framework for valuation of football players. Review of Financial Economics, 14, 281-295. doi:10.1016/j.rfe.2004.11.002
- Yaldo, L., & Shamir, L. (2017). Computational Estimation of Football Player Wages. International Journal of Computer Science in Sport, 16(1), 18-38. doi:10.1515/ijcss-2017-0002.
- Zareian, H., Elahi, A., Sajadi, S. N., Ghazi Zahedi, A. (2015). Games in Rio de Janeiro Using Intelligent Method of Multilayer Perception Networks (MLP). Strategic Studies on Youth and Sport. 14(30): 37-54. [Persian]
- Zhu, F., Lakhani, K. R., Schmidt, S. L., & Herman, K. (2015). TSG Hoffenheim: football in the age of analytics. Harvard Business School Case. Retrieved from http:// www.hbs.edu/ faculty/ Pages/ item.aspx?num=49569.
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