Sport Management Studies

Comparison of Linear Regression and Artificial Neural Network Methods for Estimating the Price of Iranian Professional Football Players

Document Type : Research Paper

Authors

1 Ph.D. Student in Sport Management, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran

2 Associated Professor in Sport Management, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran

3 Assistant Professor in Sport Management, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran

4 Assistant Professor in Sport Biomechanics, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran

Abstract
The purpose of this study was to estimate the prices of Iranian professional soccer league players, as well as to compare the regression methods and neural networks in predict that. 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 sector consisted of managers, coaches of clubs and experts familiar with the field of player buying and selling that fourteen people were selected by snowball method until reaching the saturation point. The statistical population in the quantitative section also included all footballers present in the Persian Gulf Football Professionals League in the years 2017-2018, which 226 of them were selected by Cochran formula and selected by stratified random sampling. The research instrument in qualitative method included in-depth interview with statistical sample whose reliability was calculated 81% through test-retest method. The data needed for quantitative analysis were also collected from reputable sites and the Iranian Football League. Pearson correlation coefficient, linear regression and radial neural networks were used to predict and design the model using SPSS software version 21 and R version 3.6.2. Qualitative research findings showed that player performance, personal characteristics, abilities, club characteristics and bubble-generating factors are effective in determining the price of football players. The results indicated that, despite the benefits of regression methods in predicting problems, because of the multifaceted and complex pricing of soccer players, artificial neural network models would cover more variables and be more efficient and accurate.

Keywords

Main Subjects

  1. Amir, E., & Livne, G. (2005). Accounting, valuation and duration of football player contracts. Journal of Business Finance & Accounting, 32(3&4), 549-586.
  2. Amirtash, A. R., & Mozafari, A. A. (2014). Conditions and necessity of entering premier league’s professional football clubs into the stock market. Journal of Sport Management, 6(4), 399-419. (in Persian).
  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.
  4. 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), 266–286.
  5. 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.
  6. 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.
  7. Evans, J. S. B. T. (2006). The heuristic-analytic theory of reasoning: extension and evaluation. Psychonomic Bulletin & Review, 13(3), 378–395.
  8. 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.
  9. Frick, B. (2007). The football players’ labor market: empirical evidence from the major European leagues. Scottish Journal of Political Economy, 54(3), 422–446.
  10. Frick, B. (2011). Performance, salaries, and contract length: Empirical evidence from German soccer. International Journal of Sport Finance, 6(2), 87–118.
  11. 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.
  12. 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.
  13. He, M., Cachucho, R., & Knobbe, A. (2015). Football player’s performance and mar- ket value. Proceedings of the 2nd workshop of sports analytics, European Con- ference on Machine Learning and Principles and Practice of Knowledge Discov- ery in Databases (ECML PKDD). Available at: https://dtai.cs. kuleuven.be/events/MLSA15/papers/mlsa15_ submission _ 8.pdf (Accessed 4 March 2017).
  14. Herm, S., Callsen-Bracker, H. M., & Kreis, H. (2014). When the crowd evaluates soc- cer players’ market values: Accuracy and evaluation attributes of an online com- munity. Sport Management Review, 17(4), 484–492.
  15. Izadyar, M., Memari, Zh. & Mousavi, M. H. (2016). Pricing equation for Iranian Premier League football players. Journal of Economic Research, 51(1), 25-40. (in Persian).
  16. Kaplan, T. (2010). When it comes to stats, soccer seldom counts. The New York Times. Available at: https:// mobile.nytimes.com/ 2010/ 07/ 09/ sports/ soccer/ 09soccerstats.html. (Accessed 4 March 2017).
  17. Keefer, Q. A. W. (2017). The sunk-cost fallacy in the national football league. Journal of Sports Economics, 18(3), 282–297.
  18. 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.
  19. 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, Mambai: PACKT Publishing.
  20. Lapedes, A., & Farber, R. (1987). Non-linear signal processing using neural networks. Technical Report no. LAUR- 2662 Los Alamos National Laboratory, Los Alamos, NM.
  21. Lee, Manig; Pitts, Brenda; Quartman, Jerome (2019). Research methods in sport management (H. Asadi, & A.A. Asefi, Ahmad Ali) (2nd). Tehran: University of Tehran Press. (in Persian).
  22. 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.
  23. 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.
  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, 611-624.
  25. Murphy, K. P (2012). Machine learning: A probabilistic perspective. City of Bits: MIT Press.
  26. 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, Bayeruth.
  27. Razavi, S., & tolson, B. A. (2011). A new formulation for feed forward neural networks. Neural Netw IEEE Trans, 22(10), 1588-1598.
  28. Roşca, V. (2010). Sustainable development of a city by using a Football Club. Theoretical and Empirical Researches in Urban Management, 7(16), 61-68.
  29. 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.
  30. Ruijg, J., & van Ophem, H. (2014). Determinants of football transfers. Discussion Paper 2014/01, Department of Economics & Econometrics, Amsterdam School of Economics. Available at: http: //ase.uva.nl/binaries/content/assets/subsites/Amsterdam- school- of- economics/ research/uva- econometrics/dp- 2014/1401.pdf (Accessed 14 March 2017).
  31. Salimi, M., & Tayebi, M. (2018). Management information systems in sport organizations. Isfahan, Iran: SID Publishing. (in Persian).
  32. M., & Tayebi, M. (2022). Developing a model for the success of information systems in sports organizations. International Journal of Business Information Systems, 39(1), 76-95.
  33. Satchian, V., Alizadeh, A., Dehghan, A., & Elahi, A. R. (2011). Prioritize and identification effective factors on the fans case study: Tehran Derby. Research in Sports Science, 8(10), 131-46. (in Persian).
  34. Schmeh, K. (2005). Titel, tore, transaktionen: Ein Blick hinter die Kulissen des fußball- business. Redline Wirtschaft: Heidelberg.
  35. Soltan Hoseini, 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 Study, 9(41), 16-36. (in Persian).
  36. Soltan Hosseini, M., Zebardast, M. A., Nasr Esfahani, D., Amoo Zadeh, Z., & Haji Zadeh. S. (2017). Principles of sports marketing. Esfahan: Sana Gostar Publishing. (in Persian).
  37. 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.
  38. Tunaru, R., Clark, E., & Viney, H. (2005). An option pricing framework for valuation of football players. Review of Financial Economics, 14, 281-295.
  39. Yaldo, L., & Shamir, L. (2017). Computational estimation of football player wages. International Journal of Computer Science in Sport, 16(1), 18-38.
  40. 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. (in Persian).
Sport Management Studies
Volume 14, Issue 71 - Serial Number 71
March and April 2022
Pages 117-154

  • Receive Date 17 December 2019
  • Revise Date 07 February 2020
  • Accept Date 27 February 2020

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