Document Type : Research Paper
Authors
1 physical education and sport science department, university of kurdistan
2 Faculty of Economic, University Paris 1 Panthéon Sorbonne, Paris, France
3 Artificial Intelligence Group, Computer Engineering Faculty, Tehran, Iran
Abstract
The aim of the present study was to identify performance variables affecting the pricing of Iranian football goalkeepers. The research method was qualitative, based on the thematic analysis. The statistical population of the study was consisted of all Iranian Individuals with AFC coaching professional qualifications, Iranian Premier League coaches and Football instructors. The sampling method was theorical selected and data were collected with 11 coaches and instructors. The data collection tool was an open interview, and data collection continued until theoretical saturation, meaning that the researcher did not obtain new data and code after the ninth interview, thereby halting the sampling process (9 + 2). Coding method and inductive approach to final themes were used for data analysis. NVIVO10 software was also used for data analysis. The results indicate that variables for the defensive functional variables of the goalkeeper include conceded goal, Success in one vs one situations, Sweeper Role, Shoot Control, Saving the Goal, Mistake Lead to Goal or Chance Create and Aerial Won and variables for offensive functional variables of the goalkeeper include assist, chance create, Key Pass, Passes and two-footed. Therefore, it is recommended to pay attention to the research results in designing the pricing model of the goalkeepers.
Keywords
- Abdi, S. H., Zangiabadi, M., & Talebpur, M. (2016). Determining the role of affecting factors on the evaluation of Iranian Premier League Football players. Journal of Human Resource Management in Sports, 3(2), 121-136. (in Persian).
- Beheshti, S., Rezayat, Gh. (2015). Using NVivo 10 in qualitative data analysis (1st). Tehran: Sokhanvaran Publishers.
- Byon, K. K., Zhang, J. J., & Connaughton, D. P. (2010). Dimensions of general market demand associated with professional team sports: Development of a scale. Sport Management Review, 13(2), 142-157.
- Carlos, P. (2020). A conceptual model to measure football player’s market value: A proposal by means of an analytic hierarchy process. Revista Internacional de Ciencias del Deporte, XVI(59), 24-42.
- Coluccia, D., Fontana, S., & Solimene, S. (2018). An application of the option-pricing model to the valuation of a football player in the ‘Serie A League’. Interational Journal of Sport Management and Marketing, 18(1/2), 155–168.
- Creswell, J. (2000). Research design: Qualitative, quantitative, and mixed methods approaches. New York: SAGE.
- T (2020). Eye on the prize, Football Money League. Deloitte Sports Business Group
- Derrick, Y. (2019). A data driven goalkeeper evaluation framework. Paper presented at the 13th Annual Sport Analytics Conference, Boston.
- https://www.footballbenchmark.com/library/fifa_global_transfer_market_report_2018
- He, M., Cachucho, R., & Knobbe, A. (2015). Football player’s performance and mar- ket 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), Turin, Italy.
- Izadyar, M., Memari, Z., & Mosavi, M. (2015). Equivalent to the pricing of the Iranian Premier League Football players. Economic Research, 51(1), 25-40. (in Persian).
- Majewski, S. (2016). Identification of factors determining market value of the most valuable football player. Journal of Management and Business Administration, 24(3), 91-104.
- Matesanz, D., Holzmayer, F., Torgler, B., Schmidt, S. L., & Ortega, G. J. (2018). Transfer market activities and sportive performance in European first football leagues: A dynamic network approach. PLoS One, 13(12), e0209362.
- Moradzadeh, M., Reza, S. H., & Miyali. Z. (2017). Selection of players based on effective indicators with the approach of expert evaluation algorithm and Aras technique (Case study of Shahroud University basketball team). Contemporary Researchs in Sport Management, 7(13), 1-12. (in Persian).
- 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.
- Patnaik, D., Praharaj, H., Prakash, K., & Samdani, K. (2019). A study of Prediction models for football player valuations by quantifying statistical and economic attributes for the global transfer market. Proceeding of International Conference on Systems Computation Automation and Network. New York: IEEE.
- Pavlović, V., Milačić, S., & Ljumović, I. (2014). Controversies about the accounting treatment of transfer fee in the football industry. Management: Journal of Sustainable Business and Management Solutions in Emerging Economies, 19(70), 17-24.
- Prabhnoor, S., & Lamba, P. S. (2019) Influence of crowdsourcing, popularity and previous year statistics in market value estimation of football players. Journal of Discrete Mathematical Sciences and Cryptography, 22(2), 113-126.
- Ruijg, J., & van Ophem, H. (2015). Determinants of football transfers. Applied Economics Letters, 22(1), 12-19.
- Kirschstein, T., & Liebscher, S. (2018). Assessing the market values of soccer players – A robust analysis of data from German 1. and 2. Bundesliga. Journal of Applied Statistics, 46(7), 1336-1349.
- Tunaru, R., Clark, E., & Viney, H. (2005). An option pricing framework for valuation of football players. Review of Financial Economics,14(3-4), 281-295.
- Yaldo, L., & Shamir, L. (2017). Computational estimation of football player wages. International Journal of Computer Science in Sport, 16(1), 18-38.
)