Veri Tabanlı Karar Verme Sürecinin Futbol Kulüplerinin Performansına Yansımaları

Abstract

Bu çalışma, futbol kulüplerinin yalnızca saha içi performans verilerine değil, aynı zamanda saha dışı organizasyonel, yönetsel ve sosyo-ekonomik verilere dayalı olarak şekillenen veri tabanlı karar verme (VTKV) süreçlerinin kulüp yönetimleri üzerindeki etkilerini kapsamlı biçimde incelemeyi amaçlamaktadır. Literatür taraması yöntemi esas alınarak oluşturulan bu çalışmada, çağdaş spor yöneticiliği anlayışında verinin her geçen gün daha fazla karar süreçlerinin merkezine yerleştiği ve yönetsel başarıyı doğrudan etkileyen bir araç haline geldiği vurgulanmaktadır. Bu bağlamda, veri yalnızca teknik analizlerin bir bileşeni değil, aynı zamanda kulüplerin stratejik vizyonlarını, sürdürülebilirlik planlarını ve rekabetçi konumlarını şekillendiren temel bir yapı taşı olarak değerlendirilmiştir. Çalışma kapsamında Brentford FC (İngiltere), FC Midtylland (Danimarka) ve Altınordu FK (Türkiye) gibi farklı lig yapılarında faaliyet gösteren kulüplerin örnek uygulamaları detaylı olarak analiz edilmiştir. Bu kulüplerin transfer politikalarından oyuncu gelişim modellerine, taraftar etkileşiminden mali kaynak yönetimine kadar pek çok alanda veri odaklı yaklaşımlarla başarılı sonuçlar elde ettikleri görülmektedir. Ancak bununla birlikte, futbol ekosisteminde veri okuryazarlığının yetersizliği, teknolojik altyapı eksiklikleri, geleneksel yaklaşımların hâkimiyeti ve kültürel direnç gibi faktörler, VTKV süreçlerinin önündeki başlıca engeller olarak dikkat çekmektedir. Sonuç olarak, özellikle düşük bütçeli ve gelişmekte olan kulüplerin üst düzey rekabet ortamında kalıcı başarı elde edebilmeleri için veriyi yalnızca bir yöntem değil, modern futbolun yeni ve evrensel iletişim dili olarak benimsemeleri gerektiği açıkça ortaya konulmuştur.

Keywords

• Spor yöneticiliği
• veri tabanlı karar verme
• futbol kulüpleri
• performans yönetimi
• dijital dönüşüm

Data-Driven Decision-Making Process And Its Impact On Football Club Performance

Abstract

This study aims to comprehensively examine the effects of data-based decision making (DBDM) processes, which are shaped based not only on on-field performance data of football clubs, but also on off-field organizational, managerial and socio-economic data, on club managements. In this study, which is based on the literature review method, it is emphasized that in contemporary sports management understanding, data has become more and more central to decision-making processes and has become a tool that directly affects managerial success. In this context, data is considered not only as a component of technical analysis but also as a fundamental building block that shapes the strategic visions, sustainability plans and competitive positions of clubs. Within the scope of the study, the case studies of clubs operating in different league structures such as Brentford FC (England), FC Midtylland (Denmark) and Altınordu FK (Turkey) were analyzed in detail. It is seen that these clubs have achieved successful results with data-driven approaches in many areas from transfer policies to player development models, from fan interaction to financial resource management. However, factors such as insufficient data literacy in the football ecosystem, technological infrastructure deficiencies, dominance of traditional approaches and cultural resistance stand out as the main barriers to VTKV processes. As a result, it has been clearly demonstrated that in order for developing clubs, especially those with low budgets, to achieve lasting success in a highly competitive environment, they need to adopt data not only as a method but also as the new and universal communication language of modern football.

Keywords

• Sport management
• data-driven decision-making
• football clubs
• performance management
• digital transformation

References

1. Azhar, A. A. A., Hisham, B. E. B., ve Abu Bakar, N. A. (2022). Using the enterprise architecture approach to analyse the current performance of Manchester United Football Club. Journal of Techno-Social, 14(1), 45–52. https://doi.org/10.30880/jts.2022.14.01.005
2. Baba, E., & Nagy, I. Z. (2014). The organizational adaptation of football enterprises. Annals of Faculty of Economics, 23(1), 1173–1183.
3. Bekris, E., Mylonis, E., Gkisis, I., Gioldasis, A., Sarakinos, A., ve Sotiropoulos, A. (2013). Offense and defense statistical indicators that determine the Greek Superleague teams placement on the table 2011 - 12. Journal of Physical Education and Sport, 13(3), 331–336. https://doi.org/10.7752/JPES.2013.03055
4. Brechot, M., ve Flepp, R. (2018). Dealing with randomness in match outcomes: How to rethink performance evaluation and decision-making in European club football. Social Science Research Network, https://doi.org/10.5167/UZH-174228
5. Carling, C., Reilly, T., & Williams, A. M. (2009). Performance assessment for field sports: Physiological and match notational assessment in practice. Routledge. https://doi.org/10.4324/9780203890691
6. Cintia, P., Giannotti, F., Pappalardo, L., Pedreschi, D., ve Malvaldi, M. (2015). The harsh rule of the goals: Data-driven performance indicators for football teams. IEEE International Conference on Data Science and Advanced Analytics, 1–10. https://doi.org/10.1109/DSAA.2015.7344823
7. E. Brynjolfsson, L.M. Hitt, H.H. Kim (2011). Strength in numbers: how does data-driven decision-making affect firm performance? (Working Paper: 1-33). Sloan School of Management, MIT. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1819486
8. Ersöz, G., & Gökmen, A. M. (2023). Spor Yönetiminde Dijital Dönüşüm. İnönü Üniversitesi Uluslararası Sosyal Bilimler Dergisi, 12(2), 398-420. https://doi.org/10.54282/inijoss.1368879

9. Fan, M., Chen, X., Liu, B., Zhou, F., Gong, B., & Tao, R. (2023). An analysis of financial risk assessment of globally listed football clubs. Heliyon, 9(11). https://doi.org/10.1016/j.heliyon.2023.e22886
10. Fontaine, T., Petiot, O., ve Kermarrec, G. (2024). The Role of Emotions in Intuitive Decision-Making: a Study Conducted with Expert Handball Caches. International Conference on Naturalistic Decision Making 2024, New Zealand, 1-9.
11. Holmbukt, R. A. (2024). The adoption of data-driven decision-making: A case study on influencing factors and social considerations in smaller Scandinavian companies (Yayımlanmış Yüksek Lisans Tezi). Copenhagen Business School, Kopenhag.
12. Islam, K. T., ve Nahid, M. H. (2024). Applications & implications of data-driven analytics in the football player valuation. In SHS Web of Conferences. 204, p04006. EDP Sciences. https://doi.org/10.1051/shsconf/202420404006
13. Junghagen, S. (2018). Football clubs as mediators in sponsor–stakeholder relations. Sport, Business and Management: An International Journal, 8(4), 370–386. https://doi.org/10.1108/SBM-02-2017-0007
14. Kalemba, N., & Campa, F. (2017). Managing sporting success and economic efficiency in the professional football: Identification of determinant factors through the academic literature. EAMR-Economic and Management Research, 3(2), 36–49. https://doi.org/10.26595/EAMR.2014.3.2.3
15. Karaman, T. (2023). Teknik Direktörlerin Futbolculuk Kariyerleri Üzerinden Kulüp İstikrar Düzeylerinin İncelenmesi. SPORMETRE Beden Eğitimi ve Spor Bilimleri Dergisi, 21(100. Yıl Özel Sayısı), 91-100. https://doi.org/10.33689/Spormetre.1285551
16. Karaman, T., ve Karagözoğlu, C. (2021). Türkiye Süper Ligi’nde Yer Alan Futbol Takımlarında Puan Almayı Etkileyen Faktörlerin Modellenmesi. International Journal of Sport Exercise and Training Sciences - IJSETS, 7(4), 171-181. https://doi.org/10.18826/useeabd.1000453
17. Kitman Labs. (2024, Ocak 2018). Manager recruitment: A data-driven approach. https://www.kitmanlabs.com/blog/manager-recruitment-a-data-driven-approach/
18. Li, Y., Ma, R., Gonçalves, B., Gong, B., Cui, Y., ve Shen, Y. (2020). Data-driven team ranking and match performance analysis in Chinese Football Super League. Chaos, Solitons & Fractals, 140, Article 110330. https://doi.org/10.1016/j.chaos.2020.110330
19. Lichtenthaler, U. (2020). Mixing data analytics with intuition: Liverpool Football Club scores with integrated intelligence. Journal of Business Strategy, 41(6), 27–34. https://doi.org/10.1108/JBS-06-2020-0144
20. Majumdar, A., Bakirov, R., Hodges, D., McCullagh, S., & Rees, T. (2024). A multi-season machine learning approach to examine the training load and injury relationship in professional soccer. Journal of Sports Analytics, 10(1), 47-65. https://doi.org/10.3233/JSA-240718
21. Manoli, A. E. (2014). The football industry through traditional management theories. Scandinavian Sport Studies Forum, 5, 93–109.
22. Mendes-Neves, T., Mendes-Moreira, J., ve Rossetti, R. J. F. (2021). A data-driven simulator for assessing decision-making in soccer. In Portuguese Conference on Artificial Intelligence, 698–710. Springer. https://doi.org/10.1007/978-3-030-86230-5_54
23. Meyer, J. (2024). On the Need to Understand Human Behavior to Do Analytics of Behavior. In Glückler, J., Panitz, R. (eds), Knowledge and Digital Technology: Knowledge and Space, 47-62. Springer. https://doi.org/10.1007/978-3-031-39101-9_3
24. Monsees, L. (2025). “There is a lot more potential” - Practitioner perspectives on technology and data-driven talent identification, selection, and development in a German Bundesliga academy. International Journal of Sports Science & Coaching. 20(2), 628-638. https://doi.org/10.1177/17479541241308519
25. Nappo, F., Lardo, A., Bianchi, M. T., ve Schimperna, F. (2023). The impact of digitalisation on professional football clubs. Management Control, 2, 75–95. https://doi.org/10.3280/maco2023-002006
26. Nowland, J., ve Sankara, J. (2024). New players? New managers? New stadiums? Which investments drive football club performance? Sport, Business and Management: An International Journal, 14(2), 134–150. https://doi.org/10.1108/sbm-10-2023-0124
27. Öner, İ., Karataş, Ö., & Öztürk Karataş, E. (2024). Futbol Kulüplerinde Finansal Sürdürülebilirlik. Mustafa Kemal Üniversitesi Eğitim Fakültesi Dergisi, 8(14), 289-304. https://doi.org/10.56677/mkuefder.1575336
28. Payyappalli, V. M., ve Zhuang, J. (2019). A data-driven integer programming model for soccer clubs’ decision making on player transfers. Environment Systems and Decisions, 39(4), 467–480. https://doi.org/10.1007/s10669-019-09721-7
29. Rahmati, K. (2023). Identifying and ranking key performance indicators in football clubs. International Journal of Innovation in Management Economics and Social Sciences, 3(2), 42–53. https://doi.org/10.59615/ijimes.3.2.42
30. Reuters. (2025, Nisan 2). LaLiga leads AI evolution with global outreach. https://www.reuters.com/sports/soccer/laliga-leads-ai-evolution-with-global-outreach-2025-04-02/
31. Romero, F. P., Lozano-Murcia, C., López-Gómez, J. A., Sanchez-Herrera, E. A., ve Sanchez-Lopez, E. (2021). A data-driven approach to predicting the most valuable player in a game. Computational and Mathematical Methods in Medicine, 2021, 3(4), 1-11. https://doi.org/10.1002/CMM4.1155
32. Rossi, A., Pappalardo, L., Cintia, P., Iaia, M. F., Fernández, J., ve Medina, D. (2017). Effective injury prediction in professional soccer with GPS data and machine learning. PLoS ONE 13(7):e0201264. https://doi.org/10.1371/journal.pone.0201264
33. Rüstemoğlu, R. H. (2009). Futbol sektöründe bir karar destek modeli uygulaması (Yayımlanmış Yüksek lisans tezi), İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği Anabilim Dalı, İstanbul.
34. Samur, S. (2017). The professional management of football in sports clubs. European Journal of Physical Education and Sport Science, 3(11), 294-304. https://doi.org/10.5281/zenodo.1059005
35. Shyrokostup, V. (2024). The role of data analysis in modern football: From statistics to strategy. Naukovij časopis Nacionalʹnogo pedagogìčnogo universitetu ìmenì M.P. Dragomanova, 5(178), 44–56. https://doi.org/10.31392/udu-nc.series15.2024.5(178).44
36. Singh, A. P., ve Suguna, M. (2023). Data-driven player recruitment in football. IEEE International Conference on Automation, Computing and Renewable Systems, 1–6. https://doi.org/10.1109/icacrs58579.2023.10404860
37. SportsDataCampus. (2024). Integrating big data into sports management. https://sportsdatacampus.com/integrating-big-data-into-sports-management/
38. Sulimov, D. (2024). Performance Insights-based AI-driven Football Transfer Fee Prediction. arXiv preprint arXiv:2401.16795. https://doi.org/10.48550/arXiv.2401.16795
39. Tanjung, F. S. (2024). Data-driven decisions: Leveraging analytics for strategic marketing management. Equator Journal of Management and Entrepreneurship, 12(4), 1–10. https://doi.org/10.26418/ejme.v12i4.81935
40. The Guardian. (2025, Nisan 11). Aston Villa in talks with UEFA over deal after breach of squad cost rules. https://www.theguardian.com/football/2025/apr/11/aston-villa-in-talks-with-uefa-over-deal-after-breach-of-squad-cost-rules
41. Varmus, M., Kubina, M., Miciak, M., Boško, P., ve Greguska, I. (2023). More sustainable sports organizations' operation as a result of fan involvement into the processes of decision-making and community building. Entrepreneurship and Sustainability Issues, 11(1), 1–15. https://doi.org/10.9770/jesi.2023.11.1(1)
42. Vinay Bhushan, G. L. A., ve Brojabasi, S. S. S. (2024). Data-driven decision-making: Leveraging analytics for performance improvement. Journal of Informatics Education and Research, 4(3), 129–142. https://doi.org/10.52783/jier.v4i3.1298
43. Watanabe, N. M., Shapiro, S. L., ve Drayer, J. (2021). Big data and analytics in sport management. Journal of Sport Management, 35(3), 193–197. https://doi.org/10.1123/JSM.2021-0067
44. Xue, Y., Du, E., ve Hou, Z. (2025). Sports training injuries and prevention measures using big data analysis. Molecular & Cellular Biomechanics, 22(1). https://doi.org/10.62617/mcb539