Evaluating the Performance of Football Clubs Using Digital Metrics

Section: Physical Education and Sport Sciences

Abstract

The paper evaluates the performance of football clubs according to the online measures, with special attention given to the association between the degree of digital activity, the attitude of fans, and financial health. The traditional football clubs have been long assessed with the help of the traditional performance indicators, such as the match outcome and the statistics of the players. However, the increasing role of social media and online interaction is also to be taken into consideration, and more precise assessment should be delivered with the help of digital indicators. The paper will look at the connection between online indicators e.g. social media presence, sentiment analysis and fan relations and the traditional performance indicators and financial success. The data collection was conducted using various Internet sources, including social media, official club websites and databases of fans in a year. Statistical software and correlation analysis as well as regression models were used to analyse the data. The results demonstrate that there are strong positive correlations between online measures (social media activity and sentiment) and fan loyalty as well as financial performance. There is more online engagement in clubs which have a greater fan loyalty and financial success. The paper reveals the significance of using online measures in the traditional performance evaluation paradigms to offer a more comprehensive picture of success of a club. The study can be of great importance to football clubs aiming to improve their digital engagement measures and their on-field and off-field performance.

References

  1. References
  2. Aichner, T. (2019). Football clubs’ social media use and user engagement. Marketing Intelligence & Planning, [online] 37(3), pp.242–257. doi:https://doi.org/10.1108/mip-05-2018-0155.
  3. Al-Hosaini, F.F., Basel, J.A., Baadhem, A.M., Jawabreh, O. and Anas (2023). The Impact of the Balanced Scorecard (BSC) Non-Financial Perspectives on the Financial Performance of Private Universities. [online] 12(9), pp.2903–2913. Available at: https://www.researchgate.net/publication/374083090_The_Impact_of_the_Balanced_Scorecard_BSC_Non-Financial_Perspectives_on_the_Financial_Performance_of_Private_Universities.
  4. Andri Agape Banjar Nahor and Muslimin Muslimin (2025). The Influence of Club Image, Sponsorship, and Fan Loyalty on Football Merchandise Purchase Intention. Green Inflation International Journal of Management and Strategic Business Leadership, [online] 2(3), pp.109–119. doi:https://doi.org/10.61132/greeninflation.v2i3.505.
  5. Bhattacharjee, B., Bhattacharjee, D. and Bhattacharjee, B. (2023). Performance Assessment of the Machine Learning Algorithms for First Inning Score Prediction in Cricket. Research Square (Research Square). doi:https://doi.org/10.21203/rs.3.rs-3385887/v1.
  6. Cai, J. (2024). Comprehensive Analysis of Football Player Market Valuation: Integrating Performance Metrics and Marketability Factors. Transactions on Economics, Business and Management Research, 10, pp.152–158. doi:https://doi.org/10.62051/2rybtf79.
  7. Chang, V., Sreeram Sajeev, Qianwen Ariel Xu, Tan, M. and Wang, H. (2024). Football Analytics: Assessing the Correlation between Workload, Injury and Performance of Football Players in the English Premier League. Applied Sciences, 14(16), pp.7217–7217. doi:https://doi.org/10.3390/app14167217.
  8. Chang, V., Sreeram Sajeev, Qianwen Ariel Xu, Tan, M. and Wang, H. (2024). Football Analytics: Assessing the Correlation between Workload, Injury and Performance of Football Players in the English Premier League. Applied Sciences, 14(16), pp.7217–7217. doi:https://doi.org/10.3390/app14167217.
  9. Chang, V., Sreeram Sajeev, Qianwen Ariel Xu, Tan, M. and Wang, H. (2024). Football Analytics: Assessing the Correlation between Workload, Injury and Performance of Football Players in the English Premier League. Applied Sciences, 14(16), pp.7217–7217. doi:https://doi.org/10.3390/app14167217.
  10. Corsaro, S., Ioio, G.D. and Marino, Z. (2025). The evaluation of football players: an in-depth look at the Expected Goal metric. Annals of Operations Research. doi:https://doi.org/10.1007/s10479-025-06606-8.
  11. Davis, J., Lotte Bransen, Devos, L., Jaspers, A., Meert, W., Pieter Robberechts, Jan Van Haaren and Maaike Van Roy (2024). Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned. Machine Learning, 113. doi:https://doi.org/10.1007/s10994-024-06585-0.
  12. De-la-Cruz-Torres, B., Navarro-Castro, M. and Ruiz-de-Alarcón-Quintero, A. (2025). An Expected Goals On Target (xGOT) Model: Accounting for Goalkeeper Performance in Football. Big Data and Cognitive Computing, 9(3), p.64. doi:https://doi.org/10.3390/bdcc9030064.
  13. Elsharkawi, M., Ali, R.H. and Khan, T.A. (2025). Crafting a Player Impact Metric through analysis of football match event data. Journal of Computational Mathematics and Data Science, 15, p.100115. doi:https://doi.org/10.1016/j.jcmds.2025.100115.
  14. Fan, M., Chen, X., Liu, B., Zhou, F., Gong, B. and Tao, R. (2023). An analysis of financial risk assessment of globally listed football clubs. Heliyon, [online] 9(12), p.e22886. doi:https://doi.org/10.1016/j.heliyon.2023.e22886.
  15. Galagedera, D. and Tan, J. (2024). Assessing Overall Performance of Sports Clubs and Decomposing into Their On-Field and Off-Field Efficiency. Mathematics, 12(22), pp.3554–3554. doi:https://doi.org/10.3390/math12223554.
  16. Galagedera, D. and Tan, J. (2024). Assessing Overall Performance of Sports Clubs and Decomposing into Their On-Field and Off-Field Efficiency. Mathematics, 12(22), pp.3554–3554. doi:https://doi.org/10.3390/math12223554.
  17. Girsang, Z. (2022). The Impact of Social Media Marketing on Football – Fan Loyalty. Quality in Sport, [online] 7(3), pp.28–39. doi:https://doi.org/10.12775/qs.2021.07.03.016.
  18. Gong, B., Cui, Y., Gai, Y., Yi, Q. and Gómez, M.-Á. (2019). The Validity and Reliability of Live Football Match Statistics From Champdas Master Match Analysis System. Frontiers in Psychology, 10. doi:https://doi.org/10.3389/fpsyg.2019.01339.
  19. Gregory, S., Robertson, S., Aughey, R., Spencer, B. and Alexander, J. (2024). Assigning goal-probability value to high intensity runs in football. PLoS ONE, [online] 19(9), pp.e0308749–e0308749. doi:https://doi.org/10.1371/journal.pone.0308749.
  20. Guo, S. and Yu, L. (2024). Statistical Modeling Analysis of the Relationship between Player Performance and Winning and Losing in Sporting Football Games. Applied Mathematics and Nonlinear Sciences, 9(1). doi:https://doi.org/10.2478/amns-2024-3437.
  21. Guzmán-Raja, I. and Guzmán-Raja, M. (2021). Measuring the Efficiency of Football Clubs Using Data Envelopment Analysis: Empirical Evidence From Spanish Professional Football. SAGE Open, 11(1), p.215824402198925. doi:https://doi.org/10.1177/2158244021989257.
  22. Hamdi, K., Mohamed Amine, N. and Hassan, G. (2024). European football club market value and sporting performance: the moderating effect of player transfers, fans engagement and coaching changes. Managerial Finance. doi:https://doi.org/10.1108/mf-05-2024-0363.
  23. Hamdi, K., Mohamed Amine, N. and Hassan, G. (2024). European football club market value and sporting performance: the moderating effect of player transfers, fans engagement and coaching changes. Managerial Finance. doi:https://doi.org/10.1108/mf-05-2024-0363.
  24. Hill, D.F., Skinner, J. and Grosman, A. (2025). A review of football player metrics and valuation methods: a typological framework of football player valuations. Managing Sport and Leisure, pp.1–24. doi:https://doi.org/10.1080/23750472.2025.2459727.
  25. Huettermann, M. (2021). Fan engagement in professional team sports. [online] Available at: https://www.researchgate.net/publication/357396428.
  26. Hussain, G., Naz, T., Shahzad, N. and Javed Bajwa, M. (2021). Social Media Marketing in Sports and using social media platforms for sports fan engagement. Journal of Contemporary Issues in Business and Government, [online] 27(06), p.2021. doi:https://doi.org/10.47750/cibg.2021.27.06.117.
  27. Islam, Md.S., Muhammad Nomani Kabir, Ngahzaifa Ab Ghani, Kamal Zuhairi Zamli, Nor, Md. Mustafizur Rahman and Mohammad Ali Moni (2024). ‘Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach’. Artificial Intelligence Review, 57(3). doi:https://doi.org/10.1007/s10462-023-10651-9.
  28. Kong, Y. (2025). Football Player Value Prediction: Comparing Machine Learning Models with Cross-Validation. Highlights in Science, Engineering and Technology, 128, pp.146–154. doi:https://doi.org/10.54097/5bsf6020.
  29. Liu, Y. and He, J. (2021). ‘Why Are You Running Away From Social Media?’ Analysis of the Factors Influencing Social Media Fatigue: An Empirical Data Study Based on Chinese Youth. Frontiers in Psychology, 12. doi:https://doi.org/10.3389/fpsyg.2021.674641.
  30. Lou, L., Jiao, Y. and Koh, J. (2021). Determinants of Fan Engagement in Social Media-Based Brand Communities: A Brand Relationship Quality Perspective. Sustainability, 13(11), p.6117. doi:https://doi.org/10.3390/su13116117.
  31. M. Merzah, B., Sadik, M. and Noori, A. (2025). Intelligent Scheme for Footballer Performance Evaluation Using Deep-Learning Models. International Journal of Computing and Digital Systems, 17(1), pp.1–16. doi:https://doi.org/10.12785/ijcds/1571111341.
  32. Madsen, D.O. (2025). Balanced Scorecard: History, Implementation, and Impact. Encyclopedia, [online] 5(1), p.39. doi:https://doi.org/10.3390/encyclopedia5010039.
  33. Milewski, D. and Wiśniewski, T. (2022). Regression analysis as an alternative method of determining the Economic Order Quantity and Reorder Point. Heliyon, 8(9), p.e10643. doi:https://doi.org/10.1016/j.heliyon.2022.e10643.
  34. Moreno, A. (2023). Descriptive statistics: organizing, summarizing, describing, and presenting data. [online] ResearchGate. doi:https://doi.org/10.13140/RG.2.2.31782.91203.
  35. Păvăloaia, V.-D., Teodor, E.-M., Fotache, D. and Danileţ, M. (2019). Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences. Sustainability, [online] 11(16), p.4459. doi:https://doi.org/10.3390/su11164459.
  36. Prieto-González, P., Víctor Martín, Pacholek, M., Sal-de-Rellán, A. and Marcelino, R. (2024). Impact of offensive team variables on goal scoring in the first division of the spanish soccer league: a comprehensive 10-year study. Scientific Reports, [online] 14(1). doi:https://doi.org/10.1038/s41598-024-77199-8.
  37. Shankara V, Ahmed, S., Sneha M and Guruprakash J (2024). Object Detection and Tracking for Football Data Analytics. doi:https://doi.org/10.4108/eai.23-11-2023.2343216.
  38. Sharabati, A.-A.A., Ali, A., Allahham, M.I., Hussein, A.A., Alheet, A.F. and Mohammad, A.S. (2024). The Impact of Digital Marketing on the Performance of SMEs: An Analytical Study in Light of Modern Digital Transformations. Sustainability, [online] 16(19). doi:https://doi.org/10.3390/su16198667.
  39. Siemen Kampen-Schmidt, Joerg Koenigstorfer, Uhrich, S. and Berendt, J. (2025). How differences between a sport club’s public portrayal of fan centricity and fans’ perceptions relate to fan engagement. European Sport Management Quarterly, pp.1–21. doi:https://doi.org/10.1080/16184742.2025.2525870.
  40. Stafylidis, A., Konstantinos Chatzinikolaou, Athanasios Mandroukas, Charalampos Stafylidis, Yiannis Michailidis and Metaxas, T.I. (2025). First to Score, First to Win? Comparing Match Outcomes and Developing a Predictive Model of Success Using Performance Metrics at the FIFA Club World Cup 2025. Applied Sciences, [online] 15(15), pp.8471–8471. doi:https://doi.org/10.3390/app15158471.
  41. Wang, J. and Zhai, Y. (2025). Association Between Substitutions and Match Running Performance Under Five-Substitution Rule: Evidence from the 2022 FIFA World Cup. Applied Sciences, [online] 15(17), p.9540. doi:https://doi.org/10.3390/app15179540.
  42. Wang, Y. (2024). Leveraging SNS Data for E-Sports Recommendation: Analyzing Popularity and User Satisfaction Metrics. Electronics, 14(1), p.94. doi:https://doi.org/10.3390/electronics14010094.
  43. Watanabe, W.C., Shafiq, M., Nawaz, M.J., Saleem, I. and Nazeer, S. (2024). The Impact of Emotional Intelligence on Project success: Mediating Role of Team Cohesiveness and Moderating Role of Organizational Culture. International Journal of Engineering Business Management, 16(1), pp.1–14.
  44. Zaki Ahmed, A. and Rodríguez-Díaz, M. (2020). Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines. Sustainability, 12(20), p.8683. doi:https://doi.org/10.3390/su12208683.
  45. Zare, Z., Annur Islam Sifat and Zadeh, A. (2025). Leveraging AI for sports fan engagement: Comparing traditional and transformer-based models. Intelligent Decision Technologies. doi:https://doi.org/10.1177/18724981251364377.
Download this PDF file

Statistics

How to Cite

Evaluating the Performance of Football Clubs Using Digital Metrics. (2026). Al-Rafidain Journal For Sport Sciences, 29(90), 63-81. https://doi.org/10.33899/rjss.v29i90.49630
Copyright and Licensing

How to Cite

Evaluating the Performance of Football Clubs Using Digital Metrics. (2026). Al-Rafidain Journal For Sport Sciences, 29(90), 63-81. https://doi.org/10.33899/rjss.v29i90.49630