Skip to main content
Have a personal or library account? Click to login
Data-Driven Scouting in Football: A Multiple Case Study Cover
By:  and    
Open Access
|Aug 2026

References

  1. Abate Daga, F., Veglio, F., Cherasco, G. M., & Agostino, S. (2023). The Influence of Subjective Perceptions and the Efficacy of Objective Evaluation in Soccer School Players' Classification: A Cross-Sectional Study. Children, 10(5), 767. https://doi.org/10.3390/children10050767
  2. Anzer, G., Bauer, P., & Brefeld, U. (2021). The origins of goals in the German Bundesliga. Journal of Sports Sciences, 39(22), 2525–2544. https://doi.org/10.1080/02640414.2021.1943981
  3. Ball, J., Huynh, M., & Varley, M. C. (2025). Comparing player rating systems as a metric for assessing individual performance in soccer. Journal of Sports Sciences, 43(7), 676–686. https://doi.org/10.1080/02640414.2025.2471208
  4. Barraclough, S., Till, K., Kerr, A., & Emmonds, S. (2025). An examination of signs, samples and subjective expert opinion as predictors of (de)selection in a youth male soccer academy in the UK. Journal of Sports Sciences, 1–11. https://doi.org/10.1080/02640414.2025.2490882
  5. Barron, D., Ball, G., Robins, M., & Sunderland, C. (2018). Artificial neural networks and player recruitment in professional soccer. PLOS ONE, 13(10), e0205818. https://doi.org/10.1371/journal.pone.0205818
  6. Beiderbeck, D., Evans, N., Frevel, N., & Schmidt, S. L. (2023). The impact of technology on the future of football – A global Delphi study. Technological Forecasting and Social Change, 187, 122186. https://doi.org/10.1016/j.techfore.2022.122186
  7. Cameron, S., Radwan, I., & Mara, J. (2025). What the Eyes Don't See: An Objective Assessment of Players' Contribution to Team Success in Men's Rugby League. Research Quarterly for Exercise and Sport, 96(1), 116–125. https://doi.org/10.1080/02701367.2024.2373124
  8. Cortez, A., Trigo, A., & Loureiro, N. (2021). Predicting Physiological Variables of Players that Make a Winning Football Team: A Machine Learning Approach. In Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Chiara Garau, Ivan Blečić, David Taniar, Bernady O. Apduhan, Ana Maria A. C. Rocha, Eufemia Tarantino, Carmelo Maria Torre (Ed.), Lecture Notes in Computer Science (pp. 3–15). Springer International Publishing. https://doi.org/10.1007/978-3-030-86970-0_1
  9. Cortez, A., Trigo, A., & Loureiro, N. (2022). Football match line-up prediction based on physiological variables: A machine learning approach. Computers, 11(3), 40. https://www.mdpi.com/2073-431X/11/3/40
  10. Creswell, J. W., & Poth, C. N. (2016). Qualitative inquiry and research design: Choosing among five approaches. Sage publications. https://books.google.com/books?hl=pt-PT&lr=&id=DLbBDQAAQBAJ&oi=fnd&pg=PP1&dq=Qualitative+Inquiry+and+Research+Design:+Choosing+Among+Five+Approaches.+Sage.&ots=-iu12cKUPt&sig=RoKB0l21SNbvr73-fEkqkHPhlaU
  11. Datta, M., & Rudra, B. (2022). An Intelligent Decision Support System for Bid Prediction of Undervalued Football Players. 2022 2nd International Conference on Intelligent Technologies (CONIT), 1–8. https://doi.org/10.1109/CONIT55038.2022.9847972
  12. Dubois, L.-E., & Walzak, L. (2025). Blind scouting: Using artificial intelligence to alleviate bias in selection. Personnel Review, 54(4), 953–970. https://doi.org/10.1108/PR-02-2024-0130
  13. Eisenhardt, K. M. (1989). Building Theories from Case Study Research. The Academy of Management Review, 14(4), 532. https://doi.org/10.2307/258557
  14. Frost, W., Groom, R., & Nicholls, S. B. (2025). The use of performance analysis and data driven approaches within senior men's football recruitment. International Journal of Sports Science & Coaching, 20(2), 604–616. https://doi.org/10.1177/17479541251315948
  15. Furley, P., Mehta, S., Raabe, D., & Memmert, D. (2025). Objectivity of match analysis in football: Testing the level of agreement between coaches' interpretations of video data. International Journal of Sports Science & Coaching, 20(1), 45–55. https://doi.org/10.1177/17479541241278603
  16. Gamble, P., Chia, L., & Allen, S. (2020). The illogic of being data-driven: Reasserting control and restoring balance in our relationship with data and technology in football. Science and Medicine in Football, 4(4), 338–341. https://doi.org/10.1080/24733938.2020.1854842
  17. Goes, F. R., Kempe, M., Meerhoff, L. A., & Lemmink, K. A. P. M. (2019). Not Every Pass Can Be an Assist: A Data-Driven Model to Measure Pass Effectiveness in Professional Soccer Matches. Big Data, 7(1), 57–70. https://doi.org/10.1089/big.2018.0067
  18. Harkness-Armstrong, A., Seth, L., Lowry, R., & Datson, N. (2025). Applied performance analysis practices in women's football. International Journal of Sports Science & Coaching, 20(2), 568–584. https://doi.org/10.1177/17479541241301936
  19. Hassani, M., Shang, W., Shihab, E., & Tsantalis, N. (2018). Studying and detecting log-related issues. Empirical Software Engineering, 23(6), 3248–3280. https://doi.org/10.1007/s10664-018-9603-z
  20. Kelly, A. L., Williams, C. A., Cook, R., Sáiz, S. L. J., & Wilson, M. R. (2022). A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach. Sports, 10(10), 159. https://doi.org/10.3390/sports10100159
  21. Kvale, S., & Brinkmann, S. (2009). InterViews: Learning the Craft of Qualitative Research Interviewing. SAGE.
  22. Malikov, D., & Kim, J. (2024). Beyond xG: A Dual Prediction Model for Analyzing Player Performance Through Expected and Actual Goals in European Soccer Leagues. Applied Sciences, 14(22), 10390. https://doi.org/10.3390/app142210390
  23. Monsees, L. M. (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
  24. Pappalardo, L., Cintia, P., Ferragina, P., Massucco, E., Pedreschi, D., & Giannotti, F. (2019). PlayeRank: Data-driven Performance Evaluation and Player Ranking in Soccer via a Machine Learning Approach. ACM Transactions on Intelligent Systems and Technology, 10(5), 1–27. https://doi.org/10.1145/3343172
  25. Rashid, Y., Rashid, A., Warraich, M. A., Sabir, S. S., & Waseem, A. (2019). Case Study Method: A Step-by-Step Guide for Business Researchers. International Journal of Qualitative Methods, 18, 1609406919862424. https://doi.org/10.1177/1609406919862424
  26. Rinaldi, A. M., Romano, A., Russo, C., & Tommasino, C. (2024). FPSRec: Football Players Scouting Recommendation System based on Generative AI. 2024 IEEE International Conference on Big Data (BigData), 7141–7150. https://doi.org/10.1109/BigData62323.2024.10825692
  27. Ruiz-de-Alarcón-Quintero, A., & De-la-Cruz-Torres, B. (2024). An Expected Goals on Target (xGOT) Metric as a New Metric for Analyzing Elite Soccer Player Performance. Data, 9(9), 102. https://doi.org/10.3390/data9090102
  28. Sarmento, H., Anguera, M. T., Pereira, A., & Araújo, D. (2018). Talent Identification and Development in Male Football: A Systematic Review. Sports Medicine, 48(4), 907–931. https://doi.org/10.1007/s40279-017-0851-7
  29. Shen, Q. (2025). Predicting the value of football players: Machine learning techniques and sensitivity analysis based on FIFA and real-world statistical datasets. Applied Intelligence, 55(4), 265. https://doi.org/10.1007/s10489-024-06189-0
  30. Singh, A. P., & Suguna, M. (2023). Data-Driven Player Recruitment in Football. 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), 844–850. https://doi.org/10.1109/ICACRS58579.2023.10404860
  31. Stake, R. E. (2013). Multiple case study analysis. Guilford press. https://books.google.com/books?hl=pt-PT&lr=&id=rQWT5aDHiZYC&oi=fnd&pg=PT26&dq=Stake,+R.+E.+(2006).+Multiple+case+study+analysis.+Guilford+Press.&ots=IIkQBAIsxk&sig=_rFqL4OCjqsggN33qAG-10Lg_GQ
  32. Timmermans, S., & Tavory, I. (2012). Theory Construction in Qualitative Research: From Grounded Theory to Abductive Analysis. Sociological Theory, 30(3), 167–186. https://doi.org/10.1177/0735275112457914
  33. Tisdell, E. J., Merriam, S. B., & Stuckey-Peyrot, H. L. (2025). Qualitative research: A guide to design and implementation. John Wiley & Sons.
  34. Velema, T. A., Wen, H.-Y., & Zhou, Y.-K. (2020). Global value added chains and the recruitment activities of European professional football teams. International Review for the Sociology of Sport, 55(2), 127–146. https://doi.org/10.1177/1012690218796771
  35. Wieland, V. L. S., Uysal, D., Probst, P., Grilli, M., Haney, C. M., Sidoti Abate, M. A., Egen, L., Neuberger, M., Cacciamani, G. E., Kriegmair, M. C., Michel, M. S., & Kowalewski, K.-F. (2023). Framework for a living systematic review and meta-analysis for the surgical treatment of bladder cancer: Introducing EVIglance to urology. International Journal of Surgery Protocols, 27(2), 97–103. https://doi.org/10.1097/SP9.0000000000000008
  36. Wilson, R. S., Hunter, A. H., Camata, T. V., Foster, C. S. P., Sparkes, G. R., Moura, F. A., Santiago, P. R. P., & Smith, N. M. A. (2021). Simple and reliable protocol for identifying talented junior players in team sports using small-sided games. Scandinavian Journal of Medicine & Science in Sports, 31(8), 1647–1656. https://doi.org/10.1111/sms.13969
  37. Yin, R. K. (2018). Case study research and applications (Vol. 6). Sage Thousand Oaks, CA. https://www.academia.edu/download/106905310/Artikel_Yustinus_Calvin_Gai_Mali.pdf
  38. Yunus, M., & Aditya, R. S. (2024). Talent scouting and standardizing fitness data in football club: Systematic review. Retos, 62, 1382–1389. https://doi.org/10.47197/retos.v62.107767
Language: English
Page range: 69 - 87
Published on: Aug 7, 2026
Published by: International Association of Computer Science in Sport
In partnership with: Paradigm Publishing Services

© 2026 João Malanho, António Trigo, published by International Association of Computer Science in Sport
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.