Injury Type and Recurrence as Predictors of Market Value Decline in Professional Football: A Retrospective Cohort Study of 12,948 Injuries
Abstract
Background: Professional football clubs spend billions on player transfers, yet a single injury can substantially reduce a player’s market value. While previous research has established that injuries lower value, no study has systematically compared value penalties across specific injury types or quantified the additional cost of injury recurrence.
Methods: A retrospective cohort analysis was conducted using 12,948 injury events from 2,061 professional footballers across top‑five European leagues (Premier League, La Liga, Serie A, Bundesliga, and Ligue 1) between 2008 and 2024. Mixed‑effects linear regression, Random Forest, and non‑parametric hypothesis tests (Kruskal–Wallis, Mann–Whitney U) were employed to examine the effects of injury type and recurrence on percentage change in market value, controlling for age, pre‑injury value, and position.
Results: Anterior cruciate ligament (ACL) injuries reduced market value by 6.0%, and Achilles injuries by 15.5%. Ankle injuries increased value by 14.0%, fractures by 11.0%, concussions by 10.5%, and knee injuries by 7.0%. Hamstring and muscle injuries showed no median change within the 120-day observation window. Recurrent injuries incurred an additional 8.0% penalty beyond first‑time injuries. Players under 24 gained 39.0% following injury, while players aged 30 and over lost 31.0% – a difference of 70.0 percentage points. Higher pre‑injury market value and older age were the strongest negative predictors of post‑injury valuation.
Conclusion: ACL and Achilles injuries are uniquely destructive to player market value. Most other injury types show no lasting penalty. Recurrence imposes a substantial additional penalty. Younger players recover far better than older ones. Clubs, agents, and insurers should incorporate injury type, recurrence history, and age into transfer negotiations, contract design, and player health strategies.
Keywords: Football injuries, market value, ACL, recurrence, sports economics
References
Ajjawi, O. (2025, August 20). The Premier League’s hidden drain: How injuries cost millions. Off The Pitch. https://www.offthepitch.com
Ardern, C. L., Taylor, N. F., Feller, J. A., & Webster, K. E. (2014). Fifty−five per cent return to competitive sport following anterior cruciate ligament reconstruction surgery: An updated systematic review and meta−analysis including aspects of physical functioning and contextual factors. British Journal of Sports Medicine, 48(21), 1543–1552. https://doi.org/10.1136/bjsports−2013−093398 DOI: https://doi.org/10.1136/bjsports-2013-093398
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed−effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 DOI: https://doi.org/10.18637/jss.v067.i01
Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. University of Chicago Press.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. DOI: https://doi.org/10.1023/A:1010933404324
Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5), 1287–1294. https://doi.org/10.2307/1911963 DOI: https://doi.org/10.2307/1911963
Bryson, A., Frick, B., & Simmons, R. (2013). The returns to scarce talent: Footedness and player remuneration in European soccer. Journal of Sports Economics, 14(6), 606–628. https://doi.org/10.1177/1527002511435118 DOI: https://doi.org/10.1177/1527002511435118
Carling, C., McCall, A., Le Gall, F., & Dupont, G. (2018). Injury risk and patterns in newly transferred football players: A case study of 8 seasons from a professional football club. Science and Medicine in Football, 2(1), 47–50. https://doi.org/10.1080/24733938.2017.1370123 DOI: https://doi.org/10.1080/24733938.2017.1370123
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
den Hollander, S., Kerkhoffs, G., & Gouttebarge, V. (2024). The impact of match workload and international travel on injuries in professional men’s football. Sports, 12(8), Article 212. https://doi.org/10.3390/sports12080212 DOI: https://doi.org/10.3390/sports12080212
Ekstrand, J., Waldén, M., & Hägglund, M. (2016). Hamstring injuries have increased by 4% annually in men’s professional football, since 2001: A 13-year longitudinal analysis of the UEFA Elite Club injury study. British Journal of Sports Medicine, 50(12), 731–737. https://doi.org/10.1136/bjsports-2015-095359 DOI: https://doi.org/10.1136/bjsports-2015-095359
Eliakim, E., Morgulev, E., Lidor, R., & Meckel, Y. (2020). Estimation of injury costs: Financial damage of English Premier League teams’ underachievement due to injuries. BMJ Open Sport & Exercise Medicine, 6(1), Article e000765. https://doi.org/10.1136/bmjsem−2019−000675 DOI: https://doi.org/10.1136/bmjsem-2019-000675
FIFA. (2025). Global transfer report 2024. https://digitalhub.fifa.com/m/142077cfbb75c2b0/original/Global−Transfer−Report−2024.pdf
Gümüş, H. (2026). Football player injuries and market value [Dataset]. Kaggle. https://www.kaggle.com/datasets/gumush/football−player−injuries−market−value/data
Herm, S., Callsen-Bracker, H.-M., & Kreis, H. (2014). When the crowd evaluates soccer players’ market values: Accuracy and evaluation attributes of an online community. Sport Management Review, 17(4), 484–492. https://doi.org/10.1016/j.smr.2013.12.006 DOI: https://doi.org/10.1016/j.smr.2013.12.006
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. Springer. DOI: https://doi.org/10.1007/978-1-4614-7138-7
Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives, 15(4), 143–156. DOI: https://doi.org/10.1257/jep.15.4.143
Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R² from generalized linear mixed−effects models. Methods in Ecology and Evolution, 4(2), 133–142. https://doi.org/10.1111/j.2041−210x.2012.00261.x DOI: https://doi.org/10.1111/j.2041-210x.2012.00261.x
Poli, R., Ravenel, L., & Besson, R. (2020). Scientific evaluation of the transfer value of football players. CIES Football Observatory Monthly Report, (53), 1–5. https://libra.unine.ch/handle/20.500.14713/29544
Rosen, S., & Sanderson, A. (2001). Labour markets in professional sports. The Economic Journal, 111(469), F47–F68. https://www.jstor.org/stable/2667957 DOI: https://doi.org/10.1111/1468-0297.00598
Rubio Martín, G., González Sánchez, F. J., & Manuel García, C. M. (2025). The financial dimension of football injuries according to the wisdom of crowds. Managing Sport and Leisure, 30(5), 986–997. https://doi.org/10.1080/23750472.2024.2342262 DOI: https://doi.org/10.1080/23750472.2024.2342262
Sedgwick, P. (2014). Retrospective cohort studies: Advantages and disadvantages. BMJ, 348, Article g1072. https://doi.org/10.1136/bmj.g1072 DOI: https://doi.org/10.1136/bmj.g1072
Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591–611. https://doi.org/10.2307/2333709 DOI: https://doi.org/10.1093/biomet/52.3-4.591
van Buuren, S. (2018). Flexible imputation of missing data (2nd ed.). CRC Press. DOI: https://doi.org/10.1201/9780429492259
World Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA, 310(20), 2191–2194. https://doi.org/10.1001/jama.2013.281053 DOI: https://doi.org/10.1001/jama.2013.281053
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