A significant European study has identified a systematic discount applied to football players with higher predicted injury risk, indicating that medical vulnerability translates into financial penalties within the transfer market.
Study Overview
Researchers investigated the impact of future injury risk on the market value of male professional football players. Findings show that a 1% increase in the risk of serious injury correlated with approximately a 2.29% reduction in a player's market value.
A 1% increase in the risk of serious injury correlated with approximately a 2.29% reduction in a player's market value.
Recurrent and severe injuries led to even larger valuation penalties, according to the study's econometric modeling assumptions.
Background: Players as Financial Assets
Football clubs consider players their most valuable intangible assets, investing substantially in training, salaries, and transfer fees. Injuries threaten these investments through medical costs, reduced performance, lost playing time, and decreased market value. Severe injuries can shorten careers and limit transfer opportunities, affecting clubs' financial stability and competitive success.
While medical research has focused on injury causes and prevention, less attention has been paid to how anticipated injury risk influences player valuation, which has implications for club financial planning.
Methodology
The study employed a two-stage approach: first, estimating a player’s probability of injury, and then evaluating how this predicted risk affects market value. A dataset of 5,336 player-year observations from seven major European leagues between 2006 and 2020 was used.
Stage One: Predicting Injury Risk
A logistic regression model estimated each player’s risk of future severe injury in a given season. Injury risk was predicted based on:
- Number of games missed in the previous season, categorized by severity (no, moderate, severe, highly severe).
- Age and age squared.
- Height, playing position, and footedness.
- League and year fixed effects.
Two injury models were estimated: one for severe injuries (more than five games missed) and another incorporating both severity and recurrence (multiple injuries in the same season).
Stage Two: Linking Injury Risk to Market Value
The predicted injury probability was introduced into a dynamic log-linear panel model to explain players’ market values. A System Generalized Method of Moments (System-GMM) estimator was utilized to address endogeneity, autocorrelation, and the dynamic nature of market values. Performance variables (goals, assists, cards, substitutions) were considered endogenous and instrumented. Lagged market value was included to control for unobserved player quality.
Key Findings
Injury History and Future Risk
- A strong link was observed between previous injuries and future injury risk.
- Players who missed more than 10 games in the previous season had a significantly higher probability of suffering a new severe injury.
- Highly severe past injuries nearly doubled the likelihood of future recurrent and severe injuries when recurrence was considered.
- Age displayed a non-linear relationship with injury risk, increasing early in careers, stabilizing during peak performance ages, and declining later.
Injury Risk and Market Valuation
- A 1% increase in the predicted probability of severe injury was associated with a 2.29% decrease in market value, after accounting for past valuation and performance history.
- When injury recurrence was included, the negative effect on market value became larger.
- These effects remained robust after controlling for various factors and were most pronounced among mid-tier players.
Conclusions and Implications
The findings confirm that markets penalize injury risk, particularly when injuries are severe or recurrent. The study provides evidence that injury risk significantly reduces the market value of football players, classifying injuries as a key financial and strategic risk for clubs.
Strengths and Limitations
- Strengths: The two-stage modeling approach separated injury prediction from valuation effects and addressed endogeneity. The dataset allowed for frequent and consistent valuation updates.
- Limitations: Market values were crowd-sourced estimates, which may not fully reflect actual transfer fees. Certain medical, training load, and psychological risk factors were not included due to data constraints.
Despite these limitations, the findings have implications for transfer negotiations, wage setting, insurance policies, and financial fair play regulation. Quantifying injury risk offers clubs a tool for improving strategic, financial, and sporting decision-making.