Why predicted injury risk is quietly dragging down football transfer values

By Priyanjana Pramanik, MSc.Reviewed by Susha Cheriyedath, M.Sc.Jan 28 2026

A large European study shows that clubs systematically discount players with higher predicted injury risk, revealing how medical vulnerability translates into real financial penalties on the transfer market.

Study: The effect of injury risk on players value: evidence from the main European Leagues. Image Credit: Dan Tautan / Shutterstock

In a recent study published in Humanities and Social Sciences Communications, researchers investigated how future injury risk affects the market value of male professional football players.

Their findings indicate that as the risk of serious injury increased by 1%, there was a corresponding reduction in a player’s market value of approximately 2.29%, with recurrent and severe injuries leading to even larger valuation penalties, conditional on the study’s econometric modelling assumptions.

Background: Players as Financial Assets

For football clubs, players are their most valuable intangible assets. Clubs invest heavily in players through training, salaries, and transfer fees. These investments are threatened when players are injured, not only because of medical costs but also due to reduced performance, lost playing time, and diminished market value.

Severe injuries can shorten careers and limit future transfer opportunities, directly affecting clubs’ financial stability and competitive success. While medical research has extensively studied injury causes and prevention, far less attention has been paid to how anticipated injury risk, rather than injuries already sustained, influences player valuation.

Estimates of this effect have important implications for club financial planning and decision-making.

Study Motivation and Conceptual Approach

Existing economic studies mostly focus on wages, appearances, or days missed rather than on forward-looking injury probability itself. To address this gap, the authors proposed a two-stage approach: first, estimate a player’s probability of injury, and then evaluate how this predicted risk affects market value.

They used a dataset of player valuation data between 2006 and 2020 to provide a more precise, dynamic, and forward-looking assessment for use in financial planning, insurance decisions, contracts, and transfers.

Data and Sample Characteristics

The two-stage empirical strategy was applied to an unbalanced panel of 5,336 player-year observations from seven major European leagues spanning 2006-2020.

Stage One: Predicting Injury Risk

In the first stage, a logistic regression model was used to estimate each player’s risk of future severe injury in a given season.

Injury risk was predicted based on the number of games players had missed in the previous season, classified by severity (no injury, moderate, severe, or highly severe), along with age, age squared, height, playing position, footedness, league, and year fixed effects.

Two versions of the injury model were estimated, one focusing on severe injuries (more than five games missed) and another incorporating both severity and recurrence (multiple injuries within the same season).

Stage Two: Linking Injury Risk to Market Value

In the second stage, the predicted injury probability was introduced into a dynamic log-linear panel model to explain players’ market values. Market value data were expressed in logarithmic form, meaning estimated effects represent percentage changes in valuation.

A System Generalized Method of Moments (System-GMM) estimator was used to address endogeneity, autocorrelation, and the dynamic nature of market values.

Performance variables (goals, assists, cards, substitutions) were considered endogenous and instrumented along with their lagged values, while demographic variables were assumed to be exogenous.

Lagged market value was included to control for unobserved, time-invariant player quality such as talent and reputation, supporting a causal interpretation under standard dynamic panel assumptions.

Key Findings: Injury History and Future Risk

The results showed a strong link 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.

When injury recurrence was considered, only highly severe past injuries remained a strong predictor, nearly doubling the likelihood of future recurrent and severe injuries.

Age displayed a non-linear relationship with injury risk. Risk increased in early career years, stabilized during peak performance ages, and declined later, likely reflecting changes in workload, experience, and playing time.

Key Findings: Injury Risk and Market Valuation

The second-stage analysis revealed a clear and economically meaningful effect of injury risk on market value. 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 became even larger, indicating that repeated injuries are particularly damaging to player valuation.

These effects remained robust after controlling for past market value, performance indicators, and player characteristics and were observed across the distribution of player values, with the strongest penalties occurring among mid-tier players rather than the highest-valued stars.

Conclusions: Financial Implications of Injury Risk

These findings confirm that markets strongly penalize injury risk, especially when injuries are severe or recurrent. The study provides strong evidence that injury risk significantly reduces the market value of football players, highlighting injuries as a key financial and strategic risk for clubs rather than a purely medical concern.

A major strength of the study is the two-stage modelling approach, which allowed the authors to separate injury prediction from valuation effects and address endogeneity using dynamic panel techniques.

The dataset enabled frequent and consistent valuation updates across leagues and seasons, reflecting market expectations rather than isolated transfer events.

However, the study has limitations. Market values are crowd-sourced estimates and may not fully reflect actual transfer fees or contractual details. In addition, some medical, training load, and psychological risk factors could not be included due to data constraints.

Despite these limitations, the findings have important implications for transfer negotiations, wage setting, insurance policies, and financial fair play regulation.

Quantifying injury risk offers clubs a valuable tool for improving strategic, financial, and sporting decision-making and opens avenues for future research linking detailed medical and workload data with economic outcomes.