International Edition
Latest News
Technology

Predicted Injury Risk Dragging Down Football Transfer Values

Summary of the Study: Injury Risk and Football Player Market Value This study investigates the relationship between a football player's injury history and their market value. It employs a two-stage modeling approach to first predict injury risk and…

Predicted Injury Risk Dragging Down Football Transfer Values

Summary of the Study: Injury Risk and Football Player Market Value

This study investigates the relationship between a football player’s injury history and their market value. It employs a two-stage modeling approach to first predict injury risk and then link that risk to a player’s economic valuation. Here’s a breakdown of the key aspects:

Stage One: Predicting injury Risk

* Goal: To determine factors influencing a player’s probability of future injury.
* Methodology: A model was built to predict injury risk based on:
* Past Injury History: Severity of injuries (no injury, moderate, severe, highly severe – >5 games missed) and recurrence.
* Player Characteristics: Age, age squared, height, playing position, footedness.
* Contextual Factors: League and year fixed effects.
* Key Findings:

* Players missing >10 games previously had significantly higher risk of new severe injuries.
* Highly severe past injuries nearly doubled the likelihood of future recurrent and severe injuries.
* Age had a U-shaped relationship with risk – increasing early in career, stabilizing during peak, and decreasing later.

Stage Two: Linking Injury Risk to Market Value

* Goal: To quantify the economic impact of injury risk on a player’s market value.
* Methodology:

* Predicted injury probabilities from Stage one were incorporated into a dynamic log-linear panel model.
* system-GMM estimator was used to address potential biases (endogeneity, autocorrelation, dynamic nature of market values).
* Endogenous Variables: Performance metrics (goals, assists, cards, substitutions) and their lagged values were instrumented.
* Exogenous Variables: Demographic variables were assumed to be exogenous.
* Control Variables: Lagged market value was included to account for inherent player quality.
* Key Findings:

* A 1% increase in predicted severe injury probability led to a 2.29% decrease in market value.

* The negative impact was larger when considering injury recurrence.
* The effect was consistent across player value ranges, but most pronounced for mid-tier players.

Overall Conclusions:

* markets penalize injury risk: The study provides strong evidence that injury risk significantly reduces a player’s market value,especially with severe or recurrent injuries.
* Financial & Strategic Implications: Injuries are not just medical concerns, but crucial financial and strategic risks for football clubs.
* Strengths: Two-stage modeling, dynamic panel techniques to address endogeneity, frequent valuation updates.
* Limitations: Reliance on crowd-sourced market values, lack of detailed medical/training data.
* Future Research: Integrating detailed medical and workload data with economic outcomes.

in essence, the study demonstrates that clubs do factor injury risk into player valuations, and that this risk has a measurable and ample economic impact. This has implications for how clubs approach transfers,contracts,insurance,and overall player management.

About the author: Anika Shah - Technology

MSc in Computer Science, senior reporter. Anika focuses on AI ethics, cybersecurity, and emerging hardware—frequently moderating panels at CES and Web Summit. “Anika Shah decodes tech breakthroughs and startup disruption shaping tomorrow’s digital landscape.”