Stage 3: Football Player Wage Prediction

1. Project Overview

Economic question:
Which player characteristics best predict professional football players’ wages?

Dataset: FIFA player wage dataset
Outcome variable: Wage
Method: Multiple linear regression

2. Why This Question Matters

Football is a high-value labor market.

Understanding wage predictors helps explain:

• how clubs price player experience
• whether reputation is rewarded financially
• which measurable characteristics are linked to wages

3. Dataset and Variables

4. Wage Distribution

Key point: wages are right-skewed because a small number of elite players earn much more than most players.

5. Log Wage Distribution

The log transformation reduces skewness and makes the wage distribution easier to interpret.

6. Wage by Position

This shows whether some playing roles receive higher average wages.

7. Experience and Wages

Club appearances are used as a proxy for professional experience and playing record.

8. Regression Model

The final model predicts player wage using:

• Age
• Club appearances
• National team caps

Model specification:

9. Model Performance

The model explains approximately 36% of the variation in football player wages.

10. Economic Interpretation

National team caps have the strongest positive relationship with wages. Age is negative after controlling for appearances and caps.

11. Limitations and Future Work

Limitations

• The dataset does not include contract length, injuries, or sponsorship income.
• Player quality is only partially captured by appearances and caps.
• Wages may also depend on club finances and transfer market conditions.

Future question

Do players from certain leagues or nationalities earn wage premiums after controlling for experience?

12. Conclusion and Q&A

Main takeaway:

Professional football wages are linked to measurable player characteristics, especially international experience and playing record.

Thank you. We are ready for questions.