Economic Predictors of Football Player Market Values

Gorkem Ozturk

2026-06-04

ECON 465 Final Project

Predicting Football Players’ Market Values

  • ECON 465: Introduction to Data Science

  • Final Project Presentation

  • Gorkem Ozturk

Economic Question and Motivation

Research Question:

Which player characteristics and performance statistics are associated with football players’ market values?

Why is this important?

  • Clubs spend millions on transfers

  • Market values affect transfer decisions

  • Data-driven valuation can improve decision making

Dataset Description

Dataset: Football Player Statistics and Market Values

Outcome Variable:

  • Bonservis (Market Value)

Main Predictors:

  • Goals (GLS)

  • Assists (AST)

  • Expected Goals (xG)

  • Expected Assists (xA)

  • Shots on Target (SOT)

  • Age

Outcome Variable

Regression Outcome: Bonservis

  • Measures player market value

  • Continuous numeric variable

  • Suitable for regression analysis

Objective:

Predict player market values using football performance statistics.

Distribution Analysis

Initial Findings

  • Market value distribution was highly right-skewed

  • Most players had relatively low values

  • A small number of players had extremely high values

Solution

  • Applied log transformation

  • Distribution became closer to normal

Model 1: Basic Linear Regression

Variables

  • Goals

  • Assists

  • Expected Goals

  • Age

Purpose

Estimate player market value using basic performance indicators.

Model 2: Expanded Linear Regression

Additional Variables

  • Expected Assists

  • Total Shots

  • Shots on Target

Why Build a Second Model?

To determine whether additional football statistics improve prediction accuracy.

Model Comparison

Model Description
Model 1 Basic Regression
Model 2 Expanded Regression

Result

Model 2 performed better because it included more detailed player statistics.

Main Findings

  • Goals positively affect market value

  • Assists positively affect market value

  • Expected goals are important predictors

  • Age influences player valuation

  • Detailed football statistics improve prediction performance

Limitations and Future Work

Limitations

  • Single dataset

  • Limited number of predictors

  • Market values may change over time

Future Improvements

  • Include multiple seasons

  • Include contract information

  • Include wage and transfer fee data

Conclusion

  • Football player market values can be predicted using performance statistics.

  • Regression analysis provided meaningful results.

  • The expanded model outperformed the basic model.

  • Data science methods can support football valuation and transfer decisions.

Thank You

Questions?