Evaluating Machine Learning Models for Predicting Football Players' Market Values: A Review

Abstract

In the realm of football management, the accurate assessment of player market values stands as a critical managerial task. Traditional methods reliant on expert opinions often lack transparency and can be prone to inaccuracies. This study proposes a data-driven approach employing machine learning algorithms to determine football players' market values objectively. Utilizing FIFA 20 data from sofifa.com, four regression models - linear regression, multiple linear regression, decision trees, and random forests - were evaluated for their efficacy in estimating market values. This research offers a robust framework for football clubs and player agents to enhance negotiation strategies and make well- informed decisions in the dynamic football transfer market. Key Words: Linear regression, multiple linear regression, decision trees, random forests, machine learning , FIFA video game data.