Developing A Predictive Model for Football Players’ Market Value Using Machine Learning

Football is the world’s most popular sport, and evaluating the market value of players is crucial for clubs and managers in making informed decisions regarding transfers, contracts, and financial planning. This study aims to develop a predictive model to estimate the market value of football players using machine learning (ML) algorithms and real-life statistics performance data from the top five European leagues such as English Premier League, Italian Serie A, Spanish La Liga, German Bundesliga, and French Ligue 1 between the 2017/18 and 2019/20 seasons. By reviewing past research, various ML methods such as Random Forest, LightGBM, XGBoost, and Gradient Boosting Decision Tree (GBDT) are developed. Data preprocessing techniques, including data cleaning, feature selection, feature encoding, splitting, and standardization, are applied to ensure data quality and consistency. To tune the hyperparameter of the models, RandomizedSearchCV is applied alongside cross validation. The model evaluation is conducted using regression metrics such as mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R²), to determine the most accurate model. The best-performing model is further utilised to analyse the correlation between the features and market value, offering insights into the key features that significantly impact the market value for each position.