Exploring Football Player Salary Prediction Using Random Forest: Leveraging Player Demographics and Team Associations

Abstrak

This paper explores the prediction of football player salaries using a Random Forest Regressor model, leveraging player demographics and team associations as key features. The dataset consists of 684 football players, including variables such as age, nationality, position, team, weekly salary, and annual salary. The study applies exploratory data analysis (EDA) to understand the distribution of these features and identify patterns within the dataset. Data preprocessing involves handling missing values, one-hot encoding categorical variables, and splitting the dataset into training and testing sets. The Random Forest model is trained on the preprocessed data, and its performance is evaluated using common regression metrics, including R-squared (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results show that the model explains approximately 48.5% of the variance in player salaries, with an MAE of £1.92 million and an RMSE of £2.82 million. Key predictors of salary include player age, position, nationality, and team. The analysis of feature importance reveals that categorical variables such as Nation and Team have a significant impact on salary predictions. However, the model's performance is constrained by the lack of more granular data, such as player performance metrics or external economic factors. This research provides valuable insights for football team management, helping teams understand which factors contribute to salary setting and enabling more informed decisions in player recruitment and contract negotiations. It also highlights the potential for sponsorships to target players based on these predictive attributes. Future work could explore the integration of more advanced machine learning techniques and additional player data to improve predictive accuracy and model robustness.