Football Player Performance Prediction Using Machine Learning

Abstract - The Football Player Performance Prediction project aims to build a data-driven system that enriches player evaluation and decision-making not just for teams, but for scouts and analysts as well. The Random Forest Regressor is the machine learning technique that shall turn out to be used in the model for predicting performance based on vital features such as position, club, age, and match statistics, goals, assists, pass accuracy, and tackles. The perspective is that good prediction can be accomplished through analyzing the previous match data and attributes of the player, thus ensuring optimum use of time and resources by coaches and analysts in perfecting team strategies, selecting players, and defining training programs based on AI-predicted entry results. The proposed platform brings data preprocessing, feature engineering, model training, and evaluation metrics together to result in precise and interpretable predictions. Additions in the future may include deep learning models, as well as the incorporation of real-time match data, so improve accuracy and decision-making support.