Enhancing Team Performance: A Machine Learning Approach to Sports Management

Abstract

In recent years, the integration of machine learning and data mining techniques in sports analytics has significantly improved decision-making processes in team management. This project focuses on the application of machine learning algorithms to analyze football player performance and assist in forming effective team compositions. Traditional methods rely on subjective judgments by experts, which lack transparency and consistency. The proposed system utilizes algorithms such as Support Vector Classifier (SVC), along with other data mining techniques like decision trees and regression models, to evaluate player characteristics and match them to suitable playing positions. By analyzing large volumes of sports data, including goals, passes, tackles, and other performance metrics, the system identifies key factors influencing player effectiveness. The model is trained using preprocessed and standardized datasets, ensuring accurate and reliable predictions. Comparative analysis between actual and predicted results demonstrates high accuracy, validating the effectiveness of the approach. This system not only enhances player evaluation but also supports coaches and analysts in developing better strategies, optimizing team selection, and improving overall team performance. Furthermore, the use of artificial intelligence enables predictive analytics, helping teams anticipate player performance, opponent strategies, and potential outcomes. The project highlights the potential of data-driven methods in transforming sports management, making it more efficient, objective, and performance-oriented.