The project “Sports Analytics for Football League Table and Player Performance Prediction” focuses on leveraging data analytics and machine learning techniques to evaluate player performance and predict their market value in professional football. With the increasing financial stakes in football transfers, this project aims to shift decision-making from subjective judgment to a more data-driven approach. The dataset consists of over 2,000 players from top European leagues, including attributes such as age, nationality, club, position, contract duration, and market value. The data was first cleaned and structured using SQL Server Management Studio, followed by exploratory data analysis and predictive modeling in Python using algorithms like XGBoost, Random Forest, Linear Regression, and Logistic Regression. The results were then visualized through interactive dashboards in Tableau, enabling stakeholders to analyze trends such as league-wise player valuation, positional importance, and nationality-based distribution. The project ultimately provides actionable insights for clubs, scouts, analysts, and investors to optimize player selection, improve transfer strategies, and enhance overall team performance. Bullet Points: Collected and processed football player data from reliable sources Performed data cleaning and transformation using SQL Server Conducted Exploratory Data Analysis (EDA) to identify trends and patterns Built machine learning models (XGBoost, Random Forest, Linear Regression) for prediction Evaluated models using metrics like Accuracy, MAE, RMSE, and AUC Developed interactive Tableau dashboards for visualization Identified key insights such as: EPL having the highest market value Peak player age range (25–28 years) Significant value fluctuations among players Designed a system useful for: Clubs and managers (team planning) Scouts (talent identification) Investors (financial decisions) Proposed future enhancements like real-time data integration and tactical analysis