The objective of this project was to apply a Moneyball-style, data-driven approach using SQL to analyze football players, teams, and budget efficiency, and demonstrate how relational databases can support smarter decision-making. The project involved designing a structured database with multiple related tables and writing analytical queries to evaluate player performance, value-for-money metrics, team results, transfer insights, and budget efficiency ratios. It took approximately 1–2 weeks to complete, including schema design, query development, and documentation of insights. I used SQL along with relational data modeling concepts such as joins, aggregations, and performance metrics. This was an independent academic project completed to build hands-on experience in data analysis and database management.