Multivariate Analysis on Player Performance & Market Value

Applying data cleaning, feature engineering, and skill-based variable selection to study the relationship between player

positions, performance attributes, and market value.

Used Factor Analysis to reduce 28 performance metrics into 3 core components explaining 75% of total variance and

improving model interpretability.

Built a Flexible Discriminant Analysis model to classify players into positions with 82% overall accuracy, and used MANOVA to

assess how position impacts wages and value with statistically significant results.