Contrasting Learning Pathways: Supervised and Unsupervised Analysis on FIFA 23 Player Data" is an in-depth exploration that merges sports analytics with advanced machine learning techniques. Using a blend of unsupervised methods like PCA, t-SNE, UMAP, and NMF, the study unveils hidden patterns in player roles, emphasizing the distinct nature of goalkeepers. The supervised phase, harnessing SVM and Random Forest, further underscores this distinction while revealing intricacies in classifying outfield players. This project illuminates the FIFA 23 player dataset, showcasing the synergy between football insights and modern data analysis, catering to both game enthusiasts and data science aficionados.