POSITION-BASED PLAYER PROFILING USING UNSUPERVISED LEARNING ON 2026 WORLD CUP PERFORMANCE DATA
Keywords:
Unsupervised Learning, K-Means Clustering, Player Profiling, Football Analytics, 2026 World CupAbstract
This study employs unsupervised learning to profile football players based on performance data from the 2026 FIFA World Cup, addressing a gap in the literature concerning player categorization in international tournament contexts. The dataset comprises 28 performance metrics from 512 players across 32 national teams. Following data preprocessing, K-Means clustering and principal component analysis were applied to identify natural groupings. The optimal cluster count, determined through elbow method, silhouette score (0.42), Davies-Bouldin index, and Calinski-Harabasz index, was established at k=4. This resulted in four distinct player archetypes: Defensive Anchors, characterized by strong defensive contributions and high passing accuracy; Utility Players, demonstrating versatility and high work rate but lower overall ratings; Star Forwards, exhibiting exceptional offensive output and the highest player ratings; and Supporting Attackers, showing moderate attacking contributions with limited playing time. A cross-tabulation with traditional positions revealed substantial but imperfect alignment, with goalkeepers nearly perfectly classified, while midfielders showed the most dispersed distribution. The findings validate the multidimensional nature of football performance and demonstrate the efficacy of unsupervised learning for objective player evaluation, offering practical applications for talent identification, tactical planning, and player development.
Keywords : Unsupervised Learning; K-Means Clustering; Player Profiling; Football Analytics;2026 World Cup; Performance Data.
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