Recommendation system for football player recruitment using k-nearest neighbor

Abstract

In modern professional football, achieving a competitive edge depends not only on on-field performance but also on effective off-field strategies, particularly in player recruitment. This study proposes a machine learning-based recommendation system to support talent identification and optimal player placement using statistical performance data. The model analyzes a wide range of features, including shots, expected goals, expected assists, pass types, offensive contributions, and defensive actions across field zones. The dataset undergoes preprocessing steps such as normalization (per 90 minutes) and dimensionality reduction. A key innovation of this research is the use of principal component analysis (PCA) to reduce feature dimensionality, minimizing redundancy while retaining essential information, which improves model efficiency and scalability. The refined data is then processed using the k-nearest neighbors (KNN) algorithm with cosine similarity, allowing the system to identify players with similar performance profiles based on directional similarity in a high-dimensional space. This combination enhances recommendation accuracy by focusing on performance structure rather than raw values. The resulting system provides actionable insights into player suitability and potential, offering clubs a data-driven tool for informed scouting and recruitment decisions. The approach demonstrates the effectiveness of combining PCA and KNN in optimizing football player recommendation systems.