Performance Profiles in Youth Basketball Across Different Score Contexts: An Unsupervised Machine Learning Analysis

The analysis of basketball performance has increasingly incorporated advanced analytics and machine learning methods to understand better the factors that influence offensive efficiency and game dynamics. The present study aimed to identify performance profiles in basketball using unsupervised machine learning techniques and to examine the physical load and performance indicators that differentiate these profiles. Quarters from Greek U16 basketball matches were analyzed in two contexts: quarters from games with large score differences and quarters from games with small score differences. K-means clustering was applied separately to each dataset to identify latent performance patterns. The optimal number of clusters was determined using the Elbow method and silhouette analysis, which indicated a two-cluster solution for both datasets. Cluster visualization using t-distributed stochastic neighbor embedding (t-SNE) confirmed the presence of distinct performance profiles. Independent samples t-tests revealed significant differences between clusters across several physical load indicators, including jump load, total distance covered, accumulated acceleration load, and distance covered across different speed zones (p < .001). Clusters characterized by higher movement intensity also exhibited higher basketball performance efficiency indicators. Although higher-performance clusters showed numerically higher winning proportions in both contexts (large score differences: 66.7% vs. 42.9%; small score differences: 59.4% vs. 36.7%), chi-square analyses indicated that cluster membership was not significantly associated with game outcomes. Overall, the findings suggest that performance profiles in basketball are primarily differentiated by movement intensity and physical load characteristics, highlighting the importance of integrating both physical and technical performance indicators in basketball performance analysis.