Integrating subjective assessments and wearable-derived metrics: decoding in-game load-performance relationships in collegiate basketball players.

Abstract

Background: This study aimed to investigate the relationships between well-being, recovery state, game loads, and game performance across 20 official games involving 16 highly trained collegiate basketball players. Understanding how these factors interact is essential for optimizing performance and workload management in competitive basketball.

Methods: Principal component analysis (PCA) identified six key components contributing to performance: PC1 (loads) accounted for 50% of the total variance, PC2 (well-being) explained 11%, PC3 (jump) captured 8%, PC4 (high-intensity jump) explained 6%, while PC5 (recovery) and PC6 (PlayerLoad per minute) each explained 2%. Generalized linear mixed-effects models were applied to assess the associations between these components and key performance indicators, including Performance Index Rating (PIR) and Player Total Contribution (PTC).

Results: Game loads showed a negative association with PIR (β=-1.22, 95% CI=-2.33 to -0.12, P<0.05). In contrast, high-intensity jumps were positively associated with both PIR (β=1.08, 95% CI=0.39 to 1.77, P<0.01) and PTC (β=0.99, 95% CI=0.34 to 1.64, P<0.01), explaining 43.86% of the variance in PIR and 40.17% in PTC. The effects of well-being and recovery were limited.

Conclusions: High-intensity physical activities, particularly jumps, are crucial to enhancing basketball performance, while excessive game loads can negatively impact outcomes. The limited influence of well-being and recovery suggests that their effects may be more evident over longer periods or under different contexts. Future research should focus on optimizing the balance between game loads and high-intensity actions to improve performance.