RESUMO
BACKGROUND: Limited research has investigated the association between training load and performance of basketball players during games. Little is known about how different indicators of player performance are affected by internal and external loads. The purpose of this study was to determine whether external and internal loads influence basketball players' performance during games. METHOD: This longitudinal study involved 20 professional male basketball players from a single team, classified as first-level athletes by the Chinese Basketball Association. During 34 games, external load was measured as PlayerLoad using micro-sensors, while internal load was assessed using session rating of perceived exertion (sRPE). Player performance was quantified using three metrics: Efficiency, Player Index Rating (PIR), and Plus-Minus (PM). Pearson correlation coefficients were calculated to assess the strength of the relationships between training loads and performance metrics. Linear mixed-effects models were applied to further analyze the influence of internal and external loads on basketball performance. RESULTS: Pearson correlation analysis revealed moderate positive correlations between both sRPE and PlayerLoad with Efficiency and PIR. Specifically, sRPE (r = 0.52) and PlayerLoad (r = 0.54) were both significantly correlated with Efficiency. For PIR, sRPE (r = 0.50) and PlayerLoad (r = 0.56) also demonstrated moderate correlations. These correlations were further substantiated by linear mixed-effects models, which showed that sRPE (ß = 2.21, p < 0.001) and PlayerLoad (ß = 1.87, p = 0.004) had significant independent effects on Efficiency. Similarly, sRPE (ß = 2.15, p < 0.001) and PlayerLoad (ß = 2.36, p < 0.001) significantly predicted PIR. Additionally, a significant interaction effect between PlayerLoad and sRPE was found on Plus-Minus (ß = -2.49, p < 0.001), indicating that the combination of high physical and psychological loads negatively impacted overall team performance. However, the correlation strengths for Plus-Minus were relatively low (sRPE: r = 0.16; PlayerLoad: r = 0.10). CONCLUSION: Both external and internal loads positively contribute to performance, the integration of objective (accelerometry) and subjective (sRPE) measures of load provides a comprehensive understanding of the physiological and psychological demands on athletes, contributing to more effective training regimens and performance optimization.
RESUMO
In recent years, the simplified lattice Boltzmann method without evolution of distribution functions was developed, which adopts predictor-corrector steps to solve the constructed macroscopic equations. To directly solve the constructed macroscopic equations in a single step, we propose the present one-step simplified lattice Boltzmann method and apply it to simulate thermal flows under the Boussinesq approximation. The present method is derived by reconstructing the evolution equation of the lattice Boltzmann method and constructing nonequilibrium distribution functions. This method inherits the advantages of the simplified lattice Boltzmann method, such as low virtual memory cost, convenient boundary treatment, and good numerical stability at relaxation time close to 0.5. In addition, compared to the traditional artificial compressible method (ACM), the present method is more efficient in computation when a small time step is applied in the ACM to ensure numerical stability. Several numerical examples, including natural convection in a square cavity, the porous plate problem, and natural convection in a concentric annulus, are conducted to test the accuracy of the present method. The results show that this method can accurately simulate thermal flow problems and has good numerical stability.