Peak Match Demands in Young Basketball Players: Approach and Applications

• Main Authors: Enrique Alonso, Nicolas Miranda, Shaoliang Zhang, Carlos Sosa, Juan Trapero, Jorge Lorenzo, Alberto Lorenzo
• Format: Article
• Language: English
• Published: MDPI AG 2020-03-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: basketball; worst case scenario (WCS); most intense passages; most demanding periods; peak demands (PD); performance
• Online Access: https://www.mdpi.com/1660-4601/17/7/2256

Background: The aim of this study is to describe the peak match demands and compare them with average demands in basketball players, from an external load point of view, using different time windows. Another objective is to determine whether there are differences between positions and to provide an approach for practical applications. Methods: During this observational study, each player wore a micro technology device. We collected data from 12 male basketball players (mean ± SD: age 17.56 ± 0.67 years, height 196.17 ± 6.71 cm, body mass 90.83 ± 11.16 kg) during eight games. We analyzed intervals for different time windows using rolling averages (ROLL) to determine the peak match demands for Player Load. A separate one-way analysis of variance (ANOVA) was used to identify statistically significant differences between playing positions across different intense periods. Results: Separate one-way ANOVAs revealed statistically significant differences between 1 min, 5 min, 10 min, and full game periods for Player Load, F (3,168) = 231.80, η_p² = 0.76, large, <i>p</i> < 0.001. It is worth noting that guards produced a statistically significantly higher Player Load in 5 min (<i>p</i> < 0.01, η_p² = −0.69, moderate), 10 min (<i>p</i> < 0.001, η_p² = −0.90, moderate), and full game (<i>p</i> < 0.001, η_p² = −0.96, moderate) periods than forwards. Conclusions: The main finding is that there are significant differences between the most intense moments of a game and the average demands. This means that understanding game demands using averages drastically underestimates the peak demands of the game. This approach helps coaches and fitness coaches to prepare athletes for the most demanding periods of the game and present potential practical applications that could be implemented during training and rehabilitation sessions.