The purpose of this study was to compare average physical demands and the most demanding 60-s scenarios of basketball match-play between five different age groups. Sixty-four male basketball players from five different age groups were monitored across eight regular-season home games. Physical demands were examined using a local positioning system and included total distance covered, distance >18 km·h^-1, the number of accelerations (≥2 m·s^-2) and decelerations (≤-2 m·s^-2). All four game performance variables increased significantly (58.4 - 639.2%) when calculated with rolling average techniques in comparison to average physical demand values. Furthermore, the current investigation found that while Under-12 presented the highest result in relative total distance covered (p < .001; effect size = 0.58-2.01), they also showed the lowest values in the most demanding scenarios of match play and small-to-moderate effect sizes compared with their older counterparts. Both average physical demands and the most demanding scenarios presented an increasing tendency with age when distance >18 km·h^-1 in basketball players was assessed. More specifically, the Under-12 age group achieved the lowest values and showed significant differences with the other four teams in both game analysis techniques (p < .001; effect size = 0.53 - 1.32). In conclusion, average game demands are shown to remarkably underestimate the most demanding scenarios of basketball match-play, and there are multiple significant differences between particular age groups.

Purpose: The purpose of the study was to examine the relationships between external and internal loads, and their ratio (efficiency index), with game performance between backcourt and frontcourt professional basketball players. Methods: Game loads of 14 basketball players were monitored during 6 games. External load variables measured were total distance (TD); distance >18 km·h⁻¹, commonly known as high-speed running (HSR); and number of accelerations (ACC) and decelerations (DEC) >3 m·s⁻², whereas the internal load variable measured was average heart rate (HR_mean). The ratio between external and internal load variables was calculated and defined through 4 efficiency indexes (TD:HR_mean, HSR:HR_mean, ACC:HR_mean, and DEC:HR_mean). Furthermore, basketball performance was quantified using game-related statistics. Results: TD presented a small association with basketball performance, whereas the other external load variables and the 4 efficiency indexes calculated showed trivial relationships with game-related statistics. Furthermore, HR_mean showed the greatest (small) associations with individual performance (P = .01–.02; r = .19 to .22). Regarding specific positions, the only 2 variables that presented significant differences were DEC (P = .01; d = 0.86) and DEC:HR_mean (P = .01; d = 0.81), which showed higher values in backcourt players compared with frontcourt players. Conclusions: The results suggest that the best performances of basketball players during official competition are not associated with higher game loads. This illustrates the necessity to assess basketball performance from a holistic approach and consider more than just external and internal variables to better understand the players’ performance during basketball competition.

The purpose of this study was to compare physical demands during the most demanding scenarios (MDS) of different training sessions and official matches in professional basketball players across playing positions. Thirteen professional basketball players were monitored over a 9-week competitive...

The purpose of this study was to examine the distribution of physical activities relative to the most demanding scenarios across playing positions during official basketball match-play. Twelve professional basketball players were monitored during twelve matches using a local positioning system. Peak physical demands were measured via total distance covered, distance covered >18 km·h^-1, and the number of accelerations and decelerations >3 m·s^-2 captured over 30- and 60-s rolling averages. The results showed that players were exposed to more than one high-demanding scenario in all variables and time epochs examined. Additionally, total distance covered presented the greatest number of moderate-demanding scenarios (40-80% of most demanding scenarios), whereas distance covered >18 km·h^-1, and accelerations and decelerations >3 m·s^-2 presented the greatest proportion of low-demanding scenarios (<40% of most demanding scenarios). Regarding positional differences, backcourt players generally experienced a higher number of scenarios than frontcourt players in most variables, especially in total distance covered. For this variable, scenarios between 20 and 70% of most demanding scenarios during the 30-s epoch (p < 0.001; ES = 0.420.78), and scenarios between 50 and 90% of most demanding scenarios during the 60-s epoch (p < 0.001; ES = 0.400.64) showed significant differences between backcourt and frontcourt players. Our data suggest that match physical activities are position-dependent and variable-dependent. In addition, peak physical demands appear to be repeated during basketball competition. These results may be considered by practitioners to complement average values and most demanding scenarios when prescribing individualized training programs to optimize team performance.