Fluctuations in activity demands across game quarters in professional and semiprofessional male basketball

Examination of activity demands and stoppage durations across game periods provides useful insight concerning fatigue, tactical strategies, and playing pace in team sports such as basketball. Therefore, the aims of this study were to quantify and compare game activity fluctuations across quarters in professional and semiprofessional basketball players. Video-based time-motion analyses were conducted across multiple games. Frequencies, total durations (in seconds), total distances (in meters), and mean velocities (in meters per second) were calculated for low-intensity movement (≤3 m·s^-1), high-intensity movement (>3 m·s^-1), shuffling, and dribbling activity. Frequencies were determined for jumping and upper-body activity; stoppage durations were also calculated. Separate repeated-measures analysis of variance and Cohen's d were used to identify significant differences and quantify the effect sizes between game quarters for all outcome measures, respectively. Pearson correlation analyses were performed to determine the relationship between stoppage duration and all activity measures. The results showed significantly (p ≤ 0.05) reduced dribbling (3.09 ± 0.03 m·s^-1 vs. 2.81 ± 0.01 m·s^-1) and total (2.22 ± 0.04 m·s^-1 vs. 2.09 ± 0.03 m·s^-1) activity velocities during the third compared with the first quarter in professional players. Furthermore, effect size analyses showed greater decreases in high-intensity (professional: d 1.7-5.4; semiprofessional: d 0.3-1.7), shuffling (professional: d 2.3-3.2; semiprofessional: d 1.4-2.1), and total (professional: d 1.0-4.9; semiprofessional: d 0.3-0.8) activity and increases in dribbling (professional: d 1.4-4.7; semiprofessional: d 2.5-2.8) with game progression in professional players. In semiprofessional players, stoppage duration was significantly (p ≤ 0.05) related to various low-intensity (R 0.64-0.72), high-intensity (R 0.65-0.72), and total (R 0.63-0.73) activity measures. Although not directly measured, the observed game activity fluctuations were likely because of a combination of physiological (e.g., muscle glycogen depletion, dehydration), tactical (e.g., ball control, game pace), and game-related (e.g., time-outs, player fouls) factors. Basketball coaches can use the provided data to (a) develop more precise training plans and management strategies, (b) elevate semiprofessional player performance closer to the professional level, and (c) incorporate tactical strategies to maximize the benefits of stoppages