Purpose: The purpose of this study was to quantify and compare training and competition demands in basketball. Methods: Fifteen semiprofessional male basketball players wore microsensors during physical conditioning training (PCT), games-based training (GBT), and competition to measure absolute and relative (·min⁻¹) PlayerLoad^TM (PL) and estimated equivalent distance (EED). Internal responses were calculated using absolute and relative session rating of perceived exertion (sRPE) and summated heart rate zones (SHRZ). Integrated measures were calculated as sRPE:PL and SHRZ:PL ratios. Results: PlayerLoad (arbitrary units [AU]) and EED (m) were statistically significantly (p < .05) higher during PCT (632 ± 139 AU, d = 1.36; 5,964 ± 1,312 m, d = 1.36; 6.50 ± 0.81 AU·min⁻¹, d = 2.44; 61.88 ± 7.22 m·min⁻¹, d = 2.60) and GBT (624 ± 113 AU, d = 1.54; 5,892 ± 1,080 m, d = 1.53; 6.10 ± 0.77 AU·min⁻¹, d = 2.14; 56.76 ± 6.49 m·min⁻¹, d = 2.22) than they were during competition (449 ± 118 AU; 3,722 ± 1474 m; 4.35 ± 1.09 AU·min⁻¹; 41.01 ± 10.29 m·min⁻¹). Summated heart rate zones were statistically significantly (p < .05) higher during PCT (314 ± 86 AU, d = 1.05; 3.22 ± 0.50 AU·min⁻¹, d = 1.94) and GBT (334 ± 79 AU, d = 1.38; 3.19 ± 0.54 AU·min⁻¹, d = 1.83) than they were during competition (225 ± 77 AU; 2.17 ± 0.69 AU·min⁻¹). The ratio of sRPE:PL was statistically significantly (p < .05) higher during competition (1.58 ± 0.85) than during PCT (0.98 ± 0.22, d = 1.44) and GBT (0.91 ± 0.24, d = 1.90). Conclusion: Training demands exceeded competition demands.

Purpose: To quantify and compare external and internal game workloads according to contextual factors (game outcome, game location, and score-line). Methods: Starting semiprofessional, male basketball players were monitored during 19 games. External (PlayerLoad^™ and inertial movement analysis variables) and internal (summated-heart-rate-zones and rating of perceived exertion [RPE]) workload variables were collected for all games. Linear mixed-effect models and effect sizes were used to compare workload variables based on each of the contextual variables assessed. Results: The number of jumps, absolute and relative (in min⁻¹) high-intensity accelerations and decelerations, and relative changes-of-direction were higher during losses, whereas session RPE was higher during wins. PlayerLoad^™ the number of absolute and relative jumps, high-intensity accelerations, absolute and relative total decelerations, total changes-of-direction, summated-heart-rate-zones, session RPE, and RPE were higher during away games, whereas the number of relative high-intensity jumps was higher during home games. PlayerLoad^™, the number of high-intensity accelerations, total accelerations, absolute and relative decelerations, absolute and relative changes-of-direction, summated-heart-rate-zones, sRPE, and RPE were higher during balanced games, whereas the relative number of total and high-intensity jumps were higher during unbalanced games. Conclusions: Due to increased intensity, starting players may need additional recovery following losses. Given the increased external and internal workload volumes encountered during away games and balanced games, practitioners should closely monitor playing times during games. Monitoring playing times may help identify when players require additional recovery or reduced training volumes to avoid maladaptive responses across the in-season.

Purpose: To investigate the relationships between external and internal workloads using a comprehensive selection of variables during basketball training and games. Methods: Eight semiprofessional, male basketball players were monitored during training and games for an entire season. External workload was determined as PlayerLoad^™: total and high-intensity accelerations, decelerations, changes of direction, and jumps and total low-intensity, medium-intensity, high-intensity, and overall inertial movement analysis events. Internal workload was determined using the summated-heart-rate zones and session rating of perceived exertion models. The relationships between external and internal workload variables were separately calculated for training and games using repeated-measures correlations with 95% confidence intervals. Results: PlayerLoad was more strongly related to summated-heart-rate zones (r = .88 ± .03, very large [training]; r = .69 ± .09, large [games]) and session rating of perceived exertion (r = .74 ± .06, very large [training]; r = .53 ± .12, large [games]) than other external workload variables (P < .05). Correlations between total and high-intensity accelerations, decelerations, changes of direction, and jumps and total low-intensity, medium-intensity, high-intensity, and overall inertial movement analysis events and internal workloads were stronger during training (r = .44–.88) than during games (r = .15–.69). Conclusions: PlayerLoad and summated-heart-rate zones possess the strongest dose–response relationship among a comprehensive selection of external and internal workload variables in basketball, particularly during training sessions compared with games. Basketball practitioners may therefore be able to best anticipate player responses when prescribing training drills using these variables for optimal workload management across the season.

Fox, JL, Conte, D, Stanton, R, McLean, B, and Scanlan, AT. The application of accelerometer-derived moving averages to quantify peak demands in basketball: A comparison of sample duration, playing role, and session type. J Strength Cond Res 35(12S): S58–S63, 2021—The purpose of this was to study compare peak external workload intensities in basketball using accelerometer-derived moving averages between different sample durations (0.5–5 minutes), session types (training vs. game-play), and playing roles (starting vs. bench players). Five starting and 3 bench players were monitored over a 15-week competitive season using accelerometers. For all training sessions and games, peak external workload intensities were determined using accelerometer-derived moving averages for PlayerLoad per minute (PL·min⁻¹) across sample durations of 0.5, 1, 2, 3, 4, and 5 minutes. Linear mixed-models and effect sizes (ESs) were used to compare peak PL·min⁻¹ between sample durations, session type, and playing role. Peak PL·min⁻¹ was significantly different between all sample durations (p < 0.05; ES = 0.88–5.45), with higher intensities evident across shorter sample durations. In starting players, peak intensities were significantly higher during games compared with training for all sample durations (p < 0.05; ES = 0.69–0.93). Peak game intensities were higher in starting players using all sample durations (p > 0.05; ES = 0.69–1.43) compared with bench players. Shorter sample durations produced higher peak PL·min⁻¹. Peak intensities were higher during games than training in starting players, indicating training may not adequately prepare players for the most demanding passages of game-play.