Purpose: To compare the concurrent validity of session-rating of perceived exertion (sRPE) workload determined face-to-face and via an online application in basketball players. Methods: Sixteen semiprofessional, male basketball players (21.8 [4.3] y, 191.2 [9.2] cm, 85.0 [15.7] kg) were monitored during all training sessions across the 2018 (8 players) and 2019 (11 players) seasons in a state-level Australian league. Workload was reported as accumulated PlayerLoad (PL), summated-heart-rate-zones (SHRZ) workload, and sRPE. During the 2018 season, rating of perceived exertion (RPE) was determined following each session via individualized face-to-face reporting. During the 2019 season, RPE was obtained following each session via a phone-based, online application. Repeated-measures correlations with 95% confidence intervals were used to determine the relationships between sRPE collected using each method and other workload measures (PL and SHRZ) as indicators of concurrent validity. Results: Although all correlations were significant (P < .05), sRPE obtained using face-to-face reporting demonstrated stronger relationships with PL (r = .69 [.07], large) and SHRZ (r = .74 [.06], very large) compared with the online application (r = .29 [.25], small [PL] and r = .34 [.22], moderate [SHRZ]). Conclusions: Concurrent validity of sRPE workload was stronger when players reported RPE in an individualized, face-to-face manner compared with using a phone-based online application. Given the weaker relationships with other workload measures, basketball practitioners should be cautious when using player training workloads predicated on RPE obtained via online applications.
Jordan L. Fox, Cody J. O’Grady, and Aaron T. Scanlan
Jordan L. Fox, Cody J. O’Grady, and Aaron T. Scanlan
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.
Cody J. O’Grady, Jordan L. Fox, Vincent J. Dalbo, and Aaron T. Scanlan
Purpose: To systematically quantify the external and internal workloads reported during games-based drills in basketball and identify the effects of different modifiable factors on the workloads encountered. Methods: PubMed, Scopus, MEDLINE, and SPORTDiscus databases were searched for original research published up until January 2, 2019. The search included terms relevant to workload, games-based drills, and basketball. Studies were screened using predefined selection criteria, and methodological quality was assessed prior to data extraction. Results: The electronic search yielded 8,284 studies with 3,411 duplicates. A total of 17 studies met the inclusion criteria for this review, with quality scores ranging from 9 to 10 out of 11. Factors regularly modified during games-based drills among the included studies were team size, playing area, playing and rest time, and game alterations. Games-based drills containing smaller team sizes elicited greater external and internal workloads compared to larger team sizes. Furthermore, full-court games-based drills elicited greater external and internal workloads compared to half-court drills, while continuous games-based drills elicited greater internal workloads compared to intermittent drills. Conclusions: This review provides a comprehensive collation of data indicating the external and internal workloads reported during different games-based drills in various samples of basketball players. Furthermore, evidence is provided for basketball coaches to consider when prescribing games-based drills and modifying factors during drills across the season. Current literature suggests that smaller team sizes and full-court playing areas elicit greater external and internal workloads than larger team sizes and half-court drills, respectively. Furthermore, continuous games-based drills elicit greater internal workloads than intermittent drills.
Jordan L. Fox, Robert Stanton, Charli Sargent, Cody J. O’Grady, and Aaron T. Scanlan
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.
Jordan L. Fox, Aaron T. Scanlan, Robert Stanton, Cody J. O’Grady, and Charli Sargent
Purpose: To examine the impact of workload volume during training sessions and games on subsequent sleep duration and sleep quality in basketball players. Methods: Seven semiprofessional male basketball players were monitored across preseason and in-season phases to determine training session and game workloads, sleep duration, and sleep quality. Training and game data were collected via accelerometers, heart-rate monitors, and rating of perceived exertion (RPE) and reported as PlayerLoad™ (PL), summated heart-rate zones, and session RPE (sRPE). Sleep duration and sleep quality were measured using wrist-worn activity monitors in conjunction with self-report sleep diaries. For daily training sessions and games, all workload data were independently sorted into tertiles representing low, medium, and high workload volumes. Sleep measures following low, medium, and high workloads and control nights (no training/games) were compared using linear mixed models. Results: Sleep onset time was significantly later following medium and high PL and sRPE game workloads compared with control nights (P < .05). Sleep onset time was significantly later following low, medium, and high summated heart-rate-zones game workloads, compared with control nights (P < .05). Time in bed and sleep duration were significantly shorter following high PL and sRPE game workloads compared with control nights (P < .05). Following low, medium, and high training workloads, sleep duration and quality were similar to control nights (P > .05). Conclusions: Following high PL and sRPE game workloads, basketball practitioners should consider strategies that facilitate longer time in bed, such as napping and/or adjusting travel or training schedules the following day.
Markus N.C. Williams, Vincent J. Dalbo, Jordan L. Fox, Cody J. O’Grady, and Aaron T. Scanlan
Purpose: To compare weekly training and game demands according to playing position in basketball players. Methods: A longitudinal, observational study was adopted. Semiprofessional, male basketball players categorized as backcourt (guards; n = 4) and frontcourt players (forwards/centers; n = 4) had their weekly workloads monitored across an entire season. External workload was determined using microsensors and included PlayerLoad™ (PL) and inertial movement analysis variables. Internal workload was determined using heart rate to calculate absolute and relative summated-heart-rate-zones workload and rating of perceived exertion (RPE) to calculate session-RPE workload. Comparisons between weekly training and game demands were made using linear mixed models and effect sizes in each positional group. Results: In backcourt players, higher relative PL (P = .04, very large) and relative summated-heart-rate-zones workload (P = .007, very large) were evident during training, while greater session-RPE workload (P = .001, very large) was apparent during games. In frontcourt players, greater PL (P < .001, very large), relative PL (P = .019, very large), peak PL intensities (P < .001, moderate), high-intensity inertial movement analysis events (P = .002, very large), total inertial movement analysis events (P < .001, very large), summated-heart-rate-zones workload (P < .001, very large), RPE (P < .001, very large), and session-RPE workload (P < .001, very large) were evident during games. Conclusions: Backcourt players experienced similar demands between training and games across several variables, with higher average workload intensities during training. Frontcourt players experienced greater demands across all variables during games than training. These findings emphasize the need for position-specific preparation strategies leading into games in basketball teams.
Cody J. O’Grady, Jordan L. Fox, Daniele Conte, Davide Ferioli, Aaron T. Scanlan, and Vincent J. Dalbo
Purpose: Games-based drills are the predominant form of training adopted during basketball practice. As such, researchers have begun to quantify the physical, physiological, and perceptual demands of different games-based drill formats. However, study methodology has not been systematically reported across studies, limiting the ability to form conclusions from existing research. The authors developed this call to action to draw attention to the current standard of methodological reporting in basketball games-based drill research and establish a systematic reporting standard the authors hope will be utilized in future research. The Basketball Games-Based Drill Methodical Reporting Checklist (BGBDMRC) was developed to encourage the systematic reporting of games-based drill methodology. The authors used the BGBDMRC to evaluate the current methodological reporting standard of studies included in their review published in the International Journal of Sports Physiology and Performance, “A Systematic Review of the External and Internal Workloads Experienced During Games-Based Drills in Basketball Players” (2020), which highlighted this issue. Of the 17 studies included in their review, only 38% (±18%) of applicable checklist items were addressed across included studies, which is problematic as checklist items are essential for study replication. Conclusions: The current standard of methodological reporting in basketball games-based drill research is insufficient to allow for replication of examined drills in future research or the application of research outcomes to practice. The authors implore researchers to adopt the BGBDMRC to improve the quality and reproducibility of games-based drill research and increase the translation of research findings to practice.