Purpose: To compare peak and average intensities encountered during winning and losing game quarters in basketball players. Methods: Eight semiprofessional male basketball players (age = 23.1 [3.8] y) were monitored during all games (N = 18) over 1 competitive season. The average intensities attained in each quarter were determined using microsensors and heart-rate monitors to derive relative values (per minute) for the following variables: PlayerLoad, frequency of high-intensity and total accelerations, decelerations, changes of direction, jumps, and total inertial movement analysis events combined, as well as modified summated-heart-rate-zones workload. The peak intensities reached in each quarter were determined using microsensors and reported as PlayerLoad per minute over 15-second, 30-second, 1-minute, 2-minute, 3-minute, 4-minute, and 5-minute sample durations. Linear mixed models and effect sizes were used to compare intensity variables between winning and losing game quarters. Results: Nonsignificant (P > .05), unclear–small differences were evident between winning and losing game quarters in all variables. Conclusions: During winning and losing game quarters, peak and average intensities were similar. Consequently, factors other than the intensity of effort applied during games may underpin team success in individual game quarters and therefore warrant further investigation.
Jordan L. Fox, Jesse Green, 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.
Jordan L. Fox, Cody J. O’Grady, and Aaron T. Scanlan
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.
Aaron T. Scanlan, Jordan L. Fox, Nattai R. Borges, Ben J. Dascombe, and Vincent J. Dalbo
The influence of various factors on training-load (TL) responses in basketball has received limited attention. This study aimed to examine the temporal changes and influence of cumulative training dose on TL responses and interrelationships during basketball activity.
Ten state-level Australian male junior basketball players completed 4 × 10-min standardized bouts of simulated basketball activity using a circuit-based protocol. Internal TL was quantified using the session rating of perceived exertion (sRPE), summated heart-rate zones (SHRZ), Banister training impulse (TRIMP), and Lucia TRIMP models. External TL was assessed via measurement of mean sprint and circuit speeds. Temporal TL comparisons were performed between 10-min bouts, while Pearson correlation analyses were conducted across cumulative training doses (0–10, 0–20, 0–30, and 0–40 min).
sRPE TL increased (P < .05) after the first 10-min bout of basketball activity. sRPE TL was only significantly related to Lucia TRIMP (r = .66–.69; P < .05) across 0–10 and 0–20 min. Similarly, mean sprint and circuit speed were significantly correlated across 0–20 min (r = .67; P < .05). In contrast, SHRZ and Banister TRIMP were significantly related across all training doses (r = .84–.89; P < .05).
Limited convergence exists between common TL approaches across basketball training doses lasting beyond 20 min. Thus, the interchangeability of commonly used internal and external TL approaches appears dose-dependent during basketball activity, with various psychophysiological mediators likely underpinning temporal changes.
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, Robert Stanton, Aaron T. Scanlan, Masaru Teramoto, and Charli Sargent
Purpose: To investigate the associations between sleep and competitive performance in basketball. Methods: A total of 7 semiprofessional, male players were monitored across the in-season. On nights prior to competition, sleep duration and quality were assessed using actigraphs and sleep diaries. The data were accumulated over 1 (night 1), 2 (nights 1–2 combined), 3 (nights 1–3 combined), and 4 (nights 1–4 combined) nights prior to competition. Performance was reported as player statistics (field goal and free-throw accuracy, rebounds, assists, steals, blocks, and turnovers) and composite performance statistics (offensive rating, defensive rating, and player efficiency). Linear regression analyses with cluster-robust standard errors using bootstrapping (1000 replications) were performed to quantify the association between sleep and performance. Results: The night before competition, subjective sleep quality was positively associated with offensive rating and player efficiency (P < .05). Conclusions: Strategies to increase subjective sleep quality the night before competition should be considered to increase the likelihood of successful in-game performance, given its association with composite performance metrics.
Aaron T. Scanlan, Jordan L. Fox, Nattai R. Borges, and Vincent J. Dalbo
Declines in high-intensity activity during game play (in-game approach) and performance tests measured pre- and postgame (across-game approach) have been used to assess player fatigue in basketball. However, a direct comparison of these approaches is not available. Consequently, this study examined the commonality between in- and across-game jump fatigue during simulated basketball game play.
Australian, state-level, junior male basketball players (n = 10; 16.6 ± 1.1 y, 182.4 ± 4.3 cm, 68.3 ± 10.2 kg) completed 4 × 10-min standardized quarters of simulated basketball game play. In-game jump height during game play was measured using video analysis, while across-game jump height was determined pre-, mid-, and postgame play using an in-ground force platform. Jump height was determined using the flight-time method, with jump decrement calculated for each approach across the first half, second half, and entire game.
A greater jump decrement was apparent for the in-game approach than for the across-game approach in the first half (37.1% ± 11.6% vs 1.7% ± 6.2%; P = .005; d = 3.81, large), while nonsignificant, large differences were evident between approaches in the second half (d = 1.14) and entire game (d = 1.83). Nonsignificant associations were evident between in-game and across-game jump decrement, with shared variances of 3–26%.
Large differences and a low commonality were observed between in- and across-game jump fatigue during basketball game play, suggesting that these approaches measure different constructs. Based on our findings, it is not recommended that basketball coaches use these approaches interchangeably to monitor player fatigue 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.
Aaron T. Scanlan, Robert Stanton, Charli Sargent, Cody O’Grady, Michele Lastella, and Jordan L. Fox
Purpose: To quantify and compare internal and external workloads in regular and overtime games and examine changes in relative workloads during overtime compared with other periods in overtime games in male basketball players. Methods: Starting players for a semiprofessional male basketball team were monitored during 2 overtime games and 2 regular games (nonovertime) with similar contextual factors. Internal (rating of perceived exertion and heart-rate variables) and external (PlayerLoad and inertial movement analysis variables) workloads were quantified across games. Separate linear mixed-models and effect-size analyses were used to quantify differences in variables between regular and overtime games and between game periods in overtime games. Results: Session rating-of-perceived-exertion workload (P = .002, effect size 2.36, very large), heart-rate workload (P = .12, 1.13, moderate), low-intensity change-of-direction events to the left (P = .19, 0.95, moderate), medium-intensity accelerations (P = .12, 1.01, moderate), and medium-intensity change-of-direction events to the left (P = .10, 1.06, moderate) were higher during overtime games than during regular games. Overtime periods also exhibited reductions in relative PlayerLoad (first quarter P = .03, −1.46, large), low-intensity accelerations (first quarter P = .01, −1.45, large; second quarter P = .15, −1.22, large), and medium-intensity accelerations (first quarter P = .09, −1.32, large) compared with earlier periods. Conclusions: Overtime games disproportionately elevate perceptual, physiological, and acceleration workloads compared with regular games in starting basketball players. Players also perform at lower external intensities during overtime periods than earlier quarters during basketball games.
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.