Purpose: To characterize the weekly training load (TL) and well-being of college basketball players during the in-season phase. Methods: Ten (6 guards and 4 forwards) male basketball players (age 20.9 [0.9] y, stature 195.0 [8.2] cm, and body mass 91.3 [11.3] kg) from the same Division I National Collegiate Athletic Association team were recruited to participate in this study. Individualized training and game loads were assessed using the session rating of perceived exertion at the end of each training and game session, and well-being status was collected before each session. Weekly changes (%) in TL, acute-to-chronic workload ratio, and well-being were determined. Differences in TL and well-being between starting and bench players and between 1-game and 2-game weeks were calculated using magnitude-based statistics. Results: Total weekly TL and acute-to-chronic workload ratio demonstrated high week-to-week variation, with spikes up to 226% and 220%, respectively. Starting players experienced a higher (most likely negative) total weekly TL and similar (unclear) well-being status compared with bench players. Game scheduling influenced TL, with 1-game weeks demonstrating a higher (likely negative) total weekly TL and similar (most likely trivial) well-being status compared with 2-game weeks. Conclusions: These findings provide college basketball coaches information to optimize training strategies during the in-season phase. Basketball coaches should concurrently consider the number of weekly games and player status (starting vs bench player) when creating individualized periodization plans, with increases in TL potentially needed in bench players, especially in 2-game weeks.
Daniele Conte, Nicholas Kolb, Aaron T. Scanlan, and Fabrizio Santolamazza
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.
Jordan L. Fox, Jesse Green, and Aaron T. Scanlan
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.
Nathan Elsworthy, Michele Lastella, Aaron T. Scanlan, and Matthew R. Blair
Purpose : To examine the effect of match schedule on self-reported wellness and sleep in rugby union referees during the 2019 Rugby World Cup. Methods : Following an observational design, 18 international-level male referees participating in the 2019 Rugby World Cup completed a daily questionnaire to quantify wellness status (sleep quality, mood, stress, fatigue, muscle soreness, and total wellness) and sleep characteristics (bedtime, wake-up time, and time in bed) from the previous night across the tournament. Linear mixed models and effect sizes (Hedges g av) assessed differences in wellness and sleep characteristics between prematch and postmatch days surrounding single-game and 2-game congested match schedules (prematch1, postmatch1, prematch2, and postmatch2 days). Results: During regular schedules, all self-reported wellness variables except stress were reduced (g av = 0.33–1.05, mean difference −0.32 to −1.21 arbitrary units [AU]) and referees went to bed later (1.08, 1:07 h:min) and spent less time in bed (−0.78, 00:55 h:min) postmatch compared with prematch days. During congested schedules, only wellness variables differed across days, with total wellness reduced on postmatch1 (−0.88, −3.56 AU) and postmatch2 (−0.67, −2.70 AU) days, as well as mood (−1.01, −0.56 AU) and fatigue (−0.90, −1.11 AU) reduced on postmatch1 days compared with prematch days. Conclusion: Referees were susceptible to acute reductions in wellness on days following matches regardless of schedule. However, only single-game regular match schedules negatively impacted the sleep characteristics of referees. Targeted strategies to maximize wellness status and sleep opportunities in referees considering the match schedule faced should be explored during future Rugby World Cup competitions.
Aaron T. Scanlan, Neal Wen, Patrick S. Tucker, Nattai R. Borges, and Vincent J. Dalbo
To compare perceptual and physiological training-load responses during various basketball training modes.
Eight semiprofessional male basketball players (age 26.3 ± 6.7 y, height 188.1 ± 6.2 cm, body mass 92.0 ± 13.8 kg) were monitored across a 10-wk period in the preparatory phase of their training plan. Player session ratings of perceived exertion (sRPE) and heart-rate (HR) responses were gathered across base, specific, and tactical/game-play training modes. Pearson correlations were used to determine the relationships between the sRPE model and 2 HR-based models: the training impulse (TRIMP) and summated HR zones (SHRZ). One-way ANOVAs were used to compare training loads between training modes for each model.
Stronger relationships between perceptual and physiological models were evident during base (sRPE-TRIMP r = .53, P < .05; sRPE-SHRZ r = .75, P < .05) and tactical/game-play conditioning (sRPE-TRIMP r = .60, P < .05; sRPE-SHRZ r = .63; P < .05) than during specific conditioning (sRPE-TRIMP r = .38, P < .05; sRPE-SHRZ r = .52; P < .05). Furthermore, the sRPE model detected greater increases (126–429 AU) in training load than the TRIMP (15–65 AU) and SHRZ models (27–170 AU) transitioning between training modes.
While the training-load models were significantly correlated during each training mode, weaker relationships were observed during specific conditioning. Comparisons suggest that the HR-based models were less effective in detecting periodized increases in training load, particularly during court-based, intermittent, multidirectional drills. The practical benefits and sensitivity of the sRPE model support its use across different basketball training modes.
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.
Nattai R. Borges, Aaron T. Scanlan, Peter R. Reaburn, and Thomas M. Doering
Purpose: Due to age-related changes in the psychobiological state of masters athletes, this brief report aimed to compare training load responses using heart rate (HR) and ratings of perceived exertion (RPE) during standardized training sessions between masters and young cyclists. Methods: Masters (n = 10; 55.6 [5.0] y) and young (n = 8; 25.9 [3.0] y) cyclists performed separate endurance and high-intensity interval training sessions. Endurance intensity was set at 95% of ventilatory threshold 2 for 1 hour. High-intensity interval training consisted of 6 × 30-second intervals at 175% peak power output with 4.5-minute rest between intervals. HR was monitored continuously and RPE collected at standardized time periods during each session. Banister training impulse and summated-HR-zones training loads were also calculated. Results: Despite a significantly lower mean HR in masters cyclists during endurance (P = .04; d = 1.06 [±0.8], moderate) and high-intensity interval training (P = .01; d = 1.34 [±0.8], large), no significant differences were noted (P > .05) when responses were determined relative to maximum HR or converted to training impulse and summated-HR-zone loads. Furthermore, no interaction or between-group differences were evident for RPE across either session (P > .05). Conclusions : HR and RPE values were comparable between masters and young cyclists when relative HR responses and HR training load models are used. This finding suggests HR and RPE methods used to monitor or prescribe training load can be used interchangeably between masters and young athletes irrespective of chronological age.
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.
David Suárez-Iglesias, Rubén Dehesa, Aaron T. Scanlan, José A. Rodríguez-Marroyo, and Alejandro Vaquera
Purpose: Games-based drills (GBD) are the predominant form of training stimulus prescribed to male and female basketball players. Despite being readily manipulated during GBD, the impact of defensive strategy on the sex-specific demands of GBD remains unknown. Therefore, the aim of this study was to quantify and compare the heart-rate (HR) responses experienced during 5v5 GBD using different defensive strategies (man-to-man defense vs zone defense [ZD] formations) according to player sex.
Method: HR was recorded in 11 professional male and 10 professional female basketball players while performing 5v5 GBD with different defensive strategies (man-to-man defense or ZD). HR-based training load was also calculated using the summated heart-rate zones model.
Results: During man-to-man defense, mean HR (