The association between workload, sleep, and performance in basketball players

In basketball, delivering appropriate workload stimuli while managing recovery between training sessions and competition is essential to promote favourable adaptations in players and to optimise performance. Sleep is a modifiable factor recognised as one of the most effective recovery interventions available to basketball players. While workload and sleep are conceptually important for player performance, investigations directly examining the associations between workload, sleep, and performance in basketball players are limited. To address this gap, the aims of this thesis were to: 1) review current literature to identify the associations between workload and performance in team sports; 2) review current literature examining sleep in athletes, including factors affecting sleep and subsequent performance; 3) examine player monitoring approaches used by basketball coaches, along with barriers and facilitators to player monitoring; 4) assess the impact of training and game workloads on sleep duration and sleep quality in basketball players; 5) determine the associations between acute player workloads and in-game performance; and 6) identify the cumulative effects of sleep over 1-4 nights prior to competition on in-game performance. Review of the literature revealed limited available research on the association between workload and performance in team sports including basketball. Furthermore, investigations documenting the associations between workload and sleep, and sleep and performance in athletes are lacking. The online survey demonstrated that while basketball practitioners find potential value in player monitoring, the implementation of monitoring is limited. One of the primary barriers to implementing player monitoring in basketball is a lack of understanding regarding which outcomes should be monitored, and how player monitoring data should be utilised in practice. As such, the overall findings of this thesis are extremely valuable to basketball practitioners given insight is provided regarding scenarios which may leave players susceptible to poor sleep as well as identifying which workload and sleep variables may be most useful to monitor in practice to optimise performance potential of players. The original research reported in this thesis revealed that following games where physical (PlayerLoad [PL]) and perceptual (session-rating of perceived exertion [sRPE]) demands were high, sleep duration was significantly restricted in basketball players, which may have implications for recovery following competition. Regarding workload, all investigated variables possessed non-significant relationships with in-game performance. However, overall and high-intensity external workload (PL and high-intensity inertial movement analysis [IMA] events [accelerations, decelerations, changes of direction, and jumps combined]) accumulated over the 7 days prior to competition and expressed per minute revealed small, positive associations with in-game performance in basketball players. For sleep variables, sleep efficiency captured 1 night prior to competition and subjective sleep quality accumulated over 1, 2, 3, and 4 nights prior to competition were significantly associated with in-game performance in basketball players. Furthermore, later wake times were significantly associated with favourable in-game performance in basketball players. In combination, the thesis findings support routine monitoring of player workloads and sleep across the season; however, monitoring of acute sleep variables may be more important to understand in-game performance potential than monitoring of acute workload variables in basketball players. Specifically, following competition, particularly where in-game workload demands are high, sleep duration in players appears particularly important to monitor. Suboptimal sleep duration following competition may be mitigated by adjusting training or travel schedules that enable players to increase their opportunity for sleep via later wake times. Basketball practitioners should implement objective and subjective monitoring of sleep quality leading into competition and avoid early wake times where possible to maximise sleep duration and promote favorable in-game performance

Not all about the effort? A comparison of playing intensities during winning and losing game quarters in basketball

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

A review of player monitoring approaches in basketball: Current trends and future directions

© 2017 National Strength and Conditioning Association. Fox, JL, Scanlan, AT, and Stanton, R. A review of player monitoring approaches in basketball: Current trends and future directions. J Strength Cond Res 31(7): 2021-2029, 2017-Effective monitoring of players in team sports such as basketball requires an understanding of the external demands and internal responses, as they relate to training phases and competition. Monitoring of external demands and internal responses allows coaching staff to determine the dose-response associated with the imposed training load (TL), and subsequently, if players are adequately prepared for competition. This review discusses measures reported in the literature for monitoring the external demands and internal responses of basketball players during training and competition. The external demands of training and competition were primarily monitored using time-motion analysis, with limited use of microtechnology being reported. Internal responses during training were typically measured using hematological markers, heart rate, various TL models, and perceptual responses such as rating of perceived exertion (RPE). Heart rate was the most commonly reported indicator of internal responses during competition with limited reporting of hematological markers or RPE. These findings show a large discrepancy between the reporting of external and internal measures and training and competition demands. Microsensors, however, may be a practical and convenient method of player monitoring in basketball to overcome the limitations associated with current approaches while allowing for external demands and internal responses to be recorded simultaneously. The triaxial accelerometers of microsensors seem well suited for basketball and warrant validation to definitively determine their place in the monitoring of basketball players. Coaching staff should make use of this technology by tracking individual player responses across the annual plan and using real-time monitoring to minimize factors such as fatigue and injury risk

The relationships between external and internal workloads during basketball training and games

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 workloadmanagement across the season

Game schedule congestion affects weekly workloads but not individual game demands in semi-professional basketball

To quantify and compare workloads encountered by basketball players during individual games played across 1-, 2-, and 3-day periods in the same week, and during weeks where 1, 2, and 3 games are scheduled. Eight semi-professional male players were monitored. External workload was determined as absolute and relative (·min-1) PlayerLoad (PL), and total and high-intensity jumps, accelerations, decelerations, and changes of direction (COD). Internal workload was determined as absolute and relative summated heart rate zones (SHRZ), session-rating of perceived exertion (sRPE), and RPE. Game workloads were tabulated considering the order in which they were scheduled weekly (game 1, 2, or 3), and each week considering the number of games scheduled (1, 2, or 3 games). Analysing weekly workloads, duration was higher during 3-game than 1- and 2-game weeks (P <0.05, ES = 6.65-18.19). High-intensity decelerations and COD were higher during 3-game than 1-game weeks (P <0.05, ES = 1.26-1.55). Absolute PL, jumps, accelerations, decelerations, COD, and high-intensity jumps and accelerations were higher during 3-game than 1- and 2-game weeks (P <0.05, ES = 0.69-2.63). Absolute SHRZ and sRPE were higher during 3-game than 1- and 2-game weeks (P <0.05, ES = 0.86-2.43). Players completed similar individual game workloads regardless of the number of games played on consecutive days in the week. Workloads were similar during 1- and 2-game weeks, while the addition of a third game significantly increased the overall weekly workloads encountered

A comparison of training and competition demands in semiprofessional male basketball players

© 2018 SHAPE America 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

The contribution of linear sprinting and lateral shuffling to change of direction T-Test performance in semi-professional, male basketball players

This study aims to examine the influence of linear sprint and lateral shuffle speed and acceleration on change of direction (COD) T-test performance in basketball players. Semi-professional, male basketball players (N = 9; 20.4 ± 4.5 years; 187.4 ± 7.6 cm; 86.2 ± 12.1 kg) completed 3 x 20-m linear sprinting trials, 2 x 20-m lateral shuffling trials in each direction, and 2 x COD T-test trials. Sprinting and shuffling outcome measures included 0-5, 0-10, and 5-10 m speed and acceleration, while total time during the COD T-test was taken as a further outcome measure. Correlational and regression analyses were utilised to determine the influence of each sprinting and shuffling measure on COD T-test performance. All linear speed measures were significantly (P < 0.006) related to COD T-test performance (R = -0.92 to -0.83). Only 5-10 m, right lateral shuffle speed was significantly (P = 0.019) related to COD T-test performance (R = -0.75, very large). These findings demonstrate a stronger influence of sprint speed on COD T-test performance than previously reported in basketball players, and provide novel insight regarding lateral shuffling ability. The weaker relationships observed for lateral measures on COD T-test performance are likely due to the mismatched directional demands, complexity, and non-game-specific distances embedded in the shuffling requirements of the test. As such, practical limitations of the COD T-test are highlighted in the present study, including an overt influence of linear speed and disproportionate influence on shuffling ability in the right direction on test performance. Basketball strength and conditioning staff and sport scientists should recognise that COD T-test performance may be strongly reflective of sprinting and right shuffling abilities when choosing player assessment approaches

The concurrent validity of session-rating of perceived exertion workload obtained face-to-face versus via an online application: A team case study

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. © 2020 Human Kinetics, Inc

Peak external intensity decreases across quarters during basketball games

The purpose of this study was to compare peak external intensities across game quarters in basketball. Eight semi-professional male players were monitored using accelerometers. For all quarters, peak intensities were determined via moving averages for PlayerLoad/minute (PL·min-1) using sample durations of 15 s, 30 s, 1 min, 2 min, 3 min, 4 min, and 5 min. Linear mixed models and effect sizes (ES) were used to compare peak intensities between quarters for each sample duration. Small decreases in peak PL·min-1 occurred between Quarters 1 and 4 for all sample durations (ES = 0.21-0.49). Small decreases in peak PL·min-1 were apparent between quarters 1 and 2 for 30-s, 1-min, and 3-min sample durations (ES = 0.24-0.33), and between quarters 3 and 4 for 2-5-min sample durations (ES = 0.20-0.24). Peak intensities decline across quarters with game progression in basketball, providing useful insight for practitioners to develop game-specific training and tactical strategies

Insufficient sleep in young athletes? Causes, consequences, and potential treatments

Sleep is essential in the preparation for, and the recovery from, training and competition. Despite being important for all individuals, young athletes are considered an at-risk group for reduced sleep duration and quality. The purpose of this review is to synthesise current literature relating to sleep duration and quality in young (14–25 years) athletes. Specifically, typical sleep and wake patterns, factors affecting sleep and wake patterns, and the consequences of altered sleep and wake patterns in young athletes are discussed. Scheduling training and competition in the afternoon or evening appears to result in reduced sleep duration due to less time in bed. Evidence suggests that young athletes who obtain less than 8 h of sleep per night are at a higher risk of musculoskeletal injury. An increase in sleep duration above habitual nightly sleep may be associated with favourable performance in young athletes; however, the associations between sleep quality and performance- and health-related outcomes remain unclear. © 2019, Springer Nature Switzerland AG