Are acute player workloads associated with in-game performance in basketball?

To investigate associations between acute workload and in-game performance in basketball. Eight semi-professional, male basketball players were monitored during all training sessions (N = 28) and games (N = 18) across the season. External workload was determined using absolute (arbitrary units[AU]) and relative (AU·min-1) PlayerLoadTM (PL), and absolute (count) and relative (count·min-1) low-intensity, medium-intensity, high-intensity, and total Inertial Movement Analysis (IMA) events (accelerations, decelerations, changes-of-direction, and jumps). Internal workload was determined using absolute and relative Summated-Heart-Rate-Zones workload, session-rating of perceived exertion, rating of perceived exertion, and time (min) spent working > 90% of maximal heart rate. In-game performance was indicated by the player efficiency statistic. Repeated measures correlations were used to determine associations between acute workload variables (across the previous 7 days) and player efficiency. Relative PL (r = 0.13, small) and high-intensity IMA events (r = 0.13, small) possessed the strongest associations with player efficiency of the investigated workload variables (P > 0.05). All other associations were trivial in magnitude (P > 0.05). Given the trivial-small associations between all external and internal workload variables and player efficiency, basketball practitioners should not rely solely on monitoring acute workloads to determine performance potential in players.

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.

Weekly Training Demands Increase, but Game Demands Remain Consistent Across Early, Middle, and Late Phases of the Regular Season in Semiprofessional Basketball Players.

PURPOSE: To compare weekly training, game, and overall (training and games) demands across phases of the regular season in basketball. METHODS: Seven semiprofessional, male basketball players were monitored during all on-court team-based training sessions and games during the regular season. External monitoring variables included PlayerLoad™ and inertial movement analysis events per minute. Internal monitoring variables included a modified summated heart rate zones model calculated per minute and rating of perceived exertion. Linear mixed models were used to compare training, game, and overall demands between 5-week phases (early, middle, and late) of the regular season with significance set at P ≤ .05. Effect sizes were calculated between phases and interpreted as: trivial, <0.20; small, 0.20 to 0.59; moderate, 0.60 to 1.19; large, 1.20 to 1.99; very large, ≥2.00. RESULTS: Greater (P > .05) overall inertial movement analysis events (moderate-very large) and rating of perceived exertion (moderate) were evident in the late phase compared with earlier phases. During training, more accelerations were evident in the middle (P = .01, moderate) and late (P = .05, moderate) phases compared with the early phase, while higher rating of perceived exertion (P = .04, moderate) was evident in the late phase compared with earlier phases. During games, nonsignificant, trivial-small differences in demands were apparent between phases. CONCLUSIONS: Training and game demands should be interpreted in isolation and combined given overall player demands increased as the season progressed, predominantly due to modifications in training demands given the stability of game demands. Periodization strategies administered by coaching staff may have enabled players to train at greater intensities late in the season without compromising game intensity.

Call to Action: Recommendations to Improve the Methodological Reporting of Games-Based Drills in Basketball Research.

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.

Comparing Weekly Training and Game Demands According to Playing Position in a Semiprofessional Basketball Team.

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.

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.

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 workload management across the season.

A Systematic Review of the External and Internal Workloads Experienced During Games-Based Drills in Basketball Players

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.

The Impact of Contextual Factors on Game Demands in Starting, Semiprofessional, Male Basketball Players.

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.

Losing Sleep Over It: Sleep in Basketball Players Affected by Game But Not Training Workloads.

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.

External Workload Can Be Anticipated During 5 vs. 5 Games-Based Drills in Basketball Players: An Exploratory Study.

This study determined whether external workload could be anticipated during 5 vs. 5 games-based drills in basketball. Thirteen semi-professional, male basketball players were monitored during 5 vs. 5 training drills across the season. External workload was determined using PlayerLoad™ (AU∙min-1). The reference workload for each drill was calculated across all sessions, using bootstrapping. The bootstrap mean workload and 95% confidence intervals (CI) were then calculated for session 1, sessions 1-2, and continued for remaining sessions (1-3, 1-4, etc.), and were compared with those of the reference workload. The minimum sessions to anticipate workload for each drill was identified when the first normative value fell within ±5% or ±10% of the reference workload 95% CI. The minimum sessions were then tested to determine the accuracy to which workload could be anticipated. Three to four sessions were needed to anticipate workload within ±5%, while 2-3 sessions were needed to anticipate workload within ±10%. External workload was anticipated in 0-55% of future sessions using an error range of ±5%, and in 58-89% of sessions using an error range of ±10%. External workload during 5 vs. 5 games-based drills can be anticipated in most sessions using normative values established during a short-term monitoring period with an error range of ±10%.

Validity of the Polar Team Pro Sensor for measuring speed and distance indoors.

OBJECTIVES: To assess the validity of the Polar Team Pro Sensor for measuring speed and distance indoors during continuous locomotive and change-of-direction tasks at low, medium, and high intensities. DESIGN: Descriptive validation study. METHODS: 26 recreationally-active participants (age: 32.2 ±â€¯11.0 yr; stature: 173.3 ±â€¯9.9 cm; body mass: 74.2 ±â€¯16.2 kg) completed three trials of low- (walking speed), medium- (jogging speed), and high-intensity (maximal sprinting speed) continuous locomotive and change-of-direction tasks. Participants wore back- and chest-mounted sensors to determine mean speed and total distance covered. One-way analysis of variance, t-tests, Pearson's Product moment correlation, and Bland-Altman plots were utilised to compare the speed and distance measured with the back- and chest-mounted sensors to reference measures (measured distance of the court via a trundle wheel and speed derived from measured distance and electronic timing lights). RESULTS: Speed and distance measured using the back- and chest-mounted sensors showed wide limits of agreement, which increased at high intensities for speed. The sensors typically underestimated speed and distance by as much as 2.76 km h-1 and 32.6 m, and overestimated speed and distance by as much as 4.52 km h-1 and 59.6 m across tasks and intensities compared to reference measures (168.45 and 40.00 m). CONCLUSIONS: There was low agreement between both back- and chest-mounted sensors and the reference devices for measuring speed and distance indoors. Practitioners should understand the limitations and potential for error when using the Polar Team Pro Sensors indoors to measure speed and distance during continuous locomotive and change-of-direction tasks.