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

2021 ◽  
pp. 1-8
Author(s):  
Markus N.C. Williams ◽  
Jordan L. Fox ◽  
Cody J. O’Grady ◽  
Samuel Gardner ◽  
Vincent J. Dalbo ◽  
...  
Keyword(s):  
Mixed Models ◽  
Perceived Exertion ◽  
Late Phase ◽  
Movement Analysis ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
External Monitoring ◽  
Weekly Training ◽  
The Stability ◽  
Training Demands

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.

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

2020 ◽  
pp. 1-7
Author(s):  
Markus N.C. Williams ◽  
Vincent J. Dalbo ◽  
Jordan L. Fox ◽  
Cody J. O’Grady ◽  
Aaron T. Scanlan
Keyword(s):  
Heart Rate ◽  
Mixed Models ◽  
Perceived Exertion ◽  
Movement Analysis ◽  
Rating Of Perceived Exertion ◽  
Effect Sizes ◽  
Basketball Players ◽  
Several Variables ◽  
Longitudinal Observational Study ◽  
Weekly Training

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.

Working Overtime: The Effects of Overtime Periods on Game Demands in Basketball Players

2019 ◽  
Vol 14 (10) ◽  
pp. 1331-1337 ◽  
Author(s):  
Aaron T. Scanlan ◽  
Robert Stanton ◽  
Charli Sargent ◽  
Cody O’Grady ◽  
Michele Lastella ◽  
...  
Keyword(s):  
Heart Rate ◽  
Effect Size ◽  
Perceived Exertion ◽  
Movement Analysis ◽  
Rating Of Perceived Exertion ◽  
Intensity Change ◽  
Basketball Players ◽  
Change Of Direction ◽  
Low Intensity ◽  
Medium Intensity

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.

scholarly journals Differences in Weekly Load Distribution Over Two Euroleague Seasons with a Different Head Coach

2020 ◽  
Vol 17 (8) ◽  
pp. 2812
Author(s):  
Hugo Salazar ◽  
Luka Svilar ◽  
Ane Aldalur-Soto ◽  
Julen Castellano
Keyword(s):  
External Load ◽  
Perceived Exertion ◽  
Management Program ◽  
Rating Of Perceived Exertion ◽  
Load Management ◽  
Head Coach ◽  
Basketball Players ◽  
Professional Basketball ◽  
Coaching Strategies ◽  
Weekly Training

The weekly training management and competition loads are important aspects to optimize the performance of professional basketball players. The objectives of the study were (a) to describe the weekly external load (EL), as well as the internal response (IR), of elite basketball players over two consecutive seasons with a different head coach and (b) to compare weekly loads of different competitive densities. The data were collected from 27 elite players from the same team competing in the Spanish first division league (ACB) and EuroLeague during 2017–2018 and 2018–2019 seasons. EL was measured using microsensor technology to determine PlayerLoad values, expressed in arbitrary units (AU). Session rating of perceived exertion (sRPE) was used for IR quantification. Comparisons between the two seasons and of weeks with different competitive densities were made. The inter-week load variability was moderate-high for both seasons. The highest EL values were measured during the weeks with three games (W3) (W3 > W0 > W2 > W1), while the most demanding week for players’ IR was observed during weeks with no competition (W0). Additionally, higher EL (d = 0.31) and IR (d = 0.37) values were observed in season 2018–2019 compared to 2017–2018. The results obtained in this study contributed new data on the internal and external load required by professional basketball players in weeks with different number of games and showed that different coaching strategies may demand a different external and internal workload in consecutive seasons. Furthermore, the results highlighted the need to carry out an adequate load management program.

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

2020 ◽  
Vol 15 (4) ◽  
pp. 450-456 ◽  
Author(s):  
Jordan L. Fox ◽  
Robert Stanton ◽  
Charli Sargent ◽  
Cody J. O’Grady ◽  
Aaron T. Scanlan
Keyword(s):  
Heart Rate ◽  
High Intensity ◽  
Perceived Exertion ◽  
Contextual Factors ◽  
Relative Number ◽  
Movement Analysis ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
Mixed Effect ◽  
The Impact

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.

scholarly journals Perceived training load and jumping responses following nine weeks of a competitive period in young female basketball players

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5225 ◽  
Author(s):  
Igor de Freitas Cruz ◽  
Lucas Adriano Pereira ◽  
Ronaldo Kobal ◽  
Katia Kitamura ◽  
Cristiano Cedra ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Training Session ◽  
Young Female ◽  
Rating Of Perceived Exertion ◽  
Multivariate Approach ◽  
Total Quality ◽  
Countermovement Jump ◽  
Young Athletes ◽  
Basketball Players ◽  
Weekly Training

The aims of this study were to describe the session rating of perceived exertion (sRPE), total quality recovery (TQR), and variations in countermovement jump (CMJ) height throughout nine weeks of a competitive period in young female basketball players. In total, 10 young female basketball players (17.2 ± 0.4 years; 71.8 ± 15.0 kg; 177.2 ± 9.5 cm) participated in this study. The sRPE and TQR were assessed in each training session, whereas the CMJ height was assessed prior to the first weekly training session. The magnitude-based inferences method was used to compare the sRPE, TQR, and CMJ height across the nine weeks of training. The training loads accumulated in weeks 1, 2, and 3 were likely to almost certainly be higher than in the following weeks (ES varying from 0.67 to 2.55). The CMJ height in week 1 was very likely to be lower than in weeks 2, 5, 7, and 8 (ES varying from 0.24 to 0.34), while the CMJ height of the 9th week was likely to almost certainly be higher than all previous weeks of training (ES varying from 0.70 to 1.10). Accordingly, it was observed that when higher training loads were accumulated, both CMJ and TQR presented lower values than those presented during periods with lower internal training loads. These results highlight the importance of using a comprehensive and multivariate approach to effectively monitor the physical performance of young athletes.

The Business End of the Season: A Comparison Between Playoff and Regular-Season Workloads in Professional Basketball Players

2020 ◽  
pp. 1-8
Author(s):  
Davide Ferioli ◽  
Aaron T. Scanlan ◽  
Daniele Conte ◽  
Emanuele Tibiletti ◽  
Ermanno Rampinini
Keyword(s):  
Repeated Measures ◽  
Significant Interaction ◽  
Perceived Exertion ◽  
Rating Of Perceived Exertion ◽  
Effect Sizes ◽  
Basketball Players ◽  
Professional Basketball ◽  
Weekly Training ◽  
Exploratory Investigation ◽  
Repeated Measures Analysis

Purpose: To quantify and compare the internal workloads experienced during the playoffs and regular season in basketball. Methods: A total of 10 professional male basketball players competing in the Italian first division were monitored during the final 6 weeks of the regular season and the entire 6-week playoff phase. Internal workload was quantified using the session rating of perceived exertion (s-RPE) method for all training sessions and games. A 2-way repeated-measures analysis of variance (day type × period) was utilized to assess differences in daily s-RPE between game days, days within 24 hours of games, and days >24 hours from games during the playoffs and regular season. Comparisons in weekly training, game, and total workloads were made between the playoffs and regular season using paired t tests and effect sizes. Results: A significant interaction between day and competitive period for s-RPE was found (P = .003, moderate). Lower s-RPE was apparent during playoff and regular-season days within 24 hours of games than all other days (P < .001, very large). Furthermore, s-RPE across days >24 hours from playoff games was different than all other days (P ≤ .01, moderate–very large). Weekly training (P = .009, very large) and total (P < .001, moderate) s-RPE were greater during the regular season than playoffs, whereas weekly game s-RPE was greater during the playoffs than the regular season (P < .001, very large). Conclusions: This study presents an exploratory investigation of internal workload during the playoffs in professional basketball. Players experienced greater training and total weekly workloads during the regular season than during the playoffs with similar daily game workloads between periods.

Monitoring Workload in Elite Female Basketball Players During the In-Season Phase: Weekly Fluctuations and Effect of Playing Time

2019 ◽  
Vol 14 (7) ◽  
pp. 941-948 ◽  
Author(s):  
Henrikas Paulauskas ◽  
Rasa Kreivyte ◽  
Aaron T. Scanlan ◽  
Alexandre Moreira ◽  
Laimonas Siupsinskas ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Linear Models ◽  
Training Session ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
Significant Difference ◽  
Women's Basketball ◽  
Playing Time ◽  
And Training ◽  
Basketball Teams

Purpose:To assess the weekly fluctuations in workload and differences in workload according to playing time in elite female basketball players.Methods:A total of 29 female basketball players (mean [SD] age 21 [5] y, stature 181 [7] cm, body mass 71 [7] kg, playing experience 12 [5] y) belonging to the 7 women’s basketball teams competing in the first-division Lithuanian Women’s Basketball League were recruited. Individualized training loads (TLs) and game loads (GLs) were assessed using the session rating of perceived exertion after each training session and game during the entire in-season phase (24 wk). Percentage changes in total weekly TL (weekly TL + GL), weekly TL, weekly GL, chronic workload, acute:chronic workload ratio, training monotony, and training strain were calculated. Mixed linear models were used to assess differences for each dependent variable, with playing time (low vs high) used as fixed factor and subject, week, and team as random factors.Results:The highest changes in total weekly TL, weekly TL, and acute:chronic workload ratio were evident in week 13 (47%, 120%, and 49%, respectively). Chronic workload showed weekly changes ≤10%, whereas monotony and training strain registered highest fluctuations in weeks 17 (34%) and 15 (59%), respectively. A statistically significant difference in GL was evident between players completing low and high playing times (P = .026, moderate), whereas no significant differences (P > .05) were found for all other dependent variables.Conclusions:Coaches of elite women’s basketball teams should monitor weekly changes in workload during the in-season phase to identify weeks that may predispose players to unwanted spikes and adjust player workload according to playing time.

The Concurrent Validity of Session-Rating of Perceived Exertion Workload Obtained Face-to-Face Versus Via an Online Application: A Team Case Study

2020 ◽  
Vol 15 (10) ◽  
pp. 1476-1479
Author(s):  
Jordan L. Fox ◽  
Cody J. O’Grady ◽  
Aaron T. Scanlan
Keyword(s):  
Heart Rate ◽  
Repeated Measures ◽  
Concurrent Validity ◽  
Perceived Exertion ◽  
State Level ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
Online Application ◽  
Face To Face

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.

scholarly journals The Quantification of Within-Week Session Intensity, Duration, and Intensity Distribution Across a Season in Australian Football Using the Session Rating of Perceived Exertion Method

2018 ◽  
Vol 13 (7) ◽  
pp. 940-946 ◽  
Author(s):  
Farhan Juhari ◽  
Dean Ritchie ◽  
Fergus O’Connor ◽  
Nathan Pitchford ◽  
Matthew Weston ◽  
...  
Keyword(s):  
Intensity Distribution ◽  
Mixed Models ◽  
High Intensity ◽  
Perceived Exertion ◽  
Linear Mixed Models ◽  
Training Session ◽  
Rating Of Perceived Exertion ◽  
Moderate Intensity ◽  
Low Intensity ◽  
Australian Football

Context: Team-sport training requires the daily manipulation of intensity, duration, and frequency, with preseason training focusing on meeting the demands of in-season competition and training on maintaining fitness. Purpose: To provide information about daily training in Australian football (AF), this study aimed to quantify session intensity, duration, and intensity distribution across different stages of an entire season. Methods: Intensity (session ratings of perceived exertion; CR-10 scale) and duration were collected from 45 professional male AF players for every training session and game. Each session’s rating of perceived exertion was categorized into a corresponding intensity zone, low (<4.0 arbitrary units), moderate (≥4.0 and <7.0), and high (≥7.0), to categorize session intensity. Linear mixed models were constructed to estimate session duration, intensity, and distribution between the 3 preseason and 4 in-season periods. Effects were assessed using linear mixed models and magnitude-based inferences. Results: The distribution of the mean session intensity across the season was 29% low intensity, 57% moderate intensity, and 14% high intensity. While 96% of games were high intensity, 44% and 49% of skills training sessions were low intensity and moderate intensity, respectively. Running had the highest proportion of high-intensity training sessions (27%). Preseason displayed higher training-session intensity (effect size [ES] = 0.29–0.91) and duration (ES = 0.33–1.44), while in-season game intensity (ES = 0.31–0.51) and duration (ES = 0.51–0.82) were higher. Conclusions: By using a cost-effective monitoring tool, this study provides information about the intensity, duration, and intensity distribution of all training types across different phases of a season, thus allowing a greater understanding of the training and competition demands of Australian footballers.

Player Session Rating of Perceived Exertion: A More Valid Tool Than Coaches’ Ratings to Monitor Internal Training Load in Elite Youth Female Basketball

2020 ◽  
Vol 15 (4) ◽  
pp. 548-553 ◽  
Author(s):  
Corrado Lupo ◽  
Alexandru Nicolae Ungureanu ◽  
Riccardo Frati ◽  
Matteo Panichi ◽  
Simone Grillo ◽  
...  
Keyword(s):  
Body Mass ◽  
Perceived Exertion ◽  
Team Sports ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
Mixed Effect ◽  
Maximal Intensity ◽  
Session Duration ◽  
Post Hoc

Purpose: To monitor elite youth female basketball training to verify whether players’ and coaches’ (3 technical coaches and 1 physical trainer) session rating of perceived exertion (s-RPE) has a relationship with Edwards’ method. Methods: Heart rate of 15 elite youth female basketball players (age 16.7 [0.5] y, height 178 [9] cm, body mass 72 [9] kg, body mass index 22.9 [2.2] kg·m−2) was monitored during 19 team (268 individual) training sessions (102 [15] min). Mixed effect models were applied to evaluate whether s-RPE values were significantly (P ≤ .05) related to Edwards’ data, total session duration, maximal intensity (session duration at 90–100% HRmax), type of training (ie, strength, conditioning, and technique), and whether differences emerged between players’ and coaches’ s-RPE values. Results: The results showed that there is a relationship between s-RPE and Edwards’ methods for the players’ RPE scores (P = .019) but not for those of the trainers. In addition, as expected, both players’ (P = .014) and coaches’ (P = .002) s-RPE scores were influenced by total session duration but not by maximal intensity and type of training. In addition, players’ and coaches’ s-RPE values differed (P < .001)—post hoc differences emerged for conditioning (P = .01) and technique (P < .001) sessions. Conclusions: Elite youth female basketball players are better able to quantify the internal training load of their sessions than their coaches, strengthening the validity of s-RPE as a tool to monitor training in team sports.

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