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 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.

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.

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.

The Relationship Between Training Load and Injury in Men’s Professional Basketball

2017 ◽  
Vol 12 (9) ◽  
pp. 1238-1242 ◽  
Author(s):  
Kaitlyn J. Weiss ◽  
Sian V. Allen ◽  
Mike R. McGuigan ◽  
Chris S. Whatman
Keyword(s):  
Regression Model ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Self Report ◽  
Basketball Players ◽  
Professional Basketball ◽  
Trauma Research ◽  
Mean Differences ◽  
The Relationship

Purpose:To establish the relationship between the acute:chronic workload ratio and lower-extremity overuse injuries in professional basketball players over the course of a competitive season. Methods:The acute:chronic workload ratio was determined by calculating the sum of the current week’s session rating of perceived exertion of training load (acute load) and dividing it by the average weekly training load over the previous 4 wk (chronic load). All injuries were recorded weekly using a self-report injury questionnaire (Oslo Sports Trauma Research Center Injury Questionnaire20). Workload ratios were modeled against injury data using a logistic-regression model with unique intercepts for each player. Results:Substantially fewer team members were injured after workload ratios of 1 to 1.49 (36%) than with very low (≤0.5; 54%), low (0.5–0.99; 51%), or high (≥1.5; 59%) workload ratios. The regression model provided unique workload–injury trends for each player, but all mean differences in likelihood of being injured between workload ratios were unclear. Conclusions:Maintaining workload ratios of 1 to 1.5 may be optimal for athlete preparation in professional basketball. An individualized approach to modeling and monitoring the training load–injury relationship, along with a symptom-based injury-surveillance method, should help coaches and performance staff with individualized training-load planning and prescription and with developing athlete-specific recovery and rehabilitation strategies.

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.

scholarly journals Impaired Player–Coach Perceptions of Exertion and Recovery During Match Congestion

2017 ◽  
Vol 12 (9) ◽  
pp. 1151-1156 ◽  
Author(s):  
Steven H. Doeven ◽  
Michel S. Brink ◽  
Wouter G.P. Frencken ◽  
Koen A.P.M. Lemmink
Keyword(s):  
Perceived Exertion ◽  
Training Session ◽  
Rating Of Perceived Exertion ◽  
Total Quality ◽  
Basketball Players ◽  
Subsequent Recovery ◽  
Professional Basketball ◽  
Quality Of Recovery ◽  
Degree Of Recovery

During intensified phases of competition, attunement of exertion and recovery is crucial to maintain performance. Although a mismatch between coach and player perceptions of training load is demonstrated, it is unknown if these discrepancies also exist for match exertion and recovery. Purpose:To determine match exertion and subsequent recovery and to investigate the extent to which the coach is able to estimate players’ match exertion and recovery. Methods:Rating of perceived exertion (RPE) and total quality of recovery (TQR) of 14 professional basketball players (age 26.7 ± 3.8 y, height 197.2 ± 9.1 cm, weight 100.3 ± 15.2 kg, body fat 10.3% ± 3.6%) were compared with observations of the coach. During an in-season phase of 15 matches within 6 wk, players gave RPEs after each match. TQR scores were filled out before the first training session after the match. The coach rated observed exertion (ROE) and recovery (TQ-OR) of the players. Results:RPE was lower than ROE (15.6 ± 2.3 and 16.1 ± 1.4; P = .029). Furthermore, TQR was lower than TQ-OR (12.7 ± 3.0 and 15.3 ± 1.3; P < .001). Correlations between coach- and player-perceived exertion and recovery were r = .25 and r = .21, respectively. For recovery within 1 d the correlation was r = .68, but for recovery after 1–2 d no association existed. Conclusion:Players perceive match exertion as hard to very hard and subsequent recovery reasonable. The coach overestimates match exertion and underestimates degree of recovery. Correspondence between coach and players is thus not optimal. This mismatch potentially leads to inadequate planning of training sessions and decreases in performance during fixture congestion in basketball.

scholarly journals Internal and External Load Control in Team Sports through a Multivariable Model

2021 ◽  
pp. 751-758
Author(s):  
Aitor Piedra ◽  
Toni Caparrós ◽  
Jordi Vicens-Bordas ◽  
Javier Peña
Keyword(s):  
Heart Rate ◽  
External Load ◽  
Perceived Exertion ◽  
Team Sports ◽  
Neuromuscular Fatigue ◽  
Multivariable Model ◽  
Countermovement Jump ◽  
Basketball Players ◽  
Professional Basketball ◽  
Sporting Activities

Data related to 141 sessions of 10 semi-professional basketball players were analyzed during the competitive period of the 2018-2019 season using a multivariable model to determine possible associations between internal and external load variables and fatigue. Age, height, weight, sessional rate of perceived exertion (sRPE), summated-heart-rate-zones, heart rate variability, total accelerations and decelerations were the covariates, and post-session countermovement jump loss (10% or higher) the response variable. Based on the results observed, a rise in sRPE and accelerations and decelerations could be associated with increased lower-body neuromuscular fatigue. Observing neuromuscular fatigue was 1,008 times higher with each additional sRPE arbitrary unit (AU). Each additional high-intensity effort also increased the probability of significant levels of neuromuscular fatigue by 1,005 times. Fatigue arising from demanding sporting activities is acknowledged as a relevant inciting event leading to injuries. Thus, the methodology used in this study can be used then to monitor neuromuscular fatigue onset, also enhancing proper individual adaptations to training.

scholarly journals Load monitoring system in top-level basketball team

Kinesiology ◽  
2018 ◽  
Vol 50 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Luka Svilar ◽  
Igor Jukić
Keyword(s):  
External Load ◽  
Perceived Exertion ◽  
Body Height ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
Load Monitoring ◽  
Acceleration And Deceleration ◽  
And Control ◽  
High Band

The study aimed to describe and compare the external training load, monitored using microtechnology, with the internal training load, expressed as the session rating of perceived exertion (sRPE), in elite male basketball training sessions. Thirteen professional basketball players participated in this study (age=25.7±3.3 years; body height=199.2±10.7 cm; body mass=96.6±9.4 kg). All players belonged to the same team, competing in two leagues, ACB and the Euroleague, in the 2016/2017 season. The variables assessed within the external motion analysis included: Player Load (PL), acceleration and deceleration (ACC/DEC), jumps (JUMP), and changes of direction (CoD). The internal demands were registered using the sRPE method. Pearson product-moment correlations were used to determine relationships between the variables. A significant correlation was observed between the external load variables and sRPE (range r=0.71–0.93). Additionally, the sRPE variable showed a high correlation with the total PL, ACC, DEC, and CoD. The contrary was observed with respect to the relationship between sRPE and JUMP variables: the correlation was higher for the high band and lower for the total number of jumps. With respect to the external load variables, a stronger correlation was found between PL and the total number of ACC, DEC and COD than the same variables within the high band. The only contrary finding was the correlation between PL and JUMP variables, which showed a stronger correlation for hJUMP. Tri-axial accelerometry technology and the sRPE method serve as valuable tools for monitoring the training load in basketball. Even though the two methods exhibit a strong correlation, some variation exists, likely due to frequent static movements (i.e., isometric muscle contractions) that accelerometers are not able to detect. Finally, it is suggested that both methods are to be used complementary, when possible, in order to design and control the training process as effectively as possible.

External, Internal, and Perceived Training Loads and Their Relationships in Youth Basketball Players Across Different Positions

2021 ◽  
pp. 1-7
Author(s):  
Pierpaolo Sansone ◽  
Alessandro Ceravolo ◽  
Antonio Tessitore
Keyword(s):  
Effect Size ◽  
Repeated Measures ◽  
External Load ◽  
Perceived Exertion ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
Training Dose ◽  
Load Models ◽  
Impulse Load ◽  
External Impulse

Purpose: To quantify external, internal, and perceived training loads and their relationships in youth basketball players across different playing positions. Methods: Fourteen regional-level youth male players (age: 15.2 [0.3] y) were monitored during team-based training sessions across 10 in-season weeks. The players were monitored with BioHarness-3 devices, to measure external (Impulse Load, in Newtons per second) and internal (summated-heart-rate zones [SHRZ], in arbitrary units [AU]) loads, and with the session rating of perceived exertion (sRPE, in AU) method to quantify perceived training load. Multiple linear mixed models were performed to compare training loads between playing positions (backcourt and frontcourt). Repeated-measures correlations were performed to assess the relationships between the load models, for all players and within playing positions. Results: External load (backcourt: 13,599 [2260] N·s; frontcourt: 14,934 [2173] N·s) and sRPE (backcourt: 345 [132] AU; frontcourt: 505 [158] AU) were higher in the frontcourt (P < .05, effect size: moderate), while SHRZ was similar between positions (backcourt: 239 [45] AU; frontcourt: 247 [43] AU) (P > .05; effect size: trivial). The correlations were as follows: large between the external load and SHRZ (r = .57, P < .001), moderate between SHRZ and sRPE (r = .45, P < .001), and small between the external load and sRPE (r = .26, P = .02). The correlation magnitudes were equivalent for external load–SHRZ (large) and SHRZ–sRPE (moderate) across positions, but different for the external load–sRPE correlation (small in backcourt; moderate in frontcourt). Conclusions: In youth basketball, small–large commonalities were found between the training dose (external load) and players’ responses (internal and perceived loads). Practitioners should carefully manage frontcourt players’ training loads because they accumulate greater external and perceived loads than backcourt  players do.

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.

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