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.

scholarly journals Comparing external total load, acceleration and deceleration outputs in elite basketball players across positions during match play

Kinesiology ◽  
2018 ◽  
Vol 50 (2) ◽  
pp. 228-234 ◽  
Author(s):  
Jairo Vázquez-Guerrero ◽  
Luis Suarez-Arrones ◽  
David Casamichana Gómez ◽  
Gil Rodas
Keyword(s):  
External Load ◽  
Training Programs ◽  
Body Height ◽  
Training Load ◽  
Effect Sizes ◽  
Basketball Players ◽  
Match Play ◽  
Total Load ◽  
Load Model ◽  
Acceleration And Deceleration

The aim of this study was to compare external load, calculated by an accelerometer training load model, the number and intensity of accelerations and decelerations, and the acceleration:deceleration ratio between playing positions during basketball matches. Twelve elite male basketball players (mean±SD, age: 25.5±5.2 years; (range: 19-36 years); body height 201.4±8.6 cm; body mass: 98.4±12.6 kg) were monitored during two official matches. An accelerometer training load model and the number of accelerations and decelerations were used to assess physical demands imposed on basketball players. Magnitude-based inferences and effect sizes (ES) were used to assess possible differences between positions: point guards (PG), shooting guards (SG), small forwards (SF), power forwards (PF) and centers (C). Elite basketball players in all positions presented higher maximal decelerations than accelerations (ES=2.70 to 6.87) whereas the number of moderate accelerations were higher than the number of moderate decelerations (ES=0.54 to 3.12). Furthermore, the acceleration:deceleration ratio (>3 m∙s-2) was significantly lower in players on the perimeter (PG and SG) than in PF and C (ES=1.03 to 2.21). Finally, PF had the lowest total external load (ES=0.67 to 1.18). These data allow us to enlarge knowledge of the external demands in basketball matches and this information could be used in the planning of training programs

Positional Differences in Elite Basketball: Selecting Appropriate Training-Load Measures

2018 ◽  
Vol 13 (7) ◽  
pp. 947-952 ◽  
Author(s):  
Luka Svilar ◽  
Julen Castellano ◽  
Igor Jukic ◽  
David Casamichana
Keyword(s):  
Principal Components ◽  
Perceived Exertion ◽  
Principal Component ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Change Of Direction ◽  
External Variables ◽  
Load Monitoring ◽  
External Training ◽  
Almost All

Purpose: To study the structure of interrelationships among external-training-load measures and how these vary among different positions in elite basketball. Methods: Eight external variables of jumping (JUMP), acceleration (ACC), deceleration (DEC), and change of direction (COD) and 2 internal-load variables (rating of perceived exertion [RPE] and session RPE) were collected from 13 professional players with 300 session records. Three playing positions were considered: guards (n = 4), forwards (n = 4), and centers (n = 5). High and total external variables (hJUMP and tJUMP, hACC and tACC, hDEC and tDEC, and hCOD and tCOD) were used for the principal-component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Varimax rotation mode was used to extract multiple principal components. Results: The analysis showed that all positions had 2 or 3 principal components (explaining almost all of the variance), but the configuration of each factor was different: tACC, tDEC, tCOD, and hJUMP for centers; hACC, tACC, tCOD, and hJUMP for guards; and tACC, hDEC, tDEC, hCOD, and tCOD for forwards are specifically demanded in training sessions, and therefore these variables must be prioritized in load monitoring. Furthermore, for all playing positions, RPE and session RPE have high correlation with the total amount of ACC, DEC, and COD. This would suggest that although players perform the same training tasks, the demands of each position can vary. Conclusion: A particular combination of external-load measures is required to describe the training load of each playing position, especially to better understand internal responses among players.

scholarly journals Physical Performance Differences Between Starter and Non‐Starter Players During Professional Soccer Friendly Matches

2019 ◽  
Vol 69 (1) ◽  
pp. 283-291 ◽  
Author(s):  
Jesús V. Giménez ◽  
Anthony S. Leicht ◽  
Miguel A. Gomez
Keyword(s):  
Physical Performance ◽  
Perceived Exertion ◽  
Body Height ◽  
Rating Of Perceived Exertion ◽  
Effect Sizes ◽  
Ratings Of Perceived Exertion ◽  
Professional Soccer ◽  
Performance Differences ◽  
Average Acceleration ◽  
Acceleration And Deceleration

Abstract The aim of this study was to investigate the physical performance differences between players that started (i.e. starters, ≥65 minutes played) and those that were substituted into (i.e. non‐starter) soccer friendly matches. Fourteen professional players (age: 23.2 ± 2.7 years, body height: 178 ± 6 cm, body mass: 73.2 ± 6.9 kg) took part in this study. Twenty, physical performance‐related match variables (e.g. distance covered at different intensities, accelerations and decelerations, player load, maximal running speed, exertion index, work‐to‐rest ratio and rating of perceived exertion) were collected during two matches. Results were analysed using effect sizes (ES) and magnitude based inferences. Compared to starters, non‐starters covered greater match distance within the following intensity categories: >3.3≤4.2m/s (very likely), >4.2≤5 m/s (likely) and >5≤6.9 m/s (likely). In contrast, similar match average acceleration and deceleration values were identified for starters and non‐starters (trivial). Indicators of workloads including player loads (very likely), the exertion index (very likely), and the work–to‐rest ratio (very likely) were greater, while self‐ reported ratings of perceived exertion were lower (likely) for non‐starters compared to starters. The current study demonstrates that substantial physical performance differences during friendly soccer matches exist between starters and non‐starters. Identification of these differences enables coaches and analysts to potentially prescribe optimal training loads and microcycles based upon player’s match starting status.

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.

scholarly journals Quantifying Training and Game Demands of a National Basketball Association Season

2021 ◽  
Vol 12 ◽  
Author(s):  
Jennifer L. Russell ◽  
Blake D. McLean ◽  
Sean Stolp ◽  
Donnie Strack ◽  
Aaron J. Coutts
Keyword(s):  
Body Mass ◽  
External Load ◽  
Primary Objective ◽  
Training Load ◽  
National Basketball Association ◽  
Training Data ◽  
Basketball Players ◽  
Load Monitoring ◽  
Individualized Training ◽  
And Training

Purpose: There are currently no data describing combined practice and game load demands throughout a National Basketball Association (NBA) season. The primary objective of this study was to integrate external load data garnered from all on-court activity throughout an NBA season, according to different activity and player characteristics.Methods: Data from 14 professional male basketball players (mean ± SD; age, 27.3 ± 4.8 years; height, 201.0 ± 7.2 cm; body mass, 104.9 ± 10.6 kg) playing for the same club during the 2017–2018 NBA season were retrospectively analyzed. Game and training data were integrated to create a consolidated external load measure, which was termed integrated load. Players were categorized by years of NBA experience (1-2y, 3-5y, 6-9y, and 10 + y), position (frontcourt and backcourt), and playing rotation status (starter, rotation, and bench).Results: Total weekly duration was significantly different (p &lt; 0.001) between years of NBA playing experience, with duration highest in 3–5 year players, compared with 6–9 (d = 0.46) and 10+ (d = 0.78) year players. Starters experienced the highest integrated load, compared with bench (d = 0.77) players. There were no significant differences in integrated load or duration between positions.Conclusion: This is the first study to describe the seasonal training loads of NBA players for an entire season and shows that a most training load is accumulated in non-game activities. This study highlights the need for integrated and unobtrusive training load monitoring, with engagement of all stakeholders to develop well-informed individualized training prescription to optimize preparation of NBA players.

Monitoring Training Load and Well-Being During the In-Season Phase in National Collegiate Athletic Association Division I Men’s Basketball

2018 ◽  
Vol 13 (8) ◽  
pp. 1067-1074 ◽  
Author(s):  
Daniele Conte ◽  
Nicholas Kolb ◽  
Aaron T. Scanlan ◽  
Fabrizio Santolamazza
Keyword(s):  
Perceived Exertion ◽  
National Collegiate Athletic Association ◽  
Well Being ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Division I ◽  
Basketball Players ◽  
Men's Basketball ◽  
College Basketball ◽  
Basketball Coaches

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.

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 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 Relationships Between Internal and External Training Load in Team-Sport Athletes: Evidence for an Individualized Approach

2017 ◽  
Vol 12 (2) ◽  
pp. 230-234 ◽  
Author(s):  
Jonathan D. Bartlett ◽  
Fergus O’Connor ◽  
Nathan Pitchford ◽  
Lorena Torres-Ronda ◽  
Samuel J. Robertson
Keyword(s):  
High Speed ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Learning Approaches ◽  
Network Analyses ◽  
Load Monitoring ◽  
Individualized Approach ◽  
Australian Football ◽  
Ann Models

Purpose:The aim of this study was to quantify and predict relationships between rating of perceived exertion (RPE) and GPS training-load (TL) variables in professional Australian football (AF) players using group and individualized modeling approaches.Methods:TL data (GPS and RPE) for 41 professional AF players were obtained over a period of 27 wk. A total of 2711 training observations were analyzed with a total of 66 ± 13 sessions/player (range 39–89). Separate generalized estimating equations (GEEs) and artificial-neural-network analyses (ANNs) were conducted to determine the ability to predict RPE from TL variables (ie, session distance, high-speed running [HSR], HSR %, m/min) on a group and individual basis.Results:Prediction error for the individualized ANN (root-mean-square error [RMSE] 1.24 ± 0.41) was lower than the group ANN (RMSE 1.42 ± 0.44), individualized GEE (RMSE 1.58 ± 0.41), and group GEE (RMSE 1.85 ± 0.49). Both the GEE and ANN models determined session distance as the most important predictor of RPE. Furthermore, importance plots generated from the ANN revealed session distance as most predictive of RPE in 36 of the 41 players, whereas HSR was predictive of RPE in just 3 players and m/min was predictive of RPE in just 2 players.Conclusions:This study demonstrates that machine learning approaches may outperform more traditional methodologies with respect to predicting athlete responses to TL. These approaches enable further individualization of load monitoring, leading to more accurate training prescription and evaluation.

scholarly journals Variation in the Correlation Between Heart Rate and Session Rating of Perceived Exertion-Based Estimations of Internal Training Load in Youth Soccer Players

2019 ◽  
Vol 31 (1) ◽  
pp. 91-98 ◽  
Author(s):  
Durva Vahia ◽  
Adam Kelly ◽  
Harry Knapman ◽  
Craig A. Williams
Keyword(s):  
Heart Rate ◽  
Confidence Interval ◽  
External Load ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Soccer Players ◽  
Youth Soccer ◽  
Validity Analysis ◽  
Consistent Method

Purpose: When exposed to the same external load, players receive different internal loads, resulting in varied adaptations in fitness. In adult soccer, internal training load is measured using heart rate (HR) and session rating of perceived exertion (sRPE) scales, but these have been underutilized in youth soccer. This study investigated the in-season variation in correlation between HR and sRPE estimations of training load for adolescent soccer players. Method: Fifteen male professional adolescent players were monitored for 7 months. Within-participant correlations and Bland–Altman agreement plots for HR and sRPE were calculated for each month to analyze variation over the season and for individual players to analyze the validity of the scale. Results: The monthly correlations ranged from r = .60 to r = .73 (P < .05) and the overall correlation was r = .64 (95% confidence interval, .60–.68; P < .001). Bland–Altman plots showed an agreement of methods. Conclusion: Results showed consistently large correlations for all months. sRPE is a consistent method of measure of internal training load for the entire season for youth soccer players. Validity analysis found no bias in sRPE measurements when compared with HR for all players in the study.

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