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 Combining Internal- and External-Training-Load Measures in Professional Rugby League

2014 ◽  
Vol 9 (6) ◽  
pp. 905-912 ◽  
Author(s):  
Dan Weaving ◽  
Phil Marshall ◽  
Keith Earle ◽  
Alan Nevill ◽  
Grant Abt
Keyword(s):  
Principal Components ◽  
High Speed ◽  
Perceived Exertion ◽  
Principal Component ◽  
Rugby League ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Training Dose ◽  
External Training ◽  
Training Impulse

Purpose:This study investigated the effect of training mode on the relationships between measures of training load in professional rugby league players.Methods:Five measures of training load (internal: individualized training impulse, session rating of perceived exertion; external—body load, high-speed distance, total impacts) were collected from 17 professional male rugby league players over the course of two 12-wk preseason periods. Training was categorized by mode (small-sided games, conditioning, skills, speed, strongman, and wrestle) and subsequently subjected to a principal-component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Modes that extracted more than 1 principal component were subjected to a varimax rotation.Results:Small-sided games and conditioning extracted 1 principal component, explaining 68% and 52% of the variance, respectively. Skills, wrestle, strongman, and speed extracted 2 principal components each explaining 68%, 71%, 72%, and 67% of the variance, respectively.Conclusions:In certain training modes the inclusion of both internal and external training-load measures explained a greater proportion of the variance than any 1 individual measure. This would suggest that in training modes where 2 principal components were identified, the use of only a single internal or external training-load measure could potentially lead to an underestimation of the training dose. Consequently, a combination of internal- and external-load measures is required during certain training modes.

scholarly journals Multiple Measures are Needed to Quantify Training Loads in Professional Rugby League

2017 ◽  
Vol 38 (10) ◽  
pp. 735-740 ◽  
Author(s):  
Daniel Weaving ◽  
Ben Jones ◽  
Phil Marshall ◽  
Kevin Till ◽  
Grant Abt
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
High Speed ◽  
Perceived Exertion ◽  
Principal Component ◽  
Component Analysis ◽  
Rugby League ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Training Mode

AbstractThis study aims to investigate the effect of training mode (conditioning and skills) on multivariate training load relationships in professional rugby league via principal component analysis. Four measures of training load (internal: heart rate exertion index, session rating of perceived exertion; external: PlayerLoad™, individualised high-speed distance) were collected from 23 professional male rugby league players over the course of one 12 wk preseason period. Training was categorised by mode (skills or conditioning) and then subjected to a principal component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Modes that extracted more than 1 principal component were subject to a varimax rotation. Skills extracted 1 principal component, explaining 57% of the variance. Conditioning extracted 2 principal components (1st: internal; 2nd: external), explaining 85% of the variance. The presence of multiple training load dimensions (principal components) during conditioning training provides further evidence of the influence of training mode on the ability of individual measures of external or internal training load to capture training variance. Consequently, a combination of internal and external training-load measures is required during certain training modes.

scholarly journals Differences in External Load Variables Between Playing Positions in Elite Basketball Match-Play

2020 ◽  
Vol 75 (1) ◽  
pp. 257-266
Author(s):  
Hugo Salazar ◽  
Julen Castellano ◽  
Luka Svilar
Keyword(s):  
Principal Components ◽  
External Load ◽  
Principal Component ◽  
Match Play ◽  
Change Of Direction ◽  
Time Motion ◽  
External Variables ◽  
Load Monitoring ◽  
Kaiser Criterion ◽  
Motion Profile

Abstract The purpose of this study was to describe the specific demands and structure of interrelationships of external load variables in order to generate a position-related time motion profile in elite basketball. Seventeen professional players from three different playing positions (6 guards, 4 forwards, and 7 centers) were analyzed in five friendly games. Player load per minute (PLmin) was used as an indicator of intensity to compare positions. Furthermore, high and total external variables of jumping (hJUMP and tJUMP), acceleration (hACC and tACC), deceleration (hDEC and tDEC) and change of direction (hCOD and tCOD), respectively, were used for the principal component analysis (PCA). The Kaiser criterion (eigenvalue > 1) was applied, and the Varimax rotation mode was used to extract multiple principal components. PCA showed that all positions had three or four principal components, but the configuration of each factor was different: tCOD, hCOD, hDEC and hJUMP for guards, hCOD, tCOD, tACC and hDEC for forwards, and tJUMP, hJUMP, hDEC and tACC for centers were specifically demanded in match-play. For guards and forwards, a significant correlation was found between COD variables, while for centers tCOD and PLmin had the strongest correlation. When monitoring the external load via tri-axial accelerometers in basketball match-play, each playing position showed specific physical demands. Therefore, these variables must be prioritized in load monitoring programs.

scholarly journals Internal and External Training Load in Under-19 versus Professional Soccer Players during the In-Season Period

2021 ◽  
Vol 18 (2) ◽  
pp. 558
Author(s):  
Sullivan Coppalle ◽  
Guillaume Ravé ◽  
Jason Moran ◽  
Iyed Salhi ◽  
Abderraouf Ben Abderrahman ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Training Session ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Soccer Players ◽  
Time Intervals ◽  
Professional Soccer ◽  
Global Positioning ◽  
External Training ◽  
Soccer Team

This study aimed to compare the training load of a professional under-19 soccer team (U-19) to that of an elite adult team (EAT), from the same club, during the in-season period. Thirty-nine healthy soccer players were involved (EAT [n = 20]; U-19 [n = 19]) in the study which spanned four weeks. Training load (TL) was monitored as external TL, using a global positioning system (GPS), and internal TL, using a rating of perceived exertion (RPE). TL data were recorded after each training session. During soccer matches, players’ RPEs were recorded. The internal TL was quantified daily by means of the session rating of perceived exertion (session-RPE) using Borg’s 0–10 scale. For GPS data, the selected running speed intensities (over 0.5 s time intervals) were 12–15.9 km/h; 16–19.9 km/h; 20–24.9 km/h; >25 km/h (sprint). Distances covered between 16 and 19.9 km/h, > 20 km/h and >25 km/h were significantly higher in U-19 compared to EAT over the course of the study (p =0.023, d = 0.243, small; p = 0.016, d = 0.298, small; and p = 0.001, d = 0.564, small, respectively). EAT players performed significantly fewer sprints per week compared to U-19 players (p = 0.002, d = 0.526, small). RPE was significantly higher in U-19 compared to EAT (p =0.001, d = 0.188, trivial). The external and internal measures of TL were significantly higher in the U-19 group compared to the EAT soccer players. In conclusion, the results obtained show that the training load is greater in U19 compared to EAT.

Within-Subject Correlation Between Salivary IgA and Measures of Training Load in Elite Football Players

2019 ◽  
Vol 14 (6) ◽  
pp. 847-849 ◽  
Author(s):  
Pedro Figueiredo ◽  
George P. Nassis ◽  
João Brito
Keyword(s):  
High Speed ◽  
Perceived Exertion ◽  
Immunoglobulin A ◽  
Correlation Coefficients ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Football Players ◽  
Positioning Systems ◽  
External Training ◽  
Salivary Secretory Immunoglobulin A

Purpose: To quantify the association between salivary secretory immunoglobulin A (sIgA) and training load in elite football players. Methods: Data were obtained on 4 consecutive days during the preparation camp for the Rio 2016 Olympic Games. Saliva samples of 18 elite male football players were collected prior to breakfast. The session rating of perceived exertion (s-RPE) and external training-load metrics from global positioning systems (GPS) were recorded. Within-subject correlation coefficients between training load and sIgA concentration, and magnitude of relationships, were calculated. Results: sIgA presented moderate to large negative correlations with s-RPE (r = −.39), total distance covered (r = −.55), accelerations (r = −.52), and decelerations (r = −.48). Trivial to small associations were detected between sIgA and distance covered per minute (r = .01), high-speed distance (r = −.23), and number of sprints (r = −.18). sIgA displayed a likely moderate decrease from day 1 to day 2 (d = −0.7) but increased on day 3 (d = 0.6). The training-load variables had moderate to very large rises from day 1 to day 2 (d = 0.7 to 3.2) but lowered from day 2 to day 3 (d = −5.0 to −0.4), except for distance per minute (d = 0.8) and sprints (unclear). On day 3, all training-load variables had small to large increments compared with day 1 (d = 0.4 to 1.5), except for accelerations (d = −0.8) and decelerations (unclear). Conclusions: In elite football, sIgA might be more responsive to training volume than to intensity. External load such as GPS-derived variables presented stronger association with sIgA than with s-RPE. sIgA can be used as an additional objective tool in monitoring football players.

scholarly journals The Association Between Internal and External Measures of Training Load in Batsmen and Medium-Fast Bowlers During Net-Based Cricket Training

2017 ◽  
Vol 12 (2) ◽  
pp. 247-253 ◽  
Author(s):  
Will Vickery ◽  
Ben Dascombe ◽  
Rob Duffield
Keyword(s):  
Heart Rate ◽  
Multiple Regression ◽  
High Intensity ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Stepwise Multiple Regression ◽  
Total Distance ◽  
Regression Techniques ◽  
External Training

Purpose:To examine the relationship between session rating of perceived exertion (sRPE) and measures of internal and external training load (TL) in cricket batsmen and medium-fast bowlers during net-based training sessions.Methods:The internal (heart rate), external (movement demands, PlayerLoad), and technical (cricket-specific skills) loads of 30 male cricket players (age 21.2 ± 3.8 y, height 1.82 ± 0.07 m, body mass 79.0 ± 8.7 kg) were determined from net-based cricket-training sessions (n = 118). The relationships between sRPE and measures of TL were quantified using Pearson product–moment correlations respective to playing position. Stepwise multiple-regression techniques provided key internal- and external-load determinants of sRPE in cricket players.Results:Significant correlations were evident (r = -.34 to .87, P < .05) between internal and external measures of TL and sRPE, with the strongest correlations (r ≥ .62) for GPS-derived measures for both playing positions. In batsmen, stepwise multiple-regression analysis revealed that 67.8% of the adjusted variance in sRPE could be explained by PlayerLoad and high-intensity distance (y = 27.43 + 0.81 PlayerLoad + 0.29 high-intensity distance). For medium-fast bowlers, 76.3% of the adjusted variance could be explained by total distance and mean heart rate (y = 101.82 + total distance 0.05 + HRmean – 0.48).Conclusion:These results suggest that sRPE is a valid method of reporting TL among cricket batsmen and medium-fast bowlers. Position-specific responses are evident and should be considered when monitoring the TL of cricket players.

scholarly journals In-season internal and external training load quantification of an elite European soccer team

2018 ◽  
Author(s):  
Rafael Soares Oliveira ◽  
João Paulo Brito ◽  
Alexandre Martins ◽  
Bruno Mendes ◽  
Francisco Calvete ◽  
...  
Keyword(s):  
High Speed ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Soccer Players ◽  
Soccer Teams ◽  
External Training ◽  
Daily Training ◽  
Player Positions ◽  
Level Of Effort

Elite soccer teams that participate in European competitions often have a difficult schedule, involving weeks in which they play up to three matches, which leads to acute and transient subjective, biochemical, metabolic and physical disturbances in players over the subsequent hours and days. Inadequate time recovery between matches can expose players to the risk of training and competing whilst not fully recovered. Controlling the level of effort and fatigue of players to reach higher performances during the matches is therefore critical. Therefore, the aim of the current study was to provide the first report of seasonal internal and external training load (TL) that included Hooper Index (HI) scores in elite soccer players during an in-season period. Sixteen elite soccer players were sampled, using global position system, session rating of perceived exertion (s-RPE) and HI scores during the daily training sessions throughout the 2015-2016 in-season period. Data were analysed across ten mesocycles (M: 1 to 10) and collected according to the number of days prior to a match. Total daily distance covered was higher at the start (M1 and M3) compared to the final mesocycle (M10) of the season. M1 (5589m) reached a greater distance than M5 (4473m) (ES = 9.33 [12.70, 5.95]) and M10 (4545m) (ES = 9.84 [13.39, 6.29]). M3 (5691m) reached a greater distance than M5 (ES = 9.07 [12.36, 5.78]), M7 (ES = 6.13 [8.48, 3.79]) and M10 (ES = 9.37 [12.76, 5.98]). High-speed running distance was greater in M1 (227m), than M5 (92m) (ES = 27.95 [37.68, 18.22]) and M10 (138m) (ES = 8.46 [11.55, 5.37]). Interestingly, the s-RPE response was higher in M1 (331au) in comparison to the last mesocycle (M10, 239au). HI showed minor variations across mesocycles and in days prior to the match. Every day prior to a match, all internal and external TL variables expressed significant lower values to other days prior to a match (p<0.01). In general, there were no differences between player positions. Conclusions: Our results reveal that despite the existence of some significant differences between mesocycles, there were minor changes across the in-season period for the internal and external TL variables used. Furthermore, it was observed that MD-1 presented a reduction of external TL (regardless of mesocycle) while internal TL variables did not have the same record during in-season match-day-minus.

scholarly journals Associations between Physical Status and Training Load in Women Soccer Players

2021 ◽  
Vol 18 (19) ◽  
pp. 10015
Author(s):  
Lillian Gonçalves ◽  
Filipe Manuel Clemente ◽  
Joel Ignacio Barrera ◽  
Hugo Sarmento ◽  
Gibson Moreira Praça ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Vertical Jump ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Soccer Players ◽  
Load Range ◽  
Change Of Direction ◽  
Fitness Assessment ◽  
Cohort Design ◽  
And Training

This study aimed to analyze the variations of fitness status, as well as test the relationships between accumulated training load and fitness changes in women soccer players. This study followed an observational analytic cohort design. Observations were conducted over 23 consecutive weeks (from the preseason to the midseason). Twenty-two women soccer players from the same first Portuguese league team (22.7 ± 5.21 years old) took part in the study. The fitness assessment included anthropometry, hip adductor and abductor strength, vertical jump, change of direction, linear speed, repeated sprint ability, and the Yo-Yo intermittent recovery test. The training load was monitored daily using session rating of perceived exertion (s-RPE). A one-way repeated ANOVA revealed no significant differences for any of the variables analyzed across the three moments of fitness assessments (p > 0.05). The t-test also revealed no differences in the training load across the moments of the season (t = 1.216; p = 0.235). No significant correlations were found between fitness levels and accumulated training load (range: r = 0.023 to −0.447; p > 0.05). This study revealed no differences in the fitness status during the analyzed season, and the fitness status had no significant relationship with accumulated training load.

25 Years of Session Rating of Perceived Exertion: Historical Perspective and Development

2020 ◽  
pp. 1-10 ◽  
Author(s):  
Carl Foster ◽  
Daniel Boullosa ◽  
Michael McGuigan ◽  
Andrea Fusco ◽  
Cristina Cortis ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Lactate Concentration ◽  
Training Session ◽  
Blood Lactate Concentration ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Exercise Session ◽  
Training Outcomes ◽  
External Training ◽  
Training Impulse

The session rating of perceived exertion (sRPE) method was developed 25 years ago as a modification of the Borg concept of rating of perceived exertion (RPE), designed to estimate the intensity of an entire training session. It appears to be well accepted as a marker of the internal training load. Early studies demonstrated that sRPE correlated well with objective measures of internal training load, such as the percentage of heart rate reserve and blood lactate concentration. It has been shown to be useful in a wide variety of exercise activities ranging from aerobic to resistance to games. It has also been shown to be useful in populations ranging from patients to elite athletes. The sRPE is a reasonable measure of the average RPE acquired across an exercise session. Originally designed to be acquired ∼30 minutes after a training bout to prevent the terminal elements of an exercise session from unduly influencing the rating, sRPE has been shown to be temporally robust across periods ranging from 1 minute to 14 days following an exercise session. Within the training impulse concept, sRPE, or other indices derived from sRPE, has been shown to be able to account for both positive and negative training outcomes and has contributed to our understanding of how training is periodized to optimize training outcomes and to understand maladaptations such as overtraining syndrome. The sRPE as a method of monitoring training has the advantage of extreme simplicity. While it is not ideal for the precise recording of the details of the external training load, it has large advantages relative to evaluating the internal training load.

scholarly journals Monitoring What Matters: A Systematic Process for Selecting Training-Load Measures

2017 ◽  
Vol 12 (s2) ◽  
pp. S2-101-S2-106 ◽  
Author(s):  
Sean Williams ◽  
Grant Trewartha ◽  
Matthew J. Cross ◽  
Simon P.T. Kemp ◽  
Keith A. Stokes
Keyword(s):  
Injury Risk ◽  
Perceived Exertion ◽  
Principal Component ◽  
Rugby Union ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Systematic Process ◽  
Daily Training ◽  
Monitoring Training ◽  
Generalized Linear Mixed Effects

Purpose:Numerous derivative measures can be calculated from the simple session rating of perceived exertion (sRPE), a tool for monitoring training loads (eg, acute:chronic workload and cumulative loads). The challenge from a practitioner’s perspective is to decide which measures to calculate and monitor in athletes for injury-prevention purposes. The aim of the current study was to outline a systematic process of data reduction and variable selection for such training-load measures.Methods:Training loads were collected from 173 professional rugby union players during the 2013–14 English Premiership season, using the sRPE method, with injuries reported via an established surveillance system. Ten derivative measures of sRPE training load were identified from existing literature and subjected to principal-component analysis. A representative measure from each component was selected by identifying the variable that explained the largest amount of variance in injury risk from univariate generalized linear mixed-effects models.Results:Three principal components were extracted, explaining 57%, 24%, and 9% of the variance. The training-load measures that were highly loaded on component 1 represented measures of the cumulative load placed on players, component 2 was associated with measures of changes in load, and component 3 represented a measure of acute load. Four-week cumulative load, acute:chronic workload, and daily training load were selected as the representative measures for each component.Conclusions:The process outlined in the current study enables practitioners to monitor the most parsimonious set of variables while still retaining the variation and distinct aspects of “load” in the data.

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