The diagonal positioning of the goals modifies the external training load and the tactical behaviour of young football players

2022 ◽  
Author(s):  
Albert Canton ◽  
Carlota Torrents ◽  
Bruno Gonçalves ◽  
Angel Ric ◽  
Filippo Salvioni ◽  
...  
Keyword(s):  
Training Load ◽  
Football Players ◽  
External Training

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.

Individual Response to External Training Load in Elite Football Players

2020 ◽  
Vol 15 (5) ◽  
pp. 696-704
Author(s):  
Håvard Wiig ◽  
Thor Einar Andersen ◽  
Live S. Luteberget ◽  
Matt Spencer
Keyword(s):  
External Load ◽  
High Speed ◽  
Training Load ◽  
Effect Sizes ◽  
Football Players ◽  
Individual Response ◽  
Total Distance ◽  
External Training ◽  
Tracking Devices ◽  
Very High

Purpose: To investigate within-player effect, between-player effect, and individual response of external training load from player tracking devices on session rating of perceived exertion training load (sRPE-TL) in elite football players. Methods: The authors collected sRPE-TL from 18 outfield players in 21 training sessions. Total distance, high-speed running distance (>14.4 m/s), very high-speed running distance (>19.8 m/s), PlayerLoad™, PlayerLoad2D™, and high-intensity events (HIE > 1.5, HIE > 2.5, and HIE > 3.5 m/s) were extracted from the tracking devices. The authors modeled within-player and between-player effects of single external load variables on sRPE-TL, and multiple levels of variability, using a linear mixed model. The effect of 2 SDs of external load on sRPE-TL was evaluated with magnitude-based inferences. Results: Total distance, PlayerLoad™, PlayerLoad2D™, and HIE > 1.5 had most likely substantial within-player effects on sRPE-TL (100%–106%, very large effect sizes). Moreover, the authors observed likely substantial between-player effects (12%–19%, small to moderate effect sizes) from the majority of the external load variables and likely to very likely substantial individual responses of PlayerLoad™, high-speed running distance, very high-speed running distance, and HIE > 1.5 (19%–30% coefficient of variation, moderate to large effect sizes). Finally, sRPE-TL showed large to very large between-session variability with all external load variables. Conclusions: External load variables with low intensity-thresholds had the strongest relationship with sRPE-TL. Furthermore, the between-player effect of external load and the individual response to external load advocate for monitoring sRPE-TL in addition to external load. Finally, the large between-session variability in sRPE-TL demonstrates that substantial amounts of sRPE-TL in training sessions are not explained by single external load variables.

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.

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.

External Training Load Monitoring and the Impact on Training Load Management in Collegiate Male Soccer Players

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Jacob R. Gdovin ◽  
Riley Galloway ◽  
Lorenzo S. Tomasiello ◽  
Michael Seabolt ◽  
Robert Booker
Keyword(s):  
Training Load ◽  
Soccer Players ◽  
Load Management ◽  
Load Monitoring ◽  
External Training ◽  
The Impact

The road to 21 seconds: A case report of a 2016 Olympic swimming sprinter

2019 ◽  
Vol 14 (3) ◽  
pp. 393-405 ◽  
Author(s):  
Augusto Carvalho Barbosa ◽  
Pedro Frederico Valadão ◽  
Carolina Franco Wilke ◽  
Felipe de Souza Martins ◽  
Dellano Cézar Pinto Silva ◽  
...  
Keyword(s):  
Bench Press ◽  
Morphological Changes ◽  
Training Load ◽  
Competitive Performance ◽  
Start Time ◽  
Stroke Length ◽  
Dry Land ◽  
Swimming Time ◽  
Back Squat ◽  
External Training

This study aimed to describe training characteristics as well as physical, technical and morphological changes of an elite Olympic swimming sprinter throughout his road to 21 s in the 50 m freestyle. Over a ∼2.5-year period, the following assessments were obtained: external training load, competitive performance, instantaneous swimming speed, tethered force, dry-land maximal dynamic strength in bench press, pull-up and back squat and body composition. From 2014 to 2016, the athlete dropped 3.3% of his initial best time by reducing total swimming time (i.e. the total time minus 15-m start time – from 17.07 s to 16.21 s) and improving the stroke length (from 1.83 m to 2.00 m). Dry-land strength (bench press: 27.3%, pull-up: 9.1% and back squat: 37.5%) and tethered force (impulse: 30.5%) increased. Competitive performance was associated to average (r = −0.82, p = 0.001) and peak speeds (r = −0.71; p = 0.009) and to lean body mass (r = −0.55; p = 0.03), which increased in the first year and remained stable thereafter. External training load presented a polarized pattern in all training seasons. This swimmer reached the sub-22 s mark by reducing total swimming time, which was effected by a longer stroke length. He also considerably improved his dry-land strength and tethered force levels likely due to a combination of neural and morphological adaptations.

Methods to collect and interpret external training load using microtechnology incorporating GPS in professional football: a systematic review

2019 ◽  
Vol 28 (3) ◽  
pp. 437-458 ◽  
Author(s):  
Vincenzo Rago ◽  
João Brito ◽  
Pedro Figueiredo ◽  
Júlio Costa ◽  
Daniel Barreira ◽  
...  
Keyword(s):  
Systematic Review ◽  
Training Load ◽  
Professional Football ◽  
External Training

scholarly journals Achieving a desired training intensity through the prescription of external training load variables in youth sport: More pieces to the puzzle required

2020 ◽  
Vol 38 (10) ◽  
pp. 1124-1131
Author(s):  
Sean Scantlebury ◽  
Kevin Till ◽  
Clive Beggs ◽  
Nicholas Dalton-Barron ◽  
Dan Weaving ◽  
...  
Keyword(s):  
Youth Sport ◽  
Training Load ◽  
Training Intensity ◽  
External Training

scholarly journals Monitoring Training Loads: The Past, the Present, and the Future

2017 ◽  
Vol 12 (s2) ◽  
pp. S2-2-S2-8 ◽  
Author(s):  
Carl Foster ◽  
Jose A. Rodriguez-Marroyo ◽  
Jos J. de Koning
Keyword(s):  
Information Overload ◽  
Wearable Sensors ◽  
Interval Training ◽  
Time Frame ◽  
Turnaround Time ◽  
Training Load ◽  
High Resolution Data ◽  
External Training ◽  
Monitoring Training ◽  
Time Required

Training monitoring is about keeping track of what athletes accomplish in training, for the purpose of improving the interaction between coach and athlete. Over history there have been several basic schemes of training monitoring. In the earliest days training monitoring was about observing the athlete during standard workouts. However, difficulty in standardizing the conditions of training made this process unreliable. With the advent of interval training, monitoring became more systematic. However, imprecision in the measurement of heart rate (HR) evolved interval training toward index workouts, where the main monitored parameter was average time required to complete index workouts. These measures of training load focused on the external training load, what the athlete could actually do. With the advent of interest from the scientific community, the development of the concept of metabolic thresholds and the possibility of trackside measurement of HR, lactate, VO2, and power output, there was greater interest in the internal training load, allowing better titration of training loads in athletes of differing ability. These methods show much promise but often require laboratory testing for calibration and tend to produce too much information, in too slow a time frame, to be optimally useful to coaches. The advent of the TRIMP concept by Banister suggested a strategy to combine intensity and duration elements of training into a single index concept, training load. Although the original TRIMP concept was mathematically complex, the development of the session RPE and similar low-tech methods has demonstrated a way to evaluate training load, along with derived variables, in a simple, responsive way. Recently, there has been interest in using wearable sensors to provide high-resolution data of the external training load. These methods are promising, but problems relative to information overload and turnaround time to coaches remain to be solved.

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