scholarly journals Quantification of training and match-play load across a season in professional youth football players

2021 ◽  
pp. 174795412110003
Author(s):  
Patrick C Maughan ◽  
Niall G MacFarlane ◽  
Paul A Swinton
Keyword(s):  
Perceived Exertion ◽  
Linear Models ◽  
Training Load ◽  
Effect Sizes ◽  
Future Research ◽  
Football Players ◽  
Mixed Linear Models ◽  
Match Play ◽  
Mean Values ◽  
Youth Football

The purpose of this study was to quantify load across an entire season for professional youth football players and assess the effects of stage of season, playing position and training day relative to match day (MD). Data from ratings of perceived exertion and seven global positioning system (GPS) derived measures of external training load were collected from 20 players across a 47-week season. Mixed linear models were used to assess the effects of stage of season, training proximity to match day (e.g. MD-1, MD-2) and position across each dependent variable. Training proximity to match day was found to have the most substantive effect with effect sizes ranging from small ([Formula: see text] to large ([Formula: see text]. Across training load measures, mean values collected on match day were on average 47% higher than all other sessions. Whilst significant regression coefficients were obtained for playing position (p ≤ 0.003) and stage of season (p ≤ 0.049), effect sizes were close to zero ([Formula: see text]in each instance. This study provides insight into the season-long training and match-play demands of a professional youth football team. It highlights the significant impact of match-play on load and supports the use of multiple methods of collecting training load data. Overall, there was limited variation in mean values of dependent variables across playing position, stage of the season and loading during midweek training. These findings highlight the need for future research to investigate whether greater systematic variations in training load can be used to increase physical fitness and maximise physical performance during competition.

scholarly journals Relationships Between Training Load Variables in Professional Youth Football Players

2020 ◽  
Author(s):  
Patrick Maughan ◽  
Paul Swinton ◽  
Niall MacFarlane
Keyword(s):  
Perceived Exertion ◽  
Principal Component ◽  
Training Load ◽  
Underlying Structure ◽  
Football Players ◽  
Multiple Components ◽  
Session Types ◽  
Youth Football ◽  
Load Monitoring ◽  
Multiple Variables

AbstractThis study aims to investigate the relationship between subjective and external measures of load in professional youth football players whilst accounting for the effect of training theme or competition. Data from ratings of perceived exertion and global positioning system-derived measures of external training load were collected from 20 professional youth players (age=17.4±1.3 yrs) across a 46-week season. General characteristics of training sessions were categorised based on their proximity to match day. The underlying structure of the data was investigated with principal component analysis. An extraction criterion comprising eigenvalues >1 was used to identify which components to retain. Three components were retained for training performed three days prior to match day (80.2% of variance), with two components (72.9–89.7% of variance) retained for all other modes. Generally, the first component was represented by measures of volume (Total Distance, PlayerLoad and low intensity running) whilst the second and third components were characterised by measures of intensity. Identification of multiple components indicates that load monitoring should comprise multiple variables. Additionally, differences in the underlying structure across training days that reflected different goals suggest that effective monitoring should be specific to the demands of different session types.

scholarly journals Quantifying Sub-Elite Youth Football Weekly Training Load and Recovery Variation

2021 ◽  
Vol 11 (11) ◽  
pp. 4871
Author(s):  
José E. Teixeira ◽  
Pedro Forte ◽  
Ricardo Ferraz ◽  
Miguel Leal ◽  
Joana Ribeiro ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Future Research ◽  
Total Quality ◽  
Youth Football ◽  
Age Related ◽  
Weekly Training ◽  
External Training ◽  
Recovery Status

Monitoring the training load in football is an important strategy to improve athletic performance and an effective training periodization. The aim of this study was two-fold: (1) to quantify the weekly training load and recovery status variations performed by under-15, under-17 and under-19 sub-elite young football players; and (2) to analyze the influence of age, training day, weekly microcycle, training and playing position on the training load and recovery status. Twenty under-15, twenty under-17 and twenty under-19 players were monitored over a 2-week period during the first month of the 2019–2020 competitive season. Global positioning system technology (GPS) was used to collect external training loads: total distance covered, average speed, maximal running speed, relative high-speed running distance, high metabolic load distance, sprinting distance, dynamic stress load, accelerations and decelerations. Internal training load was monitored using ratings of perceived exertion (RPE) and session rating of perceived exertion (sRPE). Recovery status was obtained using the total quality recovery (TQR) scale. The results show an age-related influence for external training load (p ≤ 0.001; d = 0.29–0.86; moderate to strong effect), internal training load (p ≤ 0.001, d = 0.12–0.69; minimum to strong effect) and recovery status (p ≤ 0.001, d = 0.59; strong effect). The external training load presented differences between training days (p < 0.05, d = 0.26–0.95; moderate to strong effect). The playing position had a minimum effect on the weekly training load (p < 0.05; d = 0.06–0.18). The weekly microcycle had a moderate effect in the TD (p < 0.05, d = 0.39), RPE (p < 0.05; d = 0.35) and sRPE (p < 0.05, d = 0.35). Interaction effects were found between the four factors analyzed for deceleration (F = 2.819, p = 0.017) and between inter-day, inter-week and age for total covered distance (F = 8.342, p = 0.008). This study provided specific insights about sub-elite youth football training load and recovery status to monitor training environments and load variations. Future research should include a longer monitoring period to assess training load and recovery variations across different season phases.

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.

Association Between Match Activity, Endurance Levels and Maturity in Youth Football Players

2019 ◽  
Vol 40 (09) ◽  
pp. 576-584 ◽  
Author(s):  
Lorenzo Francini ◽  
Ermanno Rampinini ◽  
Andrea Bosio ◽  
Darragh Connolly ◽  
Domenico Carlomagno ◽  
...  
Keyword(s):  
High Speed ◽  
Perceived Exertion ◽  
Field Tests ◽  
Work Rate ◽  
Height Velocity ◽  
Aerobic Performance ◽  
Football Players ◽  
Youth Football ◽  
Match Rate ◽  
Very High

AbstractThe aim of the study was to examine the associations between maximal and submaximal field tests with match physical activity and biological maturation in youth football players. Sixty-eight youth football players (U14, U15, U16, U17) performed maximal and submaximal field endurance tests. Biological maturity was estimated calculating the distance from peak height velocity (Y-PHV). Physical match activities were tracked using GPS units and players’ post-match rate of perceived exertion (RPE) was recorded. Mainly moderate associations were found between field tests and match activities. Large correlations were found between Yo-Yo Intermittent Recovery test level 1, distance covered at high and very high-speed running, the quantity of very high and maximal metabolic power running. Small to moderate associations between match activities and Y-PHV were observed. The magnitude of correlation between match activities and field tests increased from moderate to large when matches with an RPE>5 were considered. The results provide further evidence of the association between young football players’ aerobic performance and match work rate. Submaximal field tests demonstrate ecological validity and may constitute a practical alternative to performing maximal tests. Maturation was found to have a moderate effect on youth players’ match work rate.

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.

Relationship Between Pretraining Subjective Wellness Measures, Player Load, and Rating-of-Perceived-Exertion Training Load in American College Football

2018 ◽  
Vol 13 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Andrew D. Govus ◽  
Aaron Coutts ◽  
Rob Duffield ◽  
Andrew Murray ◽  
Hugh Fullagar
Keyword(s):  
Muscle Soreness ◽  
Perceived Exertion ◽  
Training Session ◽  
College Football ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Football Players ◽  
Unit Increase ◽  
Relationship Of ◽  
The Relationship

Context:The relationship between pretraining subjective wellness and external and internal training load in American college football is unclear.Purpose:To examine the relationship of pretraining subjective wellness (sleep quality, muscle soreness, energy, wellness Z score) with player load and session rating of perceived exertion (s-RPE-TL) in American college football players.Methods:Subjective wellness (measured using 5-point, Likert-scale questionnaires), external load (derived from GPS and accelerometry), and s-RPE-TL were collected during 3 typical training sessions per week for the second half of an American college football season (8 wk). The relationship of pretraining subjective wellness with player load and s-RPE training load was analyzed using linear mixed models with a random intercept for athlete and a random slope for training session. Standardized mean differences (SMDs) denote the effect magnitude.Results:A 1-unit increase in wellnessZscore and energy was associated with trivial 2.3% (90% confidence interval [CI] 0.5, 4.2; SMD 0.12) and 2.6% (90% CI 0.1, 5.2; SMD 0.13) increases in player load, respectively. A 1-unit increase in muscle soreness (players felt less sore) corresponded to a trivial 4.4% (90% CI −8.4, −0.3; SMD −0.05) decrease in s-RPE training load.Conclusion:Measuring pretraining subjective wellness may provide information about players’ capacity to perform in a training session and could be a key determinant of their response to the imposed training demands American college football. Hence, monitoring subjective wellness may aid in the individualization of training prescription in American college football players.

Training Periodization of Professional Australian Football Players During an Entire Australian Football League Season

2015 ◽  
Vol 10 (5) ◽  
pp. 566-571 ◽  
Author(s):  
Alexandre Moreira ◽  
Johann C. Bilsborough ◽  
Courtney J. Sullivan ◽  
Michael Cianciosi ◽  
Marcelo Saldanha Aoki ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Training Session ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Football Players ◽  
Training Intensity ◽  
Australian Football League ◽  
Football Team ◽  
Cross Training ◽  
Australian Football

Purpose:To examine the training periodization of an elite Australian Football team during different phases of the season.Methods:Training-load data were collected during 22 wk of preseason and 23 wk of in-season training. Training load was measured using the session rating of perceived exertion (session-RPE) for all training sessions and matches from 44 professional Australian Football players from the same team. Training intensity was divided into 3 zones based on session-RPE (low, <4; moderate, >4 AU and <7 AU; and high, >7 AU). Training load and intensity were analyzed according to the type of training session completed.Results:Higher training load and session duration were undertaken for all types of training sessions during the preseason than in-season (P < .05), with the exception of “other” training (ie, re/prehabilitation training, cross-training, and recovery activities). Training load and intensity were higher during the preseason, with the exception of games, where greater load and intensity were observed during the in-season. The overall distribution of training intensity was similar between phases with the majority of training performed at moderate or high intensity.Conclusions:The current findings may allow coaches and scientists to better understand the characteristics of Australian Football periodization, which in turn may aid in developing optimal training programs. The results also indicate that a polarized training-intensity distribution that has been reported in elite endurance athletes does not occur in professional Australian Football.

Constraints on visual exploration of youth football players during 11v11 match-play: The influence of playing role, pitch position and phase of play

2020 ◽  
Vol 38 (6) ◽  
pp. 658-668 ◽  
Author(s):  
Thomas B. McGuckian ◽  
Michael H. Cole ◽  
Daniel Chalkley ◽  
Geir Jordet ◽  
Gert-Jan Pepping
Keyword(s):  
Visual Exploration ◽  
Football Players ◽  
Match Play ◽  
Youth Football

Convergent Validity of CR100-Based Session Ratings of Perceived Exertion in Elite Youth Football Players of Different Ages

2020 ◽  
pp. 1-5
Author(s):  
Fabio R. Serpiello ◽  
Will G. Hopkins
Keyword(s):  
Convergent Validity ◽  
Perceived Exertion ◽  
Age Groups ◽  
Team Sports ◽  
Football Players ◽  
Ratings Of Perceived Exertion ◽  
Internal Load ◽  
Youth Football ◽  
The Relationship ◽  
Very High

Purpose: To assess the convergent validity of internal load measured with the CR100 scale in youth football players of 3 age groups. Methods: A total of 59 players, age 12–17 years, from the youth academy of a professional football club were involved in this study. Convergent validity was examined by calculating the correlation between session ratings of perceived exertion (sRPE) and Edwards load, a commonly used load index derived from the heart rate, with the data originating from 1 competitive season. The magnitude of the relationship between sRPE and Edwards load was obtained with weighted mean correlations and by assessing the effect of the change of the Edwards load on sRPE. Differences between the individuals’ intercepts and slopes were assessed by interpreting the SD representing the random effects (player identity and the interaction of player identity and scaled Edwards load). Probabilistic decisions about true (infinite sample) magnitudes accounting for sampling uncertainty were based on 1-sided hypothesis tests of substantial magnitudes, followed by reference Bayesian analysis. Results: Very high relationships exist between the sRPE and Edwards load across all age groups, with no meaningful differences in the magnitudes of the relationships between groups. Moderate to large differences between training sessions and games were found in the slopes of the relationships between the sRPE and Edwards load in all age groups. Finally, mostly small to moderate differences were observed between individuals for the intercepts and slopes of the relationships between the sRPE and Edwards load. Conclusion: Practitioners working in youth team sports can safely use the CR100 scale to track internal load.

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