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

Within-Subject Principal Component Analysis of External Training Load and Intensity Measures in Youth Soccer Training

ABSTRACT

Marynowicz, J, Lango, M, Horna, D, Kikut, K, Konefał, M, Chmura, P, and Andrzejewski, M. Within-participant principal component analysis of external training load and intensity measures in youth soccer training. J Strength Cond Res 37(12): 2411-2416, 2023-The aim of this study was to identify which combination of external training load (EL) and external intensity (EI) metrics during youth soccer training sessions captured similar or unique information. Data were collected from 18 youth soccer players during an 18-week in-season competition period using a 10-Hz global positioning system, rating of perceived exertion (RPE), and session-RPE (sRPE). External training load measures included total distance (TD, in meters), PlayerLoad (PL, in arbitrary units), high-speed running distance (HSR, in meters), and number of accelerations (ACC, n). All EL metrics were also divided by session duration (minutes) to obtain EI values. A total of 804 training observations were undertaken (43 ± 17 sessions per player). The analysis was performed by use of the principal component analysis technique. The first principal component (PC) captured 49-70% and 68-89% of the total variance in EI and EL, respectively. The findings show that from the 5 EI metrics, most of the information can be explained by either TD per minute or PL per minute, with a loading from 0.87 to 0.98 and from 0.76 to 0.95, respectively. The majority of EL information can be explained by PL (loading: 0.93-0.98), TD (loading: 0.95-0.99), ACC (loading: 0.71-0.91), or sRPE (loading: 0.70-0.93). The second PC for EL metrics is most strongly correlated with HSR, with loadings from 0.53 to 0.84. The results suggest that the majority of the information contained in the EL variables can be captured in 1 PC without losing much information. The findings suggest that stakeholders who intend to provide a fast and holistic view of EL information in a daily training environment should report TD, PL, ACC, or sRPE plus HSR to coaching staff as a metrics that provides additional unique information.

Resultant equations for training load monitoring during a standard microcycle in sub-elite youth football: a principal components approach

Applying data-reduction techniques to extract meaningful information from electronic performance and tracking systems (EPTS) has become a hot topic in football training load (TL) monitoring. The aim of this study was to reduce the dimensionality of the internal and external load measures, by a principal component approach, to describe and explain the resultant equations for TL monitoring during a standard in-season microcycle in sub-elite youth football. Additionally, it is intended to identify the most representative measure for each principal component. A principal component analysis (PCA) was conducted with a Monte Carlo parallel analysis and VariMax rotation to extract baseline characteristics, external TL, heart rate (HR)-based measures and perceived exertion. Training data were collected from sixty sub-elite young football players during a 6-week training period using 18 Hz global positioning system (GPS) with inertial sensors, 1 Hz short-range telemetry system, total quality recovery (TQR) and rating of perceived exertion (RPE). Five principal components accounted for 68.7% of the total variance explained in the training data. Resultant equations from PCA was subdivided into: (1) explosiveness, accelerations and impacts (27.4%); (2) high-speed running (16.2%); (3) HR-based measures (10.0%); (4) baseline characteristics (8.3%); and (5) average running velocity (6.7%). Considering the highest factor in each principal component, decelerations (PCA 1), sprint distance (PCA 2), average HR (PCA 3), chronological age (PCA 4) and maximal speed (PCA 5) are the conditional dimension to be considered in TL monitoring during a standard microcycle in sub-elite youth football players. Current research provides the first composite equations to extract the most representative components during a standard in-season microcycle in sub-elite youth football players. Futures research should expand the resultant equations within training days, by considering other well-being measures, technical-tactical skills and match-related contextual factors.

Relationships between RPE-derived internal training load parameters and GPS-based external training load variables in elite young soccer players

This study aimed to identify the GPS-based external training load variables that influence the internal training loads (RPE-derived parameters: RPE and session RPE - sRPE), and generate a model to predict GPS-based external load variables from RPE and perceived wellness values. Training load data for 21 elite young players were collected over 72 training sessions and 23 matches from the same competitive season, and 564 observations (training sessions, 462; matches, 102) were analysed. Considering all observations (training sessions and matches), significant moderate and large correlations (p < 0.01) were detected between RPE values and EL measures. The correlation between the GPS outcomes with both the RPE and sRPE values was higher during training sessions than during matches. Moreover, increased RPE and perceived wellness measures had a significant positive effect on external load variables (p < 0.001). The present work provides preliminary evidence of the utility of the RPE and sRPE method to quantify the training loads in young soccer players since most of the GPS-based EL indicators were moderate to highly correlated with the RPE-derived parameters. Additionally, EL variables may be estimated when combining perceived IL and subjective wellness indicators in young soccer players.

Biomechanical Load Quantification Using a Lower Extremity Inertial Sensor Setup During Football Specific Activities

Training load monitoring systems in football do not focus on lower extremities and therefore potentially neglect important information to optimise performance or reduce injury risk. The current study aims to present joint and segment angular accelerations as novel indicators to quantify lower extremity biomechanical load measured by a new inertial sensor setup. Relationships were explored with commonly used whole-body training load indicators using principal component analysis (PCA). Sixteen male amateur football players performed a linear sprint and an agility T-test. An inertial sensor setup, and local position measurement system were used to collect training load data. Hip Load, Knee Load, Thigh Load and Shank Load were introduced to quantify lower extremity biomechanical load. Three principal components were identified for both tests, explaining 91% and 86% of the variance. The indicators for the lower extremities contributed to the second principal component for both tests and provide distinct information compared to whole-body load indicators. The results show the potential to use an inertial sensor setup combined with common monitoring systems to evaluate training load, which may help optimise future performance and reduce injury risk. These relationships should be further examined during other football specific activities such as shooting or jumping.

Using Principal Component Analysis to Compare the Physical Qualities Between Academy and International Youth Rugby League Players

PURPOSE

To compare the physical qualities between academy and international youth rugby league (RL) players using principal component analysis.

METHODS

Six hundred fifty-four males (age = 16.7 [1.4] y; height = 178.4 [13.3] cm; body mass = 82.2 [14.5] kg) from 11 English RL academies participated in this study. Participants completed anthropometric, power (countermovement jump), strength (isometric midthigh pull; IMTP), speed (10 and 40 m speed), and aerobic endurance (prone Yo-Yo IR1) assessments. Principal component analysis was conducted on all physical quality measures. A 1-way analysis of variance with effect sizes was performed on 2 principal components (PCs) to identify differences between academy and international backs, forwards, and pivots at under 16 and 18 age groups.

RESULTS

Physical quality measures were reduced to 2 PCs explaining 69.4% of variance. The first PC (35.3%) was influenced by maximum and 10-m momentum, absolute IMTP, and body mass. Ten and forty-meter speed, body mass and fat, prone Yo-Yo, IMTP relative, maximum speed, and countermovement jump contributed to PC2 (34.1%). Significant differences (P < .05, effect size = -1.83) were identified between U18 academy and international backs within PC1.

CONCLUSION

Running momentum, absolute IMTP, and body mass contributed to PC1, while numerous qualities influenced PC2. The physical qualities of academy and international youth RL players are similar, excluding U18 backs. Principal component analysis can reduce the dimensionality of a data set and help identify overall differences between playing levels. Findings suggest that RL practitioners should measure multiple physical qualities when assessing physical performance.