Utilizing techniques for reducing multivariate data is essential for comprehensively understanding
the variations and relationships within both biomechanical and physiological datasets in the
context of youth football training. Therefore, the objective of this study was to identify the primary
factors influencing training sessions within a standard microcycle among young sub-elite football
players. A total of 60 male Portuguese youth sub-elite footballers (15.19 1.75 years) were continuous
monitored across six weeks during the 2019–2020 in-season, comprising the training days from match
day minus (MD-) 3, MD-2, and MD-1. The weekly training load was collected by an 18 Hz global
positioning system (GPS), 1 Hz heart rate (HR) monitors, the perceived exertion (RPE) and the total
quality recovery (TQR). A principal component approach (PCA) coupled with a Monte Carlo parallel
analysis was applied to the training datasets. The training datasets were condensed into three to five
principal components, explaining between 37.0% and 83.5% of the explained variance (proportion and
cumulative) according to the training day (p < 0.001). Notably, the eigenvalue for this study ranged
from 1.20% to 5.21% within the overall training data. The PCA analysis of the standard microcycle in
youth sub-elite football identified that, across MD-3, MD-2, and MD-1, the first was dominated by the
covered distances and sprinting variables, while the second component focused on HR measures and
training impulse (TRIMP). For the weekly microcycle, the first component continued to emphasize
distance and intensity variables, with the ACC and DEC being particularly influential, whereas the
second and subsequent components included HR measures and perceived exertion. On the three
training days analyzed, the first component primarily consisted of variables related to the distance
covered, running speed, high metabolic load, sprinting, dynamic stress load, accelerations, and
decelerations. The high intensity demands have a high relative weight throughout the standard
microcycle, which means that the training load needs to be carefully monitored and managed.
