Resultant equations for training load monitoring during a standard microcycle in sub-elite youth football: a principal components approach.
José Eduardo TeixeiraPedro ForteRicardo FerrazLuís BranquinhoRyland MorgansAntónio José SilvaAntónio Miguel MonteiroTiago Manuel BarbosaPublished in: PeerJ (2023)
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.
Keyphrases
- heart rate
- high school
- physical activity
- mental health
- virtual reality
- body composition
- high speed
- young adults
- oxidative stress
- depressive symptoms
- heart rate variability
- blood pressure
- high intensity
- randomized controlled trial
- big data
- social support
- monte carlo
- social media
- high resolution
- mass spectrometry
- atomic force microscopy
- resistance training
- artificial intelligence
- deep learning
- anti inflammatory
- quality improvement