ArticlePeerJ2023
Resultant equations for training load monitoring during a standard microcycle in sub-elite youth football: a principal components approach.
Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Principal Component Analysis Applied to In-School Inertial Measurement Unit-Derived Data During Physical Activity: A Systematic Review Highlighting Children's Behavioral Patterns.Sensors (Basel, Switzerland) · 2026Pooled it
- A Call for Consensus: A Narrative Review of GPS-Based External Training Load Monitoring in Male Youth Soccer Players.Sports (Basel, Switzerland) · 2026Review
- Ecological momentary assessment of the stress-recovery process through technology mHealth in sports: a scoping review.BMC sports science, medicine & rehabilitation · 2026Article
- Comprehensive multivariate insights into external training load in women's football: four session clusters to optimize weekly periodization.Biology of sport · 2026Article
- The Impact of Weekly Acceleration and Deceleration Loads on Neuromuscular Performance in Soccer: A Session-to-Session Analysis.Journal of sports science & medicine · 2025Observational
- Shaping Training Load, Technical-Tactical Behaviour, and Well-Being in Football: A Systematic Review.Sports (Basel, Switzerland) · 2025Review
- Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review.Frontiers in sports and active living · 2025Review
- Intra and inter variation in training load, recovery state and technical-tactical performance across a standard microcycle in Sub-elite youth football players.Frontiers in sports and active living · 2025Article
- Analyzing Key Factors on Training Days within a Standard Microcycle for Young Sub-Elite Football Players: A Principal Component Approach.Sports (Basel, Switzerland) · 2024Article
- Data Mining Paths for Standard Weekly Training Load in Sub-Elite Young Football Players: A Machine Learning Approach.Journal of functional morphology and kinesiology · 2024Article
- Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approach.Frontiers in psychology · 2024Article
- Can trainability constrain physical fitness adaptations to small-sided games and high-intensity interval training in young male basketball players? a prospective cohort study.Frontiers in physiology · 2024Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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.
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