ArticleJournal of functional morphology and kinesiology2024
Data Mining Paths for Standard Weekly Training Load in Sub-Elite Young Football Players: A Machine Learning Approach.
Article in Journal of functional morphology and kinesiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Call for Consensus: A Narrative Review of GPS-Based External Training Load Monitoring in Male Youth Soccer Players.Sports (Basel, Switzerland) · 2026Review
- Construction of the prediction model and analysis of key winning factors in world women's volleyball using gradient boosting decision tree.Scientific reports · 2025Article
- 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
- 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
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
The aim of this study was to test a machine learning (ML) model to predict high-intensity actions and body impacts during youth football training. Sixty under-15, -17, and -19 sub-elite Portuguese football players were monitored over a 6-week period. External training load data were collected from the target variables of accelerations (ACCs), decelerations (DECs), and dynamic stress load (DSL) using an 18 Hz global positioning system (GPS). Additionally, we monitored the perceived exertion and biological characteristics using total quality recovery (TQR), rating of perceived exertion (RPE), session RPE (sRPE), chronological age, maturation offset (MO), and age at peak height velocity (APHV). The ML model was computed by a feature selection process with a linear regression forecast and bootstrap method. The predictive analysis revealed that the players' MO demonstrated varying degrees of effectiveness in predicting their DEC and ACC across different ranges of IQR. After predictive analysis, the following performance values were observed: DEC (x¯
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