ArticleSports (Basel, Switzerland)2024
Loading or Unloading? This Is the Question! A Multi-Season Study in Professional Football Players.
Article in Sports (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
6 citing papers in PubMed.
- Using the Relative Pitch Size, Internal Floaters, and External Floaters During Different Small-Sided Games in Professional Soccer in the Return-to-Play Process.Journal of functional morphology and kinesiology · 2026Article
- Position-Specific External Load Between Official Matches and Small-, Medium- and Large-Sided Games in Professional Football.Journal of functional morphology and kinesiology · 2026Article
- Seasonal Analysis of Match External Load in Hungarian Second-Division Professional Football Across Three Competitive Seasons Using GPS-Derived Match-Average Data.Sports (Basel, Switzerland) · 2026Article
- Article
- Description and Classification of Training Drills, Based on Biomechanical and Physiological Load, in Elite Basketball.Sensors (Basel, Switzerland) · 2025Article
- Breathing Monitoring in Soccer: Part I-Validity of Commercial Wearable Sensors.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study examined the impact of training load periodization on neuromuscular readiness in elite football players using the Locomotor Efficiency Index (LEI) as a measure of performance optimization. Throughout the 2021/22 and 2022/23 seasons, 106 elite male players (age: 19.5 ± 3.9 years) from an Italian professional football club were monitored using Global Positioning Systems (GPS) external load data. The LEI was derived from a machine learning model, specifically random forest regression, which compared predicted and actual PlayerLoad™ values to evaluate neuromuscular efficiency. Players were categorized by weekly LEI into three readiness states: bad, normal, and good. Analysis focused on the variation in weekly LEI relative to weekly load percentage variation (large decrease, moderate decrease, no variation, moderate increase, large increase), which included total distance, high-speed distance (above 25.2 km/h), and mechanical load, defined as the sum of accelerations and decelerations. Statistical analysis showed significant differences only with variations in total distance and mechanical load. Specifically, reducing weekly loads improved LEI in players in lower readiness states, while maintaining or slightly increasing loads promoted optimal readiness. This approach enables coaches to tailor training prescriptions more effectively, optimizing workload and recovery to sustain player performance throughout a demanding season.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.