ArticleScientific reports2024
Summarizing physical performance in professional soccer: development of a new composite index.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Comparing the effect of mental fatigue-inducing models on selected cognitive and technical performance aspects in young soccer players.Scientific reports · 2026Trial
- Current approaches to measuring high-intensity locomotor actions in adult male professional soccer. A scoping review.Biology of sport · 2026Review
- Associations Between Lower-Body Fast Maximal Dynamic and Reactive Strength Tests With Acceleration Performance in Semi-Professional Male Soccer Players.European journal of sport science · 2025Article
- Uncovering the Latent Components of Physical Performance in Professional Soccer: Evidence from the Turkish First Division.Journal of functional morphology and kinesiology · 2025Article
- Return to performance: machine learning insights into how absence time following muscle injuries affects match running performance in LaLiga soccer players.Biology of sport · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aims of this study were to create a composite index to measure the overall players' physical performance in professional soccer matches and analyze the effect of individual playing time and positional differences on this composite index. A total of 830 official matches from LaLiga men's first division and Spanish Copa del Rey were analyzed, which resulted in 24,980 match observations collected from 1138 male players (forwards, n = 286; midfielders, n = 441; defenders, n = 411). The physical performance variables, which represent the locomotor demands, were collected using electronic performance tracking systems. A Partial Least-Squares Structural Equation Model (PLS-SEM) was used to measure performance. The PLS-SEM output had three significant latent components, which explained 95% of the initial variability, that were related to the acceleration-specific performance (component 1), high-intensity running-related variables (component 2), and medium intensity actions variables (component 3). Also, a linear regression analysis was used to explore relationships between playing activity time (hours-X axis) and the composite index (10-point scale-Y axis), in which a strong and positive correlation was observed between individual playing time and the composite index (r = 0.76; p < 0.001; R
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Registered trials
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