ArticleBiology of sport2025
Return to performance: machine learning insights into how absence time following muscle injuries affects match running performance in LaLiga soccer players.
Article in Biology of sport, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Return-to-Play Criteria Following Lower Limb Muscle Injuries in Soccer: A Systematic Review with Evidence Synthesis.Sports medicine (Auckland, N.Z.) · 2026Pooled it
- Return of match running performance following muscle strain injuries of varying severity in professional football.Biology of sport · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To determine how absence time after muscle injuries affects external load metrics in elite soccer players and identify which performance variables are most impacted by the injury. A total of 110 lower limb muscle injuries from LaLiga players were analysed. Following an analysis of pre- and post-injury data to identify which outcomes were affected by muscle injury, machine learning algorithms were employed to examine relationships between absence duration and performance metrics. Maximal speed, maximal acceleration, maximal deceleration, composite index (i.e., overall player performance) and sprint count during matches were the most affected variables after return to play. The multiple linear regression (MLR) model and random forest regression (RFR) presented an R
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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.