ArticleJournal of sports science & medicine2025
Beyond Playing Positions: Categorizing Soccer Players Based on Match-Specific Running Performance Using Machine Learning.
Article in Journal of sports science & medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Role of Machine Learning in Talent Identification for Team Sports: A Systematic Review.Journal of sports science & medicine · 2026Pooled it
- Classifying Soccer Players Based on Physical Capacities and Match-Specific Running Performance Using Machine Learning.Journal of sports science & medicine · 2025Article
- Potential Mechanisms of Exercise-Mediated Ferroptosis Regulation in Central Nervous System Diseases.Molecular neurobiology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Soccer players are frequently categorized by playing positions, both in the scientific literature and in practice. However, the utility of this approach in evaluating physical match performance and optimizing physical training programs remains unclear. This study compares the effectiveness of categorizing soccer players by their playing position versus using unsupervised machine learning based on match-specific running performance. Match-specific running data were collected from 40 young elite male soccer players over two seasons. Thirty-one of these players completed a 20-meter sprint test and a maximal incremental treadmill test to measure maximal oxygen uptake. Players were categorized both by playing position and by subgroups derived through
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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.