ReviewBiology of sport2023
Machine learning application in soccer: a systematic review.
Review in Biology of sport, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 4 of them syntheses that pooled it.
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
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
27 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning models for injury risk prediction in football players: A systematic review of predictors, performance, practical applications, and limitations.The Journal of international medical research · 2026Pooled it
- Brain-derived signals related to ball kicking movement in soccer and technologies employed: a systematic literature review with gap map.BMC sports science, medicine & rehabilitation · 2026Pooled it
- The Role of Machine Learning in Talent Identification for Team Sports: A Systematic Review.Journal of sports science & medicine · 2026Pooled it
- Artificial intelligence in football talent identification: a systematic review.Frontiers in sports and active living · 2026Pooled it
- Artificial Intelligence and Psychophysiological Monitoring for Integrated Performance Modeling in Elite Soccer: A Scoping Review of Applications, Evidence Gaps, and Translational Challenges.Sports (Basel, Switzerland) · 2026Review
- Evolution of spatial structure, passing network patterns, and gameplay intensity in elite women's and men's football (2020-2025).Scientific reports · 2026Article
- Deep learning for dynamic tactical formation recognition in professional football.Scientific reports · 2026Article
- Position prediction from performance and anthropometric indicators in young footballers: a machine learning approach.Scientific reports · 2026Article
- Machine learning applications in sport: a scoping review.Frontiers in psychology · 2026Review
- Predicting athletic performance in track and field athletes based on wearable physiological and psychological indicators: an interpretable machine learning study.Frontiers in physiology · 2026Article
- Optimizing football analytics: Dimensionality reduction meets machine learning for offensive and defensive player profiling.PloS one · 2026Article
- Key performance indicators of offensive transitions in elite women's football: a machine learning and explainability approach.Biology of sport · 2026Article
- Hamstring Strain Injury Risk in Soccer: An Exploratory, Hypothesis-Generating Prediction Model.Muscles (Basel, Switzerland) · 2025Article
- Beyond Playing Positions: Categorizing Soccer Players Based on Match-Specific Running Performance Using Machine Learning.Journal of sports science & medicine · 2025Article
- Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review.Frontiers in sports and active living · 2025Review
- Perceptual-cognitive skills and talent development environments in soccer: A scoping review.PloS one · 2025Article
- Goal and shot prediction in ball possessions in FIFA Women's World Cup 2023: a machine learning approach.Frontiers in psychology · 2025Article
- Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods.Diagnostics (Basel, Switzerland) · 2024Review
- Hip Muscle Strength Ratios Predicting Groin Injury in Male Soccer Players Using Machine Learning and Multivariate Analysis-A Prospective Cohort Study.Muscles (Basel, Switzerland) · 2024Article
- Analyzing Key Factors on Training Days within a Standard Microcycle for Young Sub-Elite Football Players: A Principal Component Approach.Sports (Basel, Switzerland) · 2024Article
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
Due to the chaotic nature of soccer, the predictive statistical models have become in a current challenge to decision-making based on scientific evidence. The aim of the present study was to systematically identify original studies that applied machine learning (ML) to soccer data, highlighting current possibilities in ML and future applications. A systematic review of PubMed, SPORTDiscus, and FECYT (Web of Sciences, CCC, DIIDW, KJD, MEDLINE, RSCI, and SCIELO) was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 145 studies initially identified, 32 were fully reviewed, and their outcome measures were extracted and analyzed. In summary, all articles were clustered into three groups: injury (n = 7); performance (n = 21), which was classified in match/league outcomes forecasting, physical/physiological forecasting, and technical/tactical forecasting; and the last group was about talent forecasting (n = 5). The development of technology, and subsequently the large amount of data available, has become ML in an important strategy to help team staff members in decision-making predicting dose-response relationship reducing the chaotic nature of this team sport. However, since ML models depend upon the amount of dataset, further studies should analyze the amount of data input needed make to a relevant predictive attempt which makes accurate predicting available.
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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.