ArticleSensors (Basel, Switzerland)2023
Predicting Injuries in Football Based on Data Collected from GPS-Based Wearable Sensors.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 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
- High-speed running and injury risk in soccer: a systematic review.Frontiers in public health · 2026Pooled it
- Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review.BMC medical informatics and decision making · 2026Article
- Flexible wearable medical devices: from material innovations and data processing to intelligent healthcare applications.Journal of advanced research · 2026Review
- 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
- Comparative analysis of performance data from various small-sided games with match data.BMC sports science, medicine & rehabilitation · 2026Article
- Can We Predict Adductor Strain? A Predictive Analysis of a Major League Soccer (MLS) Cohort Spanning from 2019 to 2022.Journal of functional morphology and kinesiology · 2026Article
- Development and validation of a machine learning model for non-contact injury prediction based on lower limb strength asymmetry in professional football.Scientific reports · 2026Article
- Multi-sensor fusion outperforms single indicators for fatigue prediction in university soccer players: a machine learning approach.Frontiers in physiology · 2026Article
- Trends in Semiprofessional Scottish Football Players' Acceleration and Deceleration Loads Across Playing Positions During Competitive Play and Training.Translational sports medicine · 2026Article
- AI based tool for monitoring intensity and fatigue in elite women handball.Frontiers in sports and active living · 2026Article
- Machine Learning-Based Prediction of Muscle Injury Risk in Professional Football: A Four-Year Longitudinal Study.Journal of clinical medicine · 2025Article
- Protective Equipment in Football: A Review of History, Evolution, Materials, and Contemporary Use.Sports (Basel, Switzerland) · 2025Review
- Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis.British journal of sports medicine · 2025Article
- Prediction of football injuries using GPS-based data in Iranian professional football players: a machine learning approach.Frontiers in sports and active living · 2025Article
- Exploration of the path of digital technology empowering the sustainable development of alumni football-a study based on ordinal logistic regression analysis.Frontiers in sports and active living · 2025Article
- Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis.Journal of sports science & medicine · 2024Article
- Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review.Frontiers in sports and active living · 2024Article
- Microfluidic Wearable Devices for Sports Applications.Micromachines · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing intensity and frequency of matches in professional football leagues are related to the increasing physical player load. An incorrect training model results in over- or undertraining, which is related to a raised probability of an injury. This research focuses on predicting non-contact lower body injuries coming from over- or undertraining. The purpose of this analysis was to create decision-making models based on data collected during both training and match, which will enable the preparation of a tool to model the load and report the increased risk of injury for a given player in the upcoming microcycle. For this purpose, three decision-making methods were implemented. Rule-based and fuzzy rule-based methods were prepared based on expert understanding. As a machine learning baseline XGBoost algorithm was considered. Taking into account the dataset used containing parameters related to the external load of the player, it is possible to predict the risk of injury with a certain precision, depending on the method used. The most promising results were achieved by the machine learning method XGBoost algorithm (Precision 92.4%, Recall 96.5%, and F1-score 94.4%).
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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.