ArticleSports medicine - open2022
Machine Learning for Understanding and Predicting Injuries in Football.
Article in Sports medicine - open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Applications in Non-Contact Lower Limb Sports Injury Prediction: A Systematic Review.Journal of sports science & medicine · 2026Pooled it
- A Frequency-Aware Dual-Stream Deep Learning Framework for Athlete Workload Monitoring and Injury Risk Assessment: A Multi-Dataset Validation Study in Professional Team Sports.Sensors (Basel, Switzerland) · 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
- [Progress and application of machine learning in sports injury research].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- AI-Based Electromyographic Analysis of Single-Leg Landing for Injury Risk Prediction in Taekwondo Athletes.Healthcare (Basel, Switzerland) · 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
- Machine learning framework for predicting athletic injuries and optimising performance.BMC sports science, medicine & rehabilitation · 2026Article
- Explainable temporal deep learning for athlete-independent classification of injury-labeled days in competitive distance runners: a methodological benchmark using seven-day training-load histories.Frontiers in public health · 2026Article
- SHAP-based interpretable machine learning for injury risk prediction in university football players: a multi-dimensional data analysis approach.Scientific reports · 2025Article
- Machine Learning-Based Prediction of Muscle Injury Risk in Professional Football: A Four-Year Longitudinal Study.Journal of clinical medicine · 2025Article
- Employing machine learning for early detection of poly-victimization in rural children: a survey study in China's Chaoshan region.BMC public health · 2025Article
- Kinetic Variables as Indicators of Lower Limb Indirect Injury Risk in Professional Soccer: A Systematic Review.Journal of functional morphology and kinesiology · 2025Review
- Comparison of Landing Biomechanics in Male Amateur Basketball Players with and without Patellar Tendinopathy during Simulated Games.Journal of human kinetics · 2025Article
- Advanced feature engineering in Acute:Chronic Workload Ratio (ACWR) calculation for injury forecasting in elite soccer.PloS one · 2025Article
- Response to "Comment on: Machine Learning for Understanding and Predicting Injuries in Football".Sports medicine - open · 2024Article
- Comment on: Machine Learning for Understanding and Predicting Injuries in Football.Sports medicine - open · 2024Article
- Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis.PloS one · 2024Article
- Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approach.Frontiers in psychology · 2024Article
- Development of an algorithm-based approach using neuromuscular test results to indicate an increased risk for non-contact lower limb injuries in elite football players.BMJ open sport & exercise medicine · 2023Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Attempts to better understand the relationship between training and competition load and injury in football are essential for helping to understand adaptation to training programmes, assessing fatigue and recovery, and minimising the risk of injury and illness. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods. Advances in automatic and interactive data analysis with the help of machine learning are now being used to better establish the intricacies of the player load and injury relationship. In this article, we examine this recent research, describing the analyses and algorithms used, reporting the key findings, and comparing model fit. To date, the vast array of variables used in analysis as proxy indicators of player load, alongside differences in approach to key aspects of data treatment-such as response to data imbalance, model fitting, and a lack of multi-season data-limit a systematic evaluation of findings and the drawing of a unified conclusion. If, however, the limitations of current studies can be addressed, machine learning has much to offer the field and could in future provide solutions to the training load and injury paradox through enhanced and systematic analysis of athlete data.
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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.