ReviewSports (Basel, Switzerland)2021
A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer.
Review in Sports (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
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Who cites it
31 citing papers in PubMed.
- VOeLife · 2023Trial
- The Use of Machine Learning to Estimate Ground Reaction Forces During Running: A Scoping Review of the Current Practices.Sensors (Basel, Switzerland) · 2026Article
- Countermovement Jump Force-Time Mechanics Differentiate ACL Injury Status in Elite Alpine Ski Racers.Scandinavian journal of medicine & science in sports · 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
- Sleep quality and injury risk in adolescent football players: an amplified effect for high-risk athletes revealed by quantile regression.Frontiers in public health · 2026Article
- Article
- A new conceptual framework for managing hamstring injury risk in soccer - implementing a data-informed approach: a narrative review.Biology of sport · 2026Review
- Multi-sensor fusion outperforms single indicators for fatigue prediction in university soccer players: a machine learning approach.Frontiers in physiology · 2026Article
- SHAP-based interpretable machine learning for injury risk prediction in university football players: a multi-dimensional data analysis approach.Scientific reports · 2025Article
- An Explainable Machine Learning Approach to Explain the Effects of Training and Match Load on Ultra-Short-Term Heart Rate Variability in Semi-Professional Basketball Players.Sensors (Basel, Switzerland) · 2025Article
- Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder.bioRxiv : the preprint server for biology · 2025Article
- Artificial Intelligence for Objective Assessment of Acrobatic Movements: Applying Machine Learning for Identifying Tumbling Elements in Cheer Sports.Sensors (Basel, Switzerland) · 2025Article
- Identification of Athleticism and Sports Profiles Throughout Machine Learning Applied to Heart Rate Variability.Sports (Basel, Switzerland) · 2025Article
- Predicting noncontact injuries of professional football players using machine learning.PloS one · 2025Article
- Modeling public attention behavior on badminton's ecosystem evolution via complex network theory.Frontiers in sports and active living · 2025Article
- Review
- Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis.Journal of sports science & medicine · 2024Article
- Analyzing ECG signals in professional football players using machine learning techniques.Heliyon · 2024Article
- Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review.Frontiers in sports and active living · 2024Article
- Non-contact lower limb injuries in Rugby Union: A two-year pattern recognition analysis of injury risk factors.PloS one · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of-the-art regarding machine-learning techniques in sport science, the aim of this narrative review is to provide a guideline describing a correct approach for training, validating, and testing machine learning models to predict events in sports science. The main contribution of this narrative review is to highlight any possible strengths and limitations during all the stages of model development, i.e., training, validation, testing, and interpretation, in order to limit possible errors that could induce misleading results. In particular, this paper shows an example about injury forecaster that provides a description of all the features that could be used to predict injuries, all the possible pre-processing approaches for time series analysis, how to correctly split the dataset to train and test the predictive models, and the importance to explain the decision-making approach of the white and black box models.
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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.