ArticleScientific reports2025
Predictive athlete performance modeling with machine learning and biometric data integration.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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
19 citing papers in PubMed.
- Machine learning-based personalized training models for optimizing marathon performance through pyramidal and polarized training intensity distributions.Scientific reports · 2025Trial
- Development of an individualized prediction model for dynamic adaptations in performance and immune function associated with dietary patterns in endurance athletes using machine learning.Journal of the International Society of Sports Nutrition · 2026Observational
- Article
- 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
- Predicting Next Day Heart Rate Variability Based on Training Load in Cyclists Using Machine Learning.Sports (Basel, Switzerland) · 2026Article
- Body Composition Architecture and Injury Topology in Physically Active Young Adults: A Tanglegram-Based Cophylogenetic Approach.Journal of clinical medicine · 2026Article
- Artificial Intelligence in Sports Medicine: A Decision-Centered Framework for the Future Sports Physician.Diagnostics (Basel, Switzerland) · 2026Review
- Prediction of athlete performance based on a gradient regression model.Scientific reports · 2026Article
- Can Non-Conventional Blood Biomarkers Improve Running Performance Prediction? A Proof of Concept.Life (Basel, Switzerland) · 2026Article
- Wearable Biosensing and Machine Learning for Data-Driven Training and Coaching Support.Biosensors · 2026Review
- Deep learning for genomic insights into athletic performance in sports education.Frontiers in genetics · 2026Article
- Nonlinear interaction between stroke length and stroke rate in elite swimming.Frontiers in sports and active living · 2026Article
- Machine learning prediction of ACL loading during the wide lunge: a multifactorial coupling analysis based on kinematic and electromyographic signals.Frontiers in bioengineering and biotechnology · 2026Article
- Improving sport horse selection using machine learning: Early prediction of elite-level show jumping performance.PloS one · 2026Article
- Digital twin for Taekwondo athletes: integrating sports nutrition and psychological readiness using artificial intelligence.Frontiers in public health · 2026Review
- Artificial intelligence and sports genomics: advancing precision sports science and athletic performance.Frontiers in sports and active living · 2026Review
- Evaluation of various traditional machine learning techniques for predicting the acute effect of different hamstring muscle stretching methods among male soccer players.Scientific reports · 2025Article
- Construction of the prediction model and analysis of key winning factors in world women's volleyball using gradient boosting decision tree.Scientific reports · 2025Article
- Artificial Intelligence in Endurance Sports: Metabolic, Recovery, and Nutritional Perspectives.Nutrients · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Purpose of this study is to propose a new integrative framework for athletic performance prediction based on state-of-the-art machine learning analysis and biometric data biometric scanning. By merging physiological signals i.e., Heart rate variability, oxygen consumption, muscle activation patterns, with psychological signals i.e., mental toughness, athlete engagement, group cohesion along with contextual training data, we create a hybrid model that performs superiorly as compared to traditional unidimensional models. Our exercisers were trained using a gradient boosting and neural network to learn the very complex non-linear relationships that exist between the physical and the psychological performance drivers. With a rich sample set of 480 athletes from different sports, the proposed model achieved 90% accuracy (R
Indexed as
Identifiers
What OpenQuestion holds
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.