Multimodal Machine Learning to Predict When Elite Youth Football Players Might Need Training Changes
Official title: Machine Learning-Based Short-Term Prediction of Reduced Participation Within the Competitive Microcycle in Elite Youth Football Players Using Multimodal Data
Run by Palacky University · NCT07870460 · Data updated 2026-11-12
Researchers are testing whether combining wearable and performance data can predict, within 24–72 hours, if an elite youth player will have to modify training or miss time due to health issues.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 15 Years to 19 Years |
| Sex | Men only |
| Enrollment | 30 (estimated) |
| Sponsor | Palacky University Academic / other |
| Locations | 1 site (Olomouc) |
| Status | Recruiting |
| Started | July 15, 2026 |
| Main results expected | June 30, 2027 |
| Study ends | June 30, 2028 |
| Registry record updated | October 15, 2026 |
| Trial ID | NCT07870460 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Elite youth football players in the U16 or U19 squads of SK Sigma Olomouc, with at least three years of academy training and high sensor-wearing compliance.
✕Probably not a fit
Players with low adherence to monitoring or with conditions limiting wearables may not benefit from the predictive models.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This is a prospective, longitudinal observational study conducted over one full season at an elite youth academy in Czechia. The study links continuous multimodal monitoring data—external movement from Catapult sensors and nocturnal autonomic signals from WHOOP—with staff-adjudicated daily participation outcomes to model short-term risk of reduced participation. The cohort includes about 30 male players aged approximately 16 to 19 years, generating thousands of player-days of data. Primary analysis focuses on one-step hazard of transitioning from full participation to a lower participation category within 24, 48, or 72 hours, with inter-rater reliability assessed for outcome adjudication. The study uses eight predictor domains, including maturation, anthropometry, sleep, and physiological metrics, to build machine learning models that tolerate missing or variable compliance.
Why it matters
Potential benefit: If successful, the approach could enable early identification of fatigue or health-related risks to help tailor training loads and reduce unplanned absences.
How similar studies have gone: Previous work in sports analytics has shown promise for multimodal data in predicting performance and injury risk, but this specific model for short-term participation changes is novel in this setting.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Registered player in the U16 or U19 squad of an elite football academy (SK Sigma Olomouc). * At least three years of structured football training within an organized academy programme. * Regular active participation in microcycle team training and competitive league matches. * Written informed assent by the player and written informed consent by a parent or legal guardian (for minor participants). * Monitoring compliance rate \> 80% during the initial 14-day passive run-in period. Exclusion Criteria: * Pre-existing severe musculoskeletal injury, orthopedic surgery, or neurological disorder preventing full participation at baseline. * Known cardiovascular, metabolic, or systemic disease contraindicating maximal athletic exertion. * Inability or refusal to wear continuous monitoring sensors (Catapult GNSS unit during training, WHOOP strap during sleep). * Prolonged absence from the squad due to non-medical administrative reasons (transfer, academic leave, disciplinary suspension).
Where it's running
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SK Sigma Olomouc Academy & AC Baluo, Palacký University Olomouc
Olomouc, Czechia Recruiting
Study contacts
- Study coordinator: David Prycl
- Email: david.prycl@upol.cz
- Phone: 775111781
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.