The medical, performance and data foundations behind VALITICA’s injury prediction model
| Supporting Clinical and Performance Decisions
VALITICA combines sports-medicine expertise with advanced data science to predict intrinsic muscle injuries in professional football. The model processes millions of data points across multiple variable clusters to detect early patterns that precede soft-tissue injuries.
Developed through continuous collaboration between team physicians, performance practitioners and data scientists, the system provides clear, explainable indicators that support daily medical and performance decisions.
| Data Sources Used by VALITICA
The model ingests all relevant performance, medical and contextual data available within a professional club, integrating seamlessly with existing workflows. No additional hardware is required.
Data types typically used include:
- EPTS external load metrics (distance, HSR, accelerations, decelerations).
- Internal load (heart rate, HRV trends when available, exertion markers).
- Medical records and injury history.
- Mechanical and kinematic indicators.
- Strength, power and functional test results.
- Biomarkers (optional, if provided by the club).
- Wellness and questionnaire inputs.
- Recovery cycles and treatment sessions.
- Environmental and contextual training factors.
- Match and training schedule demands.
Note: VALITICA adapts to the club’s existing data infrastructure. No changes in technology or workflows are required for implementation.
| Variable Clusters Processed by the Model
Soft-tissue injuries emerge from complex interactions across multiple dimensions. Instead of analysing metrics in isolation, VALITICA evaluates how variable clusters evolve and interact over time.
Examples of variable clusters:
- Metabolic.
- Kinematic.
- Mechanical.
- Personal characteristics.
- Injury history.
- Injury treatment & recovery progression.
- Biomarkers.
- Internal load.
- External load.
- Cumulative load.
- Thermal load.
- Foster Index.
- Borg/RPE Index.
- VAS pain scale.
- Club-specific data sources.
This multi-dimensional approach reveals subtle patterns that cannot be detected through univariate or single-cluster analysis.
| Model Architecture
VALITICA’s predictive engine uses a hybrid modelling approach that combines machine-learning techniques, statistical learning and time-series analysis. While proprietary details remain confidential, the operating principles are transparent.
Core components include:
- Multi-source data fusion.
- Time-series analysis across training cycles.
- Player-specific baseline profiling.
- Pattern detection across seasons.
- Continuous learning to enhance accuracy.
- Validation aligned with sports-medicine standards.
The model adapts to each club’s historical patterns and continuously improves as new data is collected.
| Explainability & Clinical Interpretation
VALITICA is designed to support, not replace, clinical judgement. Every risk indicator shown in the platform is explainable and aligned with the workflows of medical and performance teams.
Explainability features include:
- Transparent display of factors influencing each risk score.
- Pattern explanations linked to load, recovery and history.
- Alerts for atypical responses relative to the player’s baseline.
- Contextual guidance for further medical evaluation.
- Visual summaries designed for medical and performance teams.
This ensures that decisions remain grounded in science, context and practitioner expertise.
| Accuracy and Model Performance
Predictive performance strengthens as club data becomes more complete and consistent. VALITICA achieves high accuracy through individualised modelling and long-term pattern analysis.
Performance characteristics include:
- Up to 90% effectiveness when data quality is high and stable.
- Accuracy strengthens as long-term historical data grows.
- Player-specific models outperform generic injury frameworks.
- Continuous error reduction through season-by-season learning.
- Regular recalibration based on new club data.
Note: Accuracy depends on consistent data capture. VALITICA includes consulting to help clubs optimise their data processes and maximise reliability.
| Scientific Collaboration and Methodology
The model is built with active collaboration from sports-medicine specialists, physiotherapists, performance analysts and data scientists. The methodology reflects the operational realities of elite football.
Scientific and methodological pillars:
- Medical oversight from practitioners working in professional sport.
- Continuous input from performance and load-management experts.
- Research-driven methodology aligned with international standards.
- Internal validation using anonymised injury events from elite clubs.
This ensures clinical relevance and operational compatibility.
| Data Privacy, Ethics and Compliance
VALITICA fully complies with GDPR and the EU AI Act. All personal data is protected with industry-standard security and advanced anonymisation techniques.
Key principles:
- GDPR and EU AI Act compliant.
- Club retains full control and ownership of data.
- Multi-layer authentication and strict access logs.
- Encrypted data transfer and storage.
- Anonymisation of training datasets.
Ethical, secure and transparent data handling is central to our approach.
| Why the Science Matters
Reliable injury prediction demands rigorous data structures, robust modelling and clinically interpretable outputs. VALITICA brings these elements together, giving medical and performance teams the evidence they need to make informed decisions, reduce avoidable injuries and improve player availability throughout the season.
