In recent years, the field of sport science has undergone a profound transformation, driven by rapid advances in technology, data analysis, and computational modeling. From AI-powered performance analysis to predictive models that determine insurance premiums, mathematical models now influence decisions that affect millions of lives.
Understanding these tools is no longer optional — it is becoming a key skill set for those seeking to work at the forefront of research, coaching, performance analysis, and product development. This course bridges the gap between theoretical knowledge and practical application, showing how the same mathematical principles that govern VO₂ kinetics also power modern AI systems.
At the foundation of many modern applications in sport lies the ability to build and use models. But what makes this course unique is demonstrating how one equation can describe infinite applications:
You'll discover how the same first-order differential equation appears in exercise physiology, data smoothing, electrical circuits, and training adaptation — revealing the profound mathematical unity underlying diverse phenomena.
This course provides a hands-on, applied introduction that goes far beyond traditional textbook learning:
Interactive Learning: Over 15+ interactive charts and simulations where you can adjust parameters and see real-time results — from heart rate models to neural network training.
Real-World Applications: Learn how models determine car insurance premiums, analyze professional cycling performance, and power the AI systems you use daily.
Progressive Complexity: Start with simple linear models, progress through dynamic systems and differential equations, and finish by understanding how LLMs like ChatGPT work.
Sports Science Focus: Every concept is grounded in practical sports science applications — from critical power testing to training load management.
Cross-Disciplinary Insights: Discover how techniques from engineering, economics, and computer science can enhance your sports science practice.
By the end of this course, you'll confidently:
Understand Model Fundamentals: Recognize models as "services" that transform inputs to outputs through optimized parameters, and apply this framework across disciplines.
Navigate the Modeling Process: From observation and conceptualization to validation and real-world application, including the crucial distinction between interpolation and extrapolation.
Apply Dynamic Systems: Use differential equations to model VO₂ kinetics, training adaptation, and locomotor sports performance.
Evaluate AI and Machine Learning: Understand neural networks, the curse of dimensionality, and how LLMs connect to familiar modeling principles.
Make Informed Decisions: Recognize the difference between association and causation, understand model limitations, and appreciate the ethical responsibilities of modelers.
Think Across Disciplines: Apply mathematical analogies to borrow techniques from engineering, signal processing, and other fields to enhance your research.
Whether your interests lie in research, coaching, clinical settings, or industry, this course provides skills that significantly enhance your professional value:
High-Performance Sport: Apply dynamic models for training load optimization and performance prediction.
Sports Technology: Understand the AI and modeling principles behind wearable devices and performance analytics platforms.
Research Excellence: Use advanced modeling techniques to design better studies and interpret complex physiological data.
Industry Innovation: Bridge the gap between sports science and technology development in the rapidly growing sports tech sector.
Clinical Applications: Apply modeling principles to rehabilitation protocols and injury prevention strategies.
The combination of sports science domain knowledge with advanced modeling skills creates a powerful and increasingly rare skill set in the job market.
This course represents a fundamental shift from traditional "sage on the stage" teaching to interactive, experiential learning. Rather than passive slides, students engage with living, mathematical concepts through real-time parameter manipulation and immediate visual feedback.
The pedagogical approach is built on three core principles:
Conceptual Scaffolding: Each module builds naturally on previous knowledge, using familiar sports science contexts to introduce increasingly sophisticated mathematical concepts.
Interactive Discovery: Over 15 interactive components allow students to manipulate parameters and immediately see results, transforming abstract equations into tangible, intuitive understanding.
Cross-Disciplinary Connection: By showing how the same mathematical principles appear across physiology, engineering, economics, and AI, students develop a broader, more flexible thinking framework.
This material was originally designed and developed for the Technology and Innovation in Sport course in the Master's program at the University of Verona and University of Trento, Italy. After years of teaching and refinement, it has evolved into this comprehensive, web-based educational resource.
The 18-hour curriculum has been successfully delivered to master's students in sports science, demonstrating the effectiveness of this interactive, modeling-first approach to understanding complex physiological and performance concepts.
This educational material is made freely available under the following terms:
Want to Adapt This Content? This resource is designed to support the global sports science education community. Educators are encouraged to adapt, modify, and build upon this material for their own courses and contexts.
Have Models to Contribute? The course thrives on diverse, real-world examples. If you have interesting sports science models, datasets, or interactive demonstrations you'd like to see featured, contributions are welcomed.
Institution Collaboration If you'd like this complete 18-hour curriculum delivered at your institution, in-person workshops and collaborative teaching arrangements are available.
Research Collaboration: For those interested in the intersection of sports science education, mathematical modeling, and interactive learning technologies, research partnerships are encouraged.
For questions, collaboration opportunities, or feedback on this educational resource:
Andrea Zignoli, PhD
📧 andrea.zignoli@unitn.it
Whether you're an educator wanting to adapt this material, a researcher with interesting models to showcase, or an institution interested in hosting this curriculum, I'd love to hear from you.
No prior coding experience is required. The course is structured to gradually introduce key concepts and methods, with a focus on practical application and conceptual understanding. This resource represents years of teaching refinement and is offered freely to advance sports science education globally.