Why a course in Technology and Innovation in Sport?
Sport science now depends on technology, data analysis and computational modeling. Mathematical models are used for AI-based performance analysis, and outside sport they set insurance premiums and inform decisions that affect millions of people.
Knowing how these tools work is becoming a key skill for anyone who wants to work in research, coaching, performance analysis or product development. In this course I connect the theory to its practical use, and I show that the logic we use for VO₂ kinetics (inputs, parameters fitted to data, outputs) is the same logic behind modern AI systems.
What the course covers
Most of what technology does in sport rests on the ability to build and use models. The course starts with simple linear models, moves on to dynamic systems and differential equations, and ends with neural networks and large language models (LLMs) such as ChatGPT. Along the way we look at:
- physiological models, from VO₂ kinetics during exercise to lactate clearance after it;
- training models: the Banister fitness-fatigue model and supercompensation;
- biomechanical models: mountain bike suspension dynamics and the power equation of cycling;
- AI models: neural networks, the curse of dimensionality, and how LLMs work;
- models outside sport: insurance pricing and policy decisions.
One idea comes back several times: the same first-order differential equation describes VO₂ kinetics in exercise physiology, data smoothing, electrical circuits and training adaptation. Once you understand it in one context, you will recognize it in the others.
How the course works
The course is hands-on. Each module includes interactive charts and simulations in which you change the parameters of a model and see the result immediately, from heart rate models to a small neural network that you train by hand.
Every concept is introduced through a sport science application, such as critical power testing or training load management, and each module builds on the previous ones. Where it helps, I borrow techniques from engineering, economics and computer science.
What you should be able to do at the end
By the end of the course you should be able to:
- describe a model as a "service" that turns inputs into outputs through parameters estimated from data, and apply this view in different fields;
- follow the modeling process from observation and conceptualization to validation and use in practice, and tell interpolation from extrapolation;
- use differential equations to model VO₂ kinetics, training adaptation and performance in locomotor sports;
- explain how neural networks work, what the curse of dimensionality is, and how LLMs relate to the models seen earlier in the course;
- distinguish association from causation, recognize the limits of a model, and understand the ethical responsibilities of the people who build models;
- use analogies between disciplines to borrow techniques from engineering, signal processing and other fields in your own research.
About this course
Course origins
I originally designed this material for the Technology and Innovation in Sport course in the master's program at the University of Verona and the University of Trento, Italy. I have taught it as an 18-hour course to master's students in sport science, and after several years of teaching and revising it I turned it into this website.
Open educational resource
License and usage rights
This material is freely available on the following terms:
- Non-commercial educational use is fully permitted.
- Attribution is required: always cite this source when you use or adapt the material.
- Commercial use requires separate permission.
- Derivative works for educational purposes are encouraged, with proper attribution.
For educators and contributors
If you teach, you are welcome to adapt, modify and build on this material for your own courses.
If you have sport science models, datasets or interactive demonstrations that you would like to see featured here, contributions are welcome.
If you would like the full 18-hour course delivered at your institution, I am available for in-person workshops and shared teaching arrangements.
I am also open to research collaborations on sport science education, mathematical modeling and interactive learning technologies.
Contact and feedback
For questions, collaborations or feedback on this resource, I'd love to hear from you:
Andrea Zignoli, PhD
📧 andrea.zignoli@unitn.it
For in-person students
This website complements the official Technology and Innovation in Sport course taught at the University of Verona and the University of Trento. If you are enrolled and will take the exam, note the following: