At VeloViewer we are always fascinated by the role the WorldTour App plays in professional and individual pursuits, and were particularly intrigued with a recent academic request for the software for research purposes by Loughborough University.
PhD Researcher Alex Welburn reached out to our team for use of the App for a Time Trial (TT) optimisation study he was undertaking, focused on the critical power of two female riders.
Keen to know more, Alex sat down with us to talk about the project details and how VeloViewer will play a role in his research.
What is your role, and what is the research project?
I’m a sport scientist and a self-funded PhD researcher at Loughborough University, with over a decade of experience working in cycling across various levels — from grassroots to elite. My role spans both applied and academic domains, aiming to bridge the gap between theory and practice. This study reflects the practical application of my research, specifically around the W′ BAL model, which estimates work above critical power and models recovery. For people new to the concept, W’Bal is a mathematical model used in Sports Science to quantify an athlete’s remaining work capacity during exercise.
I believe that by individualising recovery dynamics and understanding where athletes can optimally recover and re-apply effort, we can meaningfully enhance time trial performance. This project is about testing that belief in real-world settings.
What is the study you are conducting/hypothesis?
This study explores the real-world application of the W′BAL model to optimise time trial (TT) performance. Rather than testing a specific hypothesis, the focus is on evaluating whether we can accurately predict W′ recovery during a ride, and tailor recovery dynamics to individual athletes. The W′ BAL model helps quantify work done above Critical Power (CP), where W′ acts like a finite battery. When riding below CP, W′ is replenished – but recovery depends on multiple factors. We aim to refine these dynamics to make recovery rate athlete-specific, enhancing pacing strategy and TT efficiency. The second phase of the study compares predicted outcomes to actual performance, providing post-race insights into whether riders outperformed model expectations with our adjustments. This work seeks to inform better race pacing and personalised recovery modelling.
What preparation are you undertaking for the event?
We have purposefully constrained the amount of testing we will be doing, to reflect a team environment. We will most likely get a morning and afternoon session on the same day to test. So, we have done an indoor 3 and 12 minute critical power test on the TT bike, then we will do the same but out on the road – and then finally a longer effort, again on the road, to tweak some of the parameters of the model to optimise the pacing for the event.
Why did you identify VeloViewer as a useful tool for this project?
We wanted something we could use to review the course, and then to have it in the team car with our pacing plan/instructions for the rider; as we will use it to produce the overview of the pacing plan, so we can relay instructions via radio as they won’t remember each part. So being able to effectively and easily communicate this to the rider will be a great help. We will also use it to mark out key sections, where they can stay in position, or when they need to come to the base bar.
How will the findings of the study be used?
The findings will serve two key purposes. First, they represent the application of my PhD research outside the lab and into a real-world performance setting – which is a really exciting step. It gives us the chance to evaluate and refine the W′BAL model in practice, and to develop an objective framework for making individualised pacing and recovery adjustments. We already have some potential modifications in mind, and this will be the first opportunity to test those directly.
Secondly, this study will act as a practical showcase of my work, helping me take the next step into a team-based role. While I’m not currently working within a professional setup, my goal is to move into a role as a physiologist or performance coach, where I can apply this research to directly support athletes and optimise race performance.
Check back on the blog, and keep an eye out on VV socials, as we will be sharing Alex’s findings once the study is complete.