Anybody who has ever been fortunate enough to pilot an aeroplane has probably experienced an eerie realisation that the world is far more 3 dimensional than most of us ever really think about. Up there, a couple of thousand feet or more above ground level, there is almost a sensation of diving somewhere in the middle of an aquarium while the place that once represented the 3D world feels like nothing more than the gravel and the weeds down at the very bottom of the tank. The opportunity to see the world like that has a lot to do with why many of us stop, gaze and enjoy the vista on high mountain passes.
Modelling cycling courses relies on what we call “triplets” of GPS data – latitude, longitude, elevation – and we have recently been looking for better ways to visualise data in these 3 dimensions. We were incredibly fortunate to hire a guy whose recent projects included some pretty serious geospatial work – software used to remap the bed of the Panama Canal – and the image with this blog is an example of the capability he came up with. In this image we’ve rendered the gradient of every road sector as a kind of “4th dimension”, now quite a familiar technique, but the possibilities go way beyond that.
CPL predictive race modelling typically includes projections of riders speed given power, variable power levels required to execute an Optimal Pacing Strategy, and even cadence ranges given that power, rider attributes and gearing. We can now map any of these projections onto 3D visualisations of a course and we think this opens up a world of possibilities in terms of understanding race planning and execution. Imagine being able to look at the course of a forthcoming event and see the “hotspots” where a rider will be travelling particularly slowly – often the best candidate sectors for pacing improvement – or where a particular range of gearing may be inadequate.
There is more too. One of the big benefits of Pre-Event Performance Models is scenario analysis. What is the impact of a rider’s time on course given more power (CPL Power Delta), less weight (CPL Mass Delta), less aerodynamic drag, or changes in equipment? Currently we can illustrate these impacts for the 4 quarters of a course, and for the course in aggregate, but imagine if these impacts could be visualised through every single sector. Where does a heavier but more aerodynamic wheel cost time and where does it buy time? To the best of our knowledge the cycling world has not seen this sort of analytic detail in 3D before.
We expect to incorporate more and more 3D visualisations into what we do. The image with this blog is from a rather special pacing project that came across our desk this week. In the meantime, if you the reader should have any particular visualisation ideas or problems with which we may be able to help, then we are always happy to hear them.