Endurance Analytics

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Evaluating Optimal Pacing Plans

Performance Analysis & Modelling in sport probably began in field games such as football and baseball where the objective is to turn analytical intelligence into real tactical advantage which increases the probability of victory. In cycling the highest objective or “holy grail” is free speed - faster race times for equivalent physical effort - and no application promises more of this than the development of Optimal Pacing Plans.

At CPL we’ve been developing optimal pacing algorithms since 2010, when we provided the first web based tool for experiments in variable power pacing highlighting the importance of sector-by-sector constraints on rider's ability to deliver power. This has always been a “beta” tool (further development intended) but we have recently had the opportunity to do a lot more research and development in this area. Since we are no longer the only resource for optimal pacing plans some of our conclusions may be of even greater interest in light of what others are offering.

Beware Simplification

In the same way that climbing and descending a mountain is not the same as riding an equal distance on the flat (though both propositions equal zero net elevation gain) any efforts to simplify a course take us a long way from the realities of rider-on-course modelling. Smoothing away even the smallest ups, downs, and changes of direction can seriously compromise and result in overly optimistic pacing plans. We have always done performance modelling at the highest resolutions made possible by GPS profiles and observe poorer correspondence to reality when that resolution is dialled down. So beware simplification.

Compute Time

The Optimal Pacing Plans included in our Popular Event Models leverage the full resolution of the course profile for the best achievable accuracy. In fact it takes a lot of computing power to come up with a plan from this kind of model. So much so that we don’t pretend to offer plans you can calculate in a couple of seconds on the internet. Our plans take several minutes of compute time to generate so sending you a pdf is the only way to keep things honest and useable. The ten sector summary we show you is just that - a summary – hiding the intricacies of the real model.


Can you control your power output to the nearest watt? Can you even hit an exact average for a certain interval or stretch of road? Probably not. Most people would struggle to hit this accuracy on a turbo trainer, never mind in the varying environment of road cycling. So beware pacing plans that expect you to execute in terms of single digit resolution. We work to the nearest 10 watts - just about executable – with insignificant effects on best time on course predictions. The only exception to this is in the final sector of the course where there is only one number that will solve for an overall target power.


One way to solve the optimal pacing problem is to look at the impact of small changes in sector powers on 1) time in sector & 2) target power at the course level. Logically the sectors which offer the greatest reduction in time-on-course for the least impact on target power (dt[s]/dNP if you’re into maths) may be the ones where a rider should “power up”, and vice versa. Various optimisation techniques can exploit this kind of theory but we have found better results using simulations. The way we develop Optimal Pacing Plans is conceptually similar to having a rider tackle the event 10,000 different times. Every time this rider tries a different pacing strategy – sometimes he blows up, sometimes he’s too conservative. We simply keep the details of the fastest ride where he doesn’t blow up and that is your plan.

Want to try a performance model with Optimal Pacing Plan? Simply choose an event here.

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