Endurance Analytics

Our blog - covering sports analytics and EA products

Where are you on the Power Learning Curve?

We’re celebrating an anniversary this month – 10 years since our first SRM training system arrived from the factory. As if you didn’t know it already this particular sample of German engineering has given us years of gold standard accuracy and unwavering reliability, much like most SRMs we’re aware of. We've learned an awful lot about power since the turn of the century, and the purpose of this post is to share our thoughts on the learning curve that comes with integrating power into your cycling. The accompanying graphic is an extract from a presentation we give on the subject - drop us a line if you'd like to host one.

Basics - A better heart rate monitor

The first justification for training with power is often “a more real time measure of intensity”. Power output doesn’t lag heart rate response, so it’s far superior for the monitoring of effort (the basics of pacing) and the execution of intervals. It’s also a measure of output that is not subject to external factors, so training by reference to power zones is frequently more accurate. Given such an objective measure of output it becomes clear that power is a great tool for comparing rider’s abilities and “power profiling” is the term for that. Together these are the immediate benefits of using power.

Paradigms - Descriptive analytics

The richness of power data has facilitated a number of new analytical techniques or metrics which expand on the idea of profiling rider’s ability and help to diagnose where improvements can be made. The Critical Power curve is an essential tool for expressing and explaining how a rider’s ability to deliver power decays as duration of effort increases. Simply looking at where a rider pushed his limits on this curve is a useful way of diagnosing racing performances or failures.

Several tools were developed in quick succession by Dr Andrew Coggan of the Peaks Coaching Group. Quadrant Analysis represents a way to uncover the neuromuscular demands of different types of event, pairing power and cadence data, and to improve training specificity. Training Stress Score (TSS) is a power enabled metric of training load, adjusted for intensity. Training Stress Balance (TSB) compares short term and long term TSS as a method to predict a rider’s form assuming Form = Fitness + Freshness = Chronic Training Load (long term TSS) minus Acute Training Load (short term TSS).

Finally in the area of descriptive analytics power data, used carefully and appropriately, can allow us to estimate and iteratively improve a rider’s aerodynamic drag. These are all retrospective techniques relying on the study of past ride data.

Performance modelling – Predictive analytics

The greatest opportunities in the use of power data are now in the area of forward looking, predictive analytics. This is the area CPL has been working to develop and the principle here is - “How can we use holistic performance modelling, enabled by power data, to deliver intelligence that will make riders faster, or just more successful?”

The first benefit of Performance Modelling is the ability to set real goals. When you can convert power output into a projected time on course, or a target time on course into a required power output then, aside from the motivational benefits, there are real possibilities to intelligently choose, prepare for, and target events. A simple extension of this is scenario analysis – how much faster or slower are certain conditions or rider attributes and can these be targeted?

Performance modelling, with good data, enables Equipment Optimisation. Read “free speed” (at least in terms of effort). Another route to free speed is Optimal Pacing Strategy. The fastest way to the finish line is rarely a constant power output - but it takes some serious analytics and computing power to identify the optimal strategy for a given rider. We do that.

Some cutting edge applications of performance modelling are energy budgeting and management of Anaerobic Work Capacity – the latter inspired by Dr Phil Skiba at Physfarm Training Systems.

Wherever you are on the power learning curve, or wherever your clients are, there is always more to learn. Investing in a power meter is really only the beginning. Riding a bike – fast – is no longer just a physical challenge and the more you learn, the more achievable speed is out there.

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