Endurance Analytics

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Ride Review with Post-Event Performance Modelling

The opportunities to use power based performance modelling in a pre-event context, for Goal Setting, Scenario Analysis, Equipment Optimisation (EO), and Optimal Pacing Strategies (OPS) should by now be compelling for all serious cyclists and triathletes. For the price of a few carefully collected details describing a rider's physical attributes, power-duration curve, and bike setup, it is now possible to first predict a given rider's time on a given course and then to apply scenarios on the underling model to make the same rider go faster. Analytical intelligence equals free speed...if you want to put the work in.

While the pre-event context has always been the most compelling there are also significant opportunities to use the same performance modelling techniques in a post-event context. Nobody wins a race by looking back at the day's data but but you can, perhaps, use it to win the next one...if you know what to look for and have the right tools. We have invested a lot of effort recently creating performance modelling tools for the post-event context and this article outlines what they might be able to do for you.

It's not (just) about the bike (data)

Pre-Event modelling combines data about a rider, his or her bike and equipment, the course, and the weather. Weather can mean historical trends or the actual race day forecast. We then simulate the rider's performance on the given course, under the given weather conditions. A simple simulation, based on the maxium power a rider should be able to sustain for the course duation gives us a time-on-course prediction. Many variations of power output, sector by sector, allow us to find an Optimal Pacing Strategy. Variations of rider parameters or equipment allow us to see the effect of scenarios. Variations of weather allow us to build a complete picture of which equipment is "probably" the fastest, on a "normal" day.

Post-Event modelling replaces weather forecasts with actual, observed weather. It also takes the ride power and speed data (via a .fit. .srm. .tcx, .pwx or .gc file) as a precise description of how the ride was executed. This gives us analytical possibilities and opportunities to combine and compare "model" versus "actual" performance not possible in the Pre-Event context.

Performance Modelling is more than race planning

Some people see performance-modelling as a way to predict race-day performance and then create a plan. That is Pre-Event modelling, but there is more to offer. Post-Event modelling, with the extra second-by-second information provided by the ride file, has some powerful applications. Let's review some of them:

 

The Virtual Wind Tunnel has never been more available

In our experience the most versatible, and now most used, field test protocol available for determining a rider's aerodynamic drag is the "Virtual Elevation" method a.k.a. "Chung" method (after it's inventor Robert Chung) a.k.a. the method of the "Virtual Wind Tunnel". Some very serious operations routinely use this method, in a velodrome setting, to provide riders with drag data and aerodynamic optimisation advice. In simple terms this methodology takes ride file data (power and speed) to build a "virtual elevation" (VE) profile for the ride. If you vary CdA, or let a computer do that for you, until the VE profile closely matches the actual course elevation profile of the same ride, then the CdA you finish with can be a very good estimate of the rider's actual CdA.

Post-Event Modelling, by combining the rider data, course data, ride data, and environmeental conditions, already has all of the information necessary to estimate CdA using the VE methodology. There is one caveat - wind. Rather than assume zero wind, as velodrome operators can, making VE available for any rider requires us to factor in the wind. Since we already have weather data, at times of day covering the event, and we know rider's time's of day on all sectors of the course, from the ride file, then we can do a reasonable job of factoring in air density and effective winds. VE under observed winds isn't perfect, but if estimating CdA is important to you it is better than nothing...a lot better!

Physics + Power Data = Power & Energy Attribution

Imagine a ride file says that a rider averaged 200 watts on a short climb. Now imagine we model that rider's performance on the same climb, we take all of his parameters, then plug his time into a "power given speed" model to estimate the power required to complete that climb at the given speed. If we're using the right parameters, including VE CdA, then our estimate will be 200 watts. But we have more information... Those 200 watts are composed of some watts required to overcome gravity, some to overcome aerodynamic drag, some to overcome rolling resistance, and a few others. By combining the actual power data from a ride file with theoretical power demands from a performance model we can achieve what we call a "Power Attribution" or "Energy consumption study". (Remember that energy, in joules, is just watts multiplied by time in seconds). This is an interesting capability of Post-Event Modelling.

Pacing Quality Evaluation - How well did you execute?

The identification of theoretically Optimal Pacing Strategies is one of the most compelling applications of Pre-Event Performace Modelling. But how can we know how well a strategy was executed? Sure, we can compare race normalised power with the prediction from the plan, and we can eyeball ride power and speed data relative to the sector by sector plan, but we felt that some better tooling was required.

"Pacing Quality Evaluation" is a software based comparison of actual, sector powers and speeds, from a ride file, verus a theoretically optimal plan calibrated to the rider's power-duration curve as exhibited in the ride it'self. Having looked for some magic number to describe pacing quality we eventually settled on the one number that counts...time! This analysis can also highlight something very interesting - good riders can be very good at pacing. Often the way a rider executes, with the freedom to vary power on every little hill or under every little gust of headwind, results in a time that is better than what we might predict from an "optimal" strategy simple enough to understand and execute. Nevertheless, Pacing Quality Evaluation is a fantastic gross error check and has some great applications in learning lessons and executing better in the future.

Rider Benchmarking with Virtual Competitor

We have always emphasised the application of performance-modelling in the key coaching disciplines of rider profiling, performance benchmarking, and goal setting. It is a powerful thing to be able to compare a rider's actual performance against some theoretical competitor, a better version of the rider's future self, or even a worse version of the rider's past self. A virtual competitor study, in the post-event context, makes this possible.

Real World What-If Analysis

Have you ever agonised about the race you or one of your riders lost, perhaps by a matter of a few seconds? Wouldn't it be great to know the size of the winning (or losing) margin, not in terms of time but in terms of parameters you can work to improve such as power, weight, or drag? Post-Event what-if analysis does exactly this because we can re-simulate performance given certain improvements. No more agonising.

Post-Event modelling is now available at our usual Performance Modelling link. Simply select "Post-Event Modelling" near the bottom of the page. CdA estimates via the virtual wind tunnel are open and free. The full Post-Event report, with all of the above analytical features, can be configured below.

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