Endurance Analytics

Our blog - covering sports analytics and EA products

Announcing 'Fast Aero Lab'

For a long time now we have been proponents of the message that competitive cyclists and triathletes should be paying more attention to their aerodynamics. Make no mistake - aerodynamic drag is the most important resistive force consuming a rider's hard-trained power. Riding as fast as you can is as much about aerodynamic efficiency as it is about pure fitness and power output. For many trained cyclists drag reduction also offers a lot more potential for improvement than relatively slower and harder-fought gains in fitness.

Understandably many riders and their coaches find it hard to work meaningfully on improvements in aerodynamics. You can't measure and monitor aerodynamic drag with a simple set of scales in the way that you can with weight. Measuring CdA, the metric which determines how much resistive force a rider will experience at any speed, is achieved in one of two ways. Either you go to a wind tunnel: it's fun, it's easy, but it's not very accessible either geographically or financially. Or you run field test protocols using a power meter: within the scope of any power meter equipped rider but not so accessible in terms of know-how.

Cycling Power Lab incorporates calculators which can be used to estimate CdAs from field test data but we recognise that these are more illustrative than something you would want to use frequently. For this reason we have been working hard this winter on a dedicated aero analysis site. We asked the question "just how easy can we make the execution and analysis of aerodynamic field tests?" and we believe this site takes it to the limit. Please visit www.fastaerolab.com and register - we hope you will find it a useful resource in realising the potential of CdA estimation in velodrome or road environments.

There is a bit more to the site than just analysis of CdA. Using a modified test protocol you can also estimate Crr - the coefficient of rolling resistance attributed to a combination of tyre choice, inflation, and road surface. This is also highly worth measuring and can to some extent be optimised via tyre choice and inflation levels. A third choice of protocol allows simultaneous estimation of CdA and Crr though this is more time consuming.

Another major feature is the ability to order an "online" consultation around bike position in which a specialist bike fitter will analyse images and video, in conjunction with fit measurements, to provide recommendations on the complex matter of integrative aerodynamics and biomechanics. Position consultations are provided by custom4.us, a centre of excellence in the field of biomechanics and bike fitting led by Jon Iriberri - often billed as "the bike fitter to the stars" due to extensive experience with World Tour teams and riders as well as the Spanish national team. We are very excited about this collaboration because, in this internet age, it was high time that personalised bike fitting advice be accessible online.


Somebody recently got in touch to provide feedback on Cycling Power Lab (we love your feedback, thoughts and ideas). Among other useful observations was that the site looks "a bit 90s". While we'd beg to differ - the technology we used to start CPL came out of the "millennial" decade - it's also a fair comment that has not escaped our own notice. In due course CPL will get a new look and feel.

Right now we are working on some exciting sports analytics projects which go beyond the scope of Cycling Power Lab. For that reason we thought it an opportune time to update our blog. Going forward this blog will variously cover all of these projects, hence the change of name. The opportunity to switch onto a more modern blog engine was a nice bonus.

The image that goes with this post is a tribute to cycling in the 1990s. We can still remember many of the wacky go-faster bike designs that came out of that era though we wonder if there was ever much objective aerodynamic testing. Wacky they may have been, but we have to remember that research and development is a process in which the limits have to be pushed, whether you're building bikes, shark fin saddles, or analytics software.

Many of those 90s bike designs (there are some great examples here) are of course now UCI illegal. The UCI has an important role to play in reining in innovations that could be considered to lead us into exclusive equipment arms races, not in keeping with the aesthetics of the sport, or just unsafe. At the time of writing we would join calls for them to rule definitively on the usage of disc brakes, considering in particular the last two criteria.

"Air pressure is everything"

The hour record is a hot topic at the moment and we have seen great interest in predicting how far both Alex Dowsett could go and how far Bradley Wiggins will go this Sunday June 7th. Based on comments in the media it is clear that Wiggins was hoping for unusually low air pressure this first week in June but just how much of a difference can this make?

Accompanying this article is a chart from the UK National Physics Laboratory (NPL) in Teddington, just across London from the Olympic Velodrome, which shows a time series of local air pressure during the last year. You can see that 1013mb, the typically accepted average global air pressure at sea level, is around about the average observation while 990mb and 1030mb could be considered towards the low and high ends of normal. By applying these numbers in our popular Power Calculator we see that, with some sensible estimates and at hour record pace, the difference between 990 and 1030 is very significant – at least 600 metres – and for less gifted riders might represent the difference between smashing the record and a near miss.

We can’t overemphasise the importance of air pressure when the goal is riding as fast as possible. It’s the very reason riders go to altitude – either for the physiological effects or, in the case of record attempts, to gain what could almost be considered an unfair advantage over sea level times. Our Effects of Altitude model demonstrates the extent of speed &/or power savings that can be had with increasing altitude, versus typical physiological costs. And if Wiggins does as predicted, setting an almost unacheivable mark for the hour, then just maybe the only way to beat it will be some future plan at altitude.

Turning back to the air pressure in London we have long used historical data from the NPL to consider the relative benefits of riding a time trial event based on forecast weather. One of the inputs to our Time Trial Sector Model is air pressure and we can show you just where a forecast value stands in the normal distribution of UK pressures. We display “faster days in the year”, “slower days in the year”, and relative speed and power advantages of the range of pressure percentiles.

At the time of writing the forecast for Wiggins’ ride is not looking favorable. Heat and possibly humidity can be determined by climate control at the velodrome, set at the preference of Wiggins’ team, but air pressure is forecast at a very high 1033mb. Just to recap – if that forecast comes to pass then Wiggins has been unlucky and could go 600m slower than in an atmospheric pressure at the luckier end of the scale. One final thought – track and field records in athletics have long been rectified for wind assistance...is it time cycling did the same for air pressure?

Aerodynamic interaction effects in group riding

As little as 5 years ago cycling aerodynamics was still in the wild west. Very few riders had ever seen the inside of a wind tunnel, myths prevailed, and we all had much to learn. But things have moved on...serious cyclists are now routinely using aero data to optimise race preparation, and we understand not only what positions or equipment are faster but how and why. It would help if all manufacturers were forthcoming with performance data, but we digress...

If you have seen any of the recent wind tunnel videos from Specialized, or the series presented by Chris Boardman with ITV4’s Tour de France coverage, then you’ll have noticed that with the basics well understood more efforts are being focussed on the nuances of drafting and group riding. “Interaction efforts” are the big new direction, understanding them offers a lot to anyone participating in team or mass start events, or simply with some cheeky drafting opportunities, and we ought to pay attention.

But there is a problem. Wind tunnels weren’t built for groups of riders, and they certainly weren’t built to evaluate individual drag forces on multiple riders at the same time. Studies of drafting effects began in a more practical manner with power meter data, where Broker et al. (1999) revealed that “man 2” in a 4 man team pursuit (at 57-60kph) needed about 71% of man 1’s power, while men 3 and 4 needed about 64%. More recently researchers such as Blocken et al. (2013) have used CFD (Computational Fluid Dynamics) software to simulate the theoretical effects of group riding, with similar conclusions.

Though power data can be converted to drag data using the "Martin et al." mathematical model adopted by CPL there are some problems with relying on this approach to estimate changes in a more useful number, drag. It’s really difficult for a group of riders to maintain constant wheel-wheel separation in a field test, and as we know on an intuitive level this makes a big difference to drafting effects. Meanwhile CFD simulations require simplifications which may spoil their ability to reflect the real world.

Just last month the journal “Sports Engineering” featured a key article from a team (Barrey et al, 2015) working at Monash University, Australia.  Uniquely they have worked with modifications to their wind tunnel, allowing 4 riders to be evaluated individually and simultaneously. They’ve shown how, with a 12cm separation, man 1 can expect a 5% reduction in drag, man 2 gets around 45%, while men 2 and 3 can expect around 55%. These are averages of all possible combinations of the 4 riders because, crucially, optimising these things depends on the anthropometrics and baseline CdA’s of the riders. When you plug these drag reduction factors into a power model the results compare well with the power based study mentioned above.

If anyone has put more than 4 riders together in a wind tunnel then we haven't heard of it, so CFD simulations can still add some valuable insights. For example, Blocken et al have shown how, in a pace line of upto 8 riders, there is a gradual reduction in drag force through positions progressively further back in the group. This occurs due to a gradual downstream widening of the group’s wake. In groups of 5 or fewer it is the last rider who enjoys the greatest drag reduction. But in larger groups the place to be is last-but-one, since the diminishing benefits of wake are eventually offset by the reduction in pressure drag that comes with a following rider. Important lessons here for anyone involved in group riding.

What does all of this mean for ride modelling? First of all, to do a good job of modelling team pursuit or team time trial events requires a matrix of drag reduction factors. Every rider has a baseline CdA, which falls by a certain amount given their position in the group, who is ahead and behind. Team performance still hinges on the Watts/CdA of the lead rider, rather than some notion of “group average Watts/CdA”, but the drag experienced by drafting riders can be important in modelling recovery. Secondly, the insights here will allow us to do a better job of modelling mass start events. If the type and extent of drafting achievable can be factored into an Event Model then we would expect time-on-course predictions to get even closer to reality. For the mean time, when using CPL, you may wish to apply “rule of thumb” adjustments to CdA - but watch out for some more sophisticated modelling options.

From the Wind Tunnel to the Leaderboard

Some time in the winter of 2012-13 we felt that the amount of wheel and frame manufacturer’s wind tunnel data in the public domain had reached the right critical mass to compile a database of component drag data. We used this to power our Aero Components Evaluator which allows people to estimate power or time savings achievable due to certain equipment choices on a range of simplified course layouts. 

It wasn’t long before wheel manufacturers started to get in touch volunteering their wind tunnel data for the database. We love it when manufacturers are truly open with their performance data, allowing athletes to make well informed equipment choices, and if you’re in the component business then we’re always hungry for your numbers. Williams Cycling were the first and we went on to run some performance simulations illustrating the benefits of Williams’ flagship wheels in a couple of real race scenarios. 

When Williams enhanced (and significantly quickened!) their product for 2014 they were naturally keen to translate new wind tunnel data into meaningful performance data for their customers. CPL applied Performance Modelling to compute the real-world benefit of the new wheels at both Kona and the US Pro Time Trial Championships. This sort of analysis is just one example of the power of Performance Modelling and you can read the report here.