Static versus Dynamic

Do static measurements have any value in bike fitting, or are dynamic measurements from motion capture systems and video analysis superior?

Niels Heuvelman from Cyclefit.com looks at the myths around anatomical measurements, and explains why it’s still worth making use of the good things about formulas and body segment lengths.

Ask many bike fitters, and they’ll often say static measurements don’t add much value to their fits. Instead, they’ll tell you that dynamic measurements — generally referring to motion capture systems and video analysis allowing the fitter to measure joint angles — are what’s important.

But while the general idea might be opposed, bike fitting based on formulas and body measurements can still have its place, and it’s worth taking the time to start utilising the good things about them.

Body measurements

All bike fitters I know use some form of body measurements in their daily routine. What body dimensions are measured and how they are used differs between fitters.

In general body dimensions can be used for comparisons between different riders (the more riders you have seen and measured, the easier it gets to see differences between riders), left versus right comparisons (if you measure body dimensions on both sides of the body this will give you an idea about differences) and determining a position or recommendation of certain products, such as insoles or saddles.

If you want to do this, you need more than just the body dimensions – you need a formula or algorithm to go from body dimension to positional recommendation (translating leg length to saddle height). Let’s look at each of these in more detail.

Comparisons between different riders

The Cyclefit system gives us access to a huge number of datasets containing body dimensions.

Since Cyclefit usrs have a specific toolkit to measure the body dimensions, all these are measured the same way following a certain protocol.

Figure 2 (below) is based on 67,000 data points and shows torso length plotted in relation to the leg length. As you can see from the width of the data cloud, there is a huge variation in torso length at a certain leg length.

If we look at a certain point, for example a leg length of 85cm, we see a variation in torso length from 50 to 70cm. This is a clear sign that measuring one body dimension gives us limited information and that based on one body dimension it is hard to predict other body dimensions accurately.

Figure 2: Leg length (cm) vs torso length (cm)

Figure 3 (below) gives a plot of leg length in relation to arm length, and the same conclusion can be drawn here too. Due to the huge variation, it is very hard to create an algorithm that helps us determine arm length based on a certain leg length.

What does this information tell us? Mainly, we can conclude that one-dimensional formulas in bike fitting do not work.

Despite this, they are used more than we realise. For example, most bike brands will give you a recommended frame size based on either body height, or on leg length (inseam height), with inseam length considered to be more accurate.

Figure 3: Leg length (cm) vs. arm length (cm)

As shown in the figures, there is a variation of several centimetres in both arm length and torso length in a certain population with the same leg length. Basically, this tells us that the height – length, or in other words, stack – reach relation can vary hugely for different persons with the same leg length (and this is only based on dimensions, it does not take things like flexibility into account).

The collected data also show that there is hardly any correlation between leg length and shoulder width. In other words, it does not make much sense to mount wider handlebars on larger bikes.

Figure 4 (below) shows a plot that relates shoulder width to leg length. Again, looking at the variation the conclusion should be that an off the-shelf bike most likely needs modification in order to match the rider (which as a bike fitter we already know).

Can we use algorithms at all?

Based on the data above we could conclude that algorithms in a bike fitting are not very useful, but before doing so we have to realise that all these diagrams are based on a one dimensional approach, meaning we only use one variable to calculate the other. In bike fitting we know that most adjustments depend on multiple parameters. For example, saddle height (knee extension, ankle position) and reach to handlebar (pelvic rotation, shoulder angle, elbow angle).

So, in performing a dynamic bike fit we (consciously or unconsciously) take more parameters into account. When we take more parameters into account when using algorithms, this will improve the quality of the outcome.

The algorithms used in the Cyclefit system, for example, use both leg length as well as foot length (cleat position) to calculate a saddle height. This saddle height is then corrected for different crank lengths and pedal stack height.

Figure 4: Leg length (cm) vs. shoulder width (cm)

This altogether leads to a result which is in line with what using normative ranges for angles would give us (knee flexion 30-40 degree, ankle angle 90-100 degree). Obviously, this is good, but not perfect – exactly as it would be when just using normative ranges and angles.

When we look at the other parts of a bike fit, like saddle setback, reach to the handlebar, and drop to the handlebar, we found similar results, but here as well there are quite a few things to consider such as handlebar reach, handlebar drop, lever reach, saddle length, and so on.

Conclusions

Algorithms can be used in bike fitting, but like with all protocols and techniques the more you are aware of limitations the better they work for you. The main limitation for the use of algorithms is not so much the quality of the algorithm itself, but the fact that often people expect algorithms to give them the final position.

Algorithms can be great to determine an initial position from here further optimisation can be done. In this sense, they work the same way as normative ranges in angle based bike fits.

The more parameters we have to take into account, the more difficult it is to find the optimum position using algorithms. For example, most of the algorithms currently used do not work well for modern MTB XC racing since the factor of off-road bike handling is not taken into consideration.

When you decide to use algorithms in your bike fitting process, use them the right way; meaning understand what these algorithms do, and what (minor) changes might be needed. Also, remember that algorithms will never be able to beat a well-educated fitter.

NeilsHeuvelman@innovativecycling.com

Niels Heuvelman has been in the cycling industry for 30 years. In 2012 he took over CycleFit systems and created his business Cyclefit.com, a company which serves bike fitters across Europe with software, products and education and promotes the use of static and dynamic bike fitting method.

Cyclefit is a bike fitting program based on body dimensions. It was developed around 1990, before data connections were available like they are now, and in the first years the system worked with a Videotex terminal to send the data and receive the report. Later, this changed to a full online system. Over 30 years the Cyclefit system has been used in around 500,000 fits, creating a huge range of data on body dimensions. Some of these data have been used for this article.

----- Advertisement -----

Lateral thinking - Explore new foot positioning possibilities.

Lateral, medial and rearward adjustment, with FORM Speedplay extender baseplates.

Find out more at FORM-BIKEFITTING.com