If you make an equipment change – and the most exciting example is getting a new bike – you usually want to know whether it makes you faster. This can be hard to establish. The best way is to ride exactly the same course a few times, putting out exactly the same power all the way round on both the old and new equipment/bike and to compare the average times. However, conditions vary – notably including your own condition – making it hard to conduct a good test. The new X Factors page in the Navigator can help. To understand what this does and how it works, consider the factors that affect your speed on a bike ride. The primary factors are:

  • Parcours – the hills up and down, which shape the energy demand needed for the ride;
  • Power – what you put into it.

Beyond these primary factors, there are secondary factors that affect your speed. Some of these are uncontrollable, such as:

  • Wind speed and direction;
  • Air pressure;
  • Terrain – are you riding on smooth asphalt or loose gravel?

And there are other secondary factors that often are controllable, such as:

  • Tyre pressure;
  • Chain condition;
  • Your frontal cross-sectional area (CSA);
  • Drafting, if you’re cycling with others;
  • Your current weight;
  • Your bike;
  • Other kit such as your wheels, clothes and helmet.

The key idea of the X Factors page is to separate the impact of the primary from the secondary factors on your speed. The way it does this is as follows…

First, you need to select a target ride that you want to analyse. Then you select one or more reference rides that you want to use as a baseline for predicting your speed; you can choose up to six (any more than six will be ignored):

Here, three Reference rides have been chosen. From these, Crickles will dynamically generate a machine learning model to predict speed from parcours and power. The quality of this model depends upon the consistency of the rides – if the secondary factors vary a lot and affect speed a lot the model will perform less well. Model building is also affected by technical factors aiming to ensure that the model runs quickly in real time. The quality of the model is shown on the top of the chart:

The quality varies between Poor and Excellent (as shown here).

From this model, Crickles predicts the speed of the target ride. This is graphed alongside the actual speed:

If there is no statistically significant difference between the predicted and actual speed, the text at the top of the chart will tell you so; otherwise it will tell you the average difference – 1.4 kph in this example. The elevation profile of the target is shown at the bottom of the chart. As on many of the other pages, it’s possible to zoom in on areas of the chart and to see/compare values using hover information:

Any stand-out gaps between the pink (predicted) and blue (actual) graphs are probably due to the effect of a head/tail wind or drafting or having to stop and start. To make these easier to pick out you can see a smoothed version of the charts by using the Smooth lines on graph? checkbox in the sidebar; this transforms the main chart above to this:

If you want to know whether your new bike is faster you need to ride it outdoors in the real world. Virtual rides cannot be selected and the analysis will not work on turbo rides as there is no (real) parcours. This analysis also requires the use of a power meter. Given that, if you choose rides where the uncontrollable factors are minimal and the only ‘X factor’ is isolatable such as a new bike or wheels, this page gives you an answer to the question of whether you’re faster, and by how much.

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