For cyclists who use a power meter, the FTP chart on the Navigator now explicitly states your estimated Functional Threshold Power (FTP) at the start and end of the chosen Date Range. By default, the end of the Date Range, and hence the date of the second FTP level shown, is today. This enables you to see at a glance your current FTP and how it has changed over the period. You can also reset the Date Range to see your FTP at the start and end of different periods.

We are occasionally asked about differences between the FTP estimates on Crickles and on Strava or other sites. Usually they’re all close but occasionally estimates can differ materially. There are two known reasons for this:

  1. Short protocol FTP tests – for example, some sites form an FTP estimate from an 8 minute burst. In contrast, Crickles infers FTP from efforts lasting 20 minutes or more since the definition of FTP is the power level that can be sustained indefinitely (or for an hour, depending upon what you read). Strava appears to give more precedence than Crickles to short duration power and you can see a difference in FTP estimates if you’re going full gas over that kind of timeframe.
  2. There can also be temporary differences in the time it takes for different sites to reflect an outstanding performance or set of performances in their FTP estimates. The Crickles FTP estimate shown on the chart incorporates information from all rides except the latest one.
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4 thoughts on “Enhanced FTP charts

  1. Ian, thanks for this, a great enhancement…
    How will the algorithms cope with powermeters with poor calibration? I know that my turbo overreads, by up to 10%, so outdoor rides will be recording lower power values to the indoor sessions. I suspect that there is no way of dealing with, aside from factoring, which is probably beyond the capabilities of the site?

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    1. Hi Paul,
      Thanks for the feedback! Yes, I’m afraid that realistically we have to take the numbers from the powermeter as a given. We do see a wide range of anomalies in power data. While power is in general amenable to more ambitious algorithmic analysis than we have on Crickles, I focus the finite time that I have on heart rate data, which is much more poorly treated on other sites. Mainly we use power data in comparison with the heart rate data. For example, I personally often look at the heart rate I need to generate a specific power level. Also, I compare the CSS (heart rate stress) with TSS (power-based training load) on the Navigator’s Activity tab since when TSS is greater than CSS I’m generally getting fitter and vice versa. Ian

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