You can set your Strava heart rate zones in your personal settings, or just leave then on default, based on your maximum heart rate.Ī non-linear relationship is assumed between effort and heart rate zone. It is derived from the amount of time spent in each heart rate zone, so it can be calculated for multiple sports. Strava’s Suffer Score was inspired by Eric Banister’s training-impulse (TRIMP) concept. Using my CP as my Strava FTP, Strava’s Training Load is the same as Skiba’s Bike Score (otherwise I’d get 93). Although my estimated CP is 12W lower than my FTP, xPower was 30W lower than NP. Note that for my hill reps ride, the BikeScore of 101, was considerably lower than the TSS of 117. And finally, Strava’s Training Load takes the ratio the work done at Weighted Average Power, scaled by Intensity squared, relative to one hour’s work at FTP. Skiba defines the BikeScore as the ratio the work done at xPower, scaled by the Relative Intensity squared, relative to one hour’s work at CP. It is helpful to standardise this for an individual rider, by comparing it against a benchmark, such as an all-out one hour effort.Ĭoggan proposes the Training Stress Score that takes the ratio the work done at Normalised Power, scaled by the Intensity Factor squared, relative to one hour’s work at FTP. Training LoadĪn overall assessment of a ride needs to take account of the intensity and the duration of a ride. For Coggan, the Intensity Factor is NP/FTP for Skiba the Relative Intensity is xPower/CP and for Strava the Intensity is Weighted Average Power/FTP. Intensity is defined as the ratio of the power equivalent physiological cost of the ride relative to your sustainable power. The idea of intensity is to measure severity of a ride, taking account of the rider’s individual capabilities. If you follow Strava’s suggestion of using FTP, subsequent calculations will underestimate your Training Load, which, in turn, impacts your Fitness & Freshness curves. This is important because this figure is used to calculate Intensity and Training Load. The problem is that if Strava’s Weighted Average Power is based on Skiba’s xPower, it would be more consistent to use Critical Power, as I did in the table above. Strava allows you to set your Functional Threshold Power under your personal performance settings. The emphasis of FTP is on the maximum power sustainable for one hour, whereas CP is the power theoretically sustainable indefinitely. Functional Threshold Power and Critical Power measure slightly different things. It is important for a serious cyclist to have a good idea of the power that he or she can sustain for a prolonged period. For a well-paced time trial, the variability index should be close to 1.00. These are very high figures, due to the hilly nature of the session. The variability index compares each adjusted power against average power, resulting in variability indices of 1.57 and 1.41 respectively. Both the Normalised Power of 282W and xPower of 252W were significantly higher than the straight average power of 179W. The hill reps ride included multiple bouts of high power, causing repeated accumulation of lactate and other stress related factors. This suggests that taking the average of smoothed watts raised to the power 4 gives an indication of the average level of lactate in circulation during the ride.
Plotting the actual data from a recent test on a log-log scale, I obtained a coefficient of between 3.5 and 4.7, for the relation between lactate level and watts.
The following chart is a good example, showing the rapid accumulation of blood lactate concentration at high levels of effort. Why do both metrics take the watts and raise them to the fourth power? Coggan states that many of the body’s responses are “curvilinear”. Secondly, the smoothing used for xPower is less volatile, therefore xPower will always be lower than Normalized Power (because the fourth-power scaling is dominated by the highest observations). Firstly, xPower’s exponential smoothing is more highly correlated with heart rate, so it could be argued that it does indeed correspond more closely with the underlying physiological processes. The following chart zooms into part of the hill reps session, showing the raw power output (in blue), moving average smoothing for Normalised Power (in green), exponential smoothing for xPower (in red), with heart rate shown in the background (in grey). According to Skiba, exponential decay is better than Coggan’s linear decay in representing the way the body reacts to changes in effort. Normalized Power uses a 30 second moving average, whereas xPower uses a “25 second exponential average”. The only difference between the calculations is the way that smoothing accounts for the body’s physiological delay in reacting to rapid changes in pedalling power.
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