In the version 1.20, ConnectStats supports a first version of long term (fitness) versus short term (fatigue) performance analysis. This is a bit rudimentary for now, and hopefully will improve over time.
The performance Index
The analysis is based on two fields, a summable field like distance, time or elevation gain and a second field to rescale it like heart rate, power, etc.
The analysis is based on an index built using this scalable field and summable field.
To access the analysis you need to select from the statistics field view, a field. If the field you select is summable (Distance, Time, Elevation Gain) it will use it as the summable field and choose Heart Rate as the scalable field. If you select a non summable field, it will use that as the scalable field and distance to sum.
Once the two fields are selected it will then apply a formula to get a performance index. The formula in this first version is simply to multiply the two fields, similar to a very simple TRIMP index, but in the future we could change that, for example along the line of normalised power and apply a function scaling more realistically to how the scalable field impact the distance field. This page gives some interesting comparison of the different way to do that.
Fitness (Long Term) versus fatigue (Short Term)
Given the two fields above and the performance index, then we will try to compare the long term accumulated fitness versus the short term training. We pick two periods, the short term period and the long term period, and plot the average performance index of the long term period versus the short term period.
Currently the short term period is the last seven days and the long term period is the month prior to that.
So the idea is to show how much training accumulated over a month (long term fitness) versus how much you are currently training. If your short term training is significantly above the long term fitness, you maybe over doing it. And you maybe taking it too easy or resting if the short term fitness is quite below the long term fitness.
In a future version I could parametrise both the performance index function and the periods used, depending how much people feel the idea is useful or not. So don’t hesitate to give feedback either with a review, tweet, comment or bug report.
Once you selected a field in the statistics view, tap the bottom plot to iterate between the different choices: Monthly value, performance index graph and histogram/distribution of values.
Here is my current running performance. You can see in this graph that recently I have been training a bit more which raised my long term fitness, while the toward the end november I did less running which lowered the long term fitness .