Similar Summary Stats, Different effort…

While I still need to figure out how to use the new power field from Stryd, Running Power is already a useful other way to compare the type of effort of different activities. Since I got the new pod, I have been motivated to go out run more, and looking at the last few weeks, I realised that a few runs had interestingly some similar headline stats but very different feel. So I decided to see how ConnectStats displayed the differences.

In the summary, you can see the first two have very similar heart rate average, but very different pace, while the run in Shanghai has similar pace as the first one, but higher heart rate. The run in the new territories run (Hong Kong) is also interesting to compare to the Putney run (Richmond Park). Let’s dive in.

Comparing Activities in ConnectStats

You can easily compare two activities in ConnectStats by sliding the activities in the list and selecting mark. The activity will then shows in the background of the new one you look at.

A mark will be displayed to remind you which activity is the “compare” activity.

Same Heart Rate, Different Pace

The first two run to compare have an average of 176 and 175 HR respectively but a pace of 4:50 and 5:30. You can of course just look at the pace plot on top of each other, but it’s a bit messy

Note that the map will show you both activities, useful when they are on the same location, but less when they are quite different route, as here. The pace graph clearly shows that for large part of the run the pace was faster, but not very insightful. A much better way to compare the effort is to look at the best rolling plots.

You can see that it was definitely a higher power effort, but the pace shows that the slower run had more constant pace, flatter curve. The heart rate plot shows that the max effort (left part of the curve) was similar, but the tail was lower (steeper curve on the right for the faster curve). Overall a less consistent effort, but where I pushed more at time and resulted in the same average heart rate but very different pace. The power curve interestingly shows quite a higher effort. These were two different runs, the slower one was a commute run, with a backpack and on city streets with more stop and go at light. It was also early morning, so typically not when I do my best performances…

Same Pace, Different Heart Rate

We can also compare that same activity to another run a week ago with same pace (4:49 and 4:50) but higher heart rate (181 and 176 respectively).

You can see the activity being compared to (lighter colour) has clearly higher heart rate effort through out. One interesting observation is that the lower heart rate run has steeper start, which means there were a few period where I pushed rather than a continuous effort .The pace on the other hand has a quite a different profile between the two runs

The lower heart rate run has a much steeper shape, while the other one has a quite flat profile, meaning a more constant effort.

Now for the new measure of power, again interestingly the raw graph comparison is quite useless. Hard to really see much in the difference of the effort.

 But the best rolling graph again shows a very interesting story, if somewhat consistent with the other. While the effort was about the same, the higher graph shows a much more varied effort: more power on shorter time period, but converge at the end for the overall time.

The higher heart rate was just a run where I tried to push much more through out and consistently. Quite interesting that it resulted in the higher heart rate…

If you wonder why the little bump around 5min, me too! this is an annoying little bug or quirk which I haven’t yet figured out!

Time ahead

A last graph that maybe of interest, even though in this specific case, maybe less interesting in this specific case, but it shows you the time ahead (or behind) from the compared activity. The big straight drop are pauses. So you can see that I took quite a few pauses in the lower heart rate run, and it had period where I was catching up (upward slopping) and period where I was getting behind (downward slopping). On the map, the area where I am ahead are blue, and then goes to red when I am behind. Which makes it easy to see where you are behind or ahead, especially when the run is at the same place (not the case here).

Hope you found this interesting. Happy Training.


Performance Analysis

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 .

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New Statistics Plots

In the version 1.20, I added to the main statistics page small preview graphs embedded in the table. I also rationalised somewhat the plots shown on individual fields.

Main Statistics Table

The statistics page start looking like this


For selected fields, you now see a small preview of a relevant graphs.

Here in distance it shows you the cumulative distance of the previous years, one of my favorite graph to track how you are doing on a given year compare to the previous ones.

Note that you can disable the embedded graphs with an option in settings in case you don’t like it.

For the Average Heart Rate and other non summable fields, it will show you the monthly average over the last 6 months.

Pressing the All button on the right will continue to rotate between the weekly, monthly and annual summary. The Sigma icon means it displays the total or average across all activity. If you press it, it will display the stats restricted to either the last week or last month. This enables you to see all details of the last month or week.


Here you can see that the Max Heart Rate over last week was 194, average moving pace 5:21 min/km. This enables you to see any statistics over that period easily. The weekly summary of the previous versions was limited to only a few key measures. Note that in this view the embedded plot becomes a weekly plot to compare this week’s statistics to the previous.

Field Statistics Details

If you press any field of the main statistics table, it will take you a more detail information on that fields, as for example here


This shows you two graphs and some basics stats. The first graph is a scatter plot against another variable. If you tap on that plot it will let you configure it and choose the second variable.

The bottom plot will rotate when you tap on it between a monthly summary, the performance analysis graph and an histogram of the different values as here. This post describes the performance analysis in more details.


Pressing the all button on the top right as before shows you weekly or monthly statistics.


Historical Scatter Plots I like to use

One of my initial motivation to write the app was to look at my activities using scatter plots. I was especially interested in looking at the relationship of heart rate and speed.

Here you can find information how to access them.


The first thing I look is where is my last activity in my overall history. Here you can see that I was in the middle of the pack, a bit on the high heart rate side.



It’s also interesting to check the pattern overtime. You can see here that the more recent colors are on the higher HR, slower pace area. Not good, I need to improve.


Sometimes it can also be useful to check only the recent history, using this button to rotate between all, 1m, 3m, 1y


Other times, I also want to have a more sophisticated filter for the graph, in that case I can use the search feature. If I define a search in the activity list I can then get the scatter plot only for those activities. The statistic tab will have an extra button beside running, cycling swimming and all: Search.

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Some other interesting historical relationship to look at for bikers: Power and Cadence or Power and Heart Rate

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