200 days of stats: My QS experience

I noticed that my collection of hard data hit a floor of 200 days. I therefore thought it’s a good idea to visualize my data, and draw some conclusions. They are public, because I think they can help people who take the same approach, and because there are not enough ways to say that there is a strict connection between what we do, and what we feel. My general stats are going on their steady count here:  QS Lab (link).


I set myself a very much lower target than people usually do – at around 5km/5500 steps a day. I did not want to go for the whole deal of 10km a day, because that would have upset my work goals, and also because I don’t yet believe 100% in the need of walking 10km a day. Different people will need different things, and as long as I don’t stubbornly prefer the coach to the tarmac, I’m all good. And what’s more, I completed 162% of my goal!

Goal completion

% Overall goal

There are things I like, and things I don’t like about my performance. Whereas my 5 daily km may sound lame, I very rarely walked/run less than that, and also some days were filtered away from my stats because I did not consider their output to be accurate (days in which for instance I ‘felt like’ walking less than 2km, and my band recorded 5km instead. There are also days in which I walked literally from 8am to 11pm, and those days bring an awful lot of km into my stats – but I dislike those days, because they may point to many things, but definitely not to balance. I am rather OK with the days in which I know for a fact I was able to walk, sleep, work, and mind my own rather than the days when I’m plain athletic. On the other hand, since my Jawbone UP band dropped dead in mid-November last year, and it was replaced in some 20 days, I think that my performance is even more than I count it per average, but I prefer to count the days I didn’t actually measure, too.

What I did not count in: the days until the Jawbone UP replacement, some 20 outings to the swimming pool. I will think of a way to count them, too.

Here are my daily (non-average) visualizations:

What did I learn? Well, to begin with, that accurate data really makes you feel better about yourself. I used to think that I move insignificantly, and that I spend most of my time in cooking-, working-, or reading-mode. I think that the inactive alert of the band I use really comes in handy, because I remember numerous occasions in which I avoided becoming completely numb, and usually I reduced the time of one sitting from as much as 4 numbing hours to less than 30 mins. I also learned that when you walk some 25km a day, it’s too much (especially for the back – or at least in my case) – but, like any activity that becomes steady in your daily profile, walking has its merits. And I also learned that it indeed is better to set a lower standard for yourself – with my daily average of 8.14km daily I am definitely lower than those mythical 10km, but a lot ahead of my expected 5. And being able to pursue – and satisfy one such target for more than 200 days in a row – well, that’s something!

What did I use?  J Jawbone UP band and  iOS app, IFTTT,  Google Drive spreadsheet.


I did not set a sleep goal for myself. Or maybe I did: I wanted to sleep as much as possible. I have a tendency to deprive myself of sleep, willingly, because there are so many things to do, and so little time. However, I wanted to know how much I actually sleep – I don’t know why, but I think this information will be handy one day.

Goal completion


My average is now at 8h03min – almost athletic you would say, but a look at the chart will show massive variations.Here are my visualizations:

What did I learn? I have not yet learned too much from the specific numbers of minutes I sleep. I have, instead learned what I need to do prior to sleep, and I plan to analyze next to the core sleep data the light/deep sleep quotas to make the most of it. I learned how to wake up without any alarm other than my band, and I also learned how to take short naps.

What did I use? J Jawbone UP band and  iOS app, IFTTT,  Google Drive spreadsheet.


My target as regards emails had less to do with the number of emails sent; rather, I wanted to establish a rule of no-email weekends. It did not work out  entirely, but since most of the emails were sent to friends and mostly for leisure, I cannot complain. I am going to be very strict, however, with my verdict.

Goal completion


I only sent 8.5% of my emails during the weekend. My most busy email day seems to be by far Thursday, and that’s because for a fact I set time aside for correspondence on Thursdays rather than on any other day. Here are my stats per day of week:

What did I learn? I am still crunching the data I gathered, because there are a lot of things that interest me with regard to my email sending habits. I would like to filter the gathered data per device (that’s one of the main reasons I keep in my signature a ‘Sent from -‘ line, which I assume is rather vain for the recipients, but very helpful for me). I would also like to filter outgoing mail per time of the day, and make – somehow – the connection between mail sent and completed tasks. I’m on it. For the time being, the weekday report suits my initial curiosity.

What did I use?  Gmail account, Gmail Meter, Google Drive spreadsheet.


With translations, it’s a funny thing. I of course want to have clear stats of what I worked, but that’s something a diligent translator would do while keeping an up-to-date record of the completed translations. But I was not interested in manual input, at all. So in the case of translations my goal was to see whether there is a match between what I can manually count, and what can be done via some automation. Suffice it to say, that it worked!

Goal completion


Additionally, I was of course interested in the daily output I had, both in general, and as an average. In general, well, the number of pages I translate largely depends on orders, and of the time I spent procrastinating before delivering the orders (I’m joking!). Whereas the average is more important – in the 200 day period I only worked 27 days on translations, averaging at 22p daily. That’s a lot in terms of industry standards, but definitely a lot less considering it’s not very often I translate. So here are my translation stats:

What did I use? Google API’s,  Google Drive spreadsheet

Other stats, as for instance the number of tracks I scrobble, or the number of books I’m reading are available on the general  QS Lab page. I’m currently looking into creative ways to look at the respective data (for instance the number of pages read daily, rather than the very relative percentage of an abstract book with an abstract number of pages), and the challenge is ongoing.

What and why do you track? Do you think there are other conclusions I should draw out of the data above? What are quality criteria of movement and sleep I can additionally get out of such collected data? 

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