TagTime: Stochastic Time Tracking for Space Cadets


Tag time, get it? Ah me, we crack ourselves up.

This article is co-authored with Bethany Soule.

If you want to (bee)mind how you spend your time (and oh do we ever!) then you need a time tracker.

But there’s a big problem with time tracking tools. You either have to (a) rely on your memory, (b) obsessively clock in and out as you change tasks, or (c) use something like RescueTime that auto-infers what you’re doing.

Well (a) is a non-starter, and (b) is hard if someone comes by and distracts you, for example. It can even be impossible. If you space out and start daydreaming about sugarplumbs [1], you can’t capture that. By definition, if you could think to clock out then it wasn’t spacing out. (And it might be a real problem you’re working on, rather than sugarplumbs [1], but either way.) Maybe we have attention deficit disorder or something but manual time-tracking never worked well for us. That leaves (c), software that monitors what applications or web pages you have open. That really seems to miss the boat for us. You might be hashing out an API design over email or working out an algorithm on paper. Or what if you’re testing your Facebook app, not screwing around on Facebook? [2] There’s no automated test to distinguish wasted time from productive time!

The idea is to randomly sample yourself.

We wanted something completely passive that doesn’t try to automatically infer what you’re doing. You might think “completely passive without automatic inference” is an oxymoron. But we hit on a solution: randomness. The idea is to randomly sample yourself. At unpredictable [3] times, a box pops up and asks, what are you doing right at this moment? You answer with tags each time it pings you. At first this yields noisy estimates but they’re unbiased and over time plenty accurate. It’s just a matter of getting a big enough sample size — collecting enough pings, as we call them. It doesn’t work for fine-grained tracking but over the course of weeks, it gives you a good picture of where your time goes.

Here’s one of us (Dreeves) averaging two hours a week working on TagTime since it hit the dog food point, which was almost right away since it started as a very simple Perl script:

Beeminder graph automatically generated from TagTime data

More examples of things we track automatically with TagTime — including Dreeves’s time spent working on Messy Matters — are here: beeminder.com/tagtime.

So you’ve arranged to have your computer periodically interrupt you while you’re working?

We certainly appreciate the value of not being interrupted. We’ve been using TagTime so long — going on four years — that we don’t recall how much of an adjustment period there was, but we find it doesn’t interrupt our flow at all. We think the difference is that it’s not like, say, a pop-up email notifier that pulls your train of thought off in a new direction. It’s almost the opposite: it reinforces what you’re already focused on. It can even serve to get you back on track if your mind is starting to wander. “Oh yeah, this email is entirely unrelated to the work I intended to be doing. Whoops!”

How much time do you waste on this?

Funny you should ask. TagTime knows. Dreeves spends on average 15 minutes a day (plus or minus 8 [4]) answering pings, and Bethany spends 6 minutes (plus or minus 3). It goes to show that the time you spend answering pings depends somewhat on how much thought you put into it. The taxonomy Bethany uses is simpler than Dreeves’s, plus she spends about 10 hours less at the computer per week (45 hours to Dreeves’s 56), but Dreeves also had a smaller sample period where he used a tag specifically for answering pings (hence the wider confidence interval). With zero effort you can learn the amount of time you spend at the computer versus not (TagTime autotags pings you don’t answer with “afk”).

You could also arbitrarily reduce the average amount of time spent on TagTime by reducing the rate parameter for the pinging Poisson process (i.e., how frequently it pings you), at the cost of increased variance in the time estimates. We have it set to ping every 45 minutes on average.

Other interesting tidbits about Dreeves in particular, who, as we noted, is more obsessive about his tag taxonomy: He sleeps 6.7 hours per night (when he wakes up he answers all pings he didn’t hear with the “slp” tag), spends 2 hours per week on Hacker News, and a bit over 20 hours per week with his kids.

So how can I be my own Big Brother?

We’ve open sourced the code on github, and if you know how to clone a git repository you can figure it all out just fine, which is to say it is not exactly user friendly. If you’re interested in hearing about development, and being notified if/when it is ready for consumption by normal people, you can drop us a line. There’s also a barebones implementation available on the Android Market. The source code for that is included in the TagTime git repository.

Bonus Puzzle

Suppose you frequently take the bus, arriving at the bus stop at a random time each day. The bus schedule states that, on average, buses arrive 15 minutes apart. But, paradoxically, you notice that you wait 30 minutes on average before catching a bus. How is that possible?

Follow-up question: Could something screwy like that happen with TagTime? Suppose you get paid every time you get pinged while working. (Recall that pings ping per a Poisson process.) What’s your optimal strategy for maximizing your effective hourly pay rate? (For example, if you were God you would only start working for a split second before each ping pings and then immediately stop, thus earning an unbounded amount of money per hour of work. But you’re not God.) If you’re paid d dollars for every ping that pings while you’re working and the Poisson process has rate parameter λ, how much money can you make per hour of work?

Further Reading and Resources

Addendum

Congratulations to Andy Whitten for solving the puzzle. He gives a simple example where 4 buses arrive all at once, every hour on the hour. Interestingly, it’s also possible under the constraint that the bus interarrival times are i.i.d.: With high probability the next bus arrives within a split second and with low probability it arrives in an hour.

The follow-up question is related to the waiting time paradox, and the PASTA property as pointed out by Vibhanshu Abhishek in the comments. The upshot is that memorylessness can be counter-intuitive but it has a very handy property for TagTime: There’s no way to game it. If you’re paid d dollars per ping and pings ping every 1/λ hours then you’ll make exactly as much on average as if you were just paid an hourly rate of d/λ. (For example, if pings ping hourly and you’re paid $100/ping then your effective hourly rate will converge on $100/hour.) Exquisitely fair, in expectation!

Footnotes

[1] EDIT: That’s right, sugarplumbs. They’re a sweet carpentry device. Thanks to Winter Mason for noting the distinction from sugarplums, which we totally did not mean and would be far less interesting to daydream about. Winter thought he was earning the $20 typo bounty. Ha!

[2] We’ve noticed another problem with RescueTime and its ilk, at least on Macs that don’t have focus-follows-mouse but do have “scroll-follows-mouse”: You can be scrolling around on a web page without having changed focus to it.

[3] The key to making it fully unpredictable is to use a memoryless distribution. I.e., knowing exactly when you were sampled in the past tells you nothing about when you will be sampled in the future. TagTime achieves this by pinging you according to a Poisson process, which is to say that the amount of time between each ping is drawn from an exponential distribution.

[4] That’s a 95% confidence interval, which is precisely computable by knowing the number of pings and the amount of time over which those pings were gathered.

Illustration by Kelly Savage

Tags: beeminder, time tracking