Last year, I decided to move from tracking my time using off-the-shelf applications (Time Recording, then Tap Log Record) to building my own system using Ruby on Rails so that I could tailor it to my quirks. Quantified Awesome now has more than a year of time data, and I wanted to see the patterns in how I use my time.

How I categorize my time

For ease in comparison with OECD time studies, I use the following high-level categories:

• Sleep: What it says it is. Important!
• Discretionary: Hobbies, socializing; anything optional or chosen
• Personal: Morning and evening routines, personal care, exercise
• Work: Working on IBM projects
• Business: I split this out from work because I wanted to see how much time I was spending on building my business or improving my skills

Unpaid work: Commuting and other unpaid work/business-related activities; also, tidying up, getting groceries, cooking, doing laundry, and any household tasks that I could theoretically outsource

Within the categories, I have one or two levels of detail, which I’ll discuss later.

This graph shows the major changes in how I used time this year. To account for the varying numbers of days in a month, I’ve expressed each category as a percentage of the time available for the month. The major change was the swap between working with IBM and experimenting with running my own business, but all my other categories are surprisingly stable.

Here are some basic statistics looking at the monthly and weekly variation. There’s a bit more variation on the weekly level, but it smoothens out a lot when it gets to the monthly level. Also, the overall numbers tell me I should probably work less and spend more time on discretionary activities.

OECD 2011 – Canada		Mean	~ total hours / week	Monthly STDEV	Weekly STDEV
	Sleep	35%	58	2%	3%
22%	Business + work	28%	47	5%	6%
21%	Discretionary	16%	28	5%	7%
	Personal	14%	23	2%	4%
14%	Unpaid work	7%	12	2%	3%

Sleep + personal for me = 49%; OECD 2011 stats for Canada: 42%

Sleep

I get an average of 8.3 hours of sleep per day, which is a familiar and fairly stable number, and right in line with the OECD 2011 leisure time study’s findings. Looking at the inter-day statistics for sleep, I see a standard deviation of 1.63 hours, which means my sleep pattern is a little jagged. Here’s a daily chart that shows the variation.

It doesn’t look so irregular on a weekly scale, though. I tend to be pretty good at taking it easy after I catch myself getting tired due to lack of sleep.

Business

Business-wise, I was thrilled to have a running start. Here’s billable time as a percentage of total time (out of 7 days a week). May was a little crazy because I was helping out two clients at the same time. I took time off in September and December to focus on other interests, and I’m generally scaling back consulting because I need to make myself learn how to do other kinds of business too.

Here’s some more information in a table, showing that while I don’t reach the utilization ratios I remember from my performance review days, I still do okay.

	Billable time
	% of total time	% of business time
Mar 2012	17%	61%
Apr 2012	16%	71%
May 2012	24%	76%
Jun 2012	17%	60%
Jul 2012	21%	66%
Aug 2012	18%	66%
Sep 2012	1%	5%
Oct 2012	11%	37%
Nov 2012	14%	42%
Dec 2012	2%	10%

Going forward, I should probably plan for a 25% billable : 75% marketing/overhead mix (or even more weighted towards marketing).

On average, I spend about 10 hours a week connecting with people for business, which is a surprisingly large chunk of time. It’s good, though. I’m learning a ton and helping lots of people along the way. The weekly standard deviation for this is 7.8 hours, which probably points to “introvert overload” kicking in – after an intensely social week, I’ll hibernate for a while in order to recharge.

Discretionary

All work and no play makes for a boring sort of life, so this is where discretionary activities come in. Discretionary – Social is by far the juggernaut of this category, with 46% of all discretionary time use (average per week: 13.7 hours, stdev 11.9 hours – same introvert overload kicking in). Business networking + discretionary socializing works out to an average of 20.8 hours per week, with a standard deviation of 15.1 hours. Here’s the sparkline, with a spike around the September trip where I went to a conference and hung out with family.

The graph below shows that I’m not necessarily substituting business connecting for discretionary socializing. There’s actually a very slight positive correlation between them. I do need my breaks afterwards, though.

On to other things I do with my discretionary time. Because the Social sub-category is so much bigger than the other categories, these sparklines all use different vertical axes instead of using a shared axis for inter-category comparison. They show percentage of discretionary time, with the peak time highlighted. (Remember, we can’t compare heights across categories!) The third column shows the total percentage of discretionary time spent doing that activity.

The sparklines show that my interests tend to shift. They also show some categories that I’ve forgotten to use, such as Discretionary – Family which tends to get lumped under Discretionary – Social, and Discretionary – Read – Blogs, which has become more of either Personal – Routines or Unpaid work – Travel. Looking at this, I can see that LEGO games tend to give us about three months of obsession time, which may not be a good thing. Fortunately, W- plays them too, so it’s actually “sit on the couch and chat” time, with bonus scritching of kitties who like sitting in our laps.

Unpaid Work

Duty comes before pleasure, though, so I need to make sure chores are taken care of before I settle in for some writing. Here’s how the chores worked out.

For scale: I spend about 3.3 hours a week cooking, which is really spending maybe 6-7 hours every other week or so cooking a whole batch of things. Or at least that’s what I think it works out to. The weekly data shows me that I tend to cook in cycles (mean = 3.5 hours, STDEV = 2.3 hours):

Other interesting things: Why, yes, biking and subway time are negatively correlated (coeff = -0.53). Yay biking! The weather’s been decent, actually, so I should totally break out the bicycle and bike some more. (Biking: 209 hours this year, average of 7.2 hours per week during biking season)

% of total time	Personal – Bike	Unpaid work – Subway
Nov 2011		2.4%
Dec 2011		0.6%
Jan 2012		0.9%
Feb 2012	0.1%	1.6%
Mar 2012	0.1%	4.5%
Apr 2012	7.3%	0.3%
May 2012	5.9%	0.2%
Jun 2012	4.6%	0.3%
Jul 2012	3.6%	0.7%
Aug 2012	2.5%	1.2%
Sep 2012	0.8%
Oct 2012	3.7%	1.1%
Nov 2012		6.9%
Dec 2012		3.6%

Personal

The personal category includes all the little things that keep life running, like having breakfast and brushing my teeth. On average, I spend 2.1 hours a day dressing up, eating, brushing my teeth, and so on. That’s 815 hours over the last 386 days! Biking, walking, and exercising account for 408 hours over that time span, which works out to be an hour a day. Not bad.

So, what does this mean for 2013?

I’m planning to:

• Spend less time commuting; spend more time biking and exercising – extend biking season earlier and later (November was totally bikeable, but I chickened out and got a Metropass!), and ramp up personal exercise to ~4 hours a week.
• Spend less time working as a whole (and yes, trying to not panic about this shift either); spend more time writing and doing other discretionary activities – keep business-related time to ~40 hours a week
• Spend less time working on billable projects; spend more time marketing/selling/learning (and trying not to panic about this shift) – shift to 25-30% billing as a percentage of total business time

Glad to have the numbers! You can actually see my time data on Quantified Awesome.  I’ve just added a “Split by midnight” option that makes analysis a little easier for me and other people who use the system to track their own data.

Onward!