Work analysis for Spring 2020

I just completed an analysis of all my working hours this past quarter starting on March 23rd through June 12th when the academic quarter ended. My commitments throughout the quarter included two pass/fail classes, work for the School of Medicine’s COVID-19 response, academic research with Stanford’s SHAPE lab, and a few personal projects here and there. Quite a few commitments! And I’ll get to what that meant for my actual performance later.

First, let me define what I mean by work: I track meetings, lectures, heads-down studying/programming, and reading as work. I don’t count emails, networking, and various house chores in these figures. Onwards to the stats!

I worked an average of 5.34 hours per day with a standard deviation of 3.29. My minimum hours of work per day was 0.35 hours with a maximum of 15.33 hours. I worked a total of 374 hours over the course of 70 days. Assuming a per-entry error of +/- 5 minutes and given 608 time-tracking entries through this app given an approximate error to the total account of +/- ~50.7 hours. Accordingly, I can approximate the daily error in logged time to be about +/- 13.5%. Given the way I logged my hours (using Timely’s open-window tracker and logging entries at the end of the day), this error rate seems reasonable. The underestimates and overestimates inherent in each of my time tracking entries lead me to believe that 374 hours of work over the quarter is an accurate figure.

This chart presents the distribution of work by “client” for each day throughout the quarter. A “client” is an academic course, employer, research, or personal projects. The notable trends here are:

1. My hourly expenditure on the Personal Health Dashboard decreased significantly as my Computer Graphics class and Smart Products class took on more of my mental bandwidth later on.

2. I tend to procrastinate work on academic assignments until the few days before they are due. There are a few peaks that support this claim:

   • Sun. 05/03 - Mon 05/04 for Smart Products which were the two days before my first assignment was due in the class. There was almost no other work done during that time.

   • Thu. 05/07 - Mon. 05/11 for my Remembrance Agent project that a corporate client wanted a demo of. I spent three straight days on this project working about 10 hours per day to get this project to completion.

   • Mon. 05/18 - Fri. 05/22 for my Computer Graphics class.

3. The second half of the quarter was increasing dominated by Computer Graphics work as I struggled to complete the challenging assignments at hand.

Some conclusions:

1. In this Spring 2020 quarter, I seemed to work best by focusing on one task at a time and manically-hacking away at the problem.
2. I overestimate my throughput in terms of hours spent working per day. I always felt like I worked an average of 8 hours per day but it looks like this ‘manic-recovery’ cycle actually decreases my productivity overall: after high-throughput days I need at least one day to recover where I only work 20-50% as much as the day before.

Having processed these data, I realized that I need to lay out my priorities for this upcoming Summer very clearly and make incremental progress towards those goals in a more stable manner. With these data, I have evidence that these mania-recovery cycles end up reducing my overall productivity.

Long-term, I definitely want my daily throughput to be closer to 12 hours per day, 6 days a week, which over 70 days is 720 hours or ~93% higher than my current rate. (This will include time for reading, creative projects, etc.) However, long-term habits need to start with short term goals. I’ll start out with the goal of working a consistent 8 hours per day, 6 days a week which is 480 hours over 70 days, or a ~28% increase in throughput. Hopefully this goal will involve less of the manic-recovery phases and more happiness/stability overall!

I look forward to continuing to track my hours! I’m taking a slightly-different approach to managing my tasks, priorities, personal care habits, and time-tracking; I’m using a simple spreadsheet for the coming months.

I’m so glad I have an empirical understanding of my productivity and I’m glad I’ve been able to make some important data-driven decisions about the way I spend my days.