The Great Data Dig: Getting Off My Spreadsheet High Horse
Man, let me tell you, for the last year and a half, my life was running on vibes and sheer hope. Hope is great for getting out of bed, terrible for long-term planning. I was juggling two big freelance contracts and trying to squeeze in personal learning, and everything felt like a massive push. I’d finish the week completely fried and realize I’d spent forty hours just spinning my wheels.
I needed a system. Not some fancy dashboard or SaaS solution—I needed something that told me exactly when my brain was actually working, and when I was just staring at the screen waiting for coffee to kick in. That’s how the “Weekly Virgo Prediction” system started. It’s not about astrology, even though I’m a Virgo, it’s about being incredibly anal about my own data to predict my peak output windows for the next seven days. It’s a terrible name, but it stuck.
Phase One: Shoving Data into the Blender
I started with the tools I hated the least: Google Sheets and a bunch of dusty Python scripts I’d used for college projects. My goal was simple: track inputs (sleep quality, morning mood score 1-10, caffeine intake, project type) and track outputs (actual task completion time vs. estimated time, measured in 15-minute chunks). I committed to logging these things four times a day. It was brutal.
The first few weeks were a disaster. I spent more time logging data than actually working. My sheet was an absolute mess. Date formats were inconsistent. Sometimes I forgot to log my midday crash, skewing the whole afternoon’s data. I was manually copying my calendar entries into the sheet, which took forever. I realized I wasn’t building a system; I was building a new, unpaid data entry job for myself.
I had to pivot hard. I threw out the manual entry for the schedule. I grabbed a basic Python script I found floating around forums and brutally modified it. I forced it to hook into my digital calendar through a very janky API setup (seriously, I spent two nights trying to wrestle OAuth into submission). This script started pulling and standardizing my scheduled events directly into a local CSV file.
The next struggle was the subjective stuff—mood and focus. I tried building a UI for input, but I knew I’d never use it. So I defaulted to the simplest method: a quick, three-question SMS prompt sent to my own number at 9 AM, 1 PM, and 6 PM. My phone auto-replies are structured so a second script can grab those messages and parse the raw text into numerical scores. This sounds fancy, but it’s literally just a list of ‘if message contains X, score = Y’. Crude, effective.
Phase Two: The Engine Room and The Garbage In
Once I had three weeks of slightly clean data, I moved to the analysis engine. I opened up Pandas—which I mostly learned by screaming at Stack Overflow posts—and started mashing the sleep data against the mood data against the task completion data. The initial output was useless.
My first ‘prediction’ told me I should work hardest on Tuesdays at 10 AM. Great. Except when I drilled down, the only reason Tuesday 10 AM looked good was because I had spent that time answering simple emails. It wasn’t a period of high creative output, it was a period of high busywork output. The metrics were lying to me.
I had to redefine the success metric. Instead of just “completion,” I introduced a weighted factor: cognitive load. Complex tasks (coding, deep writing) got a weight of 3. Simple tasks (emails, admin) got a weight of 1. I then ran the weekly average against the baseline data again. This required writing a loop that iteratively applied the weights, recalculating the ‘true productivity score’ for every hour slot over the 21 days of data I had accumulated.
This level of self-scrutiny only came about because of what happened last spring. I was cruising, thinking I was invincible, handling huge workloads. Then, out of nowhere, my main client decided to pivot entirely, nuking our contract mid-cycle. I had zero financial cushion because I hadn’t been tracking my time or efficiency properly—I just knew I was busy. I realized then that my work ethic was just chaos masquerading as diligence. I was too burned out to effectively chase new work, and I nearly tanked my savings. That scare forced me to build this system. It wasn’t about optimizing; it was about ensuring survival by understanding my own operational limits.
Phase Three: The “Next Up” Report
Now, every Sunday evening, the system runs the full data crunch. It pulls in the last four weeks of weighted productivity scores, identifies patterns related to sleep consistency and the preceding weekend’s activity level, and spits out the “Weekly Virgo Prediction: Next Up!” report. This report is brutal and honest. I don’t want pretty charts; I want actionable bullet points.
The final output script generates a simple HTML file (local only, thank God) that gives me three key things:
- Focus Window Peaks: High-value work is best scheduled for Tuesday, 9 AM to 12 PM, and Thursday, 2 PM to 4 PM.
- Resource Dips: Avoid critical decisions or new learning on Monday afternoons. Schedule admin and cleanup then.
- Sleep Target: Minimum 7.5 hours required for a positive Wednesday outcome.
It’s not rocket science. It’s just forcing structure onto my messy brain using data I generated myself. It works because it’s personalized and based on actual historical performance, not some generic time management guru’s advice. I finally feel like I’m steering the boat instead of just clinging to the mast. Next up? I’m integrating a simple financial tracker to see if my peak productivity windows actually correlate with higher invoicing rates. That’s going to be a whole new level of data hell, but I’m ready to dive in.
