The Day I Got Sick of Guessing and Started Tracking My Own Damn Stats
You know how it is. You spend a weekend watching the games, or tracking the markets, or whatever it is you’re trying to predict, and you think you’ve got a handle on things. Then bam! Everything goes sideways. That reliable thing you counted on suddenly tanks. That’s what started this whole mess for me.
I was relying on what I called the “Real Value” (RV) metric. It’s the safe bet, the old school way. It just looks at the big, smoothed-out averages. The team’s five-year record, the stock’s quarterly performance—that reliable stuff. And honestly, it usually keeps you out of serious trouble. But lately, it was screwing up the easy calls. It pissed me off.
So, I decided to stop trusting the mainstream noise and build my own tracker. I needed something that didn’t just look at history, but looked at momentum and the sheer, unpredictable chaos of the present moment. I called this the Value Comparison Factor, or VCF.
Setting Up the Battlefield: Grabbing the Numbers
The first thing I did was open up a fresh spreadsheet. I didn’t mess with fancy software; I just needed columns. I spent a good four hours last Tuesday night just scraping the data I needed. I pulled the last twenty data points for both sides of the comparison (could be two teams, two stocks, whatever).
For the RV column, that was easy. I just averaged everything out. I threw the high and low outliers right into the mix because RV is supposed to be steady. It doesn’t panic. It just says: “Historically, this is where we land.”
But for VCF, that required some serious elbow grease and a lot of trial-and-error weighting.
- I started by assigning a massive weight (like 40%) to the performance of the last three periods. If a team just won three straight, that mattered more than their record six months ago.
- Then, I tossed in a “Chaos Multiplier.” This was my favorite part. If the results had huge swings—if they won 5-0 one day and lost 0-5 the next—that volatility spiked the VCF score. RV hates volatility, but VCF embraces it.
- Finally, I added a subjective human factor. If I saw a coaching change, or a major new regulation dropped, I’d manually bump up the VCF score because it signaled disruption.
I didn’t care if the math was perfectly clean or statistically sound. I just wanted a number that represented gut feeling backed by recent performance trends. I built the spreadsheet so that it automatically flagged the predicted “scorer” (or winner) based on which metric was highest.
Running the Comparison: RV vs VCF Head-to-Head
For two solid weeks, I ran the simulations. Every morning, I input the real results from the day before and then checked which of my two metrics—the boring, steady RV, or the chaotic VCF—had correctly predicted the outcome.
I quickly noticed a pattern.
When the environment was stable, RV was king. When the markets were flat, or when two middle-of-the-road teams were playing, RV nailed it almost every time. It was the safe, sensible prediction. It kept saying, “Don’t overthink it, just go with the average.”
But the second things got spicy, RV choked. I watched it happen over and over. A major unexpected event would hit, and RV would barely move. It was too weighted down by old history to react fast enough.
This is where VCF shined. When the underdog suddenly got hot, VCF was already screaming about it three days earlier. When a sector started collapsing fast, VCF’s high volatility weighting pushed the score down hard. VCF was built to spot the hot streaks and the sudden crashes. It’s like the alarm bell for momentum shifts.
The Big Takeaway: So, Who Is Expected To Score?
After reviewing the fifty-plus data points I collected, the answer is simple, but crucial: Neither one scores all the time. You need both of them staring at the same situation from different angles.
My main realization was that the RV score tells you the potential of the entity—what they should achieve if everything is normal. The VCF score tells you the immediate mood and current trajectory.
If RV is high and VCF is also high, that’s a guaranteed score. That’s a fundamentally strong entity that is currently operating at peak performance. Lock it in.
But the interesting conflict is when RV is high and VCF is low. The past says they should win easily, but the present says they are stumbling badly. This means the entity is underperforming its potential. This is a trap, and RV alone would have led me right into a loss. VCF saved my bacon there.
Conversely, if RV is low (meaning historically weak) but VCF is shooting up, that means the entity is suddenly gaining massive momentum, defying its own history. This is the sleeper score, the surprising win.
I’m not throwing out my RV tracking; it’s too reliable for stability. But I learned that VCF is the essential factor for spotting breakout opportunities and avoiding sudden pitfalls. Now, when I look at a prediction, I don’t just ask, “Who usually wins?” I ask, “What is their fundamental potential (RV) and how angry/hot are they right now (VCF)?”
It totally changed how I approach my decisions. Stop looking at one metric. You need the balanced, historical view, and the chaotic, forward-looking view. That’s the real secret to predicting who is expected to score. I’m already tweaking the VCF weightings this week to make the momentum factor even more aggressive. I’ll share those results soon.