Honestly, the whole reason I even bothered digging into the Sunderland versus Derby stats was a stupid argument I had last weekend. My neighbor, Gary, is convinced that Sunderland’s defense is overrated, and he kept yapping about how Derby County consistently dominates the midfield battle, even when they lose the game. He was waving his hands, talking about “momentum shifts” and “intangible pressure.” I told him that was just nonsense fan talk. I needed numbers to shut him up. Not just the final score, but the real, nitty-gritty stats—the stuff that actually tells you who was doing the work.
So, I decided to stop arguing and start documenting. I kicked off the process by just trying to find an easy report online. That was a waste of half an hour. Every sports site just regurgitated the standard stuff: goals, shots on target, yellow cards. None of them showed the deep metrics I was truly after, like Expected Goals (xG) for open play, or how many successful press attempts happened in the final third. These stats are usually tucked away behind expensive data subscriptions or hidden deep in poorly designed football analytics portals.
I realized quickly that if I wanted the key data, I was going to have to go fetch it myself. I opened up my laptop and fired up a quick environment. I knew which specialist data site held the raw information, but it was structured terribly. The match data was locked behind four different JavaScript clicks. A simple copy-paste job wouldn’t cut it; the stats weren’t actually loaded until you scrolled down three times and pressed a little “Show Advanced Metrics” button.
Detailed Data Extraction Process
The first thing I did was write a small piece of code designed just to mimic my mouse clicks and scrolls. I had to tell it to navigate to the specific game page, wait five seconds for everything to load, and then simulate the click on the “Data Points” tab. Then it had to scroll down to where the xG data sat. This whole setup took me longer than I’d like to admit, maybe three hours of fiddling because the website kept changing the names of the buttons every time I reloaded it.
Once I managed to reliably load the metrics, I instructed the script to grab every single number it could see in the detailed summary tables. I wasn’t just taking the headline figures. I was pulling:
- The minute-by-minute xG accumulation.
- Total passes completed in the attacking half.
- The number of successful tackles made by each team’s central defensive partnership.
- Possession distribution broken down into ten-minute segments.
The raw output was a total mess. It looked like a giant wall of text with numbers jammed next to words like “td-class-metric” and “div-style-value.” It was completely unreadable. My next job was dedicated entirely to cleaning and structuring the data. I had to filter out all the rubbish code and isolate just the numbers and their labels. I dumped everything into a simple spreadsheet, organizing it column by column: Metric, Sunderland Value, Derby Value. This tedious part of the job took another two hours, mostly because I had to manually check if “45%” meant successful passes or overall possession.
Finally, I had my key data laid out plainly. The argument with Gary was purely about Derby’s midfield dominance versus Sunderland’s defense. The numbers were fascinating.
The Key Data Dump:
- Derby actually generated a higher cumulative Expected Goal value (1.92 xG) compared to Sunderland (1.45 xG), despite losing 1-0. Gary wasn’t totally wrong there.
- However, Sunderland’s defense blocked 11 shots—almost double Derby’s eight. They bent, but they absolutely did not break.
- The biggest finding: Sunderland’s central midfielders had a 92% successful pass rate in their own half, meaning Derby’s pressing, while high effort, was largely inefficient at forcing turnovers.
I packaged the spreadsheet up, sent it to Gary, and haven’t heard a peep back. The stats don’t lie, especially when you dig this deep. It feels good when you can stop listening to anecdotal garbage and anchor your argument in raw, undeniable proof.
Why do I always go this far for something so minor? It’s just how I’m wired now. If I decide something is worth looking into, I need to see the mechanism working behind the scenes. This whole process, spending half a Saturday just to win a meaningless argument with my neighbor, reminds me of my first proper job years ago. I was working retail inventory, and the computerized stock system kept misreporting quantities. My boss said it was “user error.” I knew it wasn’t. So, I spent three weeks secretly cross-referencing every single shipment and sale on paper, manually tracking the discrepancy until I had a stack of evidence a foot high proving the system itself was flawed.
They fired the software company, gave me a raise, and suddenly realized I was the guy who didn’t just point out a problem, but built the entire solution just to prove the point. That feeling—that absolute satisfaction of having verifiable, self-extracted data—that’s why I share these logs. It’s not about football; it’s about the relentless pursuit of verifiable facts, even if it requires digging through mountains of digital junk just to find them.
