The whole Ghana World Cup 2022 score thing. Man, what a frustrating time that was. Everyone knows those crucial group stage games were high-stakes, right? I had a few bucks riding on the line, and I wasn’t getting paid to wait for some TV commentator to finally decide to update the lower-third graphic.
I started out like everyone else. Had the game on the big screen, phone open to some big-name sports app, tablet open to a live betting site. Total mess. All three sources were showing the score updates on a different delay. The betting app was the fastest, but it kept freezing up exactly when a goal looked likely. The TV broadcast? Forget about it. You’d see the ball in the net, everyone would go nuts, and the little graphic would still say 0-0 for another thirty seconds. That’s enough time to miss a crucial bet, or worse, have a heart attack.
I decided I needed an instant notification system. Something that didn’t rely on some multi-million dollar media company’s slow server infrastructure. I figured, I just need the raw data, straight from the source, or as close as I can get to it. Skip the middleman, completely.
The Ugly Setup Process
I started by just watching the network traffic from one of the faster, but still not fast enough, text-only sports update sites. Not the fancy ones, just the plain HTML page with the running text updates. I wasn’t looking for an official API or anything clean. That stuff costs money or has rate limits. I just wanted the sloppy back-end data stream that fed their page.
I fired up a quick Python script. I’m not a Python guy by trade—I just use it when I need to smash something together quickly. I used a simple library, let’s just call it the web-scraping-tool-that-must-not-be-named, to just yank the entire page’s HTML every fifteen seconds. Not milliseconds. Fifteen seconds. That felt like the sweet spot between being fast and not completely overwhelming their poor server with my stupid script.
- First try was a disaster. I grabbed the whole page, which was maybe 5MB of junk, and then spent a second trying to parse it. Too slow. By the time I found the score element, another three seconds were gone.
- Second try, I got smarter. I realized the scores weren’t always in the same place. The site was moving things around slightly. I couldn’t rely on the exact line number. So I started searching for a very specific phrase. Something like: “Final Score” or “Ghana” followed by a number. I used a crude regex expression—the ugliest looking string of symbols you’ve ever seen—to just drill into the HTML soup and pull out the two numbers right after the word “Ghana” and “Opponent.”
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Third try was the charm. I isolated the actual data points. I set the script to only process the raw text that held the score. If the script ran and the two numbers it pulled were different from the last time it ran, I had it rigged to do two things:
- Display the new score in a giant, ugly red box on my second monitor.
- Play the most annoying sound effect I could find, something loud and piercing, to make sure I looked up from whatever else I was doing.
It was ugly. It was non-optimized. But it was fast. Faster than the betting app, faster than the TV, faster than any news alert.
Why the Obsession with Speed?
You might be wondering why I wasted three late nights building this Frankenstein’s monster just for some World Cup scores. It comes down to a lesson I learned the hard way a few years back, and it shaped how I look at data reliability.
I used to work this sales support job, a long, long time ago. My role was to send out quotes for custom manufacturing orders. We had this internal database that held the component costs, which fluctuated based on market supply. Crucially, the system updated the component costs every hour. I hated that delay. I mean, an hour? Things change fast.
I had this one giant quote, the biggest one of the quarter, sitting on my desk. The cost of the main component had been dropping steadily all week. I knew it was going to hit a major low point on Friday morning. I told the customer, “I’ll send the quote at 9:01 AM Friday, trust me, you want the 9:01 AM price.”
I hit the button at 9:01 AM. I was a hero. I got the quote locked in at the lowest price of the week. But here’s the kicker.
The internal component database received a manual update pushed by someone in purchasing at 9:05 AM. That manual update, which was supposed to be a corrective measure, actually reverted the cost back to the higher price from Thursday. Because my system was based on the hourly update cycle, my 9:01 AM quote was technically based on the 8:00 AM data, which was correct. But the purchasing team claimed the 9:05 AM data, which was wrong, was the new “source of truth.”
The deal collapsed into a massive argument, and the customer walked. My bonus went to zero. My boss told me I was great, but “you can’t argue with the system of record, even when the system of record is completely wrong and slow.”
I realized that day that being right isn’t enough. You have to be right AND have the fastest, most reliable source of truth, even if you have to build that source yourself. I lost a huge amount of money simply because a backend system had a 60-minute delay and a bad manual override.
So, when I was sitting there, watching the Ghana match, waiting for the scores, I wasn’t just gambling on the game. I was gambling on a slow, unreliable data feed. I wasn’t going to let that happen again. I had to build my own source, even a crude one.
That little Python script, scraping the hell out of that poor sports page, was my reliable source. It didn’t care about TV delays, or slow app loading. It just screamed the numbers at me the second they appeared in the underlying HTML. And you know what? It worked perfectly. Pure peace of mind. Simple tools for getting the job done, no fancy features required. Just the damn score, immediately.