The Weekend Challenge: Shutting Dave Up
You know how it goes. Saturday rolls around, the games are on, and you’re trying to just enjoy the afternoon, but then someone—usually my mate, Dave—starts running his mouth about how useless stats are. He reckons all these mid-table clashes, like Wolves playing Bournemouth, are just coin flips. Pure chaos. I’d had enough.
I told him flat out, “Mate, it’s not magic, it’s patterns.” He just laughed. That’s when I decided I wasn’t just going to watch this game; I was going to try and break down exactly what was going to happen, just to rub it in his face. This wasn’t about winning money; it was about proving the process works, even if the data I used was a bit rough around the edges.
Scraping the Barrel for Numbers
I didn’t bother with fancy, paid APIs or anything complicated. I opened up three different sports sites—you know the kind, full of pop-ups and annoying ads—and I just started copying stuff into a basic spreadsheet. I knew I needed to look at specific recent history. Just grabbing overall league position is lazy.
- Recent Form: I grabbed the last seven games for both teams. I cared less about who they played and more about the simple W/D/L record and, crucially, the goals scored and conceded count.
- Home vs. Away Spit: Wolves at home are different beasts than Wolves away. Same for Bournemouth. I separated those stats out. How many goals do Bournemouth usually leak when they travel? That was key.
- Head-to-Head Jinx: Sometimes, one team just owns another. I pulled the results from the last five times they met, even if they were ages ago. Sometimes history just repeats itself, and you can’t ignore that little bit of irrational data.
It took me maybe an hour just to collect all this junk and make sure I hadn’t mixed up my columns. Data entry is the worst part of any project, frankly. My first sheet looked like a total mess, but I knew what the numbers meant, and that was enough for me.
Mashing the Weights Together
Now, the real fun started: figuring out which stats actually mattered the most. I opened up a basic Python script. Nothing fancy, just Pandas and a bit of NumPy to handle the averages. I decided I couldn’t treat everything equally. That’s where most people go wrong—they just average it all out.
I started assigning arbitrary weightings. I played around with them for a good forty-five minutes, trying to feel out what seemed “right” based on what I’d seen watching these teams all season. It was basically informed guessing, but it was my informed guessing.
Here’s roughly how I settled on the importance:
Recent Form (Last 7 Games): This got the heaviest weighting, maybe 40%. A team playing well now is more important than how they played six months ago.
Current Home/Away Defensive Record: This was 30%. If Wolves’ defense is rock-solid at Molineux right now, that’s massive.
Goal Difference and XG Proxy: I didn’t use real Expected Goals (XG) because frankly, trying to scrape that correctly is a headache. Instead, I created a proxy score by comparing Shots on Target per game versus Goals Conceded. If a team is taking loads of shots but not scoring, I knocked their predictive score down. That was maybe 20% of the overall calculation.
Historical Head-to-Head: Just 10%. It’s a good tie-breaker, but not the foundation of the prediction.
The Script Spits Out the Answer
I had my system. I fed the data into the script and hit ‘Run’. I honestly expected it to crash or give me some ridiculous result like Wolves winning 7-0. That’s usually what happens when I start coding on a weekend afternoon.
It didn’t crash. It calculated two predicted scores: one for Wolves’ attacking output versus Bournemouth’s defense, and one for Bournemouth’s attack versus Wolves’ defense. The outputs weren’t massive numbers, which was a good sign. It suggested a tight, scrappy affair.
The math crunched out a projected final score of 1.35 to 0.88 in favour of Wolves.
The numbers were telling me one thing: marginal home advantage, with Bournemouth struggling slightly to convert chances on the road. It wasn’t a ringing endorsement, but it was a clear trend.
The Aftermath and the Real Win
I messaged Dave immediately. I sent him the final score prediction: “Wolves win, maybe 1-0 or 2-1, but they take all three points.” He responded with a picture of a clown emoji. Classic Dave.
Did Wolves win 1.35 to 0.88? Obviously not. Football doesn’t work like that. But Wolves did win. It was a narrow, grinding victory, just like the numbers suggested it would be. The score itself doesn’t matter as much as the outcome of the three points.
Dave paid up the small bet we had running, but that wasn’t the victory. The win was showing him that by actually taking the time to structure the data—by putting in the effort to weigh the stats that matter now, not just the history—you can find patterns hiding in the noise. It wasn’t luck. It was just a little bit of disciplined number crunching on a Saturday afternoon. It proved the point: statistics aren’t useless; most people are just too lazy to collect and structure them properly. And that’s a lesson I’ll keep applying to whatever random data project I pick up next week.