So, I got this idea buzzing around in my head a while back – trying to predict Appalachian State game outcomes. You know, just for fun, see if I could figure out some pattern.
Getting Started – The Data Hunt
First thing, I needed data. Man, finding good, clean historical game data wasn’t as straightforward as I thought. I spent a good chunk of time just poking around different sports sites, trying to scrape together scores, opponent stats, locations, dates – the whole nine yards. It felt like digging through dusty old boxes.
I grabbed stuff like:
- Past game scores (App State vs opponent)
- Win/Loss records
- Home or Away status
- Maybe some basic team stats like yards per game, points allowed (when I could find consistent numbers)
It was messy. Some years had detailed stats, others were just scores. Consistency was a nightmare.
Trying to Make Sense of It
Once I had a pile of data, I started messing with it. Nothing fancy, mind you. I’m not some data wizard. I first just looked at simple head-to-head records. Okay, App State usually beats Team X, loses to Team Y. That’s easy enough, but not really predicting, is it?
Then I tried calculating average points for and against. Figured maybe I could compare App State’s average points scored against the opponent’s average points allowed. Seemed logical, right? I plugged the numbers into some basic spreadsheets, set up some simple formulas.
Reality Check – It’s Harder Than It Looks
Well, let me tell you, predicting football is tough. My initial results were all over the place. One week, the prediction looked genius; the next, completely embarrassing. I remember one game, I think it was against Coastal Carolina a while back, my numbers pointed one way, and the game went totally the opposite. Kind of frustrating, honestly.
You realize pretty quickly that stats only tell part of the story. Injuries, player morale, a lucky bounce, weather – none of that stuff was really in my simple spreadsheet model. It’s way more complicated than just comparing past numbers.
A Few Tweaks
I didn’t give up entirely. I went back and tried adding a bit more logic. Put more weight on recent games, maybe factored in home-field advantage a bit more strongly. I also tried to be more careful about the quality of the data I was using, throwing out really old or incomplete stuff.
Did it make a huge difference? Maybe slightly better? It’s hard to say definitively. It felt a little more robust, but still far from a crystal ball.
Where It Stands Now
So yeah, that’s the story of my little Appalachian State prediction project. It’s still something I tinker with occasionally, especially during football season. The predictions are definitely not reliable enough to bet the farm on, not even close. But it was a fun process. Learned a bit about how hard it is to model something as chaotic as sports and got my hands dirty playing with data.
It’s mostly just a conversation starter now when friends talk about the upcoming game. “Well, my little spreadsheet says…” – usually followed by a laugh.
