Okay, so I saw this “North Carolina Appalachian State Prediction” thing trending and, honestly, I got curious. I’m not a huge sports bettor or anything, but I do like playing around with data. So I decided to see if I could build some sort of prediction model myself, just for fun.
First, I needed data. Lots of it. I started digging around, looking for past game scores, team stats, player performance – anything I could get my hands on. I spent a good chunk of the day just gathering information and throwing it all into a giant spreadsheet. It was messy, but hey, that’s how these things start, right?
Then came the fun part – trying to make sense of it all! I started by calculating some simple averages: points scored, points allowed, win percentages, that kind of basic stuff. I figured those would be a good starting point.
- Wins/Losses: Simple, but important!
- Points For/Against: How many points each team was scoring and giving up on average.
- Home/Away Records: See if either team had a big advantage on their home turf.
After that, I started getting a little fancier. I looked at things like strength of schedule – who had the tougher opponents? – and tried to factor that in. I even played around with weighting recent games more heavily than older ones, figuring that current form is probably more important than what happened last season.
Building the actual “model” was, well, let’s just say it was a lot of trial and error. I used some basic formulas in my spreadsheet to combine all these different factors and spit out a predicted score. It wasn’t pretty, and I’m sure a real data scientist would laugh at it, but it was my prediction model!
The Results?
To be honest, it was a mixed bag. The first few predictions I ran were way off. Like, not even close. But after tweaking the formulas and adjusting the weights of different stats, I started to get results that seemed at least * was a cool feeling that the data after I adjusted it, seems that have been working.
The whole process taught me a few things. First, data is king. The more you have, the better. Second, even a simple model can be surprisingly effective if you put some thought into it. And finally, predicting sports outcomes is hard! There’s a reason why people make a living doing this – it’s not easy to get right. This experience made me realize the challenge that is. But it was still awesome to build something from scratch and see it kind of, sort of work!
