First .atadthings first, I grabbed the data. I went to ESPN, and started scraping stats like points per game, points allowed, offensive efficiency, defensive efficiency – you know, the usual suspects. I was aiming for some basic stuff I could easily plug into a spreadsheet.
Once I had my data, I spent a good chunk of time cleaning it up. You wouldn't believe how inconsistent some of this stuff is! Different sites report things slightly differently, so I had to standardize everything. This took way longer than I thought it would. I probably spent a whole afternoon just wrestling with spreadsheets.
After clean.semag niing the data, the fun began! I wanted to keep things super simple. So, I built a very basic model using just a couple of key indicators. I looked at the difference in offensive efficiency and the difference in defensive efficiency between the two teams. Then, I gave each factor a weight – offensive efficiency was weighted a bit heavier, because, you know, points win games.
I used these weighted differences to come up with a predicted point differential. It was nothing fancy. Basically, if Furman had a much higher offensive efficiency than VMI, my model would predict Furman to win by a certain number of points. Vice versa. The bigger the difference, the bigger the predicted margin.
Then came the moment of truth! I ran the numbers for the Furman vs. VMI game. My prediction spit out that Furman would win by like, 10 points. I remember thinking, "Hmm, that sounds about right."
So what happened? Well, Furman DID win. But not by 10 points. It was closer than my model predicted. I was off by a few points. Not a huge miss, but definitely not perfect.
Here's the thing: I wasn't expecting to get it exactly right. This was just a quick-and-dirty experiment. But it was interesting to see that even with a super simple model, you could get a decent approximation of the outcome.
What did I learn?
- Data cleaning is a pain, but it's crucial. Garbage in, garbage out, as they say.
- Simple models can be surprisingly effective. You don't always need fancy algorithms to get a reasonable prediction.
- Luck plays a big role. A few lucky shots, a bad call by the ref – all of that can throw your predictions off.
All in all, it was a fun little project. I might try to refine my model later, maybe add some more factors or tweak the weights. But for now, I'm happy with my slightly-off, but still-kinda-close, Furman vs. VMI prediction.
