Okay, so, yesterday I was messing around with some prediction stuff, right? Decided to try my hand at predicting the Panama vs. Paraguay game. Why? Well, honestly, just felt like it. No deep reason.
First thing I did was grab all the data I could find. Think past game results, player stats, you know, the usual suspects. Used some APIs and web scraping to pull it all in. It was a bit of a pain, gotta say, cleaning up that data took longer than I expected.
Then, I threw it all into a basic model. Nothing fancy, just a simple logistic regression thingy I found online. Tweaked the parameters a bit, played around with the features to see what would give me the best… well, least-worst result. This part was mostly trial and error, to be honest. I’m no data scientist!
The model spat out some probabilities, which I then converted into implied odds. Like, if the model said Panama had a 60% chance of winning, I turned that into something like -150 odds. Kinda like how the sportsbooks do it.
Now, here’s where it gets interesting. I compared my predicted odds to the actual betting odds I saw on a few different sites. Found some discrepancies, as expected. My model was way more confident in Panama than the bookies were.
So, just for kicks, I placed a tiny bet on Panama based on my model’s prediction. I’m talking peanuts here, not risking the farm. I’m not recommending anyone actually bet based on this, it’s just for fun, remember?
Here’s the prediction it gave me:
- Panama win: 45%
- Paraguay win: 30%
- Draw: 25%
Which I turned into what I thought the odds should be versus what the bookies were offering. Looked like Panama was undervalued.
I’ll keep track of the results and see how it all plays out. Probably gonna lose my few bucks, but hey, it was a fun little experiment. Maybe next time I’ll try a different model, or add some more data. Who knows?
Anyway, that’s pretty much it. Just a quick and dirty prediction project. Don’t go betting your life savings based on this! It’s just for kicks, okay?
