Alright, so today I’m gonna walk you through my little experiment with “vt wake forest 0 0”. Yeah, sounds kinda cryptic, right? Basically, I was messing around with trying to predict the outcome of a Wake Forest football game. Don’t ask me why Wake Forest specifically, I just picked a team at random.
First things first, data collection. I spent a good chunk of time scraping data from various sports websites. Box scores, stats, historical performance – you name it, I grabbed it. It was a real pain, let me tell you. So many different formats and layouts. Ugh!
Once I had the data, I needed to clean it up. You know, get rid of the garbage, fill in the missing pieces (as best as I could), and generally massage it into a usable format. This part always takes way longer than you think it will. Seriously, data cleaning is like 80% of any data project.
Next up, I started playing around with different machine learning models. I tried a few basic ones, like logistic regression and a simple neural network. Nothing too fancy, just wanted to see if I could get some kind of baseline prediction. The results were… underwhelming, to say the least. Basically, it was about as accurate as flipping a coin. Not exactly groundbreaking stuff.
So, I started tweaking things. Added more features, tried different model architectures, messed with the hyperparameters. You know, the usual trial-and-error process. I even looked into some more advanced techniques like time series analysis, since I was dealing with game data over time. That helped a little, but still not great.
Here’s where things got interesting (sort of). I started incorporating some external data sources. Weather conditions on game day, injury reports, even social media sentiment analysis (yeah, I went there). The idea was that these factors could have some impact on the game’s outcome.
Did it work? Meh. A little bit. My accuracy improved slightly, but it was still nowhere near good enough to bet the house on. Maybe I need even more data, maybe my models are just too simple, or maybe predicting college football is just inherently impossible. Who knows?
- Collected data
- Cleaned data
- Trained basic models
- Tweaked models
- Added external data
Finally, after a ton of fiddling, I ended up with a model that could predict the outcome of a Wake Forest game with… well, let’s just say it was better than random chance, but not by much. It was a fun learning experience, though. I got to play around with some new tools and techniques, and I learned a lot about the challenges of data science. Plus, now I have a really complicated way to lose money betting on college football. Awesome!
The “0 0” part? That was just the predicted score difference from one of my earlier, really bad models. Pretty accurate, huh? Ha!
