Okay, so I’ve been messing around with this NFL record predictor thing, and let me tell you, it’s been a wild ride. I started with basically zero knowledge, just a love for football and a vague idea that I could somehow use data to guess how teams would do.
First, I needed data. Lots of it. I spent hours, maybe days, just digging through websites, trying to find historical NFL scores, stats, you name it. I felt like a digital archaeologist, sifting through layers of information.
I finally stumbled upon some datasets. I chose to use a dataset because it has a lot of what I want.
Once I had the data (which was a huge win in itself!), I had to figure out how to actually use it. I’m no data scientist, so I started with the basics. I learned a bit of Python, I get used to using that for some simple data tasks. I’d heard good things about a library called pandas, so I dove into that. It’s like Excel on steroids – super useful for organizing and cleaning up the messy data I had.
Data Cleaning
Speaking of messy data, that was the next hurdle. Missing values, inconsistent formatting, it was a real headache. I spent a good chunk of time just cleaning things up, making sure everything was consistent and usable. It was tedious, but I knew it was important. Garbage in, garbage out, right?
Building A Model
Then came the fun part (well, fun and frustrating): building the actual prediction model. I decided to start simple with a basic model. I used another Python library called scikit-learn. I won’t bore you with the technical details, but basically, I trained the model on the historical data, teaching it to recognize patterns and relationships between different stats and a team’s final record.
And for the training part, I split my data into “training” data and “testing” data. I “trained” the models on the historical data and used the testing data to see how they are doing.
Testing and Improving
The first few attempts were…not great. The model was spitting out predictions that were way off. But that’s part of the process, I learned. I tweaked the model, experimented with different features, and slowly but surely, the predictions started to get * felt like I was a football coach, constantly adjusting my game plan based on the results.
Finally I got some interesting * wasn’t perfect, not by a long shot, but it was actually making some decent predictions. It was a cool feeling to see something I built from scratch actually working (sort of!).
So, that’s where I’m at now. It’s an ongoing project, something I tinker with in my spare time. I’m still learning, still experimenting, and still having a blast. Who knows, maybe one day I’ll have a model that can actually predict the Super Bowl winner. But even if I don’t, it’s been a fun and rewarding journey.
