Alright, buckle up, folks! Today, I’m spilling the beans on my “napoli predictions” experiment. Let me tell you, it was a rollercoaster, but hey, that’s how we learn, right?
Where it all started
So, it all began with me just casually following Napoli and wondering, “Could I actually predict their game outcomes?”. I wasn’t aiming for some crazy high accuracy, just wanted to see if I could get a general feel for their performance.
Diving into the data
First things first, I needed data. I spent a good chunk of time scraping data from different sports websites – things like past match results, player stats, injuries, the whole shebang. It was a pain, I won’t lie. I even messed up a couple of times and had to re-scrape everything, but hey, that’s part of the process.
Choosing my weapons: Models & Features
- Logistic Regression: A classic for binary outcomes (win/lose). Thought it would be a good starting point.
- Random Forest: Because everyone loves Random Forests, right? Plus, it can handle a bunch of different features without too much fuss.
For features, I kept it relatively simple:
- Goals scored in the last 5 games
- Goals conceded in the last 5 games
- Home/Away advantage (just a simple boolean)
- Opponent strength (based on their league ranking)
Nothing too fancy, but enough to get the ball rolling.
Training & Testing – The Grind
I split the data into training (70%) and testing (30%) sets. Used scikit-learn for the models, which made things a lot easier. I tweaked the parameters a bit, but honestly, I didn’t spend too much time on hyperparameter optimization. Just wanted to get a sense of how things would perform.
The Results – Reality Check
Okay, here’s where it gets interesting. The Logistic Regression model gave me an accuracy of around 60%. Not bad, but not great either. The Random Forest performed slightly better, hitting around 65%. Still, nothing to write home about.
What went wrong (or right?)
I think the biggest issue was probably the simplicity of my features. Just goals scored and conceded don’t tell the whole story. Things like player form, tactical changes, and even just plain luck can have a huge impact on the outcome of a game. Plus, I didn’t account for draws at all, which skews the accuracy a bit.
Lessons Learned & Next Steps
So, what did I learn from all this? Well, for one, predicting football is hard! But more seriously, I realized that:
- Feature engineering is key. Need to find more relevant data points.
- I should incorporate more sophisticated models. Maybe something with time series analysis or even a neural network.
- Accounting for draws is crucial!
I’m planning on revisiting this project soon, armed with these lessons. Maybe I’ll try to incorporate some advanced stats like expected goals (xG) or possession data. Who knows, maybe one day I’ll actually be able to predict Napoli’s games with some degree of accuracy. For now, it’s back to the drawing board!
