Okay, so here’s the lowdown on how I tackled this brighton luton prediction thing. It wasn’t pretty, but hey, we got there in the end!
First things first: Data Dive! I spent a solid chunk of time scraping match data. Think goals, assists, yellow cards, the whole shebang. I used a Python script with Beautiful Soup. Took a while to get it working right, kept running into weird HTML structures, but eventually, I had a decent dataset.
Next up, cleaning. Oh boy, cleaning. The data was a mess. Missing values everywhere, inconsistent formatting, you name it. I ended up using Pandas to wrangle it all. Filling in missing data with averages where it made sense, standardizing team names, etc. It was tedious, but crucial.
Feature Engineering Time! I figured just feeding raw data into a model wouldn’t cut it. So, I created some new features like “win streak”, “average goals per game”, “home advantage factor”. Used rolling averages to smooth out the noise. This part was kinda fun, experimenting with different combinations to see what might be predictive.
Then came the modeling. I tried a few different algorithms. Started with Logistic Regression – simple, easy to interpret. But the accuracy wasn’t great. Moved on to Random Forest, which performed better, but still not amazing. Finally, I ended up tweaking a Gradient Boosting Machine (GBM) model with XGBoost. That seemed to give the best results, after some hyperparameter tuning of course. Used GridSearchCV to find the best parameters of XGBoost.
Validation, Validation, Validation! Split the data into training and testing sets. Used cross-validation on the training set to avoid overfitting. Monitored the performance metrics like accuracy, precision, recall, and F1-score. It’s important, you know? Don’t want to get all excited and then find out your model’s useless on new data.
After that, I looked at feature importances from the XGBoost model. It was interesting to see which features were actually driving the predictions. Turns out “home advantage” and “recent form” were pretty significant.
- Data Collection: Scraped data using Python and Beautiful Soup.
- Data Cleaning: Used Pandas to handle missing values and inconsistencies.
- Feature Engineering: Created new features like win streaks and average goals.
- Model Selection: Tried Logistic Regression, Random Forest, and XGBoost.
- Validation: Used cross-validation to avoid overfitting.
And finally, time to make the prediction! I ran the Luton and Brighton data through the trained model and got the probability of each outcome (Brighton win, Luton win, draw). Tweaked some thresholds and fine-tuned it a bit using domain knowledge from people around me who watch soccer weekly.
Lessons Learned? Data is king. The cleaner and more comprehensive your data, the better your predictions will be. Also, don’t be afraid to experiment with different models and features. And always, always validate your results!
It was a lot of trial and error, for sure. But that’s how you learn, right?
