Alright folks, let’s dive into my little prediction project: Lorient vs Nantes. I’m no expert, just a guy who likes tinkering and seeing what I can come up with. So, here’s the whole messy process, start to finish.
First things first, gathering the goods. I started by scraping data from a bunch of sports sites – you know, the ones with all the stats, scores, and historical match info. I used Python with Beautiful Soup and Requests. It’s pretty standard stuff, but honestly, cleaning the data was the real pain. Dates in different formats, team names spelled differently, all that jazz. Took me a solid evening just to wrangle everything into something usable.
Next up, feature engineering. This is where I tried to figure out what actually matters for predicting a match outcome. I looked at things like:
- Head-to-head records: How have these teams performed against each other in the past?
- Recent form: Wins, losses, draws in the last 5-10 games.
- Goals scored and conceded: Average goals per game, both for and against.
- Home and away advantage: Does Lorient perform better at home? Does Nantes struggle on the road?
I played around with different combinations of these features, trying to find the ones that seemed most predictive.
Model time! I kept it relatively simple. I tried a few different models:
- Logistic Regression: A classic for binary classification (win/loss).
- Support Vector Machine (SVM): Good for finding patterns in high-dimensional data.
- Random Forest: An ensemble method that combines multiple decision trees.
I used scikit-learn in Python for all this. I split my data into training and testing sets (80/20 split) and trained each model on the training data.
Evaluating the damage, I mean, the models. Accuracy was my primary metric. I also looked at precision and recall, especially for predicting wins for each team. Honestly, the results weren’t amazing. My best model, a Random Forest, got around 60-65% accuracy on the test data. Not exactly going to quit my day job, but hey, it’s better than flipping a coin, right?
The Prediction Itself: After training and testing, I fed the model the current stats for Lorient and Nantes. The model leaned towards a draw, but gave a slight edge to Lorient. Something like:
- Lorient Win: 35%
- Draw: 40%
- Nantes Win: 25%
What I learned and What I’d do differently next time.
This was a fun little project, and I learned a ton. Here’s what I’d tweak next time:
- More data: More historical match data would definitely help. I could also try including data from related leagues.
- Better features: Maybe look at things like player stats, injuries, or even betting odds.
- More advanced models: Deep learning might be overkill, but hey, worth a shot!
So, there you have it. My Lorient vs Nantes prediction adventure. It wasn’t perfect, but it was a good learning experience. I’ll keep tinkering and hopefully get better over time. Wish me luck!
