Alright, let’s dive into my basketball prediction experiment, focusing on that Greece vs. Spain game. Here’s how I tackled it, step-by-step.
First off, I gathered a bunch of data. I’m talking past game results, player stats (points, rebounds, assists, you name it), team standings, recent performance – the whole shebang. I scraped some data from sports websites, downloaded some CSV files, and even dug through some old forum posts for any insights. The more data, the better, right?
Next, I cleaned that data. This was a pain, let me tell you. Missing values, inconsistent formats, typos – it was a mess. I used Python with Pandas to handle this. I filled in missing data with averages, standardized the formats, and corrected any errors I could find. Data cleaning is seriously like 80% of the work.
Then came the fun part: feature engineering. I started with the basic stats, but I wanted to create more meaningful features. I calculated things like win percentages over the last 10 games, average point differentials, and even some simple “momentum” indicators based on recent wins and losses. I even tried incorporating some stuff like home court advantage and rest days between games.
After that, I picked a model. I decided to go with a simple logistic regression model. Why? Because it’s easy to understand and interpret. I split my data into training and testing sets (80/20 split). I trained the model on the training data and then evaluated its performance on the testing data using metrics like accuracy, precision, and recall.
The initial results weren’t great, I’ll be honest. The accuracy was around 60%, which is barely better than a coin flip. So, I started tweaking things. I tried different feature combinations, adjusted the model’s parameters, and even experimented with different types of models (like a simple decision tree).
I added some regularization to the logistic regression to prevent overfitting. I played around with the regularization strength until I got something that seemed to work okay. I also tried a few different feature selection techniques to see if I could improve the model’s performance by only using the most important features.
After a lot of trial and error, I managed to bump the accuracy up to around 70%. Still not amazing, but definitely better. I used the model to predict the outcome of the Greece vs. Spain game, and… it predicted Spain would win.
And guess what? Spain actually won! So, I felt pretty good about my model. Was it perfect? Nope. But it was a fun experiment and a good learning experience. I learned a lot about data cleaning, feature engineering, and model selection. And hey, I even got the prediction right!
Lessons learned? Data cleaning is crucial. Feature engineering can make a big difference. And sometimes, a simple model is all you need. I’m definitely going to keep tinkering with it and see if I can improve the accuracy even more. Maybe next time I’ll try a more complex model, or find even more data to feed into it. Who knows?
