Okay, so today I’m gonna walk you through my little experiment with trying to predict the Atlanta United game. No promises it’s gonna be some genius-level stuff, but hey, I learned a thing or two!
First things first, data. I figured, gotta grab some historical data. I scraped some match results, like, who played, who won, goals scored, all that jazz. Went back a couple of seasons, because, you know, recency bias and all that. Used some basic Python and Beautiful Soup to pull that data off some soccer stats website.
Next up, cleaning that mess. Data was all over the place, inconsistent formatting, you name it. Spent a good chunk of time standardizing stuff. Got rid of missing values, fixed date formats, all the boring but necessary stuff. Pandas library in Python was my best friend here.
Feature Engineering! This is where I tried to get a little fancy. I created some new features. Things like win streaks, home game advantage (just a boolean, true or false), average goals scored in the last 5 games, stuff like that. Thought those might be good indicators.
Then, I jumped into model selection. I tried a couple of different machine learning models. A simple Logistic Regression, a Random Forest, and even dabbled with a basic Neural Network using TensorFlow. Nothing too crazy, just wanted to see what performed best.
Training Time! Split my data into training and testing sets. Trained each of the models on the training data. Hyperparameter tuning was… limited. Honestly, just did some basic grid search to find some decent parameters. Didn’t want to spend forever on this.
After training, I ran each model against the testing data to see how well they predicted the outcome. I used metrics like accuracy, precision, and recall to evaluate the models. Gotta say, the results were… mixed.
Results & Lessons Learned: Logistic Regression was surprisingly decent. Random Forest was a bit better, but also more prone to overfitting. The Neural Network, well, it was just okay. Didn’t really outperform the simpler models. My conclusion? Predicting soccer is HARD! There’s so much randomness involved.
- Data is king. The more data, the better. Need to find more reliable sources.
- Feature engineering is crucial. Gotta come up with better, more insightful features.
- Don’t overcomplicate things. Sometimes a simple model is all you need.
- Soccer is unpredictable!
So yeah, that’s my little Atlanta United prediction experiment. Probably not gonna quit my day job anytime soon and become a professional soccer predictor, but it was a fun learning experience. Might try again with some more data and better features someday.
