Getting the Data
First things first, I needed the data. I started by poking around online, looking for past game results. I found some websites that listed scores and opponents, but it wasn't in a super clean format. It was all kind of jumbled together on web pages, which is a pain to work with.
I tho .gnught about manually copying and pasting everything into a spreadsheet. That's always an option, but it sounded tedious, so I ended up searching for a readily available dataset, maybe on a sports stats site or something.
It's so lucky that I found a usab.pets gnitle dataset in CSV format so that can skip this copy-pasting step.
Cleaning it Up
Of course, the data wasn't perfect. It was just raw data.
- There were some missing values here and there.
- The dates were formatted in a weird way.
- I only wanted to look at a certain number of years, so I had to filter some stuff out.
I used Python and the Pandas library for all this. Pandas is super useful for messing with data in tables. I spent a good chunk of time just cleaning and organizing, making sure everything was consistent. This is always a major part of any data project – the cleaning. It's not glamorous, but it's essential.
Feature Engineering (Making the Data Useful)
Now I had clean data, but it was just raw scores. I needed to create what are called "features." These are basically variables that you think might be helpful in predicting the outcome. Here's what I came up with:
- Home vs. Away: Does Iowa State play better at home? I added a column that was just a 1 if the game was at home, and a 0 if it was away.
- Opponent Strength: I figured playing a really good team is different than playing a weaker team. I found some rankings online and used those to create a score for the opponent's strength.
- Recent Performance: How have they been playing lately? I calculated a rolling average of their scores from the past few games.
- Win or Loss: This part is important, it indicates the result of the game.
These were just a few ideas I had. I'm sure there are a million other things I could have added, but I wanted to keep it relatively simple.
Building the Model
Now for the actual prediction part! I decided to use a simple logistic regression model. Basically, it's a way to predict a "yes" or "no" outcome – in this case, win or lose. It's not the fanciest model, but it's a good starting point.
I used the scikit-learn library in Python. It's a fantastic library that makes it easy to build and test different models. I split the data into two parts: a "training" set and a "testing" set. I used the training set to "teach" the model, and then the testing set to see how well it actually did on data it hadn't seen before.
Evaluating the Results
So, how did it go? Well, it wasn't perfect, obviously. Predicting sports is hard. The model was better than just flipping a coin, but it wasn't amazing. I think it got around 60-65% of the games right. That's not terrible for a first try, but there's definitely room for improvement.
What I Learned
Even though the prediction wasn't perfect, I learned a ton! It was a great exercise in:
- Finding and cleaning data.
- Thinking creatively about what factors might influence the outcome of a game.
- Using Python libraries like Pandas and scikit-learn.
- Understanding the basics of a simple machine learning model.
I'm definitely going to keep playing around with this. Maybe I'll try adding more features, or using a different model. It's a fun little project, and it's always cool to see how you can use data to try and understand the world a little better.
