Okay, folks, let’s dive into this “Dayton vs. St. Joseph Prediction” thing I messed around with today.
First off, I gotta admit, I’m no sports expert. But I do love tinkering with data, and I saw some folks talking about this game online, so I thought, “Why not try to build something?”
The Setup
I started by, you guessed it, grabbing some data. I found a few websites that had stats on both teams – things like points per game, rebounds, field goal percentage, you know, the usual stuff.
My main tool? Plain old Python. I’m no coding wizard, but I can throw together some basic scripts. I used a library called “requests” to pull the data from those websites, and another one called “BeautifulSoup” to make sense of the messy website code.
Wrangling the Data
This part was a bit of a headache. Every website had its data formatted differently. Some used tables, others had it buried in paragraphs. So, I spent a good chunk of time writing code to clean it all up and put it into a format I could actually work with – basically, a big spreadsheet in Python.
- Figuring out how the different websites store data
- Writing customized code to scrape each particular set of tables
- Dealing with random errors and unexpected website changes
Building a “Model” (Kind Of)
Now, I’m not going to pretend I built some fancy machine learning algorithm. My “model” was pretty simple. I basically calculated some averages for each team based on their past performance, and then compared them. I looked at things like:
- Offensive Power: How many points do they usually score?
- Defensive Strength:How many points do they usually allow?
- Recent Performance:Have they been winning or losing lately?
I added some weightage to these, I did some testing by changing them and seeing their effects on historical games.
Making the Prediction
After crunching the numbers, my little script spit out a prediction. it favored Dayton, but not by a huge margin. Keep in mind, this is just based on some basic stats. There’s no accounting for injuries, home-court advantage, or just plain luck.
The Result (and Lessons Learned)
So, how did my prediction do? Well, I’ll have to wait for the game to finish to know for sure! But regardless of the outcome, this was a fun little project. I learned a bit more about working with data, and I got to flex my coding muscles a * also reminded me that sports are unpredictable, and even the best models can be *’s part of the fun, right?
This might not be the perfect predictor, and honestly,I did spend much time, but it was a great journey to put all these together!
