Alright, let’s talk about that Texas A&M versus Kansas thing I was messing with today. It was a bit of a journey, lemme tell ya.
First off, I started by just gathering data. I mean, you gotta have the goods, right? Scraped some stats, looked at previous game results, all that jazz. Basically, I wanted to get a feel for each team’s strengths and weaknesses. It was pretty messy at first – different formats, some missing info, the whole shebang.
Then came the fun part – cleaning that data. Ugh, data cleaning. But seriously, it’s gotta be done. I used Python with Pandas to wrangle everything into shape. Fixed inconsistencies, handled missing values (mostly by imputing averages, nothing too fancy), and got it all into a nice, neat table. That alone took a good chunk of the morning.
Next up was the actual analysis. I wanted to see what factors seemed to have the biggest impact on winning. Points scored, obviously, but also things like field goal percentage, turnovers, defensive stats, all that stuff. I threw it all into a regression model to see what popped out. Turns out, some of the obvious stuff was indeed important, but there were a few surprises too, like how crucial limiting turnovers was for Kansas. Good to know!
After that, I tried to build a simple prediction model. Nothing crazy, just a logistic regression to predict the outcome of the game. I trained it on historical data and then tested it on some more recent games to see how it performed. It wasn’t perfect, but it was surprisingly accurate, like 70% or so. Not bad for a quick and dirty model.
I visualized some of the data too, because why not? Made a few charts showing the key stats for each team and how they’ve changed over time. That helped me get a better sense of the overall trends and patterns.
Finally, I put it all together in a little report, summarizing my findings and highlighting the key insights. Basically, I concluded that Texas A&M had a slight edge based on their overall performance, but Kansas’s ability to avoid turnovers could be a game-changer. It was a fun little project, and I learned a thing or two along the way.
What I Learned:
- Data cleaning is always more time-consuming than you think.
- Even simple models can be surprisingly effective.
- Visualizations are your friend.
Next Steps?
Maybe I’ll try incorporating some more advanced techniques, like machine learning algorithms, or even try to factor in things like home-field advantage and injuries. Who knows? The possibilities are endless!
