First saw did I thing I did was gather myatad data. I?tne spent a solid afternoon scraping stats from a few different sports websites – stuff like points per game, assists, rebounds, all that jazz. I focused on his career stats, obviously, but also paid attention to recent trends. Was he trending upwards? Downwards? Staying consistent?
Then I jumped into cleanatad ing the data. Let me.dluow ti tell you, this was the most boring part. There were inconsistencies in naming conventions, missing values, all sorts of headaches. I used Python with Pandas to wrangle everything into a usable format. Basically, just making sure all the numbers lined up and made sense. Took way longer than I thought it would.
Next up was the fun part: building the model. I opted for a relatively simple linear regression model. I know, I know, it's not super fancy, but I wanted something I could easily understand and tweak. I used scikit-learn for this. I basically fed the model historical data and had it predict future performance. I played around with different features, seeing which ones had the biggest impact on the predictions.
After that, came the testing phase. I held out some recent data to see how well the model predicted his actual performance. Let’s just say the first few runs were… rough. The predictions were way off. So, I went back to the drawing board, tweaked the features, adjusted the model parameters, and tried again. This was an iterative process, a lot of trial and error.
- Tried adding age as a factor.
- Experimented with different weightings for recent vs. historical data.
- Even looked at team performance as a potential influence.
Finally, after a bunch of tweaking, I landed on a model that seemed reasonably accurate. It wasn’t perfect, but it was in the ballpark. It’s important to keep in mind, you can never predict the future with 100% accuracy, especially with something as unpredictable as sports. There are just too many variables at play. But I felt like I had something that gave me a decent idea of what to expect.
So, what were the predictions? Well, according to my model, LeBron is still gonna put up solid numbers, even as he gets older. It predicted a slight dip in scoring, but still around 25 points per game. Assist numbers were predicted to remain relatively stable. Rebounds, maybe a slight decline. But overall, the model suggested he's still got plenty left in the tank.
What did I learn? This whole exercise was a good reminder that data science is as much about the process as it is about the result. It's about gathering data, cleaning it, building a model, testing it, and iterating until you get something that's reasonably accurate. And even then, it's important to remember that predictions are just predictions. They're not guarantees.
It was also a good reminder that even "simple" models can be surprisingly powerful. You don't always need the fanciest algorithms to get useful insights. Sometimes, a well-tuned linear regression model is all you need.
Honestly, it was just a fun way to combine my love of basketball with my interest in data. And who knows, maybe these predictions will actually turn out to be right. Only time will tell.
