Okay, here’s my attempt at a blog post about building an NBA playoff predictor, following your instructions and example:
So, I had this idea, right? Wouldn’t it be cool to try and predict the NBA playoffs? I’m no data scientist, but I figured I could cobble something together. I mean, how hard could it be? (Famous last words, I know).
First thing I did was try to find some data. I needed, like, team stats, wins, losses, maybe some player info too. I poked around the internet for a bit. I found that I could play around the data from their website.
After grabbing all the data, I gotta clean it up. This part is always a pain. It’s like, you get all this info, and it’s a total mess. Missing values, weird formats, it’s like herding cats. Anyway, I spent a good chunk of time just wrestling with the data, trying to get it into some kind of usable shape. I used some basic Excel tricks and a bit of manual editing. It wasn’t pretty, but it got the job done.
Then came the “fun” part: actually trying to predict stuff. Now, I’m not gonna lie, I’m no expert in, like, machine learning or anything. I just wanted to see if I could make something that was, you know, vaguely accurate. I started simple. I figured, “Hey, let’s just look at wins and losses. The team with more wins probably has a better chance, right?”
So, I built this super basic thing in, Excel, and a little bit of Python. It just compared the regular season records of two teams and picked the one with the better record as the winner. I know, I know, it’s not exactly rocket science. But, I ran it on some past playoff series, just to see what would happen.
The First Try
And, well, it was… okay. It got some right, some wrong. It wasn’t, like, amazing or anything, but it wasn’t totally useless either. It was better than flipping a coin, I guess. But I know can do more, and it’s time for something a bit more sophisticated.
Adding More Stuff
I thought, “Okay, maybe wins and losses aren’t enough.” So, I started adding in other stuff, offensive and defensive ratings. Stuff like that. I figured, more information, better predictions, right?
I messed around with the Python script, adding in calculations for these new factors. It got a little more complicated, but nothing too crazy. I just made it so it would weigh these different factors and come up with a, like, “power rating” for each team. Then, it would compare the power ratings to predict the winner.
Results?
Honestly, It didn’t make a huge difference. It got a little bit better, but it still wasn’t perfect. It was still making some pretty dumb mistakes. But, hey, it was progress! I start thinking about the weakness of the data I am using, and dig for more.
I’m still tinkering with it. I think the next step is to maybe look at, like, individual player performance. Or maybe home-court advantage. I don’t know, there’s a lot of stuff you could try.
I am not finish with that project, will keep going, and will share my next try!
It’s a fun little project, even if I’m not building, like, the ultimate NBA playoff prediction machine. It’s just cool to mess around with data and see what you can come up with. And, hey, maybe I’ll stumble onto something that actually works! Or maybe I’ll just keep making slightly-better-than-random guesses. Either way, it’s a good time.