Okay, so today I decided to mess around with some NBA data, specifically looking at a Mavericks vs. Bucks game. I’m not a data scientist or anything, just a guy who likes basketball and wanted to see what I could pull together.
Getting Started
First thing I did was try to find some data. I poked around a bit and eventually stumbled upon some game stats online. It wasn’t super organized, but it was enough to get started. It was basically just a big table of numbers showing points, rebounds, assists, stuff like that for each player.
Cleaning it Up
The data was kinda messy, so I spent a good chunk of time just cleaning it up. I copied and pasted it into a spreadsheet – nothing fancy, just to make it easier to look at. I had to manually go through and make sure everything was in the right columns and that there weren’t any weird typos or anything.
Figuring Out What to Look For
Once the data was looking a bit better, I started thinking about what I actually wanted to see. I mean, a bunch of numbers is just a bunch of numbers, right? I decided to focus on a few key things:
- Points per player: Just to see who the big scorers were.
- Rebounds: Who was dominating the boards?
- Assists: Who was setting up their teammates?
Doing Some Basic Calculations
I’m not gonna lie, I mostly just used the basic spreadsheet functions to calculate stuff. Like, I totaled up the points for each team, found the average rebounds, that kind of thing. I even highlighted the highest numbers in each category, just to make them stand out.
My (Very Basic) Conclusions
After messing around with the numbers for a while, I came to a few, very basic, conclusions. I could see which players were the main contributors for each team, who was hot that night, and who maybe had an off game. For example, one player might have had a ton of points, but very few rebounds. Another player might have had a balanced stat line across the board.
Overall, it was a fun little experiment. I didn’t discover anything groundbreaking, but it was cool to see how you can take some raw data and pull out some simple insights. It definitely made me think more about the game and the individual player performances. Maybe next time I’ll try to compare two different games, or even a whole season’s worth of data! But that sounds like a lot more work…
