Alright, folks, let me tell you about my deep dive into the Miami Heat vs. Portland game. I’m not a pro analyst or anything, just a regular dude who likes basketball and messing around with data. I decided to actually track my steps. First, I wanted to see if I could pull some interesting insights just from the raw data, y’know?
So, first things first, I grabbed the game data. I went searching all over the internet, and thank god, NBA has it available. I downloaded the box scores, play-by-play, the works. It was all there, just waiting to be poked at. I thought of using some fancy Python libraries to import and parse it but decided to keep it simple and just copy into a google sheet.
Next up, time to clean it up. The raw data is messy, right? Columns all over the place, weird abbreviations, that kind of stuff. I spent a good chunk of the afternoon just renaming columns and making sure the data was consistent. Made sure ‘MIA’ and ‘POR’ was consistent throughout. This part is boring as hell, but you gotta do it if you want anything useful later.
Started with basic stats. Who scored the most? What was the 3-point percentage for each team? Rebounds, assists… the usual stuff. Nothing earth-shattering, but it’s good to get a feel for the game. I just created pivot tables and just dragged and dropped all the data.
Then, I thought, let’s look at the play-by-play. This is where things got a bit more interesting. I wanted to see how the game flowed. Was there a particular quarter where one team dominated? How many lead changes were there? The first half, Portland came out swinging! They had like, a ten-point lead. But then Miami started hitting their shots in the third. The lead kept switching hands after that.
Tried to find patterns with the data. Okay, so I started digging a little deeper. I was curious about turnovers. Did one team cough up the ball more in critical moments? It turned out Portland had a couple of costly turnovers late in the fourth. It’s easy to see these from watching the game, but I felt like a real detective uncovering this from the data. lol.
Finally, I tried to visualize the data. Charts, graphs, the whole shebang. I messed around with some of google sheets charts and tried to make it look pretty. It’s not the prettiest thing in the world, but it gets the point across.
What did I learn? Well, besides the fact that cleaning data is a pain, I got a better understanding of how the game unfolded. I could see the momentum shifts, the key moments, and how different players impacted the game.
- Cleaning data is 80% of the job.
- Even simple stats can tell a story.
- Basketball is fun, even when you’re just looking at numbers.
I’m thinking of doing this again with another game. Maybe I’ll try to predict the outcome next time! Wish me luck.
