Alright so today I got really annoyed trying to figure out which basketball league players were actually doing well across PNG and MSA. Seriously, just staring at spreadsheets gives me a headache. Needed something way simpler, like a quick list shouting “Hey look at this guy!”
The Spreadsheet Nightmare Begins
First, I dug around online. Found player stats files for both leagues dumped on some official sites. Big, ugly Excel sheets. You know the ones – headers everywhere, numbers blending together. Felt like walking into a messy room. Spent ages just cleaning it up: deleting extra columns I didn’t need (who cares about jersey numbers for this?), making sure all the points per game columns were actually called “PPG”, that kinda boring stuff.
Throwing Python At The Problem
Manually sorting through hundreds of players? No thanks. Fired up Python. Used pandas, it’s like a power drill for data – messy but gets the job done. Wrote a little script to:
- Shove both PNG and MSA data into separate tables.
- Pick my main stats: Points per game (PPG), Rebounds (RPG), Assists (APG), and Steals (SPG). Basic stuff shows who’s working hard.
- Find the leaders for each stat in each league. Basically shouting “Max value! Gimme the name!” for every column.
Took a few tries. Kept crashing because I forgot to fix some blank spaces in the names. Python hates that. Grrr. Finally got a list popping out.
Well, That Was… Underwhelming
First results? Meh. It was just raw numbers and names. Like “J. Smith (PNG): 28.7 PPG”. Okay, cool, but… which stat is which? Is 28.7 points good? Felt like half the story. Needed more context, a punchline.
Reworked the script. Told it to also find the average for each stat across all players in that league. Then compare the leader’s number to that average. “Player X scores +8.2 PPG more than the PNG league average!” Now we’re talking! Shows not just good, but how much better.
Facing the All-Star Conundrum
Then hit another snag. Some absolute monsters kept topping multiple categories. Like, obviously Jones is gonna be way up there in points AND rebounds. It looked repetitive. Decided not to sweat it too much. Figured if someone crushed two stats, they deserved two spots on my “who’s hot” list. Added a note at the bottom like “Yeah, Jones is just that good.”
What Popped Out (And It’s Messy!)
Finally printed my top performers list. Looks kinda rough, but it tells the story quick. Here’s a snippet:
- PNG Points King: Davis (PNG): 26.1 PPG That’s a crazy +7.3 over average.
- MSA Rebound Machine: Lee (MSA): 11.8 RPG Grabs +4.1 more than most!
- Both Leagues Pass Master: Carter (PNG): 10.2 APG (+3.8) & Ramirez (MSA): 9.9 APG (+3.9). Head to head!
Honestly? The code is clunky, the output isn’t fancy, but it works. In like 30 seconds I can see who’s popping off across both leagues and by how much. Way better than scrolling through endless numbers feeling lost.
