Alright, so today I wanted to figure out something that’s been bugging me – who’s statistically the worst player in the NBA right now? I mean, “worst” is subjective, but I wanted to see if I could get some kind of objective answer using data. It’s not about hating on anyone, just pure curiosity fueled by too much time on my hands.
My Deep Dive into Basketball Stats
First, I jumped onto the internet. I figured there’s gotta be tons of stats out there, and I just needed to find the right ones. Initially, I thought about using Player Efficiency Rating (PER), but that felt too broad. It measures good things, and I was looking for, well, the opposite.
So, I started digging. I looked at things like turnovers per game, missed field goals, missed free throws… you name it. The problem was, just looking at raw numbers doesn’t tell the whole story. A guy who plays 5 minutes a game and misses one shot isn’t necessarily worse than a starter who plays 30 minutes and misses ten, right? Context matters.
I spent a good hour just browsing different basketball stats websites, trying to get a feel for what stats might be useful. I quickly realized I needed something that took playing time into account. Otherwise, I’d just end up with a list of benchwarmers who barely see the court.
Then, I started thinking about usage rate. That’s basically how often a player is involved in a play that ends with a shot, turnover, or free throw. A high usage rate means the player is handling the ball a lot. Combining that with some “negative” stats seemed like a decent approach.
Combining the Numbers
My next step was to try to combine some of these things. I grabbed data on:
- Turnovers per 36 minutes (to account for playing time)
- Field Goal Percentage (how often they make shots)
- Free Throw Percentage (same idea)
- Usage Rate (as mentioned above)
I figured that a player with a high usage rate, a high turnover rate per 36 minutes, and low shooting percentages might be a good candidate for “worst” player, at least * is important to keep in mind that a player who has high usage rate might also be a great * is just the approach for this research.
The Result
After all that data crunching, a few players started showing up when I checked those statistics.
It’s not really fair to name names, because, again, this is just a fun little experiment, and there are a million factors that go into a player’s performance. But it was interesting to see how combining different stats could highlight players who might be struggling in certain areas.
This whole thing reminded me that stats only tell part of the story. There’s hustle, defense, teamwork… all that stuff that’s hard to quantify. But hey, it was a fun way to spend an afternoon, and it made me appreciate just how complex basketball can be, even when you’re trying to figure out something as seemingly simple as who the “worst” player is.
