Alright, so, I’ve been messing around with some NHL player data lately, trying to get a handle on predicting their performance. It’s a bit of a wild ride, let me tell you.
First off, I started digging through a bunch of stats. Goals, assists, ice time, you name it. I figured, the more data, the better, right? So I spent a good chunk of time just gathering all this info from various sources. It was pretty tedious, not gonna lie. Felt like I was drowning in numbers at times.
Data Collection and Cleaning
Then came the fun part – trying to make sense of it all. I’m no data scientist, but I know my way around a spreadsheet. I started putting everything into a big table, trying to organize it in a way that made sense. There were a few hiccups along the way – some data was missing, some was just plain wrong. I had to do a lot of cleaning and double-checking to make sure I wasn’t building my predictions on shaky ground.
I also tried to get my hands on some contract projections, just to see how the money side of things lines up with player performance. There’s some talk going around about @AFPAnalytics and their contract predictions, so I will keep my eyes on that.
Building the Model
Once I had my data all sorted, I started messing with some basic models. Nothing too fancy, just some simple stuff to see if I could spot any trends. I played around with a few different approaches, seeing what worked and what didn’t.
- First Attempt: This was a pretty basic linear regression. Worked okay for some players, but totally bombed for others.
- Second Attempt: Tried adding in some more variables, like age and position. This seemed to help a bit, but still wasn’t great.
- Third Attempt: Okay, this is where things started to get interesting. I started using a different algorithm that accounts for more factors. It’s still a work in progress, but I’m seeing some promising results.
Initial Results and Next Steps
Now, I’ve got some preliminary projections, and honestly, they’re all over the place. Some of them seem pretty reasonable, while others are way out there. It really shows you how unpredictable these guys can be. Also, how important they are, no matter who they are playing for, they are all important.
I’m not claiming to have cracked the code or anything. This is definitely a work in progress. I’m planning to keep refining my model, adding more data, and trying out different techniques. It’s a bit of a rabbit hole, but it’s pretty fascinating to see how all these different factors can influence a player’s performance. I’ll keep you all posted on how it’s going.
Anyway, that’s my little adventure in the world of NHL player projections. It’s been a messy, sometimes frustrating, but ultimately pretty rewarding experience. Hope you found it at least mildly interesting!
