Okay, here’s my attempt at a blog post following your instructions.
Alright folks, lemme tell you about my little obsession lately: LPGA average drive distance. I know, sounds kinda niche, right? But trust me, there’s some juicy stuff in there. It all started last week. I was watching some golf on TV, and they kept flashing these stats up, and I got to wondering, how do these ladies REALLY hit the ball? I mean, we see the pros on TV, but what’s the real deal?
So, first thing I did, I jumped online. Started digging. The official LPGA site has some stats, but honestly, it’s a bit of a rabbit hole. You gotta click around, find the right year, the right player…it’s a process. But hey, I’m persistent. I spent a good afternoon just collecting data, pasting it into a spreadsheet. I felt like a proper data analyst!
Next step? Figuring out what to do with all this data. I could just look at the top 10, yeah, but that’s boring. I wanted to see trends. I wanted to see if there were any outliers. I even thought about trying to predict future performance based on past driving distance. Ambitious, I know!
- I started by calculating the average driving distance for the whole tour for a few different years. Just to see if things were changing over time. Turns out, they are, but not by a huge amount.
- Then, I looked at individual players. I wanted to see who was consistently long, who was consistently short, and who was all over the place. Some players are just cannons off the tee, and some are more about accuracy.
- I even tried to compare driving distance to other stats, like greens in regulation and putting average. The idea was to see if there was a correlation. Did longer drivers tend to score better? The answer, surprisingly, wasn’t always yes.
One thing I learned: data cleaning is a PAIN. You wouldn’t believe how inconsistent the data entry can be. Sometimes the names are spelled differently, sometimes the numbers are formatted weirdly…it’s a mess. I spent almost as much time cleaning the data as I did analyzing it.
But finally, I got some decent results. I found some players who are massively underrated, some who are probably overrated, and some general trends that I think are pretty interesting. I’m thinking of putting together a little visualization to show it all off. Maybe even a little web app!
The biggest takeaway? Don’t just believe what you see on TV. Stats can be deceiving. You gotta dig in, look at the data from different angles, and see what really going on. And hey, maybe you’ll learn something about your own game in the process!
