Okay, here’s my attempt at a blog post about something I fiddled with, trying to mimic that chatty, from-experience style you asked for.
Alright folks, so I was messing around with some LPGA golfer data the other day, purely for kicks, you know? Thought I’d share how it went down.
First off, I grabbed a bunch of player stats. I just used some publically available data from the LPGA website. Nothing fancy, just the basic stuff like their names, driving distance, greens in regulation (GIR), putts per round, and scoring average.
Then, I threw all that data into a spreadsheet. Yeah, old school. Google Sheets, to be exact. I could’ve used Python or something, but honestly, sometimes a spreadsheet is just the quickest way to eyeball things.
Next thing I did was just start playing around with the data. I created some charts. I looked at the relationships between driving distance and scoring average. Did the ladies who bombed it off the tee automatically score better? Nope, not always!
- Driving Distance vs. Scoring Average: Some correlation, but not as strong as I expected. Long hitters still need to putt!
- GIR vs. Scoring Average: This one was much tighter. Hitting more greens definitely seemed to translate to lower scores. No surprise there, really.
- Putts per Round vs. Scoring Average: Obviously important. The fewer putts, the better the score.
I even tried to make some simple performance indexes, combining a few of those stats. Like, I gave each golfer a score based on a weighted average of their GIR and putting stats. Just a dumb little experiment, really.
I quickly noticed that just having raw stats isn’t enough. Course difficulty plays a huge role. A 70 at one course might be a great score, while a 70 at another course might be average.
So, I then tried to normalize the scoring data a bit. I didn’t have detailed course data, so it was a rough estimate. I found the average score for each tournament and adjusted each player’s score relative to that average. Crude, but it helped a little.
After that, I tried to see if I could predict future performance. Could I use the first half of the season’s stats to predict how someone would do in the second half? Again, this was just for fun.
I used the early season stats to calculate a performance index for each golfer. Then, I compared that index to their actual performance in the second half. The correlation was… weak. Haha. Golf is hard to predict, even for the pros, I guess.
Here’s the thing I learned, golf is more than just stats. It’s about mental game, course management, luck, and a whole bunch of other stuff that you can’t easily measure. I mean, yeah, good stats are important, but they don’t tell the whole story.
Anyway, that’s the gist of it. I’ve just showed you the basic steps on what I did. It was a fun little data dive. Maybe I’ll try something similar with PGA Tour data next time. Who knows?
