Alright, so lemme tell you about this “efg swiss open gstaad” thing I tackled. Basically, I wanted to see what the fuss was about, try my hand at understanding what goes on behind the scenes, and maybe even learn something new. It’s all about golf, and while I’m not a huge golf guy, I figured there’s gotta be something interesting there.
First thing I did? Dive into the basics. I started reading up on the tournament itself. When does it happen? Who usually plays? What’s the course like? I spent a good chunk of time just Googling “efg swiss open gstaad” and clicking through all the official websites, news articles, anything I could find. Got a decent overview of the whole shebang.
Next up, I wanted to understand the players. Pulled up the list of participants. Looked at their rankings, past performances, social media (yeah, I stalked them a little). I wanted to get a feel for who the big contenders were, who the underdogs might be, and who just showed up for the free cheese and chocolate (it’s Switzerland, after all!).
Okay, so now I had the players, the tournament, the basics. Time to dig deeper. I started trying to find any available data. Scorecards from previous years, player stats on that particular course, weather conditions during the tournament…anything I could get my hands on. This was the tricky part, as a lot of this stuff isn’t readily available unless you’re a paying subscriber to some fancy golf data service. But I managed to scrape some stuff together from different corners of the internet.
With my data in hand (mostly spreadsheets and some janky text files), I tried to make sense of it all. I started plotting some basic charts and graphs. Average scores per player on each hole, historical weather data vs. scoring, stuff like that. Nothing super fancy, just trying to see if there were any obvious trends or patterns.
Then came the fun part (for me, anyway): trying to build a simple prediction model. I knew I wasn’t going to predict the winner with 100% accuracy, but I wanted to see if I could build something that was at least somewhat better than random guessing. I used some basic statistical modeling techniques (stuff I remembered from college, mostly) to try and predict the likelihood of different players performing well based on their historical data and the course characteristics. It was mostly just messing around and seeing what happened.
After I got a “model” working, I went back and simulated the tournament a bunch of times. This let me see how my model thought the tournament would play out on average, and how much variation there was in the results. It also gave me a list of potential winners, along with the probabilities of each one winning.
Finally, I just watched some of the actual tournament. Compared my predictions to what actually happened. Saw where my model was completely wrong (which was often) and where it maybe got a little lucky. It was a good learning experience, even though my predictions were pretty terrible.
What did I learn? Golf is way more complicated than it looks. There are tons of factors that go into a player’s performance. Also, building a good prediction model is hard! But it was a fun way to spend some time, learn a little bit about a sport I don’t usually follow, and brush up on my data analysis skills.
- Diving into the sport’s specifics is crucial.
- Gathering and cleaning the available data, even if it’s incomplete, is essential.
- Experimenting with basic models and simulations can be surprisingly insightful.
