Okay, so I’ve been diving deep into tennis stats lately, specifically looking at Felix Auger Aliassime’s performance. I wanted to see if I could predict his match outcomes a little better. Here’s how I went about it, and what I found.
Getting My Hands Dirty with Data
First things first, I needed data. Lots of it. I started by scraping a bunch of tennis websites. I grabbed everything I could find on Felix – match results, opponent rankings, surface types, win percentages, the whole nine yards. It was a bit of a mess at first, just a ton of raw information.
Cleaning Up the Mess
Next, I had to clean this data up. I used Python and a library called Pandas (it’s great for handling tables of data). I spent a good chunk of time just organizing everything, making sure dates were formatted correctly, getting rid of incomplete entries, and basically making it all consistent. This part wasn’t glamorous, but it was super important.
Finding Patterns (or Trying To)
With the clean data, I started exploring. I wanted to see if I could identify any obvious trends. Does Felix perform better on clay than grass? Does he struggle against higher-ranked opponents? I created some basic charts and graphs to visualize the data. I also looked at his head-to-head records against various players.
- I looked at his win percentage on different court surfaces.
- I checked his record against top 20 players.
- I compared his performance in best-of-three versus best-of-five matches.
Building a Simple Prediction “Model”
Now, I’m no data scientist, but I tried to build a very basic prediction model. I basically took the factors that seemed most important (opponent ranking, surface type, recent performance) and gave them different weights. It was a super simple formula, nothing fancy. I’m sure a real statistician would laugh, but it was a start!
Testing, Testing, 1, 2, 3
I then tested my “model” on some past matches. I fed it the data and saw if it would correctly predict the outcome. Let me tell you, the results were…mixed. It got some right, some wrong. Definitely not a sure thing!
What I Learned (So Far)
Honestly, predicting tennis matches is hard! There are so many variables. My simple model was okay, but it definitely needs work. I realized I need to factor in things like player fatigue, recent injuries, and maybe even the weather. It’s an ongoing project, and I’m still learning. My main takeaway is that while stats can give you some insights, there’s always that element of unpredictability in sports. That’s what makes it exciting, right?
