Alright, so today I’m gonna walk you through my experience messing around with some tennis data, specifically looking at a match between Ostapenko and Kasatkina. It wasn’t some crazy deep dive, more like a weekend project to scratch an itch.
First things first, I needed the data. I went scouring the web, found a decent dataset of tennis matches. It was kinda messy, you know, with different formats and missing bits here and there. So, the first step was to clean it up. I pulled up Python and Pandas, started wrangling the data into something usable. Dropped columns I didn’t need, handled missing values (mostly with averages, nothing too fancy), and made sure the data types were correct. Tedious, but necessary.
Once the data was clean-ish, I started digging into the Ostapenko vs. Kasatkina match. I filtered the dataset to isolate that specific match. Then, I wanted to see some basic stats, you know, like first serve percentage, winners, unforced errors. Just to get a feel for how the match went down. Pandas made this pretty straightforward, just a few lines of code to calculate these stats for each player.
After that, I wanted to visualize things a bit. I fired up Matplotlib and Seaborn. Created some simple bar charts to compare the key stats between the two players. Nothing groundbreaking, but it helped me see at a glance where each player had the upper hand. I also tried a scatter plot of serve speed vs. accuracy, but the data was too sparse to get anything meaningful out of it. Ah well, can’t win ’em all.
Then, I thought, “Hey, maybe I can predict something!” I looked at past matches of these two players, and tried to build a simple model to predict the outcome of their next match. I used scikit-learn and tried a few different algorithms – Logistic Regression, Support Vector Machines, the usual suspects. Split the data into training and testing sets, trained the models, and then evaluated their performance. The results weren’t amazing, honestly. Probably needed more data, or better features, or maybe tennis matches are just inherently unpredictable. Who knows?
Finally, I wrapped everything up in a Jupyter notebook. Added some markdown to explain what I did, the results I got, and the limitations of my approach. It’s nothing publishable, but it was a fun little project to learn a bit more about data analysis and tennis. Plus, it kept me busy for a weekend!
Key takeaways:
- Data cleaning is always the most time-consuming part.
- Visualization helps you understand the data better.
- Don’t expect miracles from simple models, especially with limited data.
Overall, it was a cool experience. I got to brush up on my Python skills, explore a new dataset, and learn a bit more about the Ostapenko vs. Kasatkina rivalry. Would I do it again? Probably. There’s always more data to analyze!
