Alright folks, lemme tell you about my “atp monte carlo” adventure. It was a wild ride, full of unexpected twists and turns, just like a good tennis match!
So, it all started when I decided I wanted to dive into some data analysis of ATP (Association of Tennis Professionals) matches, specifically focusing on the Monte Carlo Masters. Why Monte Carlo? Well, clay court season is my jam, and Monte Carlo always delivers drama. Plus, I figured there’d be some interesting trends to dig up.
First thing I did was scour the web for data. I spent ages hunting down a decent dataset with match-level stats. Ended up finding a CSV file on some random tennis forum. Bless those forum dwellers! The data was kinda messy, though. Missing values all over the place, weird date formats, you name it.
Next up, I fired up my trusty Python and got to cleaning. Pandas became my best friend. I spent a solid evening wrestling with that data, filling in missing values (used the median for most), converting dates, and generally making the data usable. It wasn’t pretty, but hey, data cleaning never is.
Once the data was somewhat clean, the fun began! I started with some basic exploratory data analysis (EDA). Wanted to get a feel for the dataset. Looked at things like win rates of top players, average match length, and distribution of sets played. Nothing earth-shattering, but it gave me a baseline.
Then, I decided to focus on serve performance. Specifically, I wanted to see if there was a correlation between first serve percentage and winning a match at Monte Carlo. I calculated the average first serve percentage for winners and losers and plotted it. The difference wasn’t massive, but there was a slight trend suggesting better first serve percentage helps.
After that, I tried looking at return performance. Broke down return points won for winners and losers. Found that players who won a higher percentage of return points also tended to win the match, which, duh, isn’t surprising, but it was good to see it validated in the data.
To visualize my findings, I whipped up some simple plots using Matplotlib and Seaborn. Nothing fancy, just basic bar charts and scatter plots. Made the data a bit easier to digest. Presentation is key, even if it’s just for myself!
Finally, I experimented with some simple predictive modeling. Tried to build a model that could predict the winner of a match based on serve and return stats. Used a basic logistic regression model. The accuracy wasn’t amazing (around 65%), but it was a fun exercise. I think with more data and feature engineering, I could probably improve it.
Learned a bunch of things during this project. Data cleaning is a pain but crucial. EDA is essential for understanding your data. And even simple models can give you some insights. Most importantly, I learned that ATP data is awesome and there’s so much more to explore!
Overall, the “atp monte carlo” project was a blast. It was a good reminder that data analysis doesn’t have to be complicated to be interesting. Sometimes, just digging into something you’re passionate about can lead to some cool discoveries. Now, I’m thinking about tackling Wimbledon data next. Stay tuned!
