Okay, here’s my take on sharing my “raducano” practice journey, blog style!
Alright folks, gather ‘round! Today, I’m spillin’ the beans on my “raducano” adventure. Now, before you ask, no, it ain’t a fancy Italian dish (though that does sound good!). It’s the codename I gave to my latest personal project— a deep dive into analyzing tennis data, inspired by, you guessed it, Emma Raducanu! Let’s get into it.
Phase 1: Data Hoarding (and Cleaning… Ugh)
- First things first, I needed data. Like, mountains of it. I scoured the web, looking for publicly available tennis match stats. Hit a few goldmines, and a whole lot of dead ends.
- Then came the fun part… NOT! Cleaning the data. Oh man, the inconsistencies were a nightmare. Different naming conventions, missing values, you name it. I spent a solid week wrestling with spreadsheets and Python scripts, just trying to get everything into a usable format.
- I used Pandas in python. Nothing fancy, just good old-fashioned data wrangling.
Phase 2: Feature Engineering – Making Sense of the Chaos
- Once the data was clean(ish), it was time to get creative. I started thinking about what features would be interesting to analyze.
- Aces, double faults, first serve percentage – the usual suspects. But I also wanted to dig deeper.
- I calculated things like “break point conversion rate,” “percentage of points won on second serve,” and even tried to estimate “player fatigue” based on match length and opponent ranking.
- This involved a lot of trial and error, and some late-night calculations (fueled by copious amounts of coffee, obviously).
Phase 3: Model Building – Time to Get Nerdy
- Here’s where things got really interesting. I decided to use a few machine learning models to see if I could predict match outcomes.
- Started with a simple Logistic Regression model for the baseline. Got surprisingly decent results!
- Then I tried a Random Forest, and things got even better. The model was actually picking up on some pretty subtle patterns in the data.
- I even experimented with a neural network, but honestly, it was overkill for this project. Plus, I kept running into overfitting issues, so I decided to stick with the Random Forest.
Phase 4: Visualization – Making it Pretty (and Understandable)
- All this data analysis is useless if you can’t communicate your findings effectively. So, I spent a good chunk of time creating visualizations.
- I used Matplotlib and Seaborn in Python to generate charts and graphs. Nothing too fancy, just clear and informative.
- I created visualizations of things like “Raducanu’s win rate against different opponent types,” “her serve performance over time,” and “key factors influencing her match outcomes.”
Phase 5: Sharing the Knowledge (That’s Where You Come In!)
- Finally, the moment of truth! It was time to share my findings with the world (or at least with you guys).
- I wrote up a detailed report summarizing my analysis, including the methodology, results, and key takeaways.
- I also created a presentation with all the visualizations, so people could easily grasp the main points.
- And of course, I’m sharing all of this with you today, in this very blog post!
What I Learned
This “raducano” project was a total blast! I learned a ton about data analysis, machine learning, and even a little bit about tennis strategy.
But more importantly, I learned the value of persistence. There were times when I felt like I was drowning in data, or when my models just wouldn’t cooperate. But I kept pushing through, and eventually, I got some really cool results.
So, that’s my “raducano” story. Hope you found it interesting! Now, if you’ll excuse me, I’m off to celebrate with a big bowl of pasta. (Raducano-themed, of course!).
