Okay, so today I’m gonna walk you through my little tennis tabilo project. It wasn’t anything super fancy, but I learned a ton, and maybe it’ll help someone else out there.
First, I got the idea. I’m a big tennis fan, and I always wanted to dig deeper into player stats and performance. I stumbled upon some publicly available data – match results, rankings, etc. – and thought, “Hey, I can build something cool with this!”
I started by cleaning the data. Oh man, what a mess! Dates were in different formats, player names were inconsistent, and there were tons of missing values. I used Python with Pandas to handle this. Basically, I wrote a bunch of scripts to standardize the data, fill in the blanks where I could, and remove any totally unusable entries.
Next, I moved onto the fun part: analysis. I wanted to see if I could predict match outcomes based on player rankings, head-to-head records, and recent performance. I experimented with a few different machine learning models – logistic regression, random forests, that kind of thing. Scikit-learn came in clutch here. I split the data into training and testing sets and trained the models.
The results? Not amazing, I’ll be honest. My predictions were only slightly better than random chance. But I learned a lot about feature engineering. I realized that ranking alone wasn’t enough. I needed to incorporate things like surface type (clay, grass, hard court), recent form (win/loss ratio in the last few matches), and maybe even weather conditions. I also found out that head-to-head records were surprisingly influential.
After the modeling phase, I decided to create a simple web app to visualize the data and predictions. I used Flask for the backend and HTML/CSS/JavaScript for the frontend. I set up a basic interface where you could select two players and see their stats and the model’s prediction for a hypothetical match. It was pretty basic, but it was cool to see everything come together.
Things I would do differently next time:
- Get more granular data: things like unforced errors, first serve percentage, etc. would probably improve the model’s accuracy.
- Experiment with more advanced models: maybe a neural network or something more sophisticated.
- Spend more time on feature engineering: finding those hidden variables that really influence match outcomes.
Overall, it was a fun project. I learned a ton about data cleaning, machine learning, and web development. And I got a deeper appreciation for the complexities of tennis. If you’re thinking about tackling a similar project, I say go for it! It’s a great way to learn and have some fun.
That’s it from me, good luck if you try something similar!
