Alright guys, so today I’m gonna walk you through my little experiment with predicting the arnaldi vs fils match. I’m no expert, mind you, just a regular dude who likes tennis and messing around with data.
First off, I started by gathering data. I mean, you can’t predict anything without something to base it on, right? So I scraped match results, player stats – you name it – from a few different tennis websites. Think ATP rankings, head-to-head records, recent performance on similar surfaces, that kind of jazz.
Then came the fun part: cleaning up the mess. Let me tell you, raw data is never pretty. Dates in different formats, inconsistent player names, missing values… It was a real headache. I spent a good chunk of time wrangling all that into something usable. Used some Python and Pandas, if you’re curious. Nothing too fancy, just the usual data manipulation stuff.
Next, I tried a few different prediction methods. Honestly, I just wanted to see what would stick. I started with something simple: a weighted average based on recent performance and head-to-head. Give more weight to recent matches, a little boost for winning against the opponent before, and boom – a prediction. It was surprisingly okay, but definitely not perfect.
After that, I got a little more ambitious and tried a logistic regression model. I figured, hey, maybe I can throw in a bunch of features – serve stats, return stats, even things like age and height – and let the model figure out what’s important. I used scikit-learn for this, which made things a lot easier. Getting the data into the right format for the model was still a pain, though.
I trained the model on a bunch of past matches and then used it to predict the arnaldi vs fils match. Drumroll, please… It predicted Arnaldi would win! But here’s the thing: Fils is a tough player, and I knew the model wasn’t taking everything into account. Things like player momentum and mental state are hard to quantify.
So, what happened? Well, the match is yet to be played! I’ll have to update you guys with the actual outcome and see how my predictions fared. It’s all a bit of fun, but it’s interesting to see how data can be used to make predictions, even if they’re not always right.
In the end, this was more about the process than the actual prediction. I learned a lot about data cleaning, feature engineering, and the limitations of predictive models. Plus, it gave me a good excuse to watch more tennis!
- Gathered data from tennis websites
- Cleaned and preprocessed the data using Python and Pandas
- Tried weighted average and logistic regression models
- Trained the models on past matches
- Predicted the Arnaldi vs Fils match (Arnaldi to win!)
Lessons Learned
The whole thing was a good reminder that data is just one piece of the puzzle. Gut feeling, news, and other factors still play a role. I’ll definitely be tweaking my approach for the next match prediction!
