Alright, so today I wanna chat about something I’ve been messing around with: cristian stuani. Yeah, the footballer. Stick with me, it’s not what you think!
It all started when I was trying to beef up my data analysis skills. I was bored with the usual datasets, you know, the ones everyone uses. I wanted something… different. I was watching a football match and this guy Stuani scored an absolute screamer. A lightbulb went off. Why not use football stats as a dataset?
First things first, data acquisition. I spent a good chunk of time scraping data from various football stats websites. It was a pain, I won’t lie. Lots of different formats, some sites were easier than others. I ended up using Python with Beautiful Soup and Requests libraries. Scraped everything I could find about Stuani: goals, assists, appearances, minutes played, you name it.
After I got the data, it was a HUGE mess.Data cleaning was the next big hurdle.Missing values everywhere, inconsistent formatting, weird characters… It was like cleaning up after a toddler threw a spaghetti dinner. Pandas in Python became my best friend. I filled in missing values with reasonable estimates (like average minutes played per game), standardized the formatting, and got rid of all the junk data. This took way longer than I expected, honestly.
Then came the fun part: analysis! I wanted to see if there were any interesting trends in Stuani’s performance. I used Matplotlib and Seaborn to create visualizations. Things like goals scored per season, shot accuracy, conversion rate, that kind of stuff. I even tried to see if there was any correlation between his age and his performance. Surprisingly, his goal-scoring rate seemed to peak around his early thirties!
- Goal per game analysis: I calculated his goal per game ratio over different seasons.
- Shooting accuracy: I checked the percentage of his shots that were on target.
- Minutes per goal: A key metric to understand his efficiency.
I also tried some basic predictive modeling. Nothing too fancy, just using scikit-learn to see if I could predict his goals based on other stats. The results weren’t amazing, but it was a good learning experience. It showed me how complex it is to predict something like football performance, even with a decent amount of data.
Learnings & Takeaways
What did I learn? Well, first, data cleaning is a HUGE part of any data project. Don’t underestimate it! Second, football stats can be surprisingly interesting. You can dig really deep and find some cool trends. Third, predicting football performance is HARD. There are so many factors that you can’t account for, like team dynamics, injuries, and just plain luck.
Would I do it again? Absolutely. It was a fun and challenging project that helped me sharpen my data analysis skills. Plus, I got to learn a lot more about cristian stuani, who seems like a pretty decent player!