Okay, here we go! Here’s my take on that efootball player project, blog style.
Alright folks, let’s dive into this eFootball player thing I messed around with. Been wanting to do something with game data for ages, and this seemed like a good starting point.
First thing I did was scour the internet for data. I’m talking player stats, appearances, skills – the whole shebang. Ended up finding a few decent spreadsheets and some online databases. Data is king, people! So spend time here.
Then came the fun part – cleaning. Oh boy, was that a mess! Inconsistent formatting, missing values, weird encodings… you name it. I used Python with Pandas, of course. Loaded the dataframes, poked around, filled in gaps where I could, and dropped the truly useless bits. It was tedious, but crucial.
After cleaning, I started thinking about what I wanted to do with the data. I decided to focus on a few key areas:
- Player overall ratings distribution.
- Identifying the best players in each position.
- Finding hidden gems (players with high potential but low current ratings).
To get the overall distribution, I just used Pandas and Matplotlib to make a histogram. Super simple. `df[‘overall_rating’].hist()` and boom! Done. Then I started diving into player positions.
Figuring out the best players per position was a bit more involved. I grouped the data by position (`*(‘position’)`), then sorted each group by overall rating (`.sort_values(‘overall_rating’, ascending=False)`), and took the top few players. This gave me a decent list of the top-rated guys in each spot.
Now, the “hidden gems” part was tricky. I didn’t have actual potential data, so I had to wing it. I decided to create a “potential score” based on a combination of current rating, age, and some key skills for their position. This was totally arbitrary, but hey, it’s my project! I messed around with the weights until I got a list of players who were relatively young, decent now, but had the stats to suggest they could get much better.
I even tried visualizing some of the data using Seaborn. Scatter plots of age vs. overall rating, box plots of skill distributions by position. It helped me get a better feel for the data and identify some outliers.
The whole thing was a blast. Definitely learned a lot about data manipulation, cleaning, and visualization. Plus, it was fun to apply these skills to something I’m actually interested in (gaming). I may not have discovered the next Messi, but at least I had a good time digging through the data. I will keep on this project.
