Okay, here we go, here’s my take on dealing with identifying an Italian soccer team’s Black player.
Alright, so the other day I got this idea, kinda random, right? I wanted to see if I could figure out how to quickly identify a Black player on an Italian soccer team, purely for research purposes. I’m not into any weird stuff, just trying to learn some new things with data.
First things first: Getting the Data
I started by searching online for rosters of Italian soccer teams. I’m talking Serie A teams, you know, the big leagues. Found a bunch of websites with player lists, but a lot of them were kinda messy. Some had player pictures, some didn’t. Some had player nationalities listed, some just had names. Ugh.
I ended up scraping a few different sites and tried to merge the data. Python with Beautiful Soup to the rescue! I pulled the player names, team names, and if available, their pictures. It was messy, had to clean up a ton of typos and inconsistencies.
Next Up: Identifying the Players
This is where things got tricky. I thought about a few approaches:
- Facial Recognition: I looked into some facial recognition APIs. There are a few out there that can estimate someone’s race or ethnicity based on their picture. But these are often unreliable, and I didn’t want to risk misidentification or perpetuating any harmful stereotypes. Scrap that idea pretty quickly.
- Nationality as a Proxy: I could use nationality. If a player is listed as Nigerian, Senegalese, or Ghanaian, there’s a good chance they are Black. But that’s not always accurate. Some players have dual citizenship, and some may be Italian-born with Black heritage. This felt incomplete.
- Cross-Referencing with Other Sources: I tried searching for each player’s name online, looking for news articles, interviews, or Wikipedia pages. This was time-consuming, but it often provided more information about a player’s background.
The “Semi-Manual” Approach
I ended up doing a combo of nationality and cross-referencing. I used nationality as a first pass, then Googled each player to confirm. It was a lot of work, honestly. For each player, I’d look for images, read their bios, and see if I could confirm their race from reliable sources. It wasn’t perfect, but it was the best I could do with the limited info I had.
What I Learned
This whole thing was harder than I thought it would be. Here’s the takeaways:
- Data is messy! Web scraping is never as clean as you hope.
- Identifying someone’s race based on online info is fraught with problems. It’s easy to make mistakes, and you have to be super careful about not reinforcing stereotypes.
- A more automated approach would require better data sources and more sophisticated image analysis.
Conclusion
So, did I “solve” the problem? Not really. But I learned a lot about data scraping, the limitations of online data, and the ethical considerations of trying to categorize people. It was a good exercise, even if it didn’t lead to a perfect solution. Plus, I watched a bunch of Italian soccer highlights along the way – not a total loss!
Maybe next time I’ll try something a little less… sensitive. Thoughts?
