Okay, so today I’m gonna walk you through this little project I did, messing around with some old Man United data from 2003. It was a blast from the past, and I learned a thing or two along the way.
First things first: data gathering. I started by digging around online for any available match data from Manchester United in 2003. Scraped some stats from various football websites – goals scored, assists, player lineups, the whole shebang. It was kinda messy, you know, different formats, some missing bits. Took a while to clean it up.
Then, data cleaning. This was the real grunt work. I used Python with Pandas, which is like, the go-to tool for this kind of stuff. Got rid of duplicates, filled in missing values as best I could (some educated guesses involved!), and standardized the data formats. It was tedious, but hey, gotta get it right if you want decent analysis later.
Next up: exploration. I wanted to get a feel for the data, see what was jumping out. I started plotting some simple stuff – goals per game, distribution of goals across different players. Nothing too fancy, just histograms and bar charts. It helped me understand the team’s scoring patterns and who the key players were back then.
After that, some deeper analysis. I looked into things like: Did the team perform better at home or away? Which players had the most impact in crucial matches? I even tried to calculate some basic performance metrics, like pass completion rates and shots on target, where the data allowed. It was cool to see if some of my hunches about the team actually held up in the data.
Visualization time! Numbers are cool, but pictures are better, right? I used Matplotlib and Seaborn to create some nicer-looking charts and graphs. Goal distributions over time, player contribution comparisons, that kind of stuff. Made it easier to see the story the data was telling.
- Goal Analysis: Visualizing goals scored per match.
- Player Stats: Comparing key player contributions.
- Home vs. Away: Analyzing performance differences.
Finally, some conclusions. After all that, I had a pretty good picture of Man United in 2003, at least from a statistical perspective. I could see their strengths and weaknesses, identify key players, and understand how they performed in different situations. It was a fun project, and it reminded me why I love messing around with data.
So, yeah, that was my little data adventure. Nothing too earth-shattering, but a good learning experience. Hope you found it interesting!
