Alright, let’s dive into how I messed around with Messi’s 2018 stats. It was a fun little project, sparked by a late-night debate about whether his 2018 performance was underrated.
First things first, I needed the data. I started by hitting up the usual suspects: FBref, Transfermarkt, and even some random football stats sites I found through Google. I spent a good hour just copy-pasting tables into a spreadsheet. Yeah, I know, super old-school, but it worked for me. I grabbed everything I could find: goals, assists, shots, passes, dribbles, fouls… the whole shebang.
Next up: Cleaning the mess. You wouldn’t believe how inconsistent the data was. Different sites used different naming conventions, some had missing values, and some stats were just plain wrong (I suspect some fanboy bias in there somewhere!). So, I spent a good chunk of time standardizing the names, filling in the gaps (where I could find reliable sources), and double-checking the numbers. Excel was my weapon of choice here, with a healthy dose of manual eyeballing.
Time to crunch some numbers! This is where it got interesting. I wanted to see if there were any hidden patterns or insights in Messi’s 2018 performance. I started with simple stuff: calculating his goals per game, assist rate, shot accuracy, etc. Then, I moved on to more complex metrics, like his expected goals (xG) and expected assists (xA). These are basically fancy ways of measuring how good his chances were and how likely his passes were to lead to goals.
Visualization is key, right? I wanted to make the data more accessible, so I whipped up some charts and graphs. I used Python with Matplotlib and Seaborn. Scatter plots, bar charts, you name it. I even tried a few interactive dashboards using Plotly, which were pretty cool. Seeing the data visually really helped me understand Messi’s performance in different aspects of the game.
Diving deeper: comparing Messi to himself. I pulled data from his other seasons and compared his 2018 stats to his career averages. This helped me put his performance into context. Was he scoring more or less than usual? Was he dribbling as effectively? Was he creating as many chances? The answers were surprising, to say the least.
And finally, the conclusion (or lack thereof). After all that work, did I prove that Messi’s 2018 was underrated? Well, not exactly. The data showed that he was still performing at an incredibly high level, but maybe not quite at his peak. But hey, that’s Messi for you – even his “off” years are better than most players’ best. Plus, data doesn’t tell the whole story. There’s the eye test, the intangible stuff that numbers can’t capture. But it was fun to dig into the numbers and get a different perspective.
- Tools Used: Excel, Python (Matplotlib, Seaborn, Plotly)
- Data Sources: FBref, Transfermarkt, assorted football stats sites
- Key Findings: Messi was still amazing in 2018, but maybe not quite at his absolute peak.
Overall, this was a cool little project that scratched my data-analysis itch and gave me a deeper appreciation for Messi’s skills. It also reminded me that data is just one piece of the puzzle, and that sometimes you just have to watch the games and enjoy the magic.
