Okay, here’s my attempt at a blog post, mimicking the style and tone of the example, about “algs year 4 split 2 pick rates”:
Alright, so I’ve been digging into the ALGS Year 4, Split 2 pick rates, and let me tell you, it’s been a bit of a journey. I wanted to see what legends everyone’s been running, you know, get a feel for the meta.
First, I started by just, like, poking around. Looking at different websites, trying to get a general sense. There weren’t any official aggregated information sources at that time. It was a bit messy, honestly. Lots of different opinions, some conflicting data, the usual internet chaos.
My Data Deep Dive
So, I decided to get my hands dirty. I started watching a bunch of the ALGS matches – and I mean a bunch. I pulled up Twitch, YouTube, wherever I could find the streams. Made myself some coffee, settled in, and started taking notes.
I basically created my own little spreadsheet. Nothing fancy, just a simple Google Sheet. Every time I saw a team comp, I’d jot it down. Legend by legend. It was slow, it was tedious, but I wanted to see the patterns for myself.
- Column 1: Team Name (or just “Team A”, “Team B” if I didn’t catch it)
- Column 2: Legend 1
- Column 3: Legend 2
- Column 4: Legend 3
I did this for, like, hours. Seriously, my eyes started to cross after a while. I started to see the same Legends popping up over and over. Some were obvious, some were a bit of a surprise.
After I felt like I had a decent sample size – and my eyes needed a break – I started doing some basic calculations. Just simple percentages. What percentage of teams ran this Legend? What percentage ran that one? I used some very basic calculation features in google sheet, nothing advanced was needed.
It wasn’t perfect, obviously. My sample size was limited to the matches I watched, and there’s always some bias in that. But it gave me a much better idea of what was going on than just reading random comments online.
The whole process was, shall we say, “manual.” There was no magic tool, no fancy API I could just plug into. It was just me, a spreadsheet, and a whole lot of Apex Legends gameplay. Old school, you know?
It felt good, though. Like I actually understood the data, because I’d put in the work to collect it myself. I might not be a pro analyst, but I got a pretty good handle on those pick rates, at least from my little corner of the internet.
