Alright, let’s dive into my little experiment with building a depth chart for USC football. It was a fun little side project I took on, mostly ’cause I’m a big Trojans fan and wanted to see if I could cook something up myself.
First things first, I started by gathering all the necessary data. I mean, you can’t build a depth chart without knowing who’s on the team, right? So, I spent a good chunk of time scraping info from the official USC Athletics website. Rosters, player stats from previous games, even height and weight – I wanted it all. It was kinda tedious, manually copying and pasting stuff, but hey, gotta start somewhere.
Once I had the raw data, it was time to clean it up. The website data wasn’t exactly in a format I could easily use, so I dumped it into a spreadsheet and started scrubbing. Fixed typos, standardized positions, and made sure everything was consistent. This part’s not glamorous, but it’s super important to get right if you want accurate results later on.
Next up, I started building the actual chart. I decided to use a simple coding language that I was familar with. I set up a table to represent the different positions on the field – quarterback, running back, wide receiver, offensive line, defensive line, linebackers, defensive backs, and special teams. Then, I started filling in the players based on what I knew and saw.
But here’s where it got interesting. I didn’t just want to throw names into slots. I wanted to rank them somehow. I tried to use stats from last season, maybe some expert opinions from sports websites or articles, I even looked at how many snaps they had in previous games. It was like trying to solve a puzzle, constantly moving players around based on new information.
Then came the really fun part – visualizing the chart. I wanted something that was easy to read and understand, so I experimented with a few different layouts and color schemes. I ended up settling on a basic table format, with the starting player in bold, backups listed below, and maybe some color-coding to indicate potential starters or key contributors.
Now, this wasn’t perfect, not by a long shot. Coaching decisions are always a bit of a mystery, and injuries can throw a wrench into everything. But it was a cool exercise to see how much I could figure out on my own using public data and a little bit of coding and elbow grease.
Finally, I decided to share what I had done with some friends. They were actually impressed and it sparked some cool conversations about what to look forward to next season!
The whole process was a good reminder that sometimes the most interesting projects are the ones you just decide to jump into and figure out along the way. Plus, now I have a slightly better understanding of what goes into building a real depth chart (though I still wouldn’t want to be a coach making those decisions!).
