Hey guys,

As a lot of you know, I'm currently studying biostatistics - which is a fancy word for applied statistics. I don't know much bio. For now, I've been learning a lot of theory as well as a new programming language called R (yep, just the letter). R is an open-source, free-to-use programming language that's geared towards statistical analysis. For programmers out there, it's sort of a hybrid of SQL and Python with a lot of unique functionality contributed by statisticians and coders from all over the world. The key advantage in working in R, at least from my industry's perspective, is that graphs are a breeze - and they're beautiful. I'm sure some people can/will attest that this beats the shit out of what Excel can do.

Right now I'm learning how to create good data models in one class while learning how to visualize them in this software in another. It's been very rewarding in spite of it being a lot of work since it's a 2 hour commute to school plus working full time an hour from home and then managing to make my wife really happy. It's hard out there for a pimp like me.

Anyway, I wanted to show you guys what R can do, so I took the Draft Class / TPE list from Luke and the Budget sheet and made this really quick:



I shit you not, this took about 15 minutes. Granted, I've been learning this in school so it's fresh in my head, but it honestly wasn't bad. We can gather a few things from this graph -- mostly that Esa is the reason everyone else's graphs look so skewed. I could independently shift everyone's X axis, but ... eh. Then it'd take about 25 minutes. We can also see at what stage multiple teams are in development - I mean, look at Chicago. They got one lonely guy at 1500 TPE, and the rest are very far right on the X axis (read: young) and low in TPE right now.

I wanted to create this preview to ask the audience what else they'd want to see visualized, and I want to see if I can actually handle it. There are a lot of great color pallets, different graphing tools, and of course ... regression models. One model I really want to explore is the value of the "OVR" rating versus certain ratings. I know there's been some exploration into this, after talking to old veterans for as long as I have. The consensus is that it doesn't matter, so obviously nobody will care if I tear that axiom apart and explore what OVR is actually worth.

But uh, yeah, that's it. Excited to hear some questions. Maybe I can turn this into a presser with the right questions.