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My Biggest Two Productivity Gains from 2016

On SEBASTIAN MARSHALL

Boom! It's 2017. Oh my goodness, 2016 was the best year of my life by far. Knock on wood, everything is working. It's working marvelously, even.

One of my very few regrets is that I'm doing less ad hoc writing. I published 52 essays at The Strategic Review in 2016, the first half of which got edited into the book Progression; the second half will be in the upcoming Machina (rough guess on ETA: February).

TSR roughly doubled in size, all through word of mouth. (Thank you.) But I didn't blog as much as I used to, and I used to have a lot of fun doing this.

I've also learned a lot about making things happen in the last year, that I think might be useful to you. I'm going to be blogging a little more in 2017.

So without further ado, here's two things that have been huge for me.

A Statistical Analysis of NBA Teams

On Imported Blog

Growing up in the area around Houston, TX, I developed a love of sports. Whether it be football or basketball (sorry, Astros), I love my sports teams to death. Side note, the Texans need to get their shit together.

As a self-acclaimed math nerd, I enjoy - well - math. With the Moneyball era and my love for Daryl Morey and Nate Silver, I asked why not try to make my own sports statistics analysis.

So, that's exactly what I did over the last year. I created my own model as a result of a school project. I was pleased with the overall result, though I don't know if it's gone through enough rigorous standards to be considered formal research.

There are also multiple flaws and areas of improvement that I will hopefully address in the future, but I would like to give a brief overview on my findings.

My whole philosophy is that basketball is a team sport. I felt as if too much importance is placed on an individual player in current mathematical basketball analysis. I was more interested in how a team plays together. This is tricky because teams are always changing and the same five players don't play with each other all the time. As a result, I used the six players with the most minutes to represent each team.

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