While the name of this blog is NFL Injury Analytics because football is where I began, the methods I employ are applicable across a range of sports. For example, while I wrote my dissertation on NFL injuries I currently consult for an MLB team and have worked in football, basketball, and volleyball in the past.
This About page is really two about pages. There’s me (a human), and then there’s this blog (a blog).
My name is Zach Binney. I’m a lifelong NFL fan, one of the many things I blame my father for. He was also a statistician for the Braves from 1978-1986, which instilled in me a lifelong love of baseball and stats.
I have a PhD in epidemiology from Emory University, where I have taught multiple courses in epidemiologic and statistical methods.
I’ve consulted for an NFL team on injuries and other analytic issues. Currently I consult for an MLB team. I am also a staff writer at Football Outsiders where I focus on injuries. I was a healthcare consultant and a journalist in previous lives.
So what is an epidemiologist, and why is he talking about sports injuries? To grossly oversimplify and steal my own words from one of my FO articles, epidemiologists want to do two main things:
1. Describe the distribution of diseases (for example, injuries) in a population (for example, football players) and,
2. When we see differences within populations (for example, variation by position or team or year), ask and analyze why these differences exist.
I promise to do one, both, or neither of these in every post.
I began studying sports injuries several years ago thanks to a fortuitous accident where an NFL team that offered me a freelance analytics position said “Oh, you’re in healthcare, you must know about injuries, right?” I had not even thought about doing that at the time, but it’s an extremely timely and interesting topic! I’ve been doing in-depth work in the field ever since.
My dissertation involved describing NFL injuries, assessing the impact on injuries of the 2011 collective bargaining agreement (CBA), and devising a predictive model for NFL injury risk.
So in a nutshell, I’m a sports fan with a very strong statistics background and an interest in studying injuries.
NFL Injury Analytics Blog:
The purpose of this blog is to take a rigorous but accessible, informative, and entertaining quantitative approach to sports, and especially NFL, injury data. Given a previous career as a journalist and my current academic work, I’m confident I can accomplish 1-2.5 of these goals in any given post.
Sometimes posts will be my own original research, and sometimes I’ll comment on other people’s work or interesting data points I come across related to NFL injuries. Every once in awhile I might go totally off the rails and write a non-injury football analytics post!
I will explain any statistics used in my posts and be transparent with my methods and their limitations. I’ll try and separate these sections out from the main meat of any post so you can skip it all if you want. But I love teaching epidemiology and statistics, and if you promise to read these sections I promise to try and keep them accessible and enjoyable. You might, despite your best efforts to just waste time on the internet, learn something.