It’s my favorite time of 12 months: fall which suggests it’s time for school football. I even have all the time loved college sports. Growing up, I lived in a Big Ten/SEC household and a Big East (now ACC) town which meant a deluge of faculty sports filled the tv screen from the primary kick-off in August to the last buzzer beater in April. Recently, analytics has come to dominate each sports, but because it is football season let’s start there.
The last two off-seasons in college sports have been abuzz with NIL, transfer portal, and conference realignment news. I believe the sentiment amongst most fans is captured by Dr. Pepper’s “Chaos Involves Fansville” industrial. I started to note that each conversation about conference realignment, particularly, was crammed with speculation and fueled by gut feeling. There was, nonetheless, a standard faith that some great and powerful college football Oz was crunching numbers to choose which team was value adding to which conference. I still haven’t had the chance to fulfill his man backstage, so until then I’d wish to take a shot at proposing a data-driven conference realignment.
It is a four-part blog which is able to hopefully function a fun approach to learn some latest data science tools:
- College Football Conference Realignment — Exploratory Data Evaluation in Python
- College Football Conference Realignment — Regression
- College Football Conference Realignment — Clustering
- College Football Conference Realignment — node2vec
I’ll preface this post by saying there are a lot of ways to perform exploratory data evaluation, so I’ll only be covering a couple of methods here that are relevant to conference realignment.
The Data
I took the time to construct my very own dataset using sources I compiled from across the net. These data include basic details about each FBS program, a non-canonical approximation of all college football rivalries, stadium size, historical performance, frequency appearances in AP top 25 polls, whether the college is an AAU or R1 institution (historically necessary for membership within the Big Ten and Pac 12), the variety of NFL draft picks, data on program revenue from…