I’m excited to release ncaahoopR version 1.5 today. ncaahoopR started off as me putting together a few of my side projects into a single place. It was convenient for my work flow, and I wasn’t too sure other people would even use it. Fast forward a year and half, and the current number of people using the package far exceeds what I ever could have imagined. The uptick in users, particuarly during this current season, has made me really excited about the possibilities that lie ahead.
As part of my thesis this past spring, I improved on the win probability built into my ncaahoopR package. The new win probability model has now been integrated into the package for the upcoming 2019-20 college basketball season. In this post, I provide some background on the statistical details of the model.
Win Probability Model Framework Let \(p_{ikt}\) denote the probability that team \(i\) wins game \(k\) with time \(t\) remaining in the game.
Introduction One of the more unique student sections in college basketball belongs to the Arizon State Sun Devils and their famous “Curtain of Distraction”. The Curtain of Distraction opens to reveal students in ridiculous attire trying to do all they can to break the focus of opposing free throw shooters. Perhaps the most famous attempted distraction occured when Olympic swimmer Michael appeard from behind the curatin wearing only a speedo.
Introduction One of the NCAA Men’s Basketball metrics I’ve been fascinated with lately is that of Game Excitement Index. Game Excitement Index (GEI) attempts to quantify how exciting a particular game was after it has been played. Related metrics have been implemented for NFL games by Brian Burke, NBA games by InPredict (Mike Beuoy), and for March Madness by FiveThirtyEight. One can compute GEI for college basketball games using my ncaahoopR package, which I define as follows: