It has been two months, but I finally have another article that isn't a QC Analysis. In fact, it is also a micro-analysis article (predicting specific outcomes), unlike the majority of my articles which take a macro-analysis approach (predicting the big picture). This article is a personal project that I have been working on since I started PPB, but the idea behind the methodology did not come into fruition until after the historic UMBC-UVA upset. The project is none other than a ratings system. Everyone who is someone in college basketball analytics has a rating system, so if I'm going to be prominent in college basketball analytics, it is only right if I have my own rating system too. For now, I'm calling it the Easy Points Index because the methodology seeks to determine how easily a team can get points. After all, college basketball games are determined by points, and theoretically, the team that can get them the easiest should win. The idea for this approach came from analyzing the UMBC-UVA upset in the 2018 tournament and the season-long data and analytics. Essentially, I tried to answer the question: What made 2018 UVA different than all other 1-seeds before the historic upset tainted them forever? The answer was the basis for the Easy Points Methodology, which I will illustrate in this article.
