2018 Quality Curve Analysis - Final Edition

Well, the bracket has been released, the match-ups are set, and now it is time to see how the next three weeks will likely play out. If you have read the previous three editions (and if you haven't, here are the links to three very good reads: Jan, Feb and Mar), you will recognize the chart below. It is the Final 2018 Quality Curve.

What is the QC Telling Us?

The same thing I have said all year long: "Parity exists in 2018 and parity translates to an above average number of upsets."

The Mind of the Selection Committee

As readers of my blog already know, a proven method of spotting upsets in a tournament is exploiting the knowledge gap between the Selection Committee and the data scientists. In my introduction articles for the last two seasons (links: 2016 and 2017), I took a simplistic ex post facto approach to understanding the Selection Committee's seeding principles. In short, it seemed as if the Selection Committee put an added value on conference affiliation, where teams from conferences with a better conference-RPI received seeds higher than their individual resume would suggest they deserve. In effect, these teams were over-seeded and other teams from less-valued conferences were under-seeded, and this type of match-up usually favors the under-seeded team. In the 2016 tournament, the B12 and P12 were beneficiaries of this process, yet in the tournament, teams from these conferences went 9-7 and 4-7, respectively (combined 13-14, and 7 of the B12's 9 wins can be attributed to OU's F4-run and KU's E8-run). In the 2017 tournament, the ACC was a huge beneficiary of this process, yet in the tournament, the ACC went 11-8 total with only one team making it to the S16 (6 of the ACC's 11 total wins was UNC's title run). Now that I have seen this process in action for the last two years, I know what to look for in the 2018 tournament...................unfortunately, it may not happen this way. Why? The Selection Committee has a brand new toy for the 2018 tournament called the Quad System. How they implement it will affect our ability to identify potential upsets, and this fact will be the focus of this article. (NOTE: Some sections may be really long-winded due to quality of detail, so for this article, if I feel a section needs a condensation, I will provide one and give it the label of "TL/DR", which means Too Long/Didn't Read.)

The Madness of Metrics and Match-ups

Well, I had three ideas for this article. The first idea came to me when I was putting the final touches on the 3-part series on upsets. Needless to say, what I was hoping would happen didn't happen, which means there won't be any follow-up to that series. Either during the weeks after the tournament starts or during the off-season, I will update Part 2 of the upsets article with the remainder of the withheld data. The second idea spent about two weeks off of the drawing board, then went right back on it, and the only reason it went back on the drawing board is because the idea for this article fulfills quite a few needs. First, I promised the implementation of micro-analysis in the blog, yet here we are in March and none to be found. By micro-analysis, I mean the study of the brackets from the perspective of match-ups and team-specific qualities, rather than macro-analysis, which is the study of the brackets from the big picture perspective of tournament quality and predictability. Shame on me.....until now. Second, I like to be a ground-breaker, a pioneer in the science of brackets, an innovator to the tools of the trade. I tried last year with the article on trend analysis, and yes, I am still following, testing and improving that tool. I think this article will be just as intriguing and innovative as that one. Third and final, it uses the same data I have been using all season, just in a different way. I am going to be using efficiency analysis to predict match-ups. In particular, this article will focus on using the Four Factors as a tool to determine how teams win and lose their games.