2022 NCAA Tournament - FULL ANALYSIS

It's finally here. It seems like yesterday I was putting out the introductory article yet here we are. I'm going to post everything in this article, so all the information will be in one spot. I'll also make the section headings larger than normal so you can find what you're looking for while scrolling.

2021 Quality Curve Analysis - Final Edition

I'm not going to bore you with catchy intros and wordy mantras, so let's see the final edition of the 2021 QC and the 2021 seed curve.

Another Theoretical Attempt at Meta Analysis

I received a lot of feedback on the meta-analysis article, and since I had some spare time this week with work being slow, I did some further digging/torturing into the data. The first attempt looked at meta through raw data (or more specifically, raw averages). It showed how the national averages of 2016-2019 are mostly similar to one another but different to other years because rule changes created different metas between all the years. What if there was a way to create cross-meta comparisons so that the data in the years from 2008 to 2015 becomes more relevant to the data of 2016-2019?

A Theoretical Attempt at Meta-Analysis

If you've ever played trading card games, then you'll have same familiarity with my current topic. I grew up playing all kinds of card games, but trading card games were always my favorite because of the diversity in how you could play them. You could be aggressive with quick, hard-hitting strategies, conservative with control and lock-down strategies, or clever with endless-looping combinations that "break the game" and result in an auto-win or a special end-game format to handle the "broken-game condition." The variety of different approaches results in a competitive state labelled as "Meta", where X% of the decks are Strategy A, Y% of the decks are Strategy B, Z% of the decks are Strategy C, and etc. For example, if you attended a tournament and expected X + Y > 60% of the meta, you would want to construct and play an anti-meta deck, which means your deck would beat "on average" more than 60% of the decks being played at the tournament. In more colloquial terms, it is akin to playing a "rock-type" deck when everyone else is playing a "scissors-type" deck (and hoping you don't randomly run into the less than 10% playing a "paper-type" deck).

College basketball demonstrates a meta-like quality, given the variety of offenses (passing motion, dribble-drive motion, swing, point-action reversal, shuffle, princeton) and defenses (pass-denial M2M, pack-line M2M, zone, and an occasional full-court havoc/hell press) in the game. What I am more interested in (and what this article will look at) is a statistical-based meta for the game and the tournament.

Easy Points Index

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.

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.