2021 Meta Analysis

With it being late, I'm going to rapid fire this article. First off, I used the relative data instead of the raw data so that historical comparisons can be made. I will be comparing to the 2016-2019 tournaments like I did in the previous two meta articles. Second off, when I recommend meta and anti-meta criteria, feel free to head over to Bart Torvik's site and find teams that meet this criteria. Third off, I cannot stress this enough: This is untested and high-risk, but if you're like me, you want to be on the cutting edge of new. With that, let's see what 2021's Meta looks like.

Historical Relation of 2021 Seeds

Using the Torvik Rating system, which utilizes the familiar Pythagorean win percentage methodology of Bill James, I gauge the relative strength of the 2021 seeds to their counterparts in previous years. (NOTE: I believe Torvik may have tweaked his formula because some of the data in my spreadsheet made years ago are different from the percentages on his site. As a result, the data in this method may be inaccurate and predictive ability may be higher risk.) Remember when 2018 had historically weak 1-seeds and then one of them bowed out to a 16-seed. This is another attempt at this approach. This compares all teams of a particular seed group regardless of year. Since the Torvik ratings go back to 2008 (13 total years), this will rank all seeds from 1 to 52 (4 seeds per year x 13 total years). Thus, if a seed this year is ranked #1, they are the strongest seed ever among the thirteen years, and if they are ranked #52, I'd pencil them to be upset if you get my drift. (NOTE: 11-14 seeds have different totals because they are/have been play-in game seeds, so the total number of seeds is listed "out of ##" to denote this distinction.)

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.

Late Night Thoughts

There's a lot going through my mind with the Selection Show less than 15 hours away. I've spent most of this week thinking about the operation of the tournament and how the operation of the tourney can impact a team's performance in the tourney. Here's what I've ran through my mind on numerous occasions.

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?