Seed-Group Loss Table, Part 2

In Part 1 of this article, I explored the Seed-Group Loss Table as a predictive tool. The point of the exercise was to find patterns between seed-group losses and tournament performance. Since I added more information to the article, I would highly recommend reading (or re-reading it if you read it the first time) because I discussed a few more concepts about the data. In this article, we're going to be looking at the same data points, but the focus will be upon methods instead of data. Let's explore!

2019 Quality Curve Analysis - February Edition

Yes, it is the start of a new month, and at PPB, it usually means a new update to the Quality Curve Analysis. If you want to read the January Edition, here is the link. Before I jump into the article, a few quick thoughts are in order. First, the QC made a huge shift, one I didn't see coming and one for which I most likely don't have a full explanation. Second, I said in the previous edition for a high-magnitude shift to occur, shooting would have drastically improve. Well, shooting did slightly improve, but not enough to explain the magnitude in shift, so other unexplained factors exist. Third, the shift did not affect every team across the board, and for those that experienced the shift, it doesn't appear to be at the expense of others in the QC. Fourth and final, I should definitely point out the advanced metrics data being used includes all games played on January 31 and before. With that said, let's dive right into the analysis.

Seed-Group Loss Table, Part 1

If the title of this article seems familiar, then you remember details of my blog way better than I do. I first introduced the idea of a Seed-Group Loss Table (SGLT) in the article on Unorthodox Bracket-Picking Methods. (NOTE: Re-reading that article is not required to understand concepts in this article. However, the instructions on how to construct a SGLT can be found in Step 1 of the Loss-Mapping Technique and examples of what a SGLT looks like can be seen in the bracket images). In the Loss-Mapping Technique, the SGLT did not serve any critical function other than providing a way to double-check my work (making sure the mapping adds up to the correct totals). Also in the LMT, the SGLT was presented in a tournament-dependent construct in which all of the information was relevant to the specific tournament year. In this article, I want to re-arrange the SGLT into a seed-dependent construct in which all of the information is relevant to the seed. The goal in doing this rearrangement is to find seed-specific patterns from losses (quantity of losses, quality of losses, etc.). Thus, this article's sole purpose is to explore the SGLT as a predictive tool. 

2019 Quality Curve Analysis - January Edition

First things first, Project Perfect Bracket would like you to join in celebrating its third birthday. Now that our birthday party is over (if only the articles were as long as the parties), we can move onto real issues: The first look at the 2019 Quality Curve. I warn you before proceeding, it is not pretty!

Return and Improve Model: 2018 Revisited

I was unsure about the topic I wanted to discuss for this last article before the January Edition of the Quality Curve Analysis. Of the four articles I have written for this current season, three of them have focused on the 2018 tournament and the lessons learned from it. Since the January QC Article will pivot our entire attention to the 2019 tournament and 75% of the articles leading up to it have been 2018-centric, I think one more article about 2018's wild ride would be fitting. It's not like it could hurt.

Anyways, I'm going to take a second look at the Return & Improve Model. I first revealed this model for the 2017 tournament (Link to the article if you wanted to refresh your memory). I wanted to do a quick article on it during 2018's Crunch Week, but I.R.L. things popped up on that Wednesday and forced me to put it aside. It may have been for the better since some of the findings in this article could only have been discovered ex post facto. So let's jump right into it.