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.

2021 Quality Curve Analysis - February Edition

I'm back with the next installment of the monthly QC Analysis. Let's jump right into it.

2021 Quality Curve Analysis - January Edition

I haven't written my inaugural article this late in the season since the very first season I started writing this blog in the 2015-2016 season. There's a lot I want to go over, so I'm going to keep the fluff to a minimum. I'll start by giving some of my insights on the 2020-21 season, then dive into the January QC, and conclude with my insights on prediction.

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.

New Metric: Free Throw Advantage

Over the summer, I spent a lot of time reflecting over the Four Factors model of college basketball. If you have ever read the book Basketball on Paper by Dean Oliver, then you know what I'm talking about and it is probably a good explanation as to why you are reading this blog. One of the big concerns I have always had with the Four Factors model is the Free Throw Rate (FTR). In fact, most advanced metrics analysts, including Dean Oliver, have conceded that FTR is the least significant of the Four Factors. Since I always want my understanding of the game and the numbers to be at the highest level, I sought a different solution to the Free Throw Rate component, which brings us to this article. I'll start with a crash-course on advanced metrics, then elaborate on the details of the FTR, then introduce my new concept of Free Throw Advantage.