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.

The "Experieneced Talent" Model (Post-Season Update)

What a week of college basketball (and I could make the same exasperated comment for the officiating of that week too, but that's another story for another time). In this past week alone:

• #1 NOVA, #2 UVA, #8 AUB, #13 ARI, #19 WVU, and #23 NEV lose "home games" to unranked teams
• #10 KU, #15 TENN, #20 MICH, and #25 MIA lose road games to unranked teams
• #3 PUR, #17 OKLA, and #24 UK lose both games played for the week (and to PUR's credit, both of their losses came against Top-14 teams).
• #5 XAV gets two road wins, the first to some of the best officiating I've seen in quite some time and the second to the absolute worst late-game officiating I've seen in quite some time.
• Most importantly, the Selection Committee released their annual in-season mock tournament field with the Top 16 teams and their seedings on Feb 11, exactly four weeks before the real deal on Selection Sunday Mar 11. While I will refrain from issuing my full thoughts on the contents of this release, it should suffice enough to say: "What a way to end this week!"

If you are not convinced by last week alone that the 2018 tournament is going to be a wild one, then reading this article will be a waste of your time. As I have teased on the blog for over two months, this article will detail a quirky model that I conceived to measure and/or predict the stability or instability of the tournament.

Welcome to the 2017-18 College Basketball Season

Just as the title says, Welcome to a new season of college basketball. I'm excited to bring back Project: Perfect Bracket for a third season, and as always, the goal is to do what no one has done before: Pick a "PERFECT" 63-game NCAA tournament bracket.

Before I grade myself on last season's performace, I want to give you a pretty firm outlook of what to expect on PPB for the whole season. In the first year (2015-16), I was pretty disorganized and pretty erratic in scheduling that I literally over-worked myself to the point of not producing a bracket (other than my gut picks as soon as the bracket is revealed). I definitely didn't make this mistake in the 2016-17 season, but I did make new ones (all the more from which to learn). I doubt I have it completely figured out for the 2017-18 either, but if I don't make the same planning/preparation mistakes of the last two years, then it should have my best year yet. So, what can you expect this season?

Return and Improve Model

Too Long/Didn't Read: The introduction is a very long story on how I came up with the idea of Returning Players and Improving Tournament Performance. It may waste your time! This will also be a rather quick article because I'm going to do significant back-testing to this model (and many others) over the summer and present the content in the 2017-18 season.

2017 Quality Curve Analyis - Final Edition

The 2017 NCAA Tournament bracket has been revealed and we know the 68 teams who will be playing. The best part of all: The narrowing of the basketball landscape to these 68 teams means we can finally take a look at the elusive Seed Curve (SC). This article is going to be pretty straight-forward, so here is the run-down if you are interested:

1. Catching up to speed from the March Edition to the Final Edition
2. Comparison of the 2017 QC to the 2017 SC
3. Breakdown of the 2017 SC and comparisons to previous years

2017 Quality Curve - March Edition

How's everyone doing? It's the 1st day of March, which means a lot of things: the weather gets crazy (lion vs lamb), bracket scientists begin working a little harder and sleeping a little less, and most of all, it is the day that we set aside for Quality Curve Analysis for all games concluded up to February. This is definitely going to be an interesting one, so without further ado, let's jump right into it.

The Evolution of the 2017 Quality Curve

The first place that we must begin is an analysis of how the QC has progressed over the three time points of analysis, from the January analysis to the February analysis to the current one. I say a picture is worth a thousand words, so let's see what we have.

Trends: The Least-Quantified Metric

Since I left you the entire season in suspense over the "most talked about....least quantified" metric, I thought I would end the suspense right away by putting it in the title. This way, we can focus entirely on the idea instead of the mystery. If you have ever watched a basketball (or any sporting) game, or even the unveiling of the brackets, I guarantee that you have heard a specific team's current trend "qualified" in no uncertain terms: "This team is on a 6-game winning streak," This team seems to be in shooting slump," or "This team's average points per game is X, but over their last 4 games, it is higher." Although these statements contain numerical evidence suggestive of a trend in place for a specific team, I would say these qualitative statements suggest nothing more than plain factual evidence. Yes, that team has won 6 straight games. Yes, that team is missing a lot of open shots. Yes, that team's points per game is higher now than it was 4 games ago. To see why these statements are deceptive when it comes to the concept of trends, let's jump right in.

The New Standard - Changes to the Selection Process

It's good to be back here on another Wednesday, writing another article and preparing to pick a perfect bracket this year. If you have followed recent announcements in the college basketball landscape, you will know that the NCAA scheduled a meeting on Jan 20 concerning the use of advanced metrics in the Selection Process. Though the NCAA specifically stated that this meeting was exploratory and such changes wouldn't be implemented for the 2017 tournament, this change is definitely worth exploring. For this article, I will record the known details of the meeting, examine the old rating system (the RPI rankings), and investigate the new meta of bracket prediction (when the selection committee knows/values what we have known/valued for the last decade).

"Mr. Secretary, I propose an official reading of the Minutes of the last meeting"

If you've ever been to a business meeting, my advice is to not attend one sleepy. In my opinion, the reading of the minutes of the previous session is the absolute worst part. You are essentially going over the same stuff you went into full-detail in the last meeting, but this time, its purpose is purely for record-keeping. Here is the minutes of the meeting, but I'll try to do it in a way that won't put my readers to sleep.

Who attended the meeting?

• Dan Gavitt - NCAA Senior Vice President for Basketball: He ran the meeting
• David Worlock - NCAA Director of Media Coordination and Statistics: He was the statistics expert from the NCAA's side of the table.
• Jim Schaus - Ohio University Athletic Director: Member of the 2016-17 NCAA Selection Committee - He represented the Selection Committee.
• Ken Pomeroy - Advanced Metrics Statistician for College Basketball (Link): KenPom Ratings uses a predictive approach.
• Jeff Sagarin - Advanced Metrics Statistician for College Basketball (Link): Sagarin Ratings uses a predictive approach.
• Ben Alamar - Advanced Metrics Statistician for College Basketball (Link): BPI Ratings uses a predictive approach.
• Kevin Pauga - Advanced Metrics Statistician for College Basketball (Link): KPI Ratings uses a results-based approach.
• Others attended, but I believe these are the most relevant.

2017 Quality Curve - February Edition

It has been exactly one month since the last Quality Curve (QC) analysis, and that can only mean one thing: It's time to do another one. With half of the conference schedule completed and more than 97% of the non-conference schedule also completed, we are somewhere between the 2/3- and 3/4-mark of the 2016-2017 pre-tournament season. Most of all, our data is better now than it was in January, and it is a little bit closer to its eventual mark (which occurs on Selection Sunday). Without further ado, let's get started with some preliminary factors before we hit the main event.

Preliminary Factors

You are about to enter the February Edition of the Quality Curve Analysis. Please follow these simple instructions before proceeding.

1. If you haven't read the January Edition, follow the link and read the section on "Reviewing The Changes." The efficiency data typically used for this analysis changed formats between this season and the previous season. Understanding these changes is important to understanding the data.
2. In typical QC analyses, the current data is compared to pre-tourney data from previous years in order to maximize predictive value. Since the method of calculation changed (see Step 1), I do not have the pre-tourney data for any of the previous years. Instead, I am approximating those years using their post-tourney data, which happens to be readily available. However, I have noticed sizable movements from pre-tourney to post-tourney data, so if anyone has the post-tourney data using the old methodology (Pythag), I possibly could make a workable substitute.
3. The QC uses the efficiency ratings for the Top 50 teams, which is a close approximation for the 1-12 seeds in the tournament. More often than not, teams in the Top 50 efficiency ratings do not make the tournament and teams not in the Top 50 do make the tournament. The QC gives us a picture of the college basketball landscape, whereas the seed curve (not produced until the Bracket is revealed) gives us a picture of the NCAA tournament quality.

2017 Quality Curve - January Edition

Custom has it that you always wish your readers a Happy New Year in your first article of a new year. I'm not going to do that since our New Year begins in November. Instead, I am going to dive into what could be a PPB tradition for the beginning of January: the first Quality Curve analysis of the current season. It seems fitting that we have enough information by this point in the season to examine its current state and what it may hold in store for March.

However, the 2016-17 season presents a brand new caveat: the methodology of the data set (KenPom Ratings) used to make the Quality Curve changed. Instead of blindly pasting the current curve to a historical curve and inferring tournament results from the year-of-best-fit, we first need to determine if the old way of doing things will work with the new data set.

Warming Up the Crystal Ball

Bracket scientists are all too familiar with the phrase "Bracket Crunch Time." It describes the 88 hours following the full reveal of the tournament bracket until the 11am submission deadline on opening Thursday that bracket pickers across the world have to make their 63 selections. It implies another meaning: a ton of thought, information, analysis, and effort are 'crunched' into a short amount of 'time.' They are exhausting, nerve-racking, secretive, overwhelming, and yes, most of all, exciting. Everyone -- bracket scientists, sports media, average Joes, and even first-time bracket pickers -- does it. Yet, bracket scientists shouldn't be doing what the non-scientists are doing. We should be doing much of our thought, information, analysis and effort during Brunch Time, not Crunch Time. When everyone is having their cup of tea, doing their run-of-the-mill activities, or otherwise being complacent until lunch time or even closing time arrives, this Brunch Time should be our Crunch Time when we get ahead of the curve and do the things that the non-scientists do during their Crunch Time. So that's what we're going to do right now. We are going to warm up the crystal ball in December's Brunch Time (not in March) in order to be better prepared for the real Crunch Time.

Welcome to the 2016-17 College Basketball Season

EDITOR'S NOTE: I don't what happened with Google and this article. I set this to auto-publish on 11/23 at the stroke of mid-night, and I even received the confirmation email at that time saying that it had been published, but for whatever reason, I showed up on 12/6 to type-out the 12/7's article, and I find that it has not been published. Thanks Google!!! I guess I am going to have to keep an eye out for this problem all throughout the season.

Yep, the title pretty much says it all. A new season means a new chance to accomplish our goal: Picking the perfect bracket. To do this, we must do a few things different than last year: Learn from our mistakes, expand our horizons, and prepare....prepare.....prepare.