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.

Investigating the Aggregation Model

You may have seen the term Aggregation Model (AM) used throughout PPB (Examples: Link #1 and Link #2). I was even going to do a full write-up about it in Mar 2016, but the article disappeared from my hard-drive and I had to leave you with only the hard data. I have re-written the whole article, and hopefully I didn't forget anything from the first one. Enjoy!

The Aggregation Model

The AM is a predictive bracket tool that displays the sum of all seeds for each and every round of the NCAA Tournament. Peter Tiernan from Bracket Science used a metric known as the Mad-o-meter® (not sure if it was trademarked, but giving credit to be safe), and it is an aggregation of the tournament as a whole. The AM breaks down the M-o-M aggregation to the round-level (R32, S16, E8, F4, CG, NC), and its predictive ability works best for some rounds more than others. Up to this point, PPB has focused primarily on the AM for the Elite 8 (E8AM) for this reason, but in this article, I will look at it for all rounds with a greater emphasis on the rounds for which the AM works better.

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.

Quality Curve Analysis: Final Edition

It is that time of the year. The brackets have been released, and it's time to see what this year has in store for us. If you are not familiar with Quality Curve Analysis, here are some articles you should read first (Jan Edition, Feb Edition, Mar Edition). Let's start with a consolidation of all four QCs from each analysis this season.

Bracket-Picker Checklist (Updated 3/15 5:00pm EST)

I know what you're thinking.....this wasn't supposed to come out until after the brackets were revealed. I can't get my original plan to work, so it will have to wait until next year. I didn't finish my back-up because I watched college basketball all day yesterday. Lastly, this was the absolute worst day of the year to spring forward on clocks and lose an hour (of precious data bracket analysis or sleep, your choice). Anyways, this is a quick article and probably slightly informative, and I can always come back and add more stuff to it.

Anyways, this article should probably be called a cheat sheet because it's eventually going to look like one, even though the presentation may not appear that way.

Pulse Check: Champ Week

If any of my readers are familiar with Bracket Science, you will recognize this post, but it may not be what you are expecting. During Champ Week, Pete would post an entry called the Pulse Check, which would gauge potential tournament teams based on specific criteria that he found to be useful in identifying Final Four and Champion contenders. I, as usual, am going to take a different approach. Since a lot of the data in the Pulse Check will eventually be compiled into the Spreadsheet, I am going to present below the 2016 data that will not change, and when all games have finished, the rest of the data usually in the Pulse Check will be compiled into the Spreadsheet. This will save myself a lot of repetitive work. Instead, I am going to look at the Pulse Check from years past to see if there are any patterns or tell-tale signs that will aid us in picking the Perfect Bracket. I guess you could say that I am checking the reliability of the Pulse Check.

As I stated above, here is the data that will not change from here until the start of the tournament. If you compile your own Spreadsheet, you are more than welcome to use it. Also, feel free to double check my work. I complete stuff in binges, so all it takes is one mis-click of the mouse and one team has an Elite 8 appearance that belongs to another team. (Please use the comments below to report typos.)

The Time Line, Part 3: A Macro-Analysis of the Tournament

This is the 3rd part in my series chronicling the Modern-Era (1985-present) NCAA Tournament via a macro-perspective of the game, a perspective often ignored -- accidentally and intentionally -- in most tournament analysis. It is my belief that these macro-factors have played a role in the outcomes of modern-era tournaments, especially the surprises. If you haven't read Part 1 of this series, it details many of the macro-factors in a year-by-year style that will be discussed in this article. My goal for this article is to explain the patterns of trend in the NCAA tournament from 1985 to 1992. (NOTE: 1993 and 1994 could also be included in this analysis since they share many similarities with these years. However, they are probably best viewed as transitory years between this era and the Straight-Outta-High-School (SOHS) Era, which is why I grouped them with 1995 and 1996 in Part 2.)

To start things off, let's look at the Elite 8 Aggregation Model for the years in question. As stated in Part 2 of The Time Line series, the 1985-1992 era of the tournament was mostly calm, as most Elite 8s featured an Aggregate Value between 20 and 25. There were two outlier years in 1986 and 1990 with Aggregate Values (AVs) of 37 and 40, respectively.

Quality Curve Analysis (March Edition)

Yep, it's that time again. Time for another edition of Quality Curve Analysis. If you are interested in previous analyses, here are the links to the January and February editions. If you need a reference to quality curves, seed curves, and the like, try this article. Now, let's get to the good stuff.

2016: Then and Now

The jump-off point for this analysis has to be the evolution of the QC over the last two months. If you remember from the January Analysis (or read it just before reading this one), I made a prediction about the transformation of the curve. I predicted that once teams got into conference play and began playing against better quality teams on a night-in, night-out basis, the QC would rotate: teams at the top would rotate downwards and teams as the bottom would rotate upwards. The February Analysis showed that perfectly (but logic would have driven any one to that conclusion). The March Quality Curve has also made a significant move, and one I DID NOT see coming. It was so significant of a move, I almost posted an interim update before doing this one. So, what happened??????

The Time Line, Part 2: A Macro-Analyis of the Tournament

In Part 1 of The Time Line, I detailed changes to college basketball and the NCAA tournament, primarily from the Modern Era (1985-present). I then classified the changes into two categories: structural changes to the tournament and technical changes to the game. One change did not fit into
either category, and in my opinion, it could be in a class on its own.

2007: First tournament featuring the "One-and-Done" rule. Implemented by the NBA, all draft prospects must be 1-year removed from high school (or 19 years old) in order to be draft-eligible. It does not mean that draft prospects have to attend college. As the previous NBA drafts (2003-05) began reaching critical mass with "Straight-Outta-High-School" prospects, the NBA implemented this rule to rein in scouting and recruiting operations that had become over-extended as franchises (especially those with lottery picks) had to scour the entire nation at both the high school and college level in order to get the pick right.

For those of you thinking this is going to be an definitive analysis of the one-and-done (OAD) rule, you will be sorely disappointed! Instead, I intend to address a point I made in Part 1 about the relationship between the OAD rule and tournament results. So, I'll start this examination with a familiar data piece, the E8 Aggregation Model (E8AM). The most unusual stretch of games in the E8AM takes place from 2007-2009, which many regard as the calmest years of the tournament in an otherwise volatile era. Producing both a 13 and a 14 in the AM (and there's only one possible way of doing each of these) during this stretch, I think it is no coincidence that it started at the exact same time in which the OAD rule was implemented. So, what happened?

The Time Line, Part 1: A Macro-Analysis of the Tournament

College basketball and the NCAA tournament has endured more than their due share of changes throughout their existence. However, it is hard to imagine that no other year in the history of the college game has seen more "consequential" changes to the game than the 2015-2016 year. While we can crunch every statistic, simulate every "dribble-drive offense vs pack-line defense" match-up, or back-test any system of analysis, a macro-perspective on the game can tell quite a story. The purpose of this article is to document these macro-changes to the game and to articulate a theory about the impact of these changes on the results of the NCAA tournament.

The Time Line

1975 (S): First tournament featuring at-large berths to the tournament. Previously, the tournament was comprised of only conference tournament winners. In 1974, the greatest atrocity in college basketball occurred following the ACC championship game, which show-cased #1 NC State and #2 Maryland. Arguably the two best teams in the country (save Bill Walton's UCLA team), only the winner of this game would go to the NCAA tournament. Essentially, the National Championship game was played before the NCAA brackets were even paired, leading the NCAA in the very next year to implement at-large berths for qualified teams without winning their conference tournament. Since this episode, this atrocity only happens to teams from smaller conferences.

What is the Quality Curve?

As promised, I will be presenting an in-depth explanation of the quality curve (QC), the seed curve (SC). A forward warning: This article may be more technical than practical, meaning it is intended to explain a tool rather than tell its results. So, if you are looking for insights into the 2016 Tournament, this article may not be what you are expecting. If you want to broaden your analytical approach to the tournament, you are probably going to love this article, so let's dive right in.

The Quality Curve

Just as its name would suggest, the quality curve is a line graph displaying the "quality" of the teams in a given year of the NCAA tournament. The above chart shows the quality curve of the 2016 Field in January (blue line) and the same field again in February (pink line).

Quality Curve Analysis (February Edition)

The Idea Behind Quality Curves

As stated in the January Edition, the theory states that the quality of the teams in the tournament can predict the quality of the tournament results. In layman's terms, higher quality teams in the higher seeds will result in a stable, predictable tournament (much like 2007) and lower quality teams in the higher seeds will result in an insane, unpredictable tournament (much like 2014).

What A Perfect Bracket Looks Like, Part 2

As promised in a previous article, I will try to apply a predictive tool that will better guide us in using the Aggregation Model (AM). If this predictive tool is accurate in forecasting which Aggregate Value (AV) to use, then we should be able to approximate which Elite 8 Seed Pairs (E8SP) to use so that they match the expected Aggregate Value. The predictive tool being used to forecast the AM is the Seed Curve. If you are unfamiliar with the Seed Curve, see this article; and if you are unfamiliar with the AM, AVs or E8SPs, then re-read Part 1 of this article linked in the opening sentence.

First, let's take a collective look at things, and then we'll look at the group perspective. Below is the AM from 2003-2015 displaying each of those year's AVs.

What A Perfect Bracket Looks Like, Part 1

In the 90 hours between the complete reveal of the bracket to the tip-off of the first game, bracket pickers throughout the world use a variety of information, methods, and strategies to make their 63 picks. Whether it be points per game, the W-L record, distance traveled, or even the mascot method, bracket history can be a valuable tool in making your picks. The idea is rather simple: If I want my pre-tournament bracket to look exactly like the post-tournament bracket, then I should examine the post-tournament brackets from previous years and conform this year's pre-tournament bracket to them.

Developing the System

When the 2016 Bracket is unveiled, suppose I look back to the final bracket of 2015 and decide to make my 2016 Bracket resemble the Elite 8 of the 2015. If so, the 2016 Bracket would look like the following: 1v3, 1v2, 4v7, and 1v2. Unfortunately, if this approach was used in 2015 with the 2014 results, I would have some major discrepancies, as 2014 produced these final results: 1vs11, 4v7, 1v2, 8v2. Assuming I picked the right regions, I could match two of the four Elite 8 pairings (both 2014 and 2015 produced a 1v2 and 4v7 in the Elite 8), but I would have missed badly with the other two pairings (the 1v11 in 2014 would miss the 1v3 in 2015 and the 8v2 in 2014 would miss the 1v2 in 2015). To take the analysis deeper, I'm actually picking two games incorrectly. If I picked the 8v2 in 2015 based on the 8v2 in 2014, I first have to pick the 8-seed to beat the 1-seed and then pick the 8-seed to beat the winner of the 4-5-12-13 group winner. Since the 1-seed beat both the 8-seed and the group winner, I've incorrectly picked two games with one bad pick.

The question we have to ask ourselves is how do we make this historical data mean something. One answer that I have stumbled upon is Aggregation. If we take the seeds of all four pairs (8 teams) and aggregate them together, that will give us a value to make year-to-year comparisons.

Quality Curve Analysis (January Edition)

I thought I would start Project: Perfect Bracket with a very familiar analytical tool: the Quality Curve (QC).

The Idea Behind Quality Curves

The theory behind the curves goes as follow: the quality of the teams in the tournament can predict the quality of the tournament results. In layman's terms, higher quality teams in the higher seeds will result in a stable, predictable tournament (much like 2007) and lower quality teams in the higher seeds will result in an insane, unpredictable tournament (much like 2014).