First, let me present the numbers:

Date Opponent-Location (from the opponent’s perspective) Bubble EW% WAB Result
11/09/18 UT Rio Grande Valley-Home 0.8114 0.1886
11/12/18 UTSA-Home 0.7374 0.2626
11/18/18 Wofford-Away 0.5298 0.4702
11/21/18 Florida-Neutral 0.4072 0.5928
11/22/18 Wisconsin-Neutral 0.2465 -0.2465
11/23/18 Dayton-Neutral 0.6172 0.3828
11/27/18 North Texas-Away 0.9136 0.0864
12/04/18 Notre Dame-Neutral 0.6734 0.3266
12/08/18 Wichita St.-Semi-Away 0.7885 0.2115
12/15/18 USC-Away 0.8146 0.1854
12/18/18 Creighton-Away 0.6753 0.3247
12/21/18 Northwestern-Home 0.5020 0.4980
01/02/19 Kansas-Home 0.1805 -0.1805
01/05/19 Oklahoma St.-Away 0.7919 0.2081
01/08/19 Texas Tech-Home 0.1039 -0.1039
01/12/19 TCU-Away 0.6461 0.3539
01/16/19 Kansas St.-Away 0.4673 -0.4673
01/19/19 Texas-Home 0.2625 -0.2625
01/23/19 Oklahoma St.-Home 0.5121 0.4879
01/26/19 Vanderbilt-Away 0.8944 0.1056
01/28/19 Baylor-Away 0.6289 -0.6289
02/02/19 West Virginia-Home 0.5462 -0.5462
02/04/19 Iowa St.-Away 0.4995 -0.4995
02/09/19 Texas Tech-Away 0.2959 -0.2959
02/11/19 Baylor-Home 0.3185 -0.3185
02/16/19 TCU-Home 0.3349 0.6651
02/23/19 Texas-Away 0.5635 0.4365
02/25/19 Iowa St.-Home 0.2159 -0.2159
03/02/19 West Virginia-Away 0.8136 0.1864
03/05/19 Kansas-Away 0.4440 0.5560
03/09/19 Kansas St.-Home 0.1948 -0.1948
03/13/19 West Virginia-Neutral 0.6962 -0.6962
Net WAB 1.8725

I’m not sure why they opened up their season with a couple road games against UTRGV and UTSA. Oklahoma ended up going 11-1 in their non-conference slate. That’s a solid result, but their weren’t any huge scalps. Florida on a neutral court was their best win. They followed up by going 7-11 in the Big 12, before losing to West Virginia in the first round of the conference tournament. How does WAB view this?

Out of Conference 3.28
Vs. the Big 12 -1.41

We’d expect an average bubble team to go roughly 8-4 vs. Oklahoma’s non-conference schedule. Going 11-1 there was an exceptional result. Finishing 7-12 vs. the Big 12 wasn’t great. The expected result was roughly 8.6-10.4, which is to say we’d expect an average bubble team to have a losing record against the teams Oklahoma faced. Their schedule was brutal.

Overall Oklahoma is roughly two wins ahead of what we’d expect from a bubble team facing their schedule. 19-13 might not be impressive on its own, but when 17.1-14.9 is the baseline, 19-13 starts looking fantastic. Oklahoma deserves an at-large bid to the dance.

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Now that UNC Greensboro have finished their regular season and their conference tournament, their NCAA Tournament resume is complete. I am going to discuss them through the lens of Parcells, where they have the 32nd best overall resume. However, before I get to that, I’d like to cover a pair of other resume rankings that are publicly available.

ESPN’s BPI SOR is a WAB-style measure, but with a baseline of a top-25 team. It has UNC Greensboro with the 32nd overall best resume:

http://www.espn.com/mens-college-basketball/bpi/_/view/resume

ESPN’s BPI SOR is on the team sheets available to the committee. It is both the best, and the only, resume ranking tool on the team sheets.

Bart Torvik’s T-Rank has them with the 33rd best WAB:

http://www.barttorvik.com/?sort=34&begin=20181101&end=20190501&conlimit=All&top=0&hteam=&quad=5&rpi=#

Bart’s site has a staggering amount of information and it’s worth spending a few minutes clicking around and checking it out.

It seems like there is reasonable agreement as to whether UNC Greensboro’s resume is worthy of inclusion to the dance, but not so fast. Let’s look at some of the information that will be on their team sheet:

UNCG

That is what the committee will be looking at in the meeting room. Let’s start at the top:

28-6. Solid.

Net: 58. Not great. The NET is a hybrid measure that is both predictive and descriptive. In short, it is partially predictive, so it measures how good you are in addition to how strong your resume is.

KPI: 58. KPI is an algorithm designed to mimic and predict the committee. The switch from RPI to NET might render it a bit anachronistic.

SOR: 32: Generated from ESPN’s BPI. As I noted above, it is the only resume rating on this sheet.

BPI: 77. ESPN’s ranking for UNC Greensboro. This is a predictive measure that judges how good they are, and is not a measure for how strong their resume is.

Pom: 81. The KenPom ranking for UNC Greensboro. This is a predictive measure that judges how good they are, and is not a measure for how strong their resume is.

Sag: 87. The Sagarin ranking for UNC Greensboro. This is a predictive measure that judges how good they are, and is not a measure for how strong their resume is.

The average NET win and average NET loss are two of the silliest statistics I’ve ever seen. Moving right along, let’s skip to the meat of the sheet: the four quadrants:

The first quadrant is broken down into two parts. UNC Greensboro went 0-5 against the elite quadrant one opponents, and 2-1 vs. the lower half of quadrant one. UNC Greensboro went 2-0 vs. quadrant two opponents, 6-0 vs. quadrant three opponents, and 16-0 vs. quadrant four opponents. How does this look from a WAB perspective?

Opponent-Location (from the opponent’s perspective) bubb win% WAB Quad
Kentucky-Home 0.1011 -0.1011 1
LSU-Home 0.1913 -0.1913 1
Wofford-Home 0.2471 -0.2471 1
Wofford-Neutral 0.3846 -0.3846 1
Wofford-Away 0.5434 -0.5434 1
Furman-Home 0.4542 -0.4542 1
East Tennessee St.-Home 0.5274 0.4726 1
Furman-Neutral 0.6131 0.3869 1
Furman-Away 0.7511 0.2489 2
East Tennessee St.-Away 0.8018 0.1982 2
Samford-Home 0.7491 0.2509 3
Louisiana Tech-Neutral 0.8265 0.1735 3
Samford-Neutral 0.8504 0.1496 3
Radford-Away 0.8884 0.1116 3
Mercer-Home 0.8286 0.1714 3
Samford-Away 0.8907 0.1093 3
Delaware-Home 0.9154 0.0846 4
Chattanooga-Home 0.8973 0.1027 4
Mercer-Away 0.946 0.054 4
The Citadel-Home 0.902 0.098 4
UNC Wilmington-Home 0.9039 0.0961 4
Western Carolina-Home 0.9211 0.0789 4
VMI-Home 0.9262 0.0738 4
Prairie View A&M-Away 0.9634 0.0366 4
Elon-Home 0.9335 0.0665 4
North Carolina A&T-Home 0.9409 0.0591 4
Chattanooga-Away 0.9694 0.0306 4
The Citadel-Away 0.9709 0.0291 4
Western Carolina-Away 0.9769 0.0231 4
VMI-Away 0.9785 0.0215 4
North Alabama-Away 0.9834 0.0166 4
Coppin St.-Away 0.9949 0.0051 4
Johnson & Wales NC-Away 1 0 x
Greensboro-Away 1 0 x
Net WAB: 1.2275

Please note, the locations listed above are where the opponent was playing. Kentucky and LSU hosted UNC Greensboro.

UNC Greensboro struggled against their elite competition, but beat everyone else. Let’s look at their WAB by quad:

Quad: WAB
1 -1.0622
2 0.4471
3 0.9663
4 0.8763
Net WAB: 1.2275

Should a bubble team have put up a better showing against quad one? Yes. An average bubble team would’ve expected to be 3-5. UNC Greensboro only won twice. However, an average bubble team would have expected to pick up 2-3 losses over the rest of the schedule. Our prospective bubble squad would expect to go undefeated in those 24 games only 8.37% of the time. Going 24-0 here is an impressive accomplishment.

Let’s compare them to a team listed among Eamonn Brennan’s locks: Baylor.

Baylor

Baylor is on all 138 of the http://www.bracketmatrix.com brackets, so it’s reasonable to consider them a lock.

Wins over Texas Tech, Iowa St., Texas, Oklahoma, TCU, Alabama, Oregon. Why are we still talking about them? Well, it’s because their resume isn’t actually better than UNC Greensboro’s. Really:

Opponent-Location (from the opponent’s perspective) bubb win% WAB Quad
Mississippi-Neutral 0.4773 -0.4773 1
Kansas St.-Away 0.4738 -0.4738 1
Kansas-Away 0.4489 -0.4489 1
TCU-Home 0.3393 -0.3393 1
Texas-Home 0.2666 -0.2666 1
Kansas St.-Home 0.1989 -0.1989 1
Kansas-Home 0.1834 -0.1834 1
Texas Tech-Home 0.1059 -0.1059 1
Iowa St.-Home 0.2196 0.7804 1
Oklahoma-Home 0.2864 0.7136 1
Texas Tech-Away 0.3005 0.6995 1
Iowa St.-Away 0.505 0.495 1
Wichita St.-Home 0.5911 -0.5911 2
Oklahoma St.-Home 0.519 0.481 2
Arizona-Home 0.532 0.468 2
Texas-Away 0.5686 0.4314 2
West Virginia-Home 0.5701 0.4299 2
Oklahoma-Away 0.5927 0.4073 2
TCU-Away 0.6506 0.3494 2
Oregon-Away 0.6992 0.3008 2
Alabama-Away 0.7219 0.2781 2
Oklahoma St.-Away 0.7964 -0.7964 3
West Virginia-Away 0.8279 0.1721 3
George Mason-Neutral 0.8396 0.1604 3
Stephen F. Austin-Away 0.9826 -0.9826 4
Texas Southern-Away 0.9591 -0.9591 4
South Dakota-Away 0.96 0.04 4
Prairie View A&M-Away 0.9634 0.0366 4
New Orleans-Away 0.9709 0.0291 4
Nicholls St.-Away 0.9853 0.0147 4
Southern-Away 0.9926 0.0074 4
Net WAB: 0.4714

Again, let’s break it down by quadrant:

Quad: WAB
1 0.1944
2 2.5548
3 -0.4639
4 -1.8139
Net WAB: 0.4714

Baylor paid the bills by going 8-1 against quadrant two. However, they also pulled in three losses vs. quadrants three and four. Just looking at quadrant four, a bubble team would expect to go undefeated 82.76% of the time. Baylor lost twice. Those games count too. Yes, Baylor’s resume is probably worthy of inclusion to the dance, but overall it isn’t better than UNC Greensboro’s.

Mind you, Baylor is better than UNC Greensboro:

Team Pythag
UNC Greensboro 74.59%
Baylor 87.73%

If they were to play on a neutral court, I’d expect Baylor to win 71% of the time. But, their resumes?

Team Parcells
UNC Greensboro 90.19%
Baylor 87.70%

Baylor’s record is exactly what we would expect from them. UNC Greensboro’s record is far beyond what we would expect from them. The thing is, they won the games. If our WAB baseline was .9019, Baylor would have a WAB of -1.28.

If we care about which teams have the best bodies of work, UNC Greensboro deserves an at-large bid to the NCAA Tournament.

 

 

“You are what your record says you are.” -Bill Parcells

In the world of college basketball, it’s a bit more complicated. Teams play wildly disparate schedules. Abilene Christian and Michigan State are both 23-6. We know that Michigan State has played a tougher schedule than Abilene Christian, but can we quantify that difference? Yes, but let me first define a few terms:

Pythag: Pythag (short for Pythagorean Win Expectation) is your expected win percentage against an average D1 team on a neutral court.

Parcells: The Pythag required for your record to be the median expectation. It’s quite literally what your record says you are.

WAB: Wins-Above-Bubble. The amount of wins you have minus the amount of wins an average bubble team would expect vs. your schedule.

You can find all the data available here:

https://docs.google.com/spreadsheets/d/1qmdUR_iMWIUzMBQvMwGBP-EV_64f59kaGUOVUAiKO60/edit?usp=sharing

Abilene Christian went 0-1 in quadrant one games, 0-0 in quadrant two games, 2-2 in quadrant three games, and 17-3 in quadrant four games. They also went 4-0 in games not rated by the NCAA.

Michigan State went 10-4 in quadrant one games, 4-2 in quadrant two games, 5-0 in quadrant three games, and 4-0 in quadrant four games.

Abilene Christian played one very tough game this season, at Texas Tech. They lost by 34. The question remains: can we quantify the difference between the two resumes? The answer is yes.

We have a few different tools at our disposal. One option is to put hypothetical team through both schedules and see how many games they would expect to win. So, just how hard has Michigan State’s schedule been? If you put roughly the 50th best team in the country against their schedule, they’d be expected to win about 15.12 games. That’s an expected record of 15.12 wins and 13.88 losses. It really is an absurdly tough schedule.

How about we put the 50th best team in the country against Abilene Christian’s schedule? We’d expect them to produce a record of 26.50 wins and 2.50 losses. In short, Abilene Christian would need to be 28-1 to be in the discussion for an at-large bid, presuming they don’t win their conference tournament.

The methodology I outlined about is Wins-Above-Bubble, or WAB. WAB can be calculated with any set of ratings. I run WAB for the KenPom’s, Sagarin Predictor, and Massey Power daily. It’s fairly straightforward: how many wins do you have? How many wins would a bubble team average against your schedule? The difference is your WAB. Michigan State and Abilene Christian are both 23-6, but Michigan State’s WAB is 11.38 wins clear of Abilene Christian. However, you’ll note Abilene Christian only has six losses. Even had they ran the table, they’d only generate 2.5 Wins-Above-Bubble.

Gonzaga trails Michigan State in the WAB standings by 1.1 wins. Despite that, they are in line for the #1 seed out West. There are a few reasons for this. First, they have a neutral court win over Duke. Second, on a possession-by-possession basis they have been one of the best teams in the country. Abilene Christian has not been one of the best teams in the country.

The NCAA Tournament Selection Committee has something of a dual mandate. They are tasked with selecting the best 36 at-large teams in the country, and seeding the 68 qualifiers. They are also asked to select basis on the best resumes, or “bodies of work.” If we are going by wins and losses, these are two separate things.

“If you rank teams by how good they are then it doesn’t matter who wins games. A buzzer beater in a conference tournament will be effectively irrelevant. A sport needs its wins and losses to matter.” -Jeff (@Bpredict on Twitter)

If we are to select and seed teams, by their resumes, we need to be able to accurately judge those resumes. To be able to do that, we’d need a way of solving for how impressive a record is. WAB isn’t bad. It gives us a quick and simple way of looking at how many wins you have relative to what should be expected of you. It isn’t perfect though. It doesn’t translate into Pythag.

Pythag (short for Pythagorean Win Expectation) is your expected win percentage against an average D1 team on a neutral court. This year, we have three historical monsters as far as Pythag is concerned: Duke, Gonzaga, and Virginia. Pythag is a predictive rating, formed by your level of dominance on a possession-by-possession basis. As far as Pythag is concerned, whether a last second shot to win the game goes in or not is a tiny blip on your rating. It’s merely one possession out of a couple thousand over the course of a season. Of course, that possession can have a massive effect on your resume.

Last year I took a stab at translating a resume into Pythag. That’s a tricky thing. To solve for that, you need to figure out how good a team would have to be to expect to generate their record. Adam (@cajuncooks on Twitter) was able to program a solution to this problem. Let’s look back at Michigan State.

A team with a Pythag of .9719 projects to have five or fewer losses vs. that schedule 39.95% of the time. They’d have six losses 20.09% of the time. They have seven or more losses 39.96% of the time. Yes, there is some rounding going on here, so please trust me when I say that if we had more significant digits the two batches of results would be equally likely.

I know that got a little complicated, so let me simplify: .9719 is what Michigan State’s record says they are. .9719 is what their body of work implies about them. I originally named this metric Implied Pythag, but I have since renamed it Parcells since this is what their record says they are. Interestingly, they’ve been better that on a possession-by-possession basis, producing a Pythag of .9786.

Thanks to Adam’s expertise, I can produce the Parcells rating for every team playing D1 basketball. Which brings me to UNC Greensboro. They are not one of the 36 best at-large teams in the country. They are 87th in KenPom, 103rd in Sagarin Predictor, and 74th in Massey’s power. Blending the three together, I have them as the 89th best team in the country. Yet, they’ve produced an average WAB of 1.04. Penn State is in the KenPom top 50. Saint Mary’s is in the KenPom top 40. I have them with -2.80, and -2.57 WAB respectively.

Penn State and St. Mary’s are better than UNC Greensboro. Their resumes are quite a bit worse though. Penn State has produced a Parcells of .7911. St. Mary’s has a Parcells of .7827. UNC Greensboro has produced a Parcells of .9022. Yes, you really would have to be that good to expect to go 26-5 against that schedule.

As per the KenPom’s the Southern Conference is roughly equal to the Atlantic 10. This year, the Southern merits multiple bids, regardless of who wins their conference tournament. However, if Wofford wins, we might not see UNC Greensboro rewarded for their sterling resume. UNC Greensboro is 58th in the NET rankings. St. Mary’s is 39th. As I noted before, St. Mary’s is better than UNC Greensboro. UNC Greensboro has a better body of work. It’s up to the committee to decide what they want to reward.

I’ve touched briefly on the NET Rankings. NET is quite different from RPI. RPI was 75% a measure of strength of schedule. NET is clearly designed to be predictive, but with descriptive elements and inputs. In short, it is a hybrid rating. Of course, the committee may use NET purely as a sorting tool. I rarely pay attention to the NET rankings because they aren’t relevant to the questions I am trying to answer. What I want to know:

How good you are? That’s your Pythag rating.

How good is your resume? That’s Parcells.

How many wins do you have relative to an average bubble team? That’s your WAB.

I don’t need to squint at two wildly different team sheets and try to parse which is better. I want to solve for that. Some teams get multiple quadrant one opportunities at home. Others get very few. If quadrant one and two wins are the currency that merits inclusion to the dance, power conferences will be incentivized to hoard them and play as many conference games as possible. Interestingly, the NET doesn’t punish a soft out-of-conference schedule the way the RPI did.

As for arbitrary cutoffs:

https://twitter.com/JohnGasaway/status/1102228507445415938

The committee is going to do what the committee is going to do. If they want the 36 best at-large teams, the KenPoms and such are a good guide. If they want the 36 best at-large resumes, Parcells answers that question.

I’ve made some slight changes to the bracket I produced this morning. Here is the bracket as I would have it now:

Seed Team Seed Team Seed Team Seed Team
1 Duke 1 Kentucky 1 Gonzaga 1 Virginia
16 Prairie View A&M-Iona 16 Wright St. 16 Sam Houston St. 16 St. Francis PA-Norfolk St.
8 Villanova 8 Wofford 8 North Carolina St. 8 Oklahoma
9 Ohio St. 9 Syracuse 9 Baylor 9 Florida
5 LSU 5 Iowa St. 5 Nevada 5 Maryland
12 Clemson-Texas 12 Seton Hall-Utah St. 12 New Mexico St. 12 Lipscomb
4 Wisconsin 4 Virginia Tech 4 Florida St. 4 Marquette
13 Yale 13 Vermont 13 UC Irvine 13 Murray St.
3 Purdue 3 Kansas 3 Houston 3 Texas Tech
14 Texas St. 14 Old Dominion 14 Hofstra 14 South Dakota St.
6 Louisville 6 Buffalo 6 Kansas St. 6 Iowa
11 Belmont 11 UNC Greensboro 11 Minnesota 11 Temple
7 Washington 7 Cincinnati 7 Auburn 7 Mississippi St.
10 TCU 10 Mississippi 10 VCU 10 St. John’s
2 Tennessee 2 North Carolina 2 Michigan St. 2 Michigan
15 Montana 15 Bucknell 15 Radford 15 Drake

The changes I made were with the “play-in” games. I am inviting the 36 best resumes as judged by Parcells. I am then seeding by a mix of Pythag and Parcells. I decided that the last four invited teams belong in the play-in games, regardless of their Pythags. As such, the bracket has been modified.

Here are the brackets produced by Mast, Dobbertean, Lunardi, and Bottoms. Some things I want to note:

All four included Arizona State as a #11 seed. UCF and Alabama all made all four brackets as well. In the case of Parcells, I have Alabama, UCF, and Arizona State as the first three out, in that order. I’ll add that if Belmont grabs the OVC auto-bid (something most bracketologists are presuming), then Alabama would be my last team in.

Not one of the brackets had UNC Greensboro in. Parcells has them with the 39th best resume:

https://docs.google.com/spreadsheets/d/1qmdUR_iMWIUzMBQvMwGBP-EV_64f59kaGUOVUAiKO60/edit?usp=sharing

They’d be 40th in WAB. Their resume merits inclusion It’s hard to judge teams that play against inferior competition. Perhaps they’ll win their conference tournament and render the question moot. More likely, they’ll be sitting by their TV’s on Selection Sunday praying for inclusion. Barring another regular season loss, I expect their resume to be worthy of an invite. We’ll just have to wait and see.

Houston is another interesting case. Everyone (including me) has them as a #3 seed. The thing is, they have an elite resume, but lack the Pythag to go with it. People understand that Gonzaga is a monster. Houston is not. If they pull off a road win at Cincinnati and win their conference tournament they’ll move up at least one line. Moving up two is going to be tricky.

On Thursday Andy Katz wrote this piece:

https://www.ncaa.com/news/basketball-men/article/2019-02-07/net-rankings-ncaa-tournament-what-they-mean

Since the NCAA has been rather opaque as to what the NET is, I decided to do a deep dive into Andy’s column. From the top:

AK: “The NET will be the most common word heard throughout selection week and on Selection Sunday.”

This might be true, but not necessarily in the way Andy meant it here. We will likely hear a lot about the NET quadrant 1 and quadrant 2 wins.

AK: “The purpose of the NCAA’s Evaluation Tool ranking (i.e. NET) is to sort teams into the four quadrants on the team sheets the men’s basketball selection committee uses for selection and seeding.”

Bingo.

AK: “It is not a deciding factor.”

Of course, but no one has suggested that it would be.

AK: “It is not going to determine if a team is in or out of the bracket.”

If this were true or false we’d have no way of ever finding out.

AK: “It is an organizational piece for the committee.”

OK.

AK: “And with a month left in the regular season this is what you need to know:”

AK: “The most important thing about the NET is that if you beat good teams, don’t lose to bad teams and have quality wins away from home on a road or neutral court, you’re going to have a solid net ranking.”

Is there a system that rewards losing to bad teams, or punishes you for beating good teams?

AK: “The primary component of the NET is the TVI, a results-based factor that considers the strength of the opponent and the location of the game. If you beat a team that you’re expected to beat, then it doesn’t do as much for your ranking. Losing to teams that you were expected to beat will hurt your ranking.”

Beating teams you are supposed to beat should boost your rating, and losing to teams you weren’t expected to beat should still hurt it. The question to ask is “How much” and the NCAA isn’t saying.

AK: “The committee has always, and will always, place emphasis on winning quality games, and the quadrant system introduced last season places greater importance on the location of the games. The formula of the average net efficiency (offensive efficiency minus the defensive efficiency) is a factor in computing the ranking. So, too, is the winning percentage, the adjusted winning percentage (based on where the game is played — home, neutral or road) and the scoring margin with a cap at 10 per game.”

We’ve had the quadrant system before, although switching it from the RPI can only help. For more details as to what Andy is referring to, you can check this out:

More on this graphic in a bit.

AK: “These analytical tools weren’t just created with no intention. The data scientists at Google Cloud looked at several years’ worth of data for late-season games played on neutral courts, including conference and NCAA tournament games. They used those results as test cases to measure the accuracy of the ranking system. The computer program considered many features, and it was determined that the aforementioned data points were the best ones to use for the ranking formula.”

Accuracy? Accuracy of what? Predictive accuracy? Is NET intended to be a predictive metric? Winning percentage and adjusted winning percentage are not predictive inputs. They’re descriptive. The Team Value Index (TVI) is also a descriptive metric. Thus, I ask: How did Google and the NCAA test the accuracy of a descriptive metric?

AK: “The NCAA tournament men’s selection committee and the NABC wanted to get rid of the old RPI. There was talk of at one point averaging a number of the other metrics available to the public. But the issue there would be a lack of control of each formula.”

The NET is an absolute black box as for as the public is concerned. Also, while I understand a desire to have full knowledge of the metrics you are using, averaging predictive and descriptive metrics is a bigger problem, and it’s one we’re still stuck with.

AK: “The NET treats all games the same, whether they are played in November or February. And that’s why teams that may have had strong non-conference schedules can continue to have a strong ranking even during a losing skid in conference.”

Ignoring when the games are played is a good thing.

AK: “The goal of the NET was to produce a true ranking/sorting system for the selection committee for Selection Sunday. And while the NET is being released throughout the regular season, the end result is what it will look like as a final product, not after each game.”

If they wanted us to evaluate it as a final product, they could easily have released the NET results from previous seasons.

AK: “Valuing when a game is played is an individual choice of each member of the selection committee. But where the game was played and who it was against is used in determining the NET ranking, regardless of the time of the season.”

We’ve been over this…

AK: “When the season ends — the hope is it will show — that when you played and beat good teams you got rewarded, especially if the games were away from home.”

Like Winning Points?

http://www.vegaswatch.org/2014/01/winning-points.html

AK: “Not all home games are created equal (see a Duke home win over Virginia versus a win over Boston College) but all road wins aren’t inherently valued more than a home win. Remember, the NET will help sort the teams for the selection committee to decide on selection and seeding. But it won’t be the ultimate factor in either decision.

Let’s break this down. No one was saying all road wins are greater than all home wins. No one was saying the NET was going to be decisive in selection or seeding.

Here are some of our questions about the NET:

  1. What are the various inputs weightings?
  2. How is quality of opponent weighted in the TVI
  3. What are the NET Ratings? (Not the rankings. The underlying ratings.)
  4. What is pushing Rice up into the top 200? No predictive or descriptive metric has them that high, so we cannot find any explanation for their ranking.
  5. What were the pre-tournament NET rankings (and ratings) for previous years. Not releasing them suggests that the knowledge could somehow be harmful to the perception of the NET, which is a bad sign in and of itself.

The RPI is dead. Now we need to learn a lot more about the new monarch.

 

 

 

 

 

 

With one game remaining in the season, let’s look at where PFF stands:

Current Tab -$906,150.18
% of Bankroll -453.08%
Standard Dev 476416
Two-tailed Z -1.90
ROI -10.60%

Brutality across the board. You want to know how you generate such huge losses? Here’s how:

Superb owl sham

Spoiler alert: The Patriots don’t win by exactly one 8.33% of the time. PFF’s internal system has revealed itself to be clownishly awful, and this is one final example.

Team Line Risked To Win Result
Patriots 0 11652 9247 0
Rams 2.5 3077 2930 0
NE-LAR o56 1961 2000 0

 

 

 

First off, let me note that these are the average Pythags from throughout the season. Some thoughts:

  1. That was an awful Jets team that somehow made the playoffs.
  2. The Chargers blew their best shot at a title. Also, a Colts-Chargers AFC Championship game would have felt like a Super Bowl.
  3. The Saints finished the season with a much higher Pythag than they averaged for most of the year. They’d be around .6500, if we were using their final Pythag.
  4. The Cowboys were the best team in the NFC. Whatever happened to Tony Romo?
  5. Get used to every non-Patriots team in the AFC East having a below-average Pythag.

 

San Diego Chargers 0.7093
Indianapolis Colts 0.7086
Dallas Cowboys 0.6759
New England Patriots 0.6647
Chicago Bears 0.656
Denver Broncos 0.6452
Cincinnati Bengals 0.6405
Pittsburgh Steelers 0.6399
Carolina Panthers 0.6166
Baltimore Ravens 0.614
Philadelphia Eagles 0.5951
New York Giants 0.5778
Jacksonville Jaguars 0.5732
Atlanta Falcons 0.5689
Seattle Seahawks 0.5382
Kansas City Chiefs 0.5302
New Orleans Saints 0.5173
Miami Dolphins 0.4856
Washington Redskins 0.4442
Minnesota Vikings 0.435
St. Louis Rams 0.4242
Tampa Bay Buccaneers 0.402
New York Jets 0.3955
Buffalo Bills 0.3866
Cleveland Browns 0.3647
Arizona Cardinals 0.3535
Green Bay Packers 0.3514
Detroit Lions 0.3372
San Francisco 49ers 0.2918
Houston Texans 0.2917
Tennessee Titans 0.2916
Oakland Raiders 0.2853