Franketology: First Edition, December 23, 2025

Written By Frank Molfetta

Well, out-of-conference play has mostly wrapped. So now is as good a time as any to kick of Franketology 2025-26. Plus I desperately need a distraction from my incredibly disappointing Johnnies.

However, with such an early bracketology, it’s definitely not easy to determine seeding or even select which teams belong on the right and wrong side of the bubble. The sample sizes are small. There’s wide divergence between predictives and resume metrics in many cases. There are teams with strong metrics that lack Q1 wins, etc. Some of it is a matter of philosophy. Do you apply the principles the committee will apply in March as if the field were being seeded today? If so, then you need to focus on resume metrics, which is what the committee has leaned heavily on the last 2 years. Doing that would yield some very odd results. For instance, that could conceivably lead to a world where Ok. St., Va. Tech, Tulsa and Miami (OH) make the NCAAT and St. John’s, Baylor, Indiana and NC State, do not, if you focus on WAB. Not teams that are overly deserving in any event, but certainly more deserving than Tulsa and Miami (OH), the latter of which doesn’t have a single win in the first 2 quadrants. If you focus on resume metrics averages, you could conceivably end up with Ok. St., Tulsa, Va. Tech, New Mexico and Colorado in the field, at the expense of Kentucky, Bosie St., NC St. and Indiana. Resume Metrics averages also bring St. Louis and Yale into at-large territory.

Therefore, some middle ground is necessary. There needs to be a focus on the resume metrics, no doubt, but this early in the process there needs to be more emphasis on the predictive metrics than will likely be warranted come March. So that’s what I did, attempted to weight the resume metrics against the predictive metrics, taking into account actual quadrant results so a team like Miami (OH) or Tulsa, who have merely stacked low-level wins, are not at-large caliber.

To highlight the difficult, particularly around the bubble, let’s do some blind comparisons for my last 8 in the field, plus the first 8 out of the field, in no particular order, identities below the bracket:

  1. NET 32, WAB 63, 0-2 Q1, 1-1 Q2, 8-0 Q3&4, Predictives Avg. 23.33, Resume Avg. 57.67, Best Win: NET 52, home

  2. NET 49, WAB 58, 0-1 Q1, 2-1 Q2, 6-0 Q3&4, Predictives Avg. 36.00, Resume Avg. 54.67, Best Win: NET 77, home

  3. NET 60, WAB 54, 0-1 Q1, 1-1 Q2, 9-0 Q3&4, Predictives Avg. 80.33, Resume Avg. 45.33, Best Win: NET 36, home

  4. NET 46, WAB 38, 1-2 Q1, 1-1 Q2, 7-0 Q3&4, Predictives Avg. 51.00, Resume Avg. 41.67, Best Win: NET 31, neutral

  5. NET 44, WAB 30, 1-1 Q1, 0-0 Q2, 10-0 Q3&4, Predictives Avg. 63.67, Resume Avg. 34.67, Best Win: NET 40, neutral/semi-home

  6. NET 29, WAB 36, 0-2 Q1, 1-0 Q2, 8-0 Q3&4, Predictives Avg. 33.33, Resume Avg. 37.33, Best Win: NET 75, neutral/semi-away

  7. NET 74, WAB 32, 0-0 Q1, 4-1 Q2, 7-0 Q3&4, Predictives Avg. 65.67, Resume Avg. 27.67, Best Win: NET 64, home

  8. NET 61, WAB 49, 1-2 Q1, 1-1 Q2, 6-0 Q3&4, Predictives Avg. 51.67, Resume Avg. 53.67, Best Win: NET 68, away

  9. NET 59, WAB 43, 0-1 Q1,4-1 Q2, 7-0 Q3&4, Predictives Avg. 73.67, Resume Avg. 36.00, Best Win: NET 99, neutral

  10. NET 37, WAB 24, 0-2 Q1, 4-0 Q2, 6-0 Q3&4, Predictives Avg. 46.33, Resume Avg. 21.67, Best Win: NET 46, home

  11. NET 64, WAB 84, 0-2 Q1, 1-1 Q2, 8-0 Q3&4, Predictives Avg. 41.00, Resume Avg. 85.00, Best Win: NET 119, away

  12. NET 42, WAB 50, 0-1 Q1, 3-1 Q2, 6-0 Q3&4, Predictives Avg. 32.67, Resume Avg. 47.67, Best Win: NET 56, home

  13. NET 35, WAB 42, 0-2 Q1, 1-0 Q2, 10-0 Q3&4, Predictives Avg. 37.00, Resume Avg. 46.67, Best Win: NET 98, away

  14. NET 53, WAB 60, 0-3 Q1, 2-1 Q2, 6-0 Q3&4, Predictives Avg. 46.33, Resume Avg. 68.00, Best Win: NET 59, neutral

  15. NET 48, WAB 33, 2-2 Q1, 1-1 Q2, 4-0 Q3&4, Predictives Avg. 58.00, Resume Avg. 56.00, Best Win: NET 24, neutral

  16. NET 52, WAB 53, 1-2 Q1, 2-1 Q2, 5-1 Q3&4, Predictives Avg. 56.67, Resume Avg. 52.00, Best Win: NET 41, home

Even as I was typing this out, I made an adjustment to my order. But what you see is you have it all here. Strong predictives and mediocre resume average? Number 1. 2 Quad 1 wins, but middling metrics? Nos. 14 & 16. Elite resume metrics, but middling predictives? Nos. 7 and 10. No Quad 1 wins, but strong overall metrics? Nos. 6 and 10.

So there’s something for everyone. A veritable feast of teams with varied resumes. Here’s how I sorted it all out:

So for those that have made it this far, here’s the answer key for the blind resumes above, with the true seed and seed line I gave them:

  1. Indiana - 41, 11-seed, Play-in

  2. Ohio St. - 70, First Four Out

  3. Butler - 39, 10-seed

  4. Colorado - 76, Next Four Out

  5. Cal - 37, 10-seed

  6. Villanova - 43, 11-seed, Play-in

  7. Okla. St. - 69, First Four Out (this is the team I removed from the field while typing out the blind resume exercise)

  8. Oklahoma - 74, Next Four Out

  9. Va. Tech - 72, First Four Out

  10. SMU - 42, 11-Seed, Play-in

  11. Texas A&M - 71, First Four Out

  12. Baylor - 38, 10-seed

  13. Miami - 40, 10-seed

  14. VCU - 73, Next Four Out

  15. Boise St. - 44, 11-seed, Play-in (this is the team I added while typing out the blind resume exercise)

  16. Kansas St. - 75, Next Four Out

I’m sure many will have the same visceral reaction to including Boise State that I had initially. However, it’s not right to deny a team with 2 Quad 1 wins among this group. Had to get over the visceral reaction to knowing they lost to a D2 school, because they’ve done enough since then, at least at this moment in time. Indeed, the next lowest team with 2 Quad 1 wins is Seton Hall, true seed rank 27, a 7-seed. Sending Boise St. to Dayton if the tournament started today feels like sufficient punishment for losing at home to a middling D2 squad.

On the point of Q1 wins, there’s several teams that got in without a Q1 win, and several left out without a Q1 win. A lot of that is due to having “good enough” resume metrics—resume metrics having proven key to inclusion the last 2 years—and having high predictive metrics, indicating Q1 wins are likely coming. Those teams without a Q1 W, but included are:

  • Iowa - NET 11, WAB 35, Predictives Avg. 23.33, Resume Avg. 33.33

  • Utah St. - NET 17, WAB 25, Predictives Avg. 28.33, Resume Avg. 25.00 - Got the auto bid in this bracketology, but well within range for an at-large at the moment.

  • St. Mary’s (CA) - NET 24, WAB 29, Predictives Avg. 33.00, Resume Avg. 29.33

  • Georgia - NET 20, WAB 44, Predictives Avg. 25.33, Resume Avg. 39.00

  • Baylor - NET 42, WAB 50, Predictives Avg. 32.67, Resume Avg. 47.67

  • Miami - NET 35, WAB 42, Predictives Avg. 37.00, Resume Avg. 46.67

  • Indiana - NET 32, WAB 63, Predictives Avg. 23.33, Resume Avg. 57.67

  • SMU - NET 37, WAB 24, Predictives Avg. 46.33, Resume Avg. 21.67

  • Villanova - NET 29, WAB 36, Predictives Avg. 33.33, Resume Avg. 37.33

These teams had an average NET of 27.44, and an average WAB of 38.11. Compare that to the teams listed below with a Q1 win, who were omitted from my current field. I have taken the liberty of eliminating teams with a single upset, and almost no chance of ever making the NCAAT: Bowling Green, Denver, New Orleans, and San Diego, thereby only including plausible NCAAT teams also in plausible multi-bid leagues (Power-5 plus A10, AAC, MWC and WCC in this bracketologist’s humble opinion—not to say all of those leagues are multi-bid, but it’s at least plausible this year):

  • Grand Canyon - NET 93, WAB 91, Predictives Avg. 83.33, Resume Avg. 91.33, Q3 loss

  • New Mexico - NET 55, WAB 45, Predictives Avg. 75.33, Resume Avg. 40.00

  • Notre Dame - NET 82, WAB 76, Predictives Avg. 67.00, Resume Avg. 68.00, Q4 loss

  • Rhode Island - NET 102, WAB 117, Predictives Avg. 106.67, Resume Avg. 108.33, Q3 loss

  • Richmond - NET 90, WAB 70, Predictives Avg. 104.00, Resume Avg. 74.00, 2 Q3 losses

  • South Florida - NET 63, WAB 108, Predictives Avg. 76.33, Resume Avg. 93.00, 1-5 in Q1&2

  • Stanford - NET 76, WAB 56, Predictives Avg. 77.67, Resume Avg. 49.67, Q3 & Q4 losses

  • Syracuse - NET 92, WAB 65, Predictives Avg. 73.33, Resume Avg. 82.33, Q3 loss

  • TCU - NET 66, WAB 66, Predictives Avg. 59.00, Resume Avg. 72.33, Q3 & Q4 losses

  • Texas - NET 54, WAB 64, Predictives Avg. 43.67, Resume Avg. 91.00, 1-4 Q1-3

  • Washington - NET 56, WAB 80, Predictives Avg. 54.33, Resume Avg. 67.67

The average NET of this group is 75.36 and the average WAB is 76.18, obviously large differences from the group of included 0-Q1-win teams. Many of these teams also sport bad losses (GCU, ND, URI, Richmond, Stanford, ‘cuse, and TCU) or have not taken advantage of ample opportunities (S. Fla. & Texas). The 2 that don’t fall into those categories - New Mexico and Washington, would be the 9th and 10th teams left out of my bracket, respectively. For New Mexico it’s the weak predictives average and for Washington its the atrocious WAB and resume average. Compare New Mexico them to a team like Baylor, which has similar NET and WAB, but much better averages in the other metrics.

And I think all of this underscores the futility of early bracketology exercises. There is so much uncertainty, and we’ve only seen roughly 1/3rd of the season. No doubt many of these teams discussed in detail will separate themselves (positively or negatively) in the 10-12 weeks to come. Until then, filling out a full 68 team bracket can be difficult. This is reflected over at BracketMatrix.com, where there are essentially 64 or 65 teams that have been deemed at-large caliber by the esteemed panel of Bracketologists, for just 37 spots (of course 6-8 of those at-large caliber teams should get auto bids, but bid thieves do exist). So there’s clearly a wide range of opinions among bracketologists at this point, and a lot of basketball left to be played. 

Frank Molfetta
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Franketology: March 16, 2025, Selection Sunday