The difficulty of winning 6 games in March/April and reaching the pinnacle of the sport can differ on the year. For a team to maximize its chances of cutting down the nets, it is helpful to have a high seed but also to get some breaks along the way with upsets and good matchups in the bracket.

Since 2004 and the beginning of the Bill Self era, Kansas has been to 4 Final Fours and won 2 National Championships. Thinking back on the past 22 tournaments, fans will tend to rue the missed opportunities such as 2011’s Elite 8 loss but often ignore breaks such as the 2022 path (which saw easier-than-expected games at each level along the way). What we want to analyze is exactly how many Final 4 appearances and National Championships KU fans should have reasonably expected during this period and compare to what was actually achieved.

To do so, we mapped out each of KU’s paths from the 2004 tournament through 2026. Now obviously KU didn’t appear in all of the games or face all of these opponents. But, had they continued to win, we looked at who their opponent would have been in that round. From here, we estimate KU’s chances of winning by using the final KenPom adjusted efficiency marks for both teams and historic winning percentages. In years where there is chalk–favorites winning along the path–KU’s odds of making the Final Four let alone winning the Title are much smaller.

KU Throughout the Years

Kansas has an estimated 4.44 Final Fours over this period, indicating the team has underachieved slightly (4 actual Final Fours). The odds the team makes the final weekend range from as low as 1.3% (2024) to 68.3% (2008). The table below lists each season in reverse chronological order.

An “x” marks the years KU earned a 1-seed, years that should presumably be easier paths to the Final Four. Of the 10 seasons that KU has gotten a top seed, the Jayhawks have an expected 3.31 Final Fours (with 3 actual trips to the F4 as 1’s). Color-coding makes things really stand out. Whereas the program had a run of more green/yellow from around 2007 – 2018, since that time the program hasn’t had favorable paths to the Final Four. The last three years in particular have been mostly helpless. KU had only 0.05 expected F4’s over this span.

One thing to note is that these paths are heavily dependent on KU’s relative strength as a team. The ’08 team didn’t just face a favorable path to San Antonio, it was also the best Kansas team in the KenPom era (according to KP). In order to isolate which paths were most favorable independent of KU’s own relative strength, we changed the odds to reflect a KenPom rating of 30.00 instead of whatever KU’s rating was that season. This changes up the numbers a bit, allowing us to isolate which paths were truly easiest or most difficult based on opponent quality.

Historically, a 30-rated KP team is usually a 1-seed and has some of the best odds to cut down the nets in April of anyone that year. Given KU’s paths, we can see that such a team has as high of odds as 61.9% and as low as odd as 8.5% regarding making the Final Four. Of any of the seasons KU actually did make the Final Four, the odds of a KP30 team doing so were at least 29.1% (2018). So KU’s actual success in March has been helped by favorable paths in those seasons. We can also see that benefit to being a 1-seed with the average odds for a KP30 team with a 1-seed in KU’s paths being 37.9% (keeping with historic F4 odds for 1’s) while for non-1-seed paths the average odds of a Final Four drops to 24.0%.

Looking again at just the three most-recent seasons, a KP30 team in KU’s spots in the brackets would have been expected to make just 0.35 Final Fours¹. KU’s success, or lack thereof in recent NCAA Tournaments isn’t solely due to them having down seasons; its also due to more-difficult paths, though this is in part due to them playing tougher paths because of worser seeds. But KU also had 4-seeds in 2004 (47.8%) and 2006 (26.5%) with far more-favorable paths than what they’ve seen recently.

The next thing we will examine is KU’s likelihood of winning a National Championship. Same exercise as before, but now we are factoring in the odds KU also wins the final two games of its would-be NCAA Tournament paths.

KU has had four real chances to win a title and converted on two of them. If we add all of the odds together, KU is expected 1.31 National Championships over this span. I don’t think enough fans recognize how solid 2 NC’s over this period has been. It’s difficult to win 6 games in a row in this tournament. Again, KU’s paths have been more achievable win the team earns a 1-seed. The only two seasons where a non-1-seed Kansas team had a better than 2% chance of winning the Title was 2014 and 2012.

If we strip away KU’s actual strength and look at the paths to the Title from a KP30 team, we see the easiest paths of 2022 and 2011 still only result in a national championship less than 30% of the time. It pays to be a 1-seed, but not all 1-seed paths are equal. 2013 was especially difficult with 4-seed Michigan (#4 overall on KP) and potentially 3-seeded Florida (#2 overall) along the way.

2025 and 2026 were the hardest seasons for a KP30 team, not just because KU was in tougher spots in the bracket as a 7-seed and a 4-seed, but also because of how much better elite teams have gotten. Just reaching the level of 30.00 no longer guarantees a team a 1-seed (2025 saw a record-tying 6 teams with an adjusted efficiency of 30.00 and 2026 will have 8 or perhaps 9 teams reach this mark).

Looking at a few other seasons, the 2014 one stands out as a potentially-doable run given a few breaks (this was the season where 7-seed UConn beat 8-seed Kentucky for the National Championship). With a healthy Embiid, perhaps Kansas makes more noise. Considering KU was a 2-seed in the same exact spot just a year later, the 2.4% probability of 2015 shows just how much things can differ season-to-season. 2008 slips quite a bit when we compare it to other seasons, proving that the strength of that Jayhawk team really helped it secure the title. 2023 was the last time KU had a real chance, with difficult games in the regional (UConn/Gonzaga) being counteracted by potentially easier games in the F4 and CG rounds (Miami FL/San Diego St.).

In closing, a KenPom team rated 30.00 and given KU’s path in each of the last 22 brackets would be expected to win 2.21 NC’s and go to 6.68 F4’s. In order to arrive at the actual NC’s KU has won in the Self era (2), a hypothetical replacement team with the same KP rating each season would need to be +29.47. For 4 Final Fours, this number is lower (+25.57) but still very good overall.

1. This works out to a 32% chance of at least one Final Four. ↩︎

Note: First posted 3/18/2026.

The 2026 NCAA Tournament is underway with First Four games having been played yesterday and later this evening. While we don’t have the final lines for the R64 for Tennessee’s opponent (either Miami (OH) or SMU) or Florida’s (Prairie View/Lehigh), we do have opening round lines for the other 30 games. 3/19 update: Miami and Prairie View won.

First, let’s look at the historic winning percentage and point differential for all favored seeds in the First Round.

This is since 1985, so 40 tournaments’ worth. The 1-seed’s average margin is 23.8 points and so on. Let’s now look at the current average betting lines by better seed for 2026 (includes all R64 games):

• 1 v. 16: -31.3
• 2 v. 15: -23.5
• 3 v. 14: -20.0
• 4 v. 13: -13.8
• 5 v. 12: -10.0
• 6 v. 11: -5.3
• 7 v. 10: -3.4
• 8 v. 9: -0.3

This is further evidence of a college basketball trend many have been noticing, namely the growing chasm between the very best of the sport and the rest of the sport. While the opening round games between seeds 6 through 11 are in keeping with the historic gaps of these spots, seeds 5 or better are stronger relative to the worse seeds than they’ve historically been. There is almost no chance a 16 knocks off a 1 this season, and moving on down we can see that the 2/15 matchup in 2026 is basically a 1/16 matchup in previous seasons, a 3/14 matchup is a surer result in favor of the better team than a typical 2/15 game, and the 4/13 matchup this year is expected to be somewhere in the middle of a typical 2/15 slash 3/14 First Round contest. While a 12-over-5 upset wouldn’t be shocking this year¹, even this gap is noticeably wider compared to prior seasons.

The years 2000, 2004, 2007, 2017, and 2025 are unique in that they are the only ones where no seed worse than a 12 won an opening round game (in 2000 and 2007 all 12’s won their R64 games as well). There is a good chance this happens again. Using the historic line to winning percentage table, we estimate the chance that all top four seeds advance to the weekend at 56.6%. For something that has happened only 12.5% of the time, the projected chalkiness of the 2026 bracket is notable².

Potential Evolution of the Tournament

We plan on writing more of the phenomenon later, but essentially what is happening is that the best, richest programs are acquiring non-power conference talent from the portal during the offseason and this not only makes the good teams better it makes the mid and low-majors worse. We can see this through KenPom data; currently there are 8 teams with an AdjEM of +30.00 or better, which would be an all-time high. Last season there were 6 teams, which at the time was tied for the highest number in all seasons since 1997. Going all the way back to 2023 (I know, so long ago) there were exactly 0 at season’s end reach that +30.00 threshold. And this was hardly some outlier, in 2022 there was only 1.

Good teams are getting better, and the winners of one-bid leagues (seeds 12 on down) are getting worse. The gap is widening from both directions.

If this continues, we project this will create an evolution in the Tournament. Less exciting will be the First Round games, where chalk predominates more years than not (though upsets when they do occur will become more appreciated), but games in the Second Round and later will see better matchups and more madness. More 4/5 coin-flip games. Fewer 14’s and 15’s getting beat in the R32 by double digits, so the 2’s playing the 7/10 winner and 3’s playing the 6/11 winner will be competitive.

The first time the Round of 64 saw all 5-seeds and better win (2000), we saw two 8-seeds and a 5-seed make the Final Four. Interestingly, the growing gap doesn’t really help the 1-seeds, who have historically already been dominant in the first weekend. Instead, it seeds 2-5 secure a more-likely First Round victory and better odds to make the Final Four. 1-seeds who have to face the 4/5 in the S16 and (more likely) a 2/3 in the E8 are going to get to the final weekend less often than they do when they get a break somewhere in the bracket³.

Closing Thoughts

Assuming the 68-team bracket in its current iteration persists, and we hope it does, there will likely be a push from fans for more upsets, and at some point we hope more regulation when it comes to player movement and NIL. There is still the potential for more mid-majors to grow into competitive teams (think McNeese recently) which helps shore up the bottom of the bracket more. We might even see one-bid leagues manipulate their conference tournaments more to protect the higher seeds, so that the ASUN for instance sends Central Arkansas (KP #152) instead of Queens (#183), which would help when it comes to opening round upset potential.

1. ESPN BPI projects that all 5-seeds win 43.2% of the time, with the 5-seeds earning a collective 0.76 estimated wins. Only 6 times out of 40 (15%) have the 5-seeds gone 4-0 against the 12’s. ↩︎
2. If we include 5-seeds, the chances that all of the top 20 teams win their R64 games is 27.5%. Given the historic precedent is 5.0%, this is a five-fold increase. ↩︎
3. The 1-seeds are 24-0 against 12/13 seeds in the S16 and 21-7 against seeds 6/7/10/11 in the E8. When playing the 4/5 in the S16, the 1’s are 83-29 (74.1%). When playing the 2/3 in the E8, the 1’s are 45-34 (57.0%). 1’s have disproportionately enjoyed better paths with earlier-round upsets than other seeds, so as this changes expect there to be fewer 1’s make Final Fours. ↩︎

KU received a 4-seed on Selection Sunday and will face 13-seed Cal Baptist on Friday night. Here are some listed chances for a KU Round of 64 win:

• KU -14.5 points (implied 93.0% looking at historic lines)
• KU ML -1200/+750 CBU (implied 88.7% looking at equal expected $ outcome)
• ESPN BPI: 92.6%
• KenPom: 86.7%
• Torvik: 85.4%
• EvanMiya: 92.4%
• Historic 4 over 13: 79.4%

The range spans from under 80% to 93% depending on source. 4-seeds have won almost exactly 4 of 5 First Round games since 1985, but this year the gap between the top teams and bottom teams is wider than average. This doesn’t guarantee Kansas a win of course, but it does provide more margin of error for the Jayhawks. Should KU go on to win that one, here are some odds for KU’s success moving forward by successive round.

Source	R32	S16	E8	F4	CG	NC
KenPom	86.7%	45.5%	11.4%	5.1%	1.7%	0.5%
Torvik	85.4%	43.0%	10.9%	4.9%	1.5%	0.5%
Miya	92.4%	40.4%	8.0%	3.7%	0.9%	0.2%

The betting odds imply a KU national championship 0.8% of the time, a bit higher than these models but still quite low.

So expectations are low (and should be). But KU has a real chance to make the second weekend, and as a 4-seed should be expecting to do so, and after that they just need to believe anything can happen. KU’s path isn’t unfair or harder than it should be or something. They just need to show up and play hard.

The NCAA Tournament officially starts tonight, 3/17/2026 with the First Four (play in) games. For the last few seasons we have been grading how the committee did in selecting and seeding the field. The exercise below is similar. In order to grade the committee, we aren’t comparing them to how we think they should have seeded the field, rather we are seeing how consistently they applied their preferences throughout the bracket. Consistency is found by correlating the final S-curve to a weighted-model ranking and playing around with the inputs until the closest correlation (highest R²) can be found.

The committee uses team sheets to help it compare the various teams it reviews. The team sheet contains quite a bit of information, which we’ve divided into four different components. These components are essentially:

• Schedule strength
• Raw winning percentage
• Success by Quadrants
• Metrics

Within these components, sub-components are present. Each component will show the overall weight the committee inherently applied to the 2026 bracket. Also, each of the components will be explored in more detailed based upon what the committee members are looking at when they sit down to select and seed the field of 68 each March.

Schedule Strength (1.0%)

The committee looks at overall SOS but also sees each team’s conference and non-conference SOS ranks. The overall SOS is most important, but the committee has also historically looked at non-con SOS as it is within a team’s control to play a tournament-worthy non-con SOS. For 2026, the schedule component was relatively small (1%), but it did have some predictive power.

Raw Winning Percentage (2.9%)

This component is unique in two ways. First, it doesn’t have sub-components. Its just a teams overall record (in D-1 games). Second, it arguably should be 0%. Yes, the 1-seeds will have higher raw winning percentages than teams lower down on the S-curve, but this should accounted for by other components.

Miami (OH)’s 28-1 record and inclusion into the field (S-curve ranking of 44) helps draw this number up. At 2.9% the number is low enough that we aren’t too concerned that it was factored into the committee’s decision making, but any higher and we would be getting worried.

Success by Quadrants (30.9%)

The team sheet as well as the source website used to run our calculations show each prospective team’s results by Quadrant, from Quad 1 to Quad 4. Pundits will often compare “blind resumes” which show a team’s Quad 1 results compared to another team’s to argue or support the inclusion of a certain team over another. While many data analysts have argued against the usage of quadrants, the committee very much sees them and does weight them.

The components we used this year were three-fold. First was Q1 results (both overall wins and winning percentage). Second was Q1+Q2 results (again both overall wins and winning percentage). Third was the effect of bad losses (Q3 and Q4). This approximates how the committee likely looks at these metrics. They are impressed with both quantity and quality in terms of the top wins, but in terms of the easier games, want to just make sure teams are avoiding bad losses.

Interestingly for 2026, the committee was far more impressed with Q1+Q2 results (26.4%) than just Q1 results alone (4.5%). Bringing in Q2 games at a higher weight helped teams like UConn (7-3 in Q1 but 18-4 in Q1+Q2) and Iowa State (8-7 in Q1 but 18-7 in Q1+Q2).

Lastly, the bad loss sub-component was incorporated as a multiple to the first two components. We toggled this multiple on and off to see if it had an effect, and it did so we kept it as a sub-component.

Computer Metrics (65.2%)

The computer rankings made up nearly 2/3 of the input the committee considered. This is up from last season (55.7%) and the season before (58.5%).

We divided the metrics into the two buckets the committee basically does–efficiency metrics and resume metrics. The efficiency metrics include: NET, KenPom, Torvik, and BPI. The resume metrics include: WAB, SOR, and KPI. The metrics are weighted more toward the NCAA’s internal metrics (NET and WAB), but each listed number on the team sheet is included as they are what the committee members see.

The split between efficiency metrics and resume metrics was 32.1% and 33.1% respectively. The committee looked at both elements, how well a team did at winning games and how well they won these games. Arguably the resume metrics should be far higher than efficiency metrics, but both are quite correlated to one another regardless so this isn’t so bad.

Correlation to S-Curve

The R² was 0.9694 this season, making it better than 2025 (0.9489) and 2024 (0.8793). Compared to prior recent iterations, the committee in 2026 did a good job. Let’s look at where it was off the most

Teams Over-seeded by the Committee

Given the preferences the committee had, these three teams are finding themselves with better spots on the S-curve than they likely should have been:

Illinois (10 on the S-curve, 16 on the weighted-model)

The Illini have very strong resume metrics (8 in NET, 6 in KenPom) but their Q1 results (7-8) and resume metrics (18 in WAB) leave much to be desired. This resulted in the difference of a seed, from 4 to 3. How to handle strong teams with poorer resumes is going to always be a question the committee has to deal with, but here it seems like they over-borrowed from the efficiency metric side compared to how they were doing so elsewhere.

Kentucky (25 on S-curve, 29 on weighted-model)

Mark Pope’s team got the top 7-seed instead of the top 8-seed, helping the Wildcats avoid a potential R32 matchup against an elite 1-seed (not that their second round game against Iowa State would be easy). Kentucky was hovering around the last 7-seed/first 8-seed spot aside from its strong SOS metric (6th overall). They may have gotten a name-brand boost as well. Not egregious, but nevertheless helpful for UK.

Texas (42 on S-curve, 48 on weighted-model)

This one may be the most controversial. Not only was it a six spot difference, it also vaulted the Longhorns into the bracket instead of San Diego State (45 on weighted model) or Oklahoma (46). The Longhorns are 17-14 (they also have one non-D1 win) which includes a Q3 loss against a decent (23rd overall) SOS. Their combined resume metrics placed them in 48th, below even Auburn (44th). While nearly every bracketologist got this pick correct, it is weird Texas so easily got into the field.

Teams Under-seeded by the Committee

Just as three teams were over-seeded, we found three under-seeded teams for 2026:

Vanderbilt (17 on S-curve, 10 on the weighted-model)

The Commodores knocked of Florida in the SEC Tournament and finished as runners-up to Arkansas, but only reached the spot of the final 5-seed. Others have noticed how indefensible it is for Vandy to be a 5-seed. Looking at the weighted model, the only ranking where Vandy was worse than a top 12 team was in the lowly-correlated overall winning percentage (29) and SOS (20) components…but even there they weren’t egregiously an outlier. KU might have gotten a break to get a 4-seed, because objectively Vanderbilt should have been in front of the Jayhawks.

Utah State (33 on S-curve, 25 on the weighted-model)

Utah State was not happy with its seeding when announced on Sunday, and while it is common to hear complaints this time of year so at first we weren’t necessarily sympathetic to the Aggies, we’re now in complete agreement with them. Not only should Utah State have been a 7-seed instead of a 9, they should have comfortably been on the 7-line. Like Kentucky above who got promoted, the difference isn’t necessarily negligible in that the second round game is more winnable when playing a 2-seed instead of a 1 (Arizona is the 1-seed in Utah State’s potential path). Historically speaking, since 1985 the winner of the 7/10 game has gone on to win the R32 game 54 times for a Sweet 16 success rate of 34%. The winner of the 8/9 game, who almost always plays the 1-seed, gets to the Sweet 16 only 15% of the time.

In terms of the components, Utah State had a lower SOS component (89), and its Q3 loss didn’t help it. Still, an overall Q1+Q2 record of 13-5 to go along with decent metrics (30 in efficiency, 29 in resume) should have had it higher on the S-curve than it was.

San Diego State (49 on S-curve, 45 on the weighted-model)

It was only four spots, but the Aztecs have a case for inclusion into March Madness based on how the committee seeded the field elsewhere. They didn’t have any exceptional metric or component, but cumulatively they were within the top 45 teams. SDSU was 9-10 in Q1+Q2 games which hurt, but other first four teams (Texas was 7-13, SMU was 9-13, NC State was 11-12) were likewise not very impressive. While it wouldn’t have got them in, having SDSU in front of Oklahoma (19-15) and Auburn (17-16), the first two teams out, would have better recognized the Aztecs’ 21-11 record against way worse records from power conference teams.

Bracket Matrix

Just as with last season, the Bracket Matrix was more consistent than the committee itself despite not establishing the preferences we used to best fit the committee to a weighted model. We were able to get an R² of 0.9780 when we compared the Bracket Matrix’s S-curve compared to the weighted model that fit best to the actual S-curve. The Bracket Matrix consensus had Vandy as a 4-seed (and more 3-seeds than 5-seeds in various mock brackets) and an overall ranking of #14. It also had Utah State (#30 overall) a bit better than the official committee did.

The consensus bracket also had all 68 teams correct (well, 37 at-larges). Auburn was in 16% of the mock brackets, the highest for any team to miss the field. Texas had the lowest odds according to the Bracket Matrix, but it was still in 80% of mocks on Selection Sunday.

In total, very little controversy compared to most years. The committee was more consistent than it had been in prior years though its preferences did shift some. The biggest misses were in the area of seeding, but even here it only really had an effect on a few teams.

The NCAA Tournament selection committee uses Team Sheets and a number of metrics and data to determine how to seed the bracket, and we’ve graded them on this by seeing how consistent they’ve been at applying a standard across the board. Using this same idea, that applying a hidden weighting system to the pool of D1 teams will produce an unbiased S-curve ranking, we have created a mock bracket. As of end of games Tuesday, 3/10/2026.

Below is the S-curve.

1. Duke
2. Michigan
3. Arizona
4. Florida
5. UConn
6. Houston
7. Michigan St.
8. Gonzaga
9. Nebraska
10. Illinois
11. Iowa St.
12. Purdue
13. Vanderbilt
14. Alabama
15. Virginia
16. Kansas
17. Texas Tech
18. Arkansas
19. Louisville
20. Wisconsin
21. North Carolina
22. St. John’s (NY)
23. BYU
24. Tennessee
25. Miami (FL)
26. Georgia
27. Clemson
28. Kentucky
29. Saint Louis
30. Utah St.
31. TCU
32. UCLA
33. Saint Mary’s (CA)
34. Texas A&M
35. Villanova
36. NC State
37. Ohio St.
38. Missouri
39. Iowa
40. UCF
41. SMU
42. Texas
43. Santa Clara
44. Indiana
45. Oklahoma
46. Auburn
47. South Fla.
48. Yale
49. Troy
50. Akron
51. Liberty
52. UNI
53. High Point
54. Hofstra
55. Utah Valley
56. Hawaii
57. North Dakota St.
58. Tennessee St.
59. Wright St.
60. UMBC
61. Queens (NC)
62. Howard
63. Siena
64. Furman
65. Idaho
66. LIU
67. Boston U.
68. Bethune-Cookman

Right off the bat, we had to change some things up. According to the weighted team sheet rankings, Michigan was ahead of Duke, but since the Blue Devils beat the Wolverines head-to-head, we are confident in saying the order would be Duke #1, Michigan #2.

As we are in the midst of Champ Week, only some of the mid and low-major conference champions in this bracket are official auto-qualifiers. The projected AQ’s listed is the team with the best NET rating still left in that conference tournament. The AQ teams in italics have yet to officially qualify.

Because of taking the best NET rating, this bracket projects Akron as the MAC Tournament winner, not Miami (OH). The RedHawks get left out in this scenario, due to poor metrics and and an abysmal SOS. In fact, Miami (OH) isn’t even that close using the weighted team sheet rankings. I don’t know what the committee is going to do should Miami (OH) not win their conference tournament, but if they apply one standard across the board, it will be an unprecedented snubbing. My prediction was that Miami (OH) would make it due to their strong WAB and resume metrics. Currently Miami (OH) is 31st in WAB and 21st in SOR.

On the flip side, Auburn would get in under this scenario. The Tigers would land the final spot and have to play in Dayton, but imagine the uproar if this occurred. Yet, given the committee’s historical preferences, the weighted team sheet has them in.

For Kansas, the Jayhawks sit at the precarious #16 spot, earning the final 4-seed but certainly in danger of sliding to a 5-seed. Losing both the Arizona St. and Cincinnati games are hurting a team that could be a comfortable 3-seed. Win either one, and the numbers look quite a bit better across the board. Every game matters. Now KU can still move up based on how it does in Kansas City. At the moment, it would be better for KU to have Texas Tech lose in the Big 12 Tournament. Also we should root against Arkansas, Louisville, Virginia, and Alabama.

The bubble looks like this:

Last Four In:

• Santa Clara
• Indiana
• Oklahoma
• Auburn

First Four Out:

• Belmont
• New Mexico
• Stanford
• San Diego St.

Also in bubble consideration is South Fla. The Bulls are in the Field conditionally based on them being the highest NET team in the American, but should they lose in their conference tourney, they could still get in as an at large. The weighted team sheet ranking here would likely have them out, but this doesn’t mean the committee would agree.

Belmont suffered a bad loss to Drake in the MVC quarters and Stanford’s defeat to Pittsburgh was disastrous for the Cardinal’s chances. But both New Mexico and San Diego St. are alive in the Mountain West with chances to improve their resumes.

Looking at the wider bubble picture, power conference teams are sitting prettier than the mid-majors nearby. Of the final 10 at large spots (all the projected 10 and 11-seeds), the weighted team sheet rankings show 9 power conference teams and only 1 mid-major. Spots 47-56 on the S-curve (which include South Fla. which is a strange bubble-case) make up the first 10 spots outside the Tournament and contain 6 mid-majors to 4 power conference teams.

If the committee does end up producing a bracket similar to the one above, there will no doubt be outrage from many pundits and teams outside the power conference structure, claiming bias. What this exercise has shown is not that there isn’t a disadvantage to now-power teams, but rather that it is structural not bias-related. Most mid-major teams, even the very good ones, have glaringly worse team sheet numbers in a variety of categories, from Quadrant 1 wins to SOS, when compared to teams in the middle of the power conferences. And while mid-major coaches complain about the difficulty of scheduling more difficult, very few good mid majors are foregoing buy games and looking to play multiple games against better fellow mid-majors in the non-con. Had Miami (OH), VCU, Belmont, and New Mexico gotten together to play each other in non-conference, each team could have added 3 Q1/Q2 games with at least one of them being a Q1 game. Sure, there is uncertainty if scheduling in the off season due to the fluctuation of team strength, but this makes the old bracket busters concept worth revisiting. Teams in the better mid-major conferences should reserve a weekend in mid-February to compete in cross-conference 2 or 3-game showcase tournaments that serve as a sort of play-in to at-large consideration. Some would lose and see their resumes get worse, but the risk would be worth the reward of winning multiple Q1/Q2 games.

The current Bracket Matrix projections show roughly a 0.92 correlation to this one, and when the dust settles my guess is that the actual team sheet weights for 2026 will converge with what Bracket Matrix has. Each season is different, and the committee has been talking more about WAB so this metric will likely receive more attention from the members and should have a higher weight than it currently does.

For previous versions of this, see 2024/2025, 2023, 2022.

Kansas wrapped up its home slate with another Senior Day victory (43 in a row), beating K-State by a 104-85 margin. This ran the Jayhawks’ record at Allen Fieldhouse to 13-2 for the 2026 season, its only losses being to UConn and Cincinnati. Kansas had a number of impressive wins at the Phog in 2026, including against Iowa State, Arizona and Houston.

Since 2010, the first year that KenPom kept pregame winning percentage estimates, Kansas has gone 254-17 (93.7%) at the Phog. This jumps out as impressive, but the question becomes, how much of this is attributable to Kansas having a consistent standard of top talent and elite teams, and how much of this is attributable to the building? This question is what we are exploring.

Pomeroy’s data allows us to account for important features such as team strength, opponent strength, and even home-court advantage¹; presenting us with an unbiased look at how likely it is a team is going to win a certain game. Its important that these numbers are unbiased, as this serves as the basis for judging exactly how special THE PHOG truly is.

Note: in keeping with earlier versions of this post, we will be capitalizing THE PHOG to signify the uniqueness and magic of Allen Fieldhouse in terms of its ability to produce wins for KU. It is a real effect as our numbers will show.

Let’s start with an example, the most-recent example, of a Kansas home game so we can wrap our heads around the exercise. KU was given a 94.3% chance of winning against K-State on Senior Day in KenPom’s numbers. Since KU won, they earned 0.057 Wins Above Expectation (WAE), the metric which determines how many net home wins Kansas has in comparison to Pomeroy’s expectation. If we accumulate WAE over a period of time, it gives us a better idea of KU’s success at home in comparison to an unbiased, reasonable expectation.

Since the beginning of the 2010 season, Pomeroy’s system has expected KU to win 233.43 games in that span, or 20.57 fewer than they actually have. This would be a 86.1% winning rate, not the 93.7% rate Kansas actually has. It also works out to a difference of 1.21 wins per season. So far, THE PHOG effect is giving KU an addition 1+ win per season, which can be the difference in a seed-line by the time March rolls around.

To further gage how impressive this is, we ran a simulation 10,000 times to see how likely it would be that Kansas would win 254 games given its opponent history over this span. Exactly zero times does the simulation project KU to win 254 games; in fact the maximum number of wins randomly assigned was 251. Looking at the distribution in terms of variation, the math estimates that KU would only win 254 games once in 36,040 times if KU’s over-success at home were just a random event.

Other Arenas?

The necessary follow up is to ask the reasonable question, well is this something that happens somewhere else? While we can’t easily test each arena, following the 2025 season we did explore a series of other notable home courts to see how much help they provided to their programs. This can be reviewed in the 2025 update (scroll down on page), but we will briefly represent the results here:

• Mackey Arena (Purdue) +12.49 WAE
• Pauley Pavilion (UCLA) +11.60
• Hilton Coliseum (Iowa St.) +8.54
• Rupp Arena (Kentucky) +7.67
• Dean Smith Center (North Carolina) +4.11
• McCarthy Athletic Center (Gonzaga) +2.08
• Cameron Indoor Stadium (Duke) +0.75

Purdue lost 5 home games this season despite having a solid team, so Mackey Arena’s WAE has undoubtedly decreased. Iowa State went 16-1 at Hilton in 2026, but did so as a talented team playing a weak non-conference, so Hilton’s WAE likely increased only slightly. Kentucky went 14-4 at Rupp and no doubt lost ground. UCLA did go 17-1 at Pauley so is likely second on this list, and the others (North Carolina, Gonzaga, and Duke) all went undefeated at home so their home arenas will move up a touch on this ledger, though Duke’s underlying talent means its home court aura was less of a reason for its undefeated play in 2026.

Once we normalize for games played, Allen Fieldhouse is in the 100.00%ile (z-score of 4.03) and all others are below the 99%ile (roughly a 2.33 z-score). We’re talking magnitudes of difference here.

Betting Odds

Another way of looking at this is to consider betting odds. If a team is consistently outperforming what odds makers consider is reasonable, this adds further credibility to THE PHOG. With captured historical betting data going all the way back to the 2008 season, we ran a test to see what a hypothetical bettor would make had he put $10 on KU for each home game at the Fieldhouse in that era.

In theory, the market should balance out KU’s massive home court advantage and this bet not be profitable, but in reality we’ve found a market inefficiency. Kansas has won enough times to earn the bettor $252.44 over that span. Employing another strategy, a bettor who starts with $100 and wagers 10% of his collective pot each game would now have a total of $1,026.45 (10x growth) over these past 19 seasons.

Essentially this strategy would be an investment play, where one puts money into a fund and continues to reinvest dividends. This yields a 13.0% annual return (with 16 positive years and only 3 negative ones). By comparison, investing in the S&P 500 over this time results in 10.9% annual growth and a $663.02 pot of money. Betting on KU to win at Allen Fieldhouse has been a safer and more profitable investment strategy than the S&P 500 over the past 19 years, which is insane statement for anyone who knows about sports betting, the stock market, and financial theory.

Closing

After a down 2025, KU bounced back at home in 2026, going 13-2 against a very difficult schedule. KU was true underdogs an astonishing four times at Allen Fieldhouse this season, winning 3 of them (2 of them by double-digits). Kansas has now won 4 of 5 games as betting underdogs at THE PHOG in the Self-era.

Other bits of info include the fact KU hasn’t lost since 2010 when given at least a 90% chance of winning pre-game by KenPom. This is 148 for 148, and an underappreciated feature of THE PHOG, and has earned 6.10 WAE for KU. Kansas has been upset at home, sure, but the teams that do get a rare win at Allen Fieldhouse are at least ones with a pulse. Teams in the 80%-89% are the danger zone for some reason, with KU winning “only” 85.2% of these games (exactly as expected per KenPom, with -0.02 WAE). Regarding KenPom home underdog games for the Hawks, KU is 6-1 for a WAE of 3.49. When the building is energized, it clearly has an effect on lifting the team.

1. By home court advantage, what he applies is an algorithmically-derived number that can be utilized across the board. All teams have home court advantages; we are trying to isolate Kansas’ in comparison to the others’. ↩︎

Miami (OH) is in the midst of a magical season. The RedHawks are currently (3/5/2026) 30-0 and poised to return to the NCAA Tournament for the first time since 2007. Miami (OH) had 15 consecutive seasons with a losing record before a solid 25-9 campaign last year, but they’ve leveled up even more in Coach Travis Steele’s fourth season.

With a record as good as Miami (OH)’s is, one would normally think them to be a shoe-in to the NCAA Tournament, but things aren’t this straightforward. Let’s first consider their chances at getting the automatic bid. Although undefeated, the Red Hawks aren’t a super strong team according to the metrics. They are 88th right now in KenPom, 53rd in NET, and 83rd in Torvik. They aren’t the strongest team in the MAC, either. Akron sits at 63rd in KenPom (69th in Torvik) and would be favored in a neutral-site matchup. In fact, Torvik has Akron as the MAC tournament favorites, with Miami (OH) projected to win their conference tourney only 30.6% of the time. There are more than a few teams that Miami (OH) can reasonably lose to in the MAC.

Ok, so what? Even if Miami (OH) loses in the conference tournament, they will still have a great record (30-something and 1) and will get an at-large bid. Right?

This is where things get interesting. Recently former coach Bruce Pearl said that Miami (OH) shouldn’t get an at-large spot in this scenario, causing a ruckus in which the administration and coach at Miami (OH) responded with indignity.

Here’s the interesting thing, however. Both sides have a good point. What Miami (OH) has done is truly impressive, yet at the same time theirs good reason to doubt they could’ve qualified for an at-large spot had they been in a power conference and played a tougher schedule. What we want to do is to examine the RedHawks’ tournament resume as the committee will and explain more about this process.

It is our conjecture that the NCAA Tournament committee has a challenging job and is overly criticized. Yes, sometimes they get it wrong. But consider what they are tasked with. They are asked to rank a number of different basketball teams playing in different conferences and oftentimes wildly different schedules. It is tough enough to try and compare the 6th-best team in the Big 12 with the 6th-best team in the Big 10, now try comparing a top team from the MAC to average teams in the SEC or how MWC teams stack up against A10 teams.

In order to guide the committee, the room has team sheets which show a team’s numbers and its wins and losses based on things which can be compared, that is quadrants. If Team A is 5-4 in Q1 games and Team B is 3-6 in Q1 games, the committee can easily judge that, in terms of Q1 record alone, Team A is preferrable. Now it may be that Team B has done better at avoiding bad losses, has a better overall strength and schedule and computer ratings, etc. that wind up seeding Team B better than Team A, but this process helps the committee make some sense of the numbers. Here are a list of important things the committee considers:

• Wins and overall record against quadrants
• Computer efficiency metrics (i.e. KenPom, NET)
• Resume metrics (i.e. WAB)
• Strength of Schedule

Let’s bring in Bracket Matrix to get a consensus look at where things stand today. The consensus thinks Miami (OH) is in the tournament (they are given the MAC auto-bid at this point by many systems), albeit as an 11-seed. Should Miami (OH) lose to Akron in the MAC finals, how much this would change their status remains to be seen, but they’d likely be a quintessential bubble team.

To see why this is, put yourself in the committee’s shoes. Instead of romanticizing the dream season of the mid-major, look at things objectively. In this scenario, Miami (OH) would be 33-1. Sure, this is impressive. But they also currently have the 344th best SOS according to Warren Nolan. Digging into their resume a bit more, you’d see these marks by quadrant:

Team	Q1-A Record	Q1 Record	Q2 Record	Q3 Record	Q4 Record
Miami (OH)	0-0	0-0	1-0	10-0	16-0

The first thing we’d notice is the goose egg in terms of all Q1 games, followed by the sole Q2 win. Miami (OH) has gone 3-0 in non-D1 games, which don’t really count at all, meaning their overall 30-0 record is really 27-0…still impressive but you get what’s happening. This initial strong element of the resume has started getting chipped away.

Looking at a few computer metrics, here is how Miami (OH) looks

• 53 in NET
• 88 in KenPom
• 30 in WAB

The efficiency metrics aren’t great, but helpful to Miami (OH) is the WAB number. This metric shows the RedHawks as having the caliber of resume of a typical 8 seed.

Let’s compare this to a similarly-placed team in Bracket Matrix, TCU. The Horned Frogs’ team sheet looks something like this:

Team	Q1-A Record	Q1 Record	Q2 Record	Q3 Record	Q4 Record
TCU	3-5	5-7	5-1	3-1	7-1

• 42 in NET
• 45 in KenPom
• 36 in WAB

Looking at these two resumes, which one looks more impressive? This what Miami (OH) is up against. Yes, the 30-0 (or 33-1 potentially) looks great, but once you look at what is presented on the team sheets, it stacks up poorly against teams that have a number of quality wins.

Having said that, TCU going 10-2 in Q3/Q4 should be pulling the Horned Frogs down, and it is. TCU is currently projected to be the lowest 10-seed, i.e. just barely avoiding the First Four play-in. But it is better to have a few “hiccup” games against Q3/Q4 opponents if you win enough Q1 games than it is to go unbeaten against Q3/Q4 teams while not playing (or winning) many Q1/Q2 games.

Another factor helping TCU here is SOS, which sits at 46. This is the same thing for other non-Miami (OH) bubble teams. Ohio State has the 23rd-ranked SOS. Santa Clara’s is 87th. VCU’s is 101st. New Mexico’s is 93rd. Even the other mid-majors on the bubble have way-better SOS than Miami (OH).

What’s really holding Miami (OH) in the at-large picture at all is its WAB and the fact it has avoided bad losses (nearly every game it played, would it have lost, would be a “bad loss”).

Pearl’s Comments and the response

Bruce Pearl’s comments were not taken well, with Miami (OH)’s administration mentioning that Pearl is saying as such to promote Auburn, where his son is the head coach. Auburn was on the bubble but has lost 7 of its last 9 to fall to 16-14 on the year. The Tigers still have an outside shot of making the Big Dance if they can rack up a few wins in the SEC Tournament, but despite having the 3rd-best overall SOS, they’ve lost too many games at the moment to be in the field.

But let’s consider his point. He’s saying that Miami (OH) has benefitted from an incredibly easy schedule, and that were they to play in a power conference, they’d likely be out of the tournament. To explore this, we compared the team with the closest WAB to Miami (OH), Georgia (29), who is conveniently another SEC team.

Georgia’s gone 21-9 this year, but has won 7 Q1 games while only losing 1 Q3 game (and 0 Q4 losses). They’ve racked up impressive wins against Alabama, Cincinnati, Arkansas, Missouri, and Kentucky. We can’t say that Miami (OH) has done this. Maybe they could have, but we can’t say they did. That’s why Georgia’s WAB ranking feels truly “earned” and the committee will likely have them in the 7-8 seed range.

To Miami (OH)’s point, sure the MAC is easier than the SEC, but they’re undefeated, something that many bubble teams can’t say when looking at just Q3/Q4 results. New Mexico has two Q3 losses. TCU lost to lowly New Orleans at home (Q4) earlier in the year. Santa Clara has a Q4 loss as well. Some of the other teams on the bubble (Ohio St., Indiana, VCU) have taken care of games against Q3/Q4 opponents but have paltry records in Q1/Q2. What’s really the difference between going 1-8 in Q1 games versus going 0-0?

What people tend to ignore is that the committee functions in a certain way, namely they function by being able to compare each team against those around it. After a rough draft seed list is created, they go through the “scrubbing” process which is effectively taking two similar teams and using a fine-tooth comb to separate their resumes. The better team moves up a spot and is compared to the team above it. The worse team moves down and is compared to the team below it. Do this for a few iterations, and you’ve made a consensus decision on how the S-curve should be 1-68.

Because of this process, what the committee wants to see is that teams are easier to compare. It is easier to compare teams with similar(ish) Q1 contests, SOS marks, etc. than it is to compare a Miami (OH) and a TCU. The committee is trying to weight wins and losses, and this gets tougher to do when a team’s opponents played are skewed to the low-end of the quadrant scale.

To combat this, the NCAA has consistently signaled the importance of strength of schedule. Teams with poor SOS marks get dinged, and not just on the bubble. Iowa State fell a few spots on the S-curve (and missed a chance at a 1-seed) a couple years back because of its poor non-con SOS. They don’t want to be told they have to parse one team’s results against competitive teams against that of another’s games against non-competitive ones.

WAB, Hypothetic Results, and Scheduling

Running concurrent to this is the recent inclusion of Wins Above Bubble (WAB), which allows a team to play a weak schedule yet still build a resume as long as they have a very good record (i.e. undefeated or 1 loss). This is what Miami (OH) has done in 2026.

The metric is saying that Miami (OH)’s 30-0 record is equally as impressive as Georgia’s 21-9 record against a far harder schedule. What Bruce Pearl is saying is that Miami (OH) would still struggle to achieve something the likes of which Georgia has done against Georgia’s schedule. And what we’re saying is that both are right.

Had Miami (OH) played Georgia’s schedule, a computer metric like KenPom would project the RedHawks to have a 15-15 record. Even if we gave hypothetical Miami (OH) a few more wins playing that schedule, 17-13 isn’t likely getting a team in (that’s about 62nd in WAB).

The better response for Miami (OH) than to insult Pearl would be to point out that most teams couldn’t finish 30-0 against the RedHawks’ schedule. If Georgia had played the schedule Miami (OH) did, computers would project them to be at 28-2 or 29-1…only 21% of the time would we think they could run the table. Yes, they’d be favored in every game. But eventually someone would trip them up, they’d have an off-night, etc. With five total games on the schedule in which they’d be favored to win less than 90% of the time, it isn’t unlikely they’d drop one along the way.

Now the committee doesn’t really consider hypothetical results as such, that’s what WAB is attempting to control, but it does show how the argument becomes circular.

The other element within this is scheduling. A harder schedule for Miami (OH) would have allowed us to see how they would have done against opponents closer to the quality of Georgia’s (or TCU’s or Ohio State’s). Now Miami (OH) couldn’t schedule better within its conference. The MAC is the 17th-best conference per KenPom, truly a mid-major league. The MAC schedule has provided Miami (OH) with a number of Q3/Q4 games and its only Q2 contest (Akron). I believe the committee would be sympathetic to the RedHawks if them being in a weaker league was the only reason for their poor SOS numbers. Where Miami (OH) has been hurt, however, is in the weakness of its non-conference schedule.

The RedHawks have the 364th ranked non-con SOS, having played three non-D1 teams as well as a slew of other low-majors. Playing 8 teams outside of the top 300 in KenPom in your non-con while also playing a mediocre conference slate is a recipe for a disastrous SOS metric. For most teams at the non-power conference level, this matters not since they aren’t trying to get an at-large bid. But for Miami (OH), it might become a nightmare.

Miami (OH)’s coach tried to justify this. As ESPN reported:

Coach Travis Steele recently told ESPN that high-major squads have refused to schedule the RedHawks due to the potential for an upset.

This has become the typical sentiment from mid-major coaches and supporters of these teams. But it isn’t based on much evidence. Looking at the rest of the MAC and using KenPom numbers, Miami (OH) had the worst non-con schedule by far. Its average non-con opponent (excluding non-D1 teams) had a rating of -9.91. The average non-con opponent of all the other MAC teams was -1.97. This is a difference of about 5.6 points per game easier for Miami (OH) than others in their league faced.

Obviously they could have scheduled harder. But even the claim that high-major teams refused to schedule Miami (OH) is far-fetched. Fellow MAC school Eastern Michigan faced four power conference foes in the non-con. The Eagles played at Pitt, Louisville, Cincy, and Butler. These were all Q1/Q2 games. Other teams in the MAC were also able to schedule power conference opponents.

The more obvious solution here is that Miami (OH) didn’t expect to be 30-0 on March 5, and they just weren’t concerned with building an at-large resume when they scheduled last off-season. They were hoping to play well during the regular season to get a good seed in the conference tournament and hopefully get a bid through automatic qualification. Which is fine, a majority of the conferences are one-bid leagues. But don’t pretend your abysmal SOS is the fault of other programs.

Projecting Miami (OH) in the Field

Wrapping this up, what do we think about Miami (OH)’s chances? We’ve been all over the map in this analysis, but this is kind of everything the committee will be considering (well, besides the hypothetical scenarios). Obviously Miami (OH) can take care of things by winning its conference tournament. But what if they don’t?

Let’s say Miami (OH) wins its final regular season game (at home against Ohio). This is actually a key game, as it will allow the RedHawks to have 1 loss at most following the conference tournament. I don’t think Miami (OH) gets in if they lose the regular season finale and in the MAC tournament.

If they finish the regular season 31-0 and lose in the conference tournament, preferably in the championship game to Akron (Q2 opponent), this is where things get interesting. Their WAB would drop a bit (likely to around 38-40), which by itself would get them in the field but not by much. Combined with weak efficiency metrics, no Q1 wins or even games played, and of course that SOS, it is going to be close.

Either way, if Miami (OH) finishes at these records here are the chances I give them:

• (34-0) – 100%. 10-seed estimate.
• (33-1) – 60%. 11-seed First Four.
• (32-1) – 30%. Out but if in 11-seed.
• (31-1) – 25%. Out but if in 11-seed.
• (32-2) – 0%. A 2-loss team with that bad SOS won’t get in.

Bid thieves in leagues like the A10, MWC, and WCC (as well as any power league) are also lurking which would change this calculus. At the moment, Torvik projects Miami (OH) in the field 67% of the time, with them earning an at-large bid 49.6% of the time, a true coin flip.

The 2010 Kansas team, despite its untimely end, was the paradigm of a well-constructed roster. It had veteran leadership alongside young pro talent, a compliment of shooting to go along with interior play, stars who took over in the big moments to go along with role players who did the little things, and continuity that ensured everyone was on the same page.

Taking a look at its top 4 players, here are their value stats:

Player	PPGAB	Per100 AB	WAR/36
Marcus Morris	+4.14	+9.61	+5.29
Cole Aldrich	+4.02	+8.62	+5.27
Sherron Collins	+3.48	+6.05	+5.03
Xavier Henry	+2.39	+4.99	+3.70

The final value stat, WAR/36 just normalizes the season WAR to a 36-game schedule so we can compare across seasons.

All four of these players would go on to play at least some in the NBA. Marcus Morris was a very underrated player who continued to improve and was a very efficient Jayhawk even by his sophomore season.

Now let’s take a look at the 2026 roster’s top 4 players, through 30 games of the season.

Player	PPGAB	Per100 AB	WAR/36
Darryn Peterson	+5.54	+12.32	+4.48
Melvin Council	+4.03	+7.15	+5.89
Flory Bidunga	+4.03	+7.68	+5.73
Tre White	+2.39	+4.46	+4.05

What stands out is that, by all value metrics, the 2026 top four is as good or slightly better than the 2010 top four. Peterson’s WAR is a bit lower due to him being out of the lineup for 11 games, but other than that the stars of both teams are at worst comparable.

So why does it feel like the 2026 team is so much worse than 2010? Obviously this is due to the rest of the rosters. Let’s take a look at 2010’s role players to begin:

Player	PPGAB	Per100 AB	WAR/36
Markieff Morris	+1.22	+4.00	+2.06
Tyrel Reed	+0.78	+2.89	+1.53
Tyshawn Taylor	+0.26	+0.65	+1.37
Elijah Johnson	-0.03	-0.27	+0.18
Thomas Robinson	-0.69	-5.52	-0.31
Brady Morningstar	-1.30	-3.48	-0.20

In total, these role players in 2010 had the following value:

+23.22 Points Against Bubble (+0.65 per game)

+4.46 WAR

Contrast this with the 2026 role guys.

Player	PPGAB	Per100 AB	WAR/36
Kohl Rosario	-0.31	-1.54	+0.25
Jayden Dawson	-0.35	-2.22	+0.10
Elmarko Jackson	-0.49	-1.63	+0.39
Jamari McDowell	-1.51	-4.87	-0.63
Bryson Tiller	-1.94	-4.28	-0.63

The cumulative totals for these five rotation players are:

-133.48 Points Against Bubble (-4.45 per game)

-0.52 WAR

This final table should contrast things nicely:

Season	Stars PAB/gm	Role Guys PAB/gm	Total PPGAB1
2010	+14.03	+0.65	+14.68
2026	+13.96	-4.45	9.51

Basically, 2026’s role players are spotting a typical opponent 4-and-a half points a game that the stars have to make up. Most nights they can, but some nights, when shots aren’t falling or things aren’t going well, this can result in an unexpected loss.

Back in 2010, KU could get some value from the likes of Tyshawn, or when it went to its bench was able to maintain a solid level of play thanks to the efforts of Markieff and Tyrel Reed. Even with the struggles of freshman T-Rob and Brady Morningstar, Kansas was an elite team.

1. These totals won’t add up to each team’s season totals for PPGAB as these totals exclude coach technicals, walk-ons, and other non-rotation players (those playing fewer than 10% of possible minutes). ↩︎

Since our last write-up in the 2026 season, a lot has happened. KU now sits at 10-3 and in a tie for third place in the Big 12, only 1 game back of first, and is positioned as the 10th best team in Bracket Matrix (corresponding to a 3-seed in the NCAA Tournament). KU is far closer to the optimistic outlook we presented about 6 weeks ago, though things haven’t turned out exactly like we imagined.

Let’s start with Elmarko Jackson, a player Kansas fans have been overlooking due to the on-again, off-again status of Darryn Peterson (more below on him). In our Christmas Break Recap earlier in the season, we had this to say about Elmarko:

While everyone has bad games, Jackson needs to find a way to make his bad games not outright terrible ones. Either way, Self is likely playing him too many minutes.

At that point Jackson had not been playing well, matching his disappointing freshman season, but ever since the third game of conference play he’s really turned things around. In fact when you look at the numbers, its kind of shocking. In these 10 most-recent games he’s played in (he missed the Utah game due to injury), Elmarko is putting up +2.96 PPGAB/+8.95 Per100, doing this both on offense and defense. For the season he’s now a -0.22 PPGAB/-0.70 Per100 player, solidifying KU’s bench.

Jackson had his best game of his career against Oklahoma State last night, going for 14 points, 4 assists, and 3 rebounds on efficient shooting numbers; but also defending well. This earned Jackson a team-MVP in our system and career-first +10 outing. While defense tends to fluctuate some game-to-game based on matchups and what the opponent does, Elmarko’s season-long defensive value is in the green (+1.41 Per100) indicating he has become a reliable defender.

Now onto Darryn Peterson. Peterson looked absolutely unstoppable out the gates against the Cowboys, scoring 13 points before the first TV timeout, and he coasted to a 23-point, 18-minute performance before sitting the bulk of the second half. Peterson checking himself out for cramps was met with much hostility by the fans, and their frustration is understandable if not a bit overblown.

In Peterson’s defense, he added an estimated 8.63 points over a hypothetical bubble-level player last night. On a per possession basis this is even more impressive given he only played 18 minutes (+26.71 Per100!), but the main point here is that if you can only get some of Darryn, you take it. Kansas building a big first half lead thanks in large part to his shooting and defensive attention allowed the team to play less-than-inspired ball the second half and still win comfortably. This has happened more than once this season–that Peterson doesn’t finish a game but KU not really need him to because he helped them grow a lead during the first half. Fans would be better to appreciate this aspect.

Still, the unpredictability of the situation isn’t ideal. Peterson checking himself out and casually sitting there or riding an exercise bike (while not returning) reeks of self-interest (i.e. show the scouts his obviously superior talent while not risking further injury) that can hurt the team long term. Will he be able to go a full game if/when the team needs him in the NCAA Tournament? If he doesn’t do so in the regular season (or does so sparingly), what confidence is there he can do so when the games really matter?

Let’s put aside a few talking points that have been debunked. Many fans were speculating that Peterson was “done” back in November/December. They were clearly wrong. Darryn has played in 11 of 13 conference games and finished one with a sore ankle (Colorado). He also effectively won the Texas Tech game down the stretch with two late 3’s. He has clearly added value to this team during the season. His POCWAB is also +2.48, a solid mark (third on the team) that can also isolate player value by weighting quality opponents and utilizing total performance.

Finally, let’s explore some conceptual elements of player value. Conceptually, when Peterson plays, he makes the team better by scoring efficiently (you need points to win), forcing opponents to concentrate on guarding him (which should help his teammates get better shots), and eating minutes (meaning KU’s other starters can rest while the bench doesn’t need to play as much). That’s why fans should recognize the difference between playing 20 minutes vs. 0. Peterson has played 57% of available minutes in conference play. While it would be better at 75-80%, this is far better than him sitting.

But concept is different from result. In games Darryn has played, KU is a +9.23 team, while in games he’s missed they’re a +10.99 team. Some of this may be due to KU loading up against non-power conference teams during the portion of the season Peterson was injured (and outperforming the adjustment factor against these weaker foes), but if we isolate only the games against top 100 KenPom teams, KU is a +8.41 team with and a +10.52 without. They’ve played well with him; they’ve played well without him. They’ve had down games with him and down games without him. This isn’t to say that KU would be as good or better without him for a full season or that KU’s ceiling isn’t higher with him, but that sometimes numbers show funny things.

According to Hoop Explorer, KU is only slightly better with Peterson on the floor than they are with him off it (about 1.5 points per 100). This puts him behind Council, Bidunga, Tiller, and McDowell. While On/Off data can fluctuate, more data is needed (likely 100 games worth), and the function of who replaces you matters (that’s one reason why Flory’s On/Off is so positive, KU is better with 2 bigs and doesn’t have a good one behind him); Peterson’s impact should be higher than it is.

KU’s offense has been better in DP games (+4.38 vs. +2.07) but its defense has been far inferior (+4.85 vs. +8.92). With defense being a bit less predictable maybe randomness is at play, but at the same time KU really locks in defensively without DP. And while Peterson has good defensive skills, he doesn’t always have the motor to play 100% and will give up occasional open looks or transition opportunities. On the season he is right at bubble-level defensively (+0.08 PPGAB/+0.18 Per100), which when paired with his elite offensive game isn’t necessarily bad.

Player Value Thru 26 Games in 2026

For posterity, here was how things stood on 2/19/2026. KU has 5 regular season games remaining and at least 1 Big 12 Tournament and 1 NCAA Tournament game to play.

Erasing a 15-point comeback in under 5 minutes, Kansas’ win against TCU to move to 11-4 (1-1) was significant in a lot of ways. It prevented KU from going 0-2 in conference play for the first time since 1991. It added a win that will help its overall resume come March (certainly a loss would have hurt). It was only the 13^th time a team erased as large of a deficit with as little time as it did (15 points or more in 5 minutes or less). These factors are all important from a historical or resume perspective, but one area we cannot determine yet is the area of momentum.

Momentum here refers to the team’s momentum and how the rest of the season will play out. With KU being without its star player for much of the year, and now getting him back (for the most part), now the other Jayhawks are trying to learn how to play with Darryn Peterson while still making winning plays. Certainly Peterson has been good in his return(s) to the court. But not everyone has been as good.

Looking at game scores, KU has been a +8.78 team in the games Peterson has missed and a +8.29 team in the games he’s played. If we ignore the non-power opponents (we’ll explain why more later), then the split gets a bit worse with Peterson, +4.96 with and +6.72 without.

The offense has been noticeably better with Peterson (+5.57 with, +0.54 without), meaning that the defense has been that much worse with him (+2.71 with, +8.24 without). And while it might be tempting to blame Peterson for this, he’s actually graded out as an adequate defender both using the charting method and the eye test.

How good will KU be the rest of the way? That depends of course, but we wanted to break down this team’s chances from a pessimistic and optimistic outlook.

Pessimistic Outlook

Let’s start at the pessimistic level of things. Even though it was nice for KU to avoid losing to TCU, under this view there’d be no reason to think it will lead to clear skies moving forward. After all, the last two teams have put up 81 and 100 points (in game paces where they were expected to score 78.5 and 88.4 against a bubble team’s defense). With more game film and rotations being set, teams in conference are scouting KU’s propensity to switch and purposefully taking advantage of mismatches. Can KU make an adjustment? If it does change its defense to play more straight up, does this hurt it in other areas (such as 3-point defense, which has been solid)?

More specifically to position, KU’s interior has looked shaky. Now we shouldn’t conflate Flory Bidunga (mostly solid, +3.39 per game) with Bryson Tiller (routinely overmatched, -3.32 per game), but the fact is that KU is only comfortable with these two bigs, and this is likely another reason why KU’s front court overall has struggled (-0.78 per game). Tiller has played far more than he should, in turn Flory is asked to do more than he needs to, and on top of this foul trouble biting you is always a lingering concern. Mbiya and Calderon haven’t been reliable pieces. The last pessimistic angle we’ll look at is the splits between mid/low-major teams and the power opponents. In 15 games, KU has played 5 “bye” games, that is the games against the sub-100, non-power conference teams, and dominated them to the tune of an average margin of 29.2 points. And while dominating bad teams is a sign that your team should be good (and helps the metrics sites predict a team’s overall strength), it would be a mistake to assume there is a pure linear relationship between all 365 teams.¹

Contrast this performance to the 10 games KU has played against power conference opponents. Going 6-4 in these games, KU has outscored these opponents by only 1.5 points per game. We mentioned the Kansas front court earlier, and in the case of Flory Bidunga, these splits show themselves as well. After adjusting for opponent strength and location, Bidunga’s value against bye teams (+5.71) is noticeably better than his value against better opponents (+2.24). If KU’s only real presence inside sees his value wane when playing good teams, how will that help in remaining conference and tournament games?

Look, the comeback win and getting Darryn Peterson doesn’t change the fact the team has shown itself to have real flaws.

Optimistic Outlook

Now we’ll turn to the happier view of things. KU sits at 11-4 and a WAB of +2.3, indicating a 4-seed or potentially a 5-seed as things sit now. All this without Darryn Peterson for games such as Duke, Tennessee, UConn, and Notre Dame. KU has shown it can win games without him (i.e. Tennessee) as well as put everything together with him (i.e. Missouri). With more games and more reps, guys will naturally find their roles and KU’s talent will begin to win out.

Let’s look at offense/defense splits. Far from being a concern, it is actually a reason for optimism. KU’s offense is functioning as a top 20 (if not better) group with Peterson, not around 50 as they’ve been for the whole season. And while the defense has gone down recently, the video shows this isn’t due to Peterson as much as it is due to scouting KU’s propensity to switch. Once Self makes defensive adjustments, and he has the pieces for this team to be a good defense (Council, Bidunga, Rosario, even Peterson), this team’s defense should be very competitive.

Another positive factor is that KU’s depth has gotten better. Peterson getting 30 minutes or more a game would mean that others (White, Council) are asked to do less. It also means KU doesn’t have to play a guard or wing who isn’t ready to get major minutes. Jackson or McDowell or Rosario or Dawson or whoever should be able to get comfortable by coming in for a few minutes and playing hard on defense. In particular, Rosario has shown some ability to do this, and Jackson had a number of key steals late against TCU. Peterson’s elite offensive skill allows for more specialization elsewhere.

Winning breeds winning. This team expects it’s going to win moving forward after taking TCU’s best shot and responding. It showed grit and never stopped fighting. Energy, a very tough thing to quantify yet nevertheless a very important part of the game, is something players and teams have to learn to play at. KU showed tremendous energy late against TCU to steal the win, and there is good reason to think they can repeat this.

The Louisville game is another data point which adds some optimism. No, the game didn’t count. But it was a competitive contest in which a good team playing on its home court attempted to win but was outmatched by Darryn Peterson, etc. This shows us that KU has the talent and team to beat other good teams (this was an estimated game score of +19, which is a good enough performance to win any other game on the team’s schedule).

The final sign of optimism is the obvious fact that good players do not make teams worse but in fact better. In short, KU has Darryn Peterson so chin up. Sure, KU hasn’t been better with Peterson yet. But why think that will continue? KU was better with its best player on the floor in 2025 (Hunter Dickinson), 2024 (Kevin McCullar), 2023 (Jalen Wilson), 2022 (Ochai Agbaji), etc. even if there were times in the season where the team struggled despite having its best piece. After 10 more competitive conference games with Peterson in the line-up, this reality has to show itself in the numbers.

Peterson is looking healthier and healthier. He’s playing more minutes each game he returns, even if his legs are tiring out late in games. He’s played KU’s last 4 competitive, non-bye games (with KU going 3-1). Despite people over-reading into the situation, he truly wants to play and he truly makes the team better.

1. A mistake we admittedly make in our opponent adjustments to calculate PAB/WAR. But we need a simple way to adjust to opponent and believe that because “bye” games serve only a small portion of the schedule, it won’t matter by season’s end. ↩︎