Kansas dropped its third home game of the season on Saturday, falling to 19-10 on the season. Of these 10 losses, the team can look back on most of the games and count a number of times it squandered away chances to close out strong and win the game. We will look back at how KU has done down the stretch in its tight games.

Date	Opponent	Time Left	Score	End Result
November 8, 2024	vs. North Carolina	8:30	79-78	W, 92-89
November 12, 2024	vs. Michigan State	8:50	52-52	W, 77-69
November 26, 2024	vs. Duke	9:40	59-59	W, 75-72
December 8, 2024	@ Missouri	2:20	63-65	L, 67-76
December 31, 2024	vs. West Virginia	6:00	48-50	L, 61-62
January 11, 2025	@ Cincinnati	7:00	38-37	W, 54-40
January 25, 2025	vs. Houston	10:00	48-48	L, 86-92 2OT
January 28, 2025	vs. UCF	9:00	66-67	W, 91-87
February 1, 2025	@ Baylor	5:00	63-61	L, 70-81
February 15, 2025	@ Utah	4:30	60-60	L, 67-74
March 1, 2025	vs. Texas Tech	8:20	61-60	L, 73-78

The above 11 games for Kansas are games in which these two conditions apply:

• KU is either ahead or behind by 2 points or fewer (or tied).
• There is fewer than 10 minutes but more than 2 minutes to go in the 2nd half.

While these conditions filter a set of games, we also avoided cherry-picking KU’s best chance of winning, as that would unjustly bias the results. We want to know how KU does “down the stretch” in games it has a realistic chance of either winning or losing. KU has won many games comfortably but has also lost four games in which it never was within reasonable striking distance over the last 10 minutes of play.

At 5-6, KU’s been a bit worse down the stretch than its opponents, but at first glance this doesn’t look terrible. Wins early against Michigan State and Duke are basically the only thing holding its resume together at the moment. But looking more closely at the numbers makes this mediocre record appear much worse. Here are some reasons why.

• KU was favored in all of the games except Duke. Right off the bat KU should be shooting for something like 8-3 in these games.
• Many of these losses occurred when KU was in the midst of a comeback run and had momentum. Other teams have responded better to “game pressure.”
• As the home team, KU is 2-3 in these games. Winning close road games is usually tougher than close home ones, and this team is not taking advantage of Allen Fieldhouse. If it can’t win these games at the Phog, how can it win these games in neutral arenas?
• KU’s losses have been in a variety of ways…blow late leads with over 99% win probability (Houston), blowing huge leads (Baylor), fighting back and letting a team with its back on the ropes get second chances to counter (West Virginia, Utah, Missouri, Texas Tech), losing by poor shooting (West Virginia), losing by allowing the other team tons of good looks (Texas Tech) losing by getting beat on the glass (Utah), etc. Basically, KU has proven it can lose in about any way there is to lose despite playing well enough (for 80% + of the game) to win these games. If it can lose in about any way possible, this is felt among the team and can trigger further meltdowns.
• As the season goes on the team has gotten worse in close games. It started out 3-0 in such contests but has lost 7 of its last 9 since.

Clutchness as a forecastable factor is an oft-debated idea in sports, but it can be defined as performing well in high-leverage situations. The closer and later the game is to the finish, the more important each possession becomes. A missed shot with your team down 2 and 1 minute to play is far more weighty than a missed shot with your team down 2 and 39 minutes to play¹.

Using the charting system, which assigns comparative value to the player making the determining play of each possession (both offense and defense), we can assign a Win Probability Added % (WPA) to each play, accumulate these plays, and get a clutchness metric for each player on the team.

WPA can be taken from one of three sources (at least): Bart Torvik, ESPN, and KenPom. Each of these websites publishes the chances of winning at each stage of the game. We match these probabilities to the players and get cumulative WPA’s for each game, and in turn for the season.

Player	Cumulative WPA
TEAM – KANSAS JAYHAWKS 2025	-2.868
Dajuan Harris	-0.286
Zeke Mayo	-1.988
Rylan Griffen	-1.203
K.J. Adams	+0.944
Hunter Dickinson	+1.370
A.J. Storr	-1.566
Flory Bidunga	+1.205
David Coit	-0.614
Rakease Passmore	-0.227
Zach Clemence	+0.126
Shakeel Moore	-0.452
Coach Self Technical Fouls	-0.178

Given the odds of winning at each tip-off, Kansas as a team has underperformed by nearly 3 wins on the season. This is the biggest indicator that KU hasn’t played well in the clutch. If KU were 22-7 going into the second Houston game it wouldn’t be doing anything special, just winning at the rate a team of its caliber is expected to win, but overall it would be close to where a typical Bill Self-led Jayhawk team is at this point of the season.

Individually, KU is getting clutch play from its interior trio of Dickinson, Bidunga, and Adams. Hunter has been fairly consistent all year, though he does have let down games from time-to-time. Flory was playing elite basketball during the first three months of the season or so but really struggled in February. However, K.J. Adams has had a few excellent games recently to boost his WPA. In fact, Adams’ performance against Texas Tech added a season-high 0.556 wins. KU truly squandered an all-time game from the senior forward.

After this, there is some fall-off. Dajuan Harris is hovering around zero but can’t get over the hump. His defensive lapse and turnover late against Texas Tech cost the Jayhawks a lot of WPA (others also missed shots and didn’t make plays). Combined with other instances (poor defense in Baylor’s comeback, missed FT’s against Houston), it’s been a disappointing year in the clutch for Juan. But he hasn’t been the worst, in fact far from it.

Diggy Coit, Rakease Passmore, and Shak Moore are negative-clutch players but also not the main guys in the game during clutch moments. Passmore has been relegated to the bench and Moore is injured. Coit has played the most in big moments and has had some good games (West Virginia) alongside others where he misses big shots and gets exploited on defense due to his size. But none of these players have been the main problems.

KU’s three biggest transfers–Zeke Mayo, Rylan Griffen, and A.J. Storr–are combining for -4.757 WPA. This is dreadful and the biggest factor as to why KU’s underachieving. It isn’t the “Big 3” returnees, it’s the new guys. Mayo basically lost the Houston game for Kansas (through missed defensive assignments and live-ball turnovers) and he hasn’t been good in other big spots either. Like Mayo, Griffen tends to give up hugely important baskets and his offense hasn’t been consistent enough to make up for these lapses. A.J. Storr is unique in that he’s been bad all-around. Not even needing to play in that many late-game moments, Storr’s poor performance in the first half and other non-clutch moments have hurt the team’s win probabilities throughout the season.

Note that walk-ons haven’t played in any high-leverage situations…they’ve only entered games which have effectively been decided. Self’s technical fouls have cost the team nearly 1/5 of a win. His T at Utah gave up 2 points in what became a close game down the wire, although one might argue it inspired the subsequent comeback.

Concluding Thoughts

The glass half-full approach to this would be that KU is still good enough talent wise to beat good teams and that if it gets the right draw, upsets, etc. in March it could still make a deep run. However, if clutchness is a real factor, then this team seems destined to lose in the cruelest way come the NCAA Tournament.

Update #1 – After Houston Game #2

KU fought hard but fell short of victory in a 59-65 defeat to Houston in the 30th game of the season. KU only had a 12.4% chance of winning at tip, so this loss won’t hurt the team’s clutchness factor that much. Still, it was another game where KU was within a possession of the lead either way in the last 10 minutes yet lost. KU is now 5-7 in such games.

On the season, KU has lost 2.99 games more than expected…in other words the computers think KU should be 22-8 instead of 19-11. Hopefully this “reversion” will occur in the NCAA Tournament and KU can be a “good team that is under-seeded” and take advantage.

Player	Cumulative WPA
TEAM – KU 2025	-2.992
Dajuan Harris	-0.282
Zeke Mayo	-2.092
Rylan Griffen	-1.312
K.J. Adams	+1.065
Hunter Dickinson	+1.408
A.J. Storr	-1.599
Flory Bidunga	+1.227
David Coit	-0.629
Rakease Passmore	-0.275
Zach Clemence	+0.126
Shakeel Moore	-0.452
Bill Self Technical Fouls	-0.178

Update #2 – Season’s End

KU had a few more close games to end the season, defeating Arizona on senior night and UCF in the Big 12 Tournament before losing to Arizona (Big 12 Quarterfinals) and Arkansas (First Round of NCAA’s).

Player	Cumulative WPA
TEAM – KANSAS 2025	-3.580
Dajuan Harris	-0.312
Zeke Mayo	-1.890
Rylan Griffen	-1.531
K.J. Adams	+1.684
Hunter Dickinson	+0.983
A.J. Storr	-1.850
Flory Bidunga	+1.063
David Coit	-0.950
Rakease Passmore	-0.275
Zach Clemence	+0.126
Shakeel Moore	-0.450
Self Technicals	-0.178

A terrible close to the season saw Hunter Dickinson’s WPA drop to below +1.00. He still finished with positive WPA, but in the biggest game of the season, he had his worst effort. On the flip side, K.J. Adams posted 8 consecutive positive-WPA games to earn the team honors as the clutchest player on the year. When he went down with KU up 3 late in the NCAA Tournament game, his team melted without him on the floor. Dajuan Harris had negative WPA as well, though not as bad as the newcomers. Though as with Hunter, his mistakes down the stretch in the NCAA Tournament game were more costly.

KU finished 21-13 but “should” have won 24 to 25 games in that span. The lack of clutch play and player development hurt this team’s chances at making a deep run.

1. Hunter Dickinson’s missed FG attempts with KU down 3 and about a minute left against Tech took KU’s WPA down 11.9%. A miss in the first half only cost KU 2.3% WPA. ↩︎

The above X post was chosen as a fairly representative reflection of KU fans’ thoughts on the state of the 2025 team (as well as the two prior seasons). Although the sentiment is put in different ways, whenever Kansas has struggled or failed to achieve a certain level of success in recent seasons, a large swath of the fanbase has been quick to criticize or blame Dajuan Harris and/or K.J. Adams.

Frankly Jayhawk Takeover’s post is mild compared to others. His criticism is a bit more indirect as well, putting the blame more on Self than Harris/Adams, whom he doesn’t even name. But while he’s entitled to his opinion, his sentiment couldn’t be more wrong. Both Harris and Adams have been key pieces over the last few seasons. Adams was the third-best Jayhawk (marginally below McCullar and Dickinson) in 2024 and Harris has been KU’s third-best player (or second-best if looking at total value given his durability) in 2025. Both were also value-adding starters on a 2023 team which earned a 1-seed despite almost no quality depth.

Sometimes KU’s three main returnees (Harris, Adams, and Dickinson) are derided online as the “big 3,” with the sentiment being that as long as these guys are in Lawrence, KU cannot have success, or that Self is unjustifiably relying on their contributions while not playing others who could do better. While lumping in different players and treating them as equally important to team success isn’t normally justified, since fans often look at certain players this way, we felt it insightful to do so utilizing value metrics as we compare groups of players to each other.

Thus begins our look into player value splits. These splits will tell a much different narrative than the one commonly assumed by the vocal majority of Jayhawk fans. Each split first divides the roster into different groups so we can blindly compare value metrics. This should strip the bias, “eye test,” etc. and get a purely objective look at value estimates.

Note: The value metrics presented below are through 24 games in KU’s 2025 season.

Split # 1

Group 1	43.5% minutes played	+8.57 cumulative PPGAB
Group 2	45.7% minutes played	-0.27 cumulative PPGAB
Group 3	9.9% minutes played	+1.25 cumulative PPGAB

Which of these groups looks to be the best? Group 1 accounts for nearly half of minutes played, yet generates 8.57 points of value above a bubble-team. Group 2 plays a bit more but is essentially a bubble-team, losing 0.27 points to this benchmark. Group 3 has a limited impact in terms of minutes but is adding a bit of value.

If we looked at the team in this way, no one would be criticizing Group 1 as a collective, but Group 2 would face a ton of heat for holding the team back. One cannot say that the players who make up Group 2 aren’t getting enough playing time as a whole, either. Group 2 has played slightly more minutes than Group 1.

Group Reveal:

• Group 1 – Returning Veterans (Harris, Adams, Dickinson, Clemence)
• Group 2 – Incoming Transfers (Mayo, Griffen, Storr, Coit, Moore)
• Group 3 – True Freshmen (Bidunga, Passmore)

Since Clemence hasn’t played much this season due to his backup role and injury, Group 1 is for all intents and purposes the “Big 3.” To spell this out further, the “Big 3” are collectively adding over 8.5 points to KU’s skill level when compared to a bubble-team¹. The incoming transfers have been reduced to a collective bubble-level output. It’s tough to see how anyone with eyes would blame the returnees as a group (or even individually) more than those incoming transfers who’ve struggled. For Group 3, we see a freshman having an excellent season (Bidunga) and one having a terrible one (Passmore). Freshmen are usually judged less-harshly, as they don’t have the same experience as returnees. Yet KU is getting more from Bidunga than any of the five veteran newcomers.

Split # 2

Group 1	62.6% minutes played	+1.28 cumulative PPGAB
Group 2	36.5% minutes played	+8.27 cumulative PPGAB

Once again, we see a clear favorite emerge. Group 1 is slightly ahead of bubble-level value, but it is Group 2 which is producing the bulk of the value for the 2025 Kansas Jayhawks.

Group 1 is the perimeter players, the guards and wings. It includes Harris, Mayo, Griffen, Storr, Coit, Passmore, and Moore. Group 2 is the interior players, that is Adams, Dickinson, Bidunga, and Clemence.

This split is stark, yet how often do we hear praise lauded on the posts specifically or specific criticism about the backcourt collective? It’s just odd how certain narratives take root while others are not discussed. Now, it wouldn’t be right to be overly critical of Zeke Mayo (who has been solid) just because he’s in the backcourt, but as far as narratives go, that’s exactly what the harsher critics of Harris, Adams, or Dickinson are doing.

Split #3

Player 1	74.1% minutes	+9.55 PP100AB
Player 2	40.6% minutes	+7.90 PP100AB
Player 3	75.4% minutes	+4.84 PP100AB
Player 4	77.9% minutes	+3.53 PP100AB
Player 5	63.4% minutes	+2.85 PP100AB
Player 6	46.7% minutes	+0.45 PP100AB
Player 7	37.5% minutes	-1.98 PP100AB
Player 8	4.3% minutes	-2.57 PP100AB
Player 9	41.1% minutes	-3.63 PP100AB
Player 10	25.4% minutes	-4.40 PP100AB
Player 11	8.8% minutes	-15.90 PP100AB

These players could be ascertained by simply looking at the minutes played distribution, but without names attached, one would normally want to play those at the top more than those at the bottom. For instance, Player 2 seems like he should be playing more. From there, the minutes distribution looks reasonable, with Players 9 and 10 looking like they haven’t earned a spot in the rotation at the moment. In addition, one would assume that the ire from the fanbase would be due to the play of Players toward the bottom half (starting around player 6 or 7 and on down) while praising those toward the top. Let’s look at the same list, with names included this time.

Hunter Dickinson	74.1% minutes	+9.55 PP100AB
Flory Bidunga	40.6% minutes	+7.90 PP100AB
Dajuan Harris	75.4% minutes	+4.84 PP100AB
Zeke Mayo	77.9% minutes	+3.53 PP100AB
K.J. Adams	63.4% minutes	+2.85 PP100AB
Rylan Griffen	46.7% minutes	+0.45 PP100AB
David Coit	37.5% minutes	-1.98 PP100AB
Zach Clemence	4.3% minutes	-2.57 PP100AB
A.J. Storr	41.1% minutes	-3.63 PP100AB
Shakeel Moore	25.4% minutes	-4.40 PP100AB
Rakease Passmore	8.8% minutes	-15.90 PP100AB

Let’s reexamine the list now that we have names listed. The top five players include each of the “Big 3,” with Hunter Dickinson grading out as KU’s best overall player and Dajuan Harris besting everyone on roster who plays on the perimeter. Even Adams, who has struggled to finish this season, is comfortably adding above-bubble value.

Conversely, Bidunga has earned more minutes than he’s gotten this season. We’ll discuss his role later, and the role of the bigs in a bit. Given that KU wants to play 3 perimeter players alongside 2 bigs, it needs to find minutes for someone beside Harris/Mayo. Griffen has gotten the starting nod of late, and for good reason as he’s the best of the rest so to speak. But even Rylan has been inconsistent, and his value is just marginally above that of a bubble-level player.

From here, we’ve seen some bright spots from Diggy Coit and fewer good moments from A.J. Storr, Shak Moore, and Rakease Passmore. From this list, it’s tough to suggest that any of these sub-bubble guys playing more minutes would help the team.

Now onto the interior rotation. Perhaps the only thing the consensus view from fans is getting right is that Flory Bidunga has shown himself to be valuable and should get more minutes. One can excuse him not being used by Self as much early in the season when he was yet unproven, but after filling in nicely for Hunter (when he was ejected the final 10 minutes against Duke) and K.J. (when he was injured in late January), he’s proven himself to be a serious force. In fact, Flory was KU’s best player in K.J.’s absence, adding 7.78 PPGAB of value while playing more minutes than he normally does (about 70% of possible minutes in Adams’ absence).

This isn’t to knock K.J., which is what people often do when looking at the rotation. Adams has been the better player since his return (Flory has really struggled in the last few outings), muddying the debate even further. Some are blaming Adams for the recent poor play of Flory (an asinine suggestion which is so dumb it becomes impossible to refute—as once Flory begins to play well again no one will start crediting K.J. for Flory’s re-emergence but use it as further evidence that Flory should play while K.J. sits), but it’s clear that like any freshman, Bidunga is going to have off nights. Still, we’re left with the question. How should Self best utilize the front court rotation to ensure the best possible chance for his team to win games?

With 80 minutes to use (40 minutes among 2 spots), the 3 bigs should average 26.7 minutes. We’d argue giving Hunter the most (close to 30-32), Flory a bit less (25-27), and K.J. the remainder (22-24) seems ideal. However, these are just ranges that don’t take into account opponent quality, opponent style, foul trouble, and which player is playing the best that game. Self has to use his judgment and the feel of the game to determine how KU best matches up.

As discussed in Split #2, KU’s frontcourt is carrying it this season. Any combination of Adams/Dickinson/Bidunga has been value-adding while KU plays poorly when it tries to “go small” and play with four guards/wings around a single big. That’s the biggest indictment on the fans and conventional wisdom. Flory’s emergence as a possible starter alongside Dickinson wasn’t suggested by anyone until it was discovered in the second half of the West Virginia game when Adams (and Storr, Griffen, etc.) was benched for poor play. Although both Dickinson and Bidunga are centers, KU was able to defend and score adequately to narrow the deficit and get the Hawks back in the game. Simply put, crafting a rotation that fulfills some secondary concern (like “spacing”) matters less than playing the best talent. While position and complementary pieces still matter in a rotation, coaches have some flexibility. Small lineups work, provided the guards are skilled scorers who can hit open shots and put pressure on bigger opponents (i.e. to force turnovers). Two-big lineups can also work, provided the posts are active on the glass and take advantage of their size. For KU in 2025, the skill is clearly in the front court so as many minutes as can reasonably be had by 2 of Adams/Dickinson/Bidunga is going to be what is best for the team.

If it were up to some fans, KU would go small more for “spacing” and to “get Storr going.” But Storr, like Griffen or others on the wing, has had plenty of opportunities to get going. He just hasn’t played well. Some have blamed this on not enough playing time, but Flory Bidunga proves a player can produce with limited minutes. Flory has played fewer minutes than A.J. has this season, yet he is putting up a season that would rank in the 90^th percentile of all seasons put up by KU rotation pieces since 1993, while Storr is currently below Nick Timberlake’s 2024 performance and sits in the 13^th percentile to this point of the season.

If we look beyond minutes played, the myth Storr has had limited opportunities becomes stranger to suggest. Storr is putting up shots when he plays; he’s not being hidden. According to KenPom he is third on the team in shot percentage (24.6%), far in front of players like Dajuan Harris (17.1%) or K.J. Adams (14.8%). These numbers also support the idea that Harris’ and Adams’ value comes from intangibles and not from scoring…that Harris and Adams are “role players.” Storr has not produced winning moments for KU this season, and in two of KU’s losses, Storr’s negative-value efforts surpassed the team’s final margin of defeat.

If fans take to social media in order to express frustration, they should do so with a modicum of basketball IQ and blame the players who are underperforming.

Split #4

Player 1	+1.346 WPA
Player 2	+1.207 WPA
Player 3	+0.518 WPA
Player 4	+0.126 WPA
Player 5	-0.106 WPA
Player 6	-0.198 WPA
Player 7	-0.225 WPA
Player 8	-0.413 WPA
Player 9	-0.730 WPA
Player 10	-1.412 WPA
Player 11	-1.503 WPA

WPA, or Win Probability added, is calculated by looking at each play made during the game and assigning the relative win percentage difference to the player responsible for that play. WPA is adjusted to not overweight the importance of defensive rebounds (they are important but defensive stops aren’t entirely due to the player who gets the ball after a missed shot). For the season, KU has a -1.214 WPA, meaning the Jayhawks have lost 1.214 more games than they were expected to. To be sure, KU has been mostly un-clutch this season².

These calculations provide another look at player value, in that we can see who is performing in the higher-leverage situations. Players who dominate the easier opponents but struggle against good teams in key spots might be fooling us with elevated value scores. WPA provides a different look.

At the top we have the following four players with positive WPA. Note a commonality?

• Dickinson: +1.346
• Bidunga: +1.207
• Adams: +0.518
• Clemence: +0.126

Again, we decreased the importance of defensive rebounds and played around with this metric, so this is not due to positional advantages per se. KU’s most clutch players are its post players. While this probably doesn’t bode well for March, when it is rumored that guards carry teams, this blame should be on the shoulders of the underperforming backcourt than the frontcourt. Has a single fan produced the narrative that the backcourt is the reason KU will struggle to win, while simultaneously defending the efforts of the bigs? Yet, consistently, we see that the 2025 Jayhawk frontcourt has out-produced the backcourt on a variety of value-metrics.

When we get to the rest of the list, keep in mind that more high-leverage opportunities equate to a higher likelihood of a WPA further away from 0. The rest of the list is as follows.

• Harris: -0.106
• Coit: -0.198
• Passmore: -0.225
• Moore: -0.413
• Griffen: -0.730
• Storr: -1.142
• Mayo: -1.503

For all the hate Juan gets, he’s been KU’s best backcourt option in not only PAB but also the clutchness factor of WPA. Contrast Juan with Zeke. Zeke’s play against Houston cost him an astounding 0.849 WPA, but even aside from his choke job against the Cougars he’s not been good against the better opponents or in many key situations. Zeke was also bad in the losses to Missouri, Iowa State, Baylor, and Kansas State. Now, we don’t want to overstate this. Mayo is also needing to do more than he should given the weakness of the team’s other wing scorers. He’s been a reliable piece on the whole. His play in key situations has to improve if KU’s going to make a deep run this March. But it hasn’t only been Zeke. Both Rylan Griffen and A.J. Storr, though they’ve been in fewer high-leverage situations than Mayo, have produced significant negative-WPA seasons. Not a single one of the 3 most-important transfer additions has been the slightest-bit clutch for KU. If we broke it down by the “Big 3” returnees (Harris, Adams, and Dickinson) and the “Big 3” transfers (Mayo, Griffen, and Storr), we’d show that the Big 3 returnees are collectively adding 1.757 wins while the Big 3 returnees are collectively losing 3.375 wins to what this KU team is expected to have on the season.³

In closing, Harris and Adams (and Dickinson) have done well enough to be seen as positive pieces to the 2025 KU team. While Bidunga has outplayed Adams on the whole, this hasn’t been the case in 3 of the last 4 recent games. Nevertheless, KU’s front-court is best with all 3 bigs rotating among 2 spots, and it is certainly better than the backcourt. Blaming K.J. for imperfection is missing the forest for the trees. Harris is KU’s best guard, something no one acknowledges.⁴ It is hardly his fault KU has failed to recruit or bring in talented transfer options to supplement his role. The sooner fans realize this, the better they can get closure with whatever happens for this team the rest of the way.

Addendum: 2024 Splits

Big 3 (Harris, Adams, Dickinson)	50.0% minutes played	+6.11 cumulative PPGAB
All Others in Rotation	48.6% minutes played	-1.69 cumulative PPGAB

Looking back to 2024, we see that the trio collective of Harris, Adams, and Dickinson was worth over 6 points per game of value compared to bubble, while the remaining scholarship pieces were well-below that level of performance. In 2024, Harris struggled while Adams played much better than he has in 2025, but the idea is the same. The “Big 3” as a group were getting KU on its way to 1-seed possibilities, but the team was held back by the sub-bubble play of the others.

Of course it would be wrong to not mention that Kevin McCullar, though not part of the Big 3 of the 2025 roster, was very much a member of this group for the 2024 team. McCullar was KU’s best player on a Per 100 possession basis (+6.73) and was worth +3.98 points per game in the games he was healthy enough to play. It was the combined efforts of the others that were a disaster.

For that reason, it’s tough to listen to critics who place heavy blame on any of Harris/Adams/Dickinson for the team’s record the past two seasons. KU would have missed the tournament last season without them, and they would be a bubble-team this year without the trio. While its true that each has limitations and there are valid critiques to each player’s overall game, the fanbase’s level of hate for Harris, Adams, and/or Dickinson is way too extreme. There’s no proportionality when comparing them to the rest of the roster.⁵

1. In other words, a team that has all bubble players + Harris/Adams/Dickinson would be favored by about 8.5 against a bubble team on a neutral floor. For reference a 1-seed is typically about 12 points better than a bubble team. The “Big 3” get KU over 70% of the way to 1-seed status, or about to 3-seed/4-seed status alone. ↩︎
2. Close losses include West Virginia, Houston, and the Baylor debacle. KU has won some close games early (North Carolina, Duke) but on the whole isn’t getting the best record given its skill-level. This can be looked at one of two ways. The pessimistic view is that KU cannot close against good teams and is destined for an early exist in the NCAA’s. The optimistic view is that KU is good enough to beat anyone and that their poor play in clutch situations is mostly “bad luck” that can be remedied. ↩︎
3. This doesn’t change much if you play with the relative value of a defensive rebound. It isn’t like WPA is overweighing Dickinson’s importance due to his defensive rebounding prowess. He’s just been more clutch than the others, with Mayo being most-unreliable in big moments. ↩︎
4. Almost no one acknowledges that Harris has been one of KU’s 2 best backcourt players either. We’d grant the possibility that Zeke is more important than Juan (Mayo is a better scorer to be sure), but after that there is a wide gap between Harris/Mayo and the rest of the backcourt. Yet it is Juan who gets the lion’s share of the blame. How absurd! ↩︎
5. In 2023, Harris + Adams combined for 2.56 PPGAB while the rest of the roster was at +4.35. When we adjust for minutes played, Harris + Adams as a duo were better than the combined efforts of the other rotation players. At no point in the last 3 seasons has it been statistically acceptable to place the majority of the blame on them for the team’s struggles. ↩︎

Franchise rankings are determined by allocating 100 points for the Super Bowl winner that season and then half as much for teams making it as far as the prior round. Therefore, the Super Bowl Runner-up gets 50 points, the Conference Championship losers 25 points, the Divisional losers 12.5 points, and finally the Wild-Card losers 6.25 points. The final column sets the top franchise to 1 and then rates the other franchises in terms of a portion of #1.

Additionally, the list shows the total appearances per round, including appearances even if that round hadn’t been devised yet. For instance, the Wild-Card round didn’t exist until the 70’s, but if a team made the Super Bowl in say, the 1960’s, it would show up as a Wild-Card appearance in the above table (as well as the Divisional and Conference rounds). Or, for teams that receive a bye past the Wild-Card into the Divisional, they still show up as having reached the Wild-Card round that season. That Wild-Card column can also be understood as a showing of a franchise’s total number of playoff appearances.

There are 32 current and total NFL franchises in the 59-year Super Bowl era. There have been a number of name changes, so the current franchise name and colors are listed in the table above. The Baltimore Ravens are treated like an expansion team (instead of the continuation of the old Cleveland Browns) following official NFL record keeping.

To compare franchises, divide one franchise into another. For instance, the #3 Dallas Cowboys (0.988) have a franchise that is 1.36 x that of the #5 Kansas Chiefs (0.725). Inversely, the Chiefs are 0.73 that of the Cowboys (divide 0.725 into 0.988). Speaking of the Chiefs, they were 0.311 of the top franchise and ranked in the middle at #16 before Mahomes became the starter in the 2018 season. They still have a bit of a gap to #4 San Francisco, but they have surpassed Green Bay thanks as they share Super Bowl titles with the Packers but have more appearances.

Despite having never won a Super Bowl, Minnesota (0.532) can boast a storied history of playoff appearances and NFC Championships. Minnesota grades out as a 2.33 x franchise compared to the New Orleans Saints (0.228), the worst franchise which has won the Super Bowl.

The franchise with the most playoff appearances is the Dallas Cowboys with 36. They also have the most Divisional appearances at 29. San Francisco has made 19 Conference championship games, and the New England Patriots have been to 11 Super Bowls, which is the most. New England and Pittsburgh share the record of 6 Super Bowls, and the Steelers edge out the Pats in terms of which franchise can claim being the best over the past 59 seasons.

Tom Brady, whose career lasted 23 seasons for the Patriots and Buccaneers, has contributed to 1,019 points or 0.953 of the entire 59-year history of #1 Pittsburgh Steelers. If we exclude franchises he played for (as his numbers are included in their totals), the only other franchises which have a better history than Tom Brady are Dallas and San Francisco. Patrick Mahomes, to his credit, is already at 0.421 for his career (putting him in front of the bottom 17 franchises)¹.

1. This counts only Mahomes as a starter. His rookie season (in which his team lost in a Wild-Card game) is not counted as he did not appear in the postseason. ↩︎

Published 1/31/2025

Not many more players in the history of Kansas basketball have been as polarizing as Dajuan Harris and K.J. Adams. Both have been starters on the three most-recent KU teams and both are seen as being responsible for the success or lack thereof since 2023.¹

We’ll table the contributions of K.J. Adams for now, and focus on Dajuan Harris, particularly his play this current season. A common refrain from many is that Harris is not good enough to be KU’s starting point guard, or he should play limited minutes, or even that the team would be better starting someone else (like Shak Moore). With Harris’ injury and a DNP, we got a glimpse of the post-Dajuan era, and it wasn’t pretty. Kansas nearly lost to a middle-of-the-pack conference team at home. Ironically enough, it was the defensive plays and free throws of K.J. Adams which helped seal the win.

Nevertheless, we’ve wanted to compare the play of Dajuan Harris to others for some time, as a way to contextualize the discussions around the only Jayhawk to have played the last five seasons at KU. Specifically, we were most concerned with this season. Harris in 2021 was not good, and even in 2022 he was the weakest link of the starting rotation by far. No one is denying this. Furthermore, the biggest critiques currently Harris faces is from his play this season, and they really aren’t warranted.

Harris Compared to Players in Kansas’ History

First, we will compare how Harris stacks up to others in Kansas history using the fullest statistical data we have, which is defensive charting and the PPGAB/Per100AB calculations. With video available in KU seasons from 1993-2025 (albeit not complete), we have a good idea as to the contributions of the 194 players who’ve played for Kansas over that time. In these 33 seasons there’ve been 321 player-seasons with at least 10% of minutes played, indicating we have plenty of KU players over the years to compare Dajuan Harris to.

How does he stack up? Using a combination of the 3 main value metrics—PPGAB, Per100AB, and WAR—we place 2025 Dajuan Harris in the 66.3%ile, or #102 out of 321. If we just look at PG seasons since 1993, this places him at #18 out of 63, two spots behind his solid 2023 season. This is about in the middle of all PG starters since ’93 (#18 with 33 seasons played).² Harris’s Per100 metric, which is minutes-neutral, places him in the 70.8%ile, right next to 1993 Adonis Jordan and 2004 Keith Langford.

There might be some room for critiquing Juan’s play over the course of last season (he was at the 34.4%ile) and certainly his play as an underclassman was subpar. But it’s a mystery why he gets so much heat in a year where he’s been better than the other options in the backcourt. Let’s compare the 2025 perimeter rotation next.

Harris Compared to 2025 KU Guards

Through 20 games, Harris edges out Zeke Mayo as KU’s most-valuable perimeter player this season. This might be partly due to timing, as Mayo had his worst game of the season against Houston. So come season’s end, maybe it will be Mayo then Harris. Either way, Harris is clearly more valuable than the other options. Let’s compare the team’s 7 perimeter rotation players in terms of minutes-neutral Per100AB:

• Harris +4.35
• Mayo +3.90
• Griffen +1.60
• Coit -1.82
• Moore -2.47
• Storr -4.24
• Passmore -15.66

Harris’ would-be replacements, Coit and Moore, have struggled more often than Juan has this season. Why would KU want to give up 6 to 7 points per 100 possessions by leaving Harris out of the game longer than Juan needs for his normal in-game breaks, occasional foul trouble, etc.? Perhaps during games where Moore or Coit is playing well and Harris is not you might see diminished minutes for Harris so the hot hand can continue playing, but the general trend of the rotation should be for Juan to get the most minutes.

Harris Compared to Big 12 Guards

The last response of Juan’s critics normally accepts that Harris is KU’s best option as point guard this year, but pivots and says this is due to poor recruiting at the guard position. We’ll let the reader judge for himself how good KU’s recruiting has been, but we can judge this idea in terms of how Harris stacks up to other guards in the Big 12. If Harris is in the middle-of-the-pack or below compared to the other Big 12 guards, this would be a problem for a team which wants to win the league and get a high seed in March. Ultimately, for KU to win as a team it needs its players to win their individual match-ups. If Harris is not doing this at a high-enough rate, it means the other positions have to pick up the slack if KU is going to reach its regular season goals heading into March Madness.

Unfortunately, we do not have charted stats for the other 15 Big 12 teams. We can’t fully say how good a different player’s defensive coverage is, or how much more value a player’s shot-creation is adding or subtracting. Therefore, we need to use a different set of statistical information. This won’t be as robust or accurate as the charting process, but it is the best we have when comparing players on other teams. We will use 3 sources and a total of 5 metrics to analyze and rank the Big 12 guards through the end of January, 2025.

First, we will use Bart Torvik. We will use Torvik’s algorithm to generate the list of Big 12 point and combo guards. Setting the limit to all players who’ve played 25%+ minutes, and filtering to include only the “pure point guards,” “scoring point guards” and “combo guards,” we get a population of 48 guards. This works out to an average of 3 per team, which is fitting as it aligns with the common practice of having three ball-handlers rotate at the 1 and 2 positions throughout the game.

Now that we have the list of 48 Big 12 ball-handling guards, we will use Torvik’s (offensive) value score, PRPG! as well as his defensive PRPG! metric. Combining these two numbers gets us a total PRPG! estimate, which serves as Torvik’s estimate to the relative value of a player. Total PRPG! is our first metric.

We will also use Torvik’s calculation of Box Plus-Minus (BPM). BPM is an older value metric than PRPG! and can be calculated a bit differently depending on the source. BPM is probably the most similar to CtH’s PPGAB or Per100AB, just with less information than what we gather. BPM is our second metric.

The third metric is taken from Evan Miyakawa. It is BPR, or Bayesian Performance Rating. This rating utilizes On/Off +/- data to estimate a player’s value. While we have our own concerns with how “noisy” (and accurate) this data is, its value is that it is conceptually different from other value metrics. Also, it should in theory provide more insight to defense.

The fourth metric is College Basketball Reference’s Win Share/40. While Torvik’s PRPG! is a per game metric, WS/40 is minutes-neutral. We don’t want to overly bias players who play large minutes against those who play fewer.

The fifth metric is CBB Ref’s PER, Player Efficiency Rating. This rating is offense-heavy, and if anything is done to hedge. We don’t want to bias anything toward Harris, and by including PER we are doing the opposite.

These five metrics are recorded for each Big 12 guard, and then normalized to find a mean and standard deviation among the population. From here, corresponding z-scores are calculated for each category.

Next, we average the z-scores among these categories to find an average z-score and then find the associated percentile to make the ranking more intuitive.

This exercise places Dajuan Harris as the 9th best 1-or-2 guard in the Big 12, at the 78.5%ile and ahead of anyone else on 11 of the other 15 Big 12 teams. We’ll grant that both Houston and Iowa State have better backcourts, but Harris would objectively start on 14 of the 16 Big 12 teams this season.

If the critic’s point was simply that Harris is not elite, no one would disagree, but Harris is viewed much more negatively than that. And for no reason. It’s factually wrong that Harris in 2025 is not a good college basketball player at the high-major level.

2/14/2025 Update – Juan up to #5 among Big 12 PG/CG’s

Through 24 games, Dajuan Harris is now grading out as the 5th best B12 PG/CG. There are 42 such players with at least 25% minutes played for their teams, with Harris in the 80.6th%ile among his B12 peers. He’s now grading slightly better than the Iowa State guards (Lipsey, Gilbert), which shouldn’t surprise anyone who saw the KU/ISU matchup in Lawrence.

Note that there are some B12 guards classified as “wings” which aren’t included, with some having classifications changed with more games being played. It’s unlikely that including these players would change the calculation much (some would grade better than Harris, but most would be below him). Either way, for Harris to be in the 80th percentile of ball-handling guards in the B12 shows he has value. When comparing Juan to his Kansas counterparts-Shakeel Moore (33.5th percentile) and David Coit (27.0th percentile), it puts into perspective that KU is better with Harris in the game.

1. While not the point of this blogpost, Harris and Adams were starters on a 2023 team which won the Big 12 and earned a 1-seed. Sure, they lost in the Round of 32 (by 1, without Bill Self). They were also starters on a 2024 team that, despite its flaws, was a 4-seed despite losing its best wing scorer in Kevin McCullar. Now the Round of 32 is, by definition, a disappointment for a program at KU’s level, but it’s not like the Harris/Adams era is colored by missed tournaments or even bubble-teams. When viewed in the context of the overall landscape of college basketball, KU’s been quite good the last couple seasons. There needs to be more common sense in assessing the state of the program. ↩︎
2. We haven’t adjusted WAR, which accumulates over a season, so it is more likely than not that Harris’s 2025 season relative to KU history improves rather than declines. ↩︎

The 2025 Kansas Jayhawks enter the New Year after falling in their conference opener (for the first time in over 3 decades), and are now 9-3 (0-1) on the season. This year’s team has had some good wins, but it has also had some bad losses. With the majority of conference play still to come, let’s recap the season so far and look ahead to what might be.

Season So Far – Player Breakdown

After deriving an estimated level of value in terms of points against a “bubble-level” player or a replacement player, there are four different ways to assess player value. The first, or PPGAB +/-, rates a player’s contribution in terms of points per game above or below an average player on a bubble-team. Generally, KU rotation level players should strive to be above 0.00, and the average number of positive-bubble players KU has in a season has been 6.1 since 1993¹. This year, with Harris, Mayo, Griffen, Adams, Dickinson, and Bidunga; KU is right at its average.

Hunter Dickinson, at +4.44 per game, is having the 24th best PPGAB +/- season in the last 33 seasons of Kansas basketball. This is the 94.8%ile of all player-seasons. He’s nearly a full point-per-game better than his 2024 campaign (+3.46), with this being due to better defense (his offense has actually declined). He’s averaging 15.1 and 10.4 per game on 27.7 minutes a contest. He leads in blocks, is second in steals, and third in assists. Dickinson’s Per 100 is second on the team behind Bidunga (more on him later), and is currently at the 94.0%ile of all KU players since ’93 who’ve qualified². Hunt’s minutes are down some this season, which is good, but we’ll see if he is asked to play more now that conference play has commenced. Hunter is adding 2.05 WAR, again leading the team. Similarly, his POCWAB is +0.92, leading the team in this metric as well. Simply put, Hunter is better than most of the bigs he faces on the block and cleans up defensive possessions well with boards. He does get hurt when teams make him guard the ball-screen and can fluster him at times when he’s on offense. But he’s the fourth-best player on KenPom for a reason, folks³.

Zeke Mayo, at +3.02 per game, is KU’s hottest player, averaging 26 PPG over his last 3 games. Zeke puts to bed the lie that it is impossible to adjust to a higher level of competition or that Self’s system isn’t conducive to transfer-ins. He’s been tremendous, and is adding value above-bubble on both ends of the floor. Mayo’s Per 100 value puts him at the 81.3%ile among KU players, very close to senior-year Steve Woodberry (1994). Mayo also doesn’t have a real weakness. He’s not a lock-down defender nor excellent ball-handler, but he can cover his man and handle it full-court in limited stretches. Expect him to continue to get big minutes.

Flory Bidunga is a +2.54 per game player despite playing only 12.6 minutes per game. Such high value in such limited playing time has skyrocketed the true freshman to the upper echelon of the Per 100 list. Bidunga’s value per possession is the 11th best since ’93 (96.7%ile), among other elite interior players like 2009 Cole Aldrich and 2012 Thomas Robinson. Will Flo keep up such play if he gets more playing time, something that seems to be on the way? We shall see. But he has been a bright spot thanks to his hustle, rebounding, elite finishing ability at the rim, and overall feel for the game.

Dajuan Harris is having an underrated season, generating +2.14 PPGAB and +4.03 Per 100. He’s in-between sophomore year Jacque Vaughn (1995) and senior year Adonis Jordan (1993) at the moment. Given the criticism of Juan, one would think he was having a terrible year. Harris’s WAR and POCWAB is 3rd on the team, outranking Flory due to Harris having more minutes played and more clutch moments against the better teams. One concern is that Harris is playing worse of late–only +0.54 per game over his past 5 games–but trends come and go throughout the course of any season.

There’s a noticeable decline following these top four players. K.J. Adams is technically an above-bubble player on the season, with a +0.62 PPGAB score and +1.24 Per 100. His corresponding WAR of +0.76 is also okay, and would be over +2.25 if the season extended to the average 36 games this year. But, Adams has a negative POCWAB, which shows he hasn’t been clutch against the tougher opponents on the whole.⁴ This was exacerbated by a season-low -7.59 game-score against West Virginia in a 1-point loss.

Rylan Griffen has been similar to K.J. on a value-scale. He’s at +0.52 PPGAB and +1.43 Per 100 in fewer minutes than Adams, making his WAR a bit less. Griffen is also below 0 on POCWAB, with poor outings against N.C. State and West Virginia being the major culprits⁵. Much has been made about his struggles shooting the ball, but his 56.0% true shooting isn’t bad. But he’s not a high margin scorer, nor does he create many opportunities for others.

David Coit has been KU’s best player not in the green, with a -0.59 PPGAB and -1.76 Per 100. Despite being a scorer at the lower levels, he’s also played within his role as a bench energy-guard who can hit the open shot and given effort on both ends.

Rakease Passmore, at -1.89 PPGAB, would be KU’s worst rotation-player in this stat if he played more. His Per 100 of -17.15 certainly is. It’s doubtful that Passmore qualifies with 10% of minutes played by season’s end.

A.J. Storr, who is sitting at -2.03 PPGAB, is KU’s lowest-earning player. He’s also got the second-worst WAR and worst POCWAB on the year. In fact, Storr has only added to his team’s chances of winning in 2 games this season, first against mid-major Oakland and then again at Creighton. He has also had 3 games where his play was neutral in helping the team win, meaning the remaining 7 games (including North Carolina, Michigan State, Duke, Missouri, and West Virginia) were games in which his play hurt the team.

Zach Clemence and Shakeel Moore haven’t played 10% of minutes and thus don’t need much discussion so far. Each has dealt with injuries and had limited impact on the results of the season thus far.

What Comes Next?

Now the fun begins. Kansas is reeling, having lost 3 of 5 and a home game to a West Virginia team that wasn’t supposed to be that good. With losing comes criticism, and there is much consternation about this team.

Hopefully, some of that has been answered above. There are four players (Hunt, Flo, Zeke, Juan) who have been comfortably above-bubble players all season, and have clutched up well enough to help the team win games. While not perfect, these four should be seen favorably. Everyone else warrants more serious scrutiny.

Kansas has struggled with K.J. Adams on the floor recently, though it’s not all the time. Both Creighton and now West Virginia took advantage of his inability to hit the outside shot and sagged off him. While all teams do this to some degree, Adams had very poor games in these two losses, contributing to his negative POCWAB. When he was benched for Flory Bidunga, the Jayhawks made their run to cut an 18-point deficit to 4. The game was eventually tied, with Adams on the court (though he didn’t contribute positively to the effort), but the clear message from Flory’s performance was that Flo could do things K.J. just can’t. Rebound, finish inside, and even cover the roll man on the high-ball screen. Yes, Flory can’t shoot from the outside. But neither can K.J. Might as well play 2 bigs who are elite at the rim/on the glass more.

Even worse has been A.J. Storr, someone who arguably cost KU against West Virginia and could have lost the North Carolina or Duke games had other Jayhawks not rallied and clutched up. One doesn’t hear enough criticism about his play. Some even pine for him to start. While our sympathies are with those who want K.J. to sit more until and unless he starts showing some value, the replacement cannot be A.J.

Our starting line-up, followed by the bench in terms of use, is as follows:

1. PG Dajuan Harris
2. SG Zeke Mayo
3. SF Rylan Griffen
4. C Flory Bidunga
5. C Hunter Dickinson

Yes, there are 2 centers listed. This recognizes that KU won’t have anything resembling a stretch-4 or point-forward, but rather two more-traditional bigs. Can Self make it work? No reason to think it couldn’t be an improvement. After all, he played with 2 bigs from 2004 through 2016, winning multiple conference titles, going to 6 Elite 8’s and 2 Final 4’s, and winning a national championship. Mayo and Harris will have to make plays with the ball, utilizing the bigs for screens and finding them in places where they can be effective. Hunter can also pop out, and his outside shot (which is okay, not great) becomes a more-valuable option when you have Flory (a great offensive rebounder) underneath.

The bench, in terms of minutes-played, should be as follows:

• K.J. Adams
• David Coit
• Shakeel Moore
• A.J. Storr

This makes a 9-man rotation, with it being necessary for each player to get in the first half for a minimum of 4 or 5 minutes. No excuse. If the game is close, the starters will need blows early so their legs are fresh for a longer run in the second half. If KU builds a comfortable lead, it makes sense to rest the starters some, and if KU is trailing then that must be because some of the starters aren’t playing well and something needs to be tried.

Adams has still been the best of the rest. He’s athletic, a versatile and solid defender, and his energy isn’t matched by other options at the 4 once you pull Flory or Hunter. Putting in another guard, such as Coit or Moore, and shifting Griffen down to the 4 makes the shooting better but it makes the defense much worse.

Coit, as a backup, can spell Mayo, Harris, or even play alongside them in a 3-guard look. Moore is slotted in as Harris’ backup, though he needs closer to 10 minutes, not the 1:40 per game he got against N.C. State and West Virginia. He did get 14 against Brown, in a runaway Jayhawk victory.

A.J. Storr stays in the rotation, despite showing nothing this season to merit it. Storr’s reputation and history as a skilled scorer and adept foul-drawer is what keeps him getting playing time. He should spell Griffen at the 3 primarily, but could arguably get some minutes at the 4 if Adams isn’t working out that night.

What Self decides to do shall be seen. Unfortunately, he doesn’t seem all that willing to sit Harris for long. Harris is averaging 35.3 minutes in the 7 games against power-conference teams. Now, this hasn’t always been the worst move. Moore wasn’t available in all of these games. Harris had great games against Michigan State and N.C. State, and held his own against North Carolina, Duke, Missouri, and West Virginia despite slow starts in the Mizzou and WV games. Harris is +12.01, unadjusted, against these 7 teams this year in total. What one hopes is that Moore’s presence can eat minutes up that Harris can use resting, allowing KU to tire out opposing guards while helping Harris be at his max for crunch time. Why else is Moore on the team?

The biggest issue is how he handles the 4-spot, really the 3 and 4 spot. Flory, getting only 12.6 minutes per game, is being wasted. Getting this number up to 20, 22, even 24 could change the dynamic of the team enough to help it. Going with a 2-big line-up means shooters need to shoot, with Griffen, Coit, even Storr being the wings alongside Harris/Mayo in the backcourt. At the moment Griffen seems to be the best choice. We won’t completely write-off Storr just yet.

The Real Issue – Energy

Focusing on each individual misses that basketball is a team game, and that how someone approaches things affects everyone else. KU brought zero energy to start the Creighton, Missouri, and West Virginia games⁶, trailing by 10+ points within 10 minutes of each. They had double-digit deficits in each second-half by more than just 10, and each time their comebacks were thwarted in part due to how far behind they got. Had KU only gotten down by 10-12 against West Virginia, instead of 18, it’s likely they would have won. Cutting Missouri’s 24-point lead to 2 in the span of about 10 minutes was impressive, but why not keep the game within arm’s length so your eventual run gets you in front?

Energy, or lack thereof, has been tied to poor road performances recently. KU is 3-9 in true road games the last two seasons, and 1-8 over its most recent 9 road contests. This is unheard of. These games are marked by poor energy, with a number of them being effectively over by halftime. Again, this is where utilizing the bench can help.

1. Minimum of 10% minutes played. ↩︎
2. Minimum of 10% minutes played. ↩︎
3. Pomeroy’s rankings are a bit suspect, but if someone is included on them, he certainly isn’t a net negative to the team he plays for. ↩︎
4. K.J. certainly showed his worth against Duke and Flagg, earning a positive game-score. ↩︎
5. In fact, Griffen’s performance against N.C. State is the team’s worst clutch performance this season, given the opponent’s quality and being blind to the performance of the team. ↩︎
6. KU’s energy wasn’t great against Michigan State, although the Spartans didn’t seem to have much either. Eventually both teams worked into the game and it got better, with KU’s talent winning out. ↩︎

Below are the market-derived odds for various championships in sports. These odds are updated manually as of the date listed, so may not be current. This is the most unbiased look at what the market views as the true odds for a team to win a title.

NCAA Basketball Champions (April 2026) – As of 3/30/2026 – Final Four

• Michigan – 37.5%
• Arizona – 32.9%
• Illinois – 17.1%
• Connecticut – 12.5%

With the Final Four set, the winner of Michigan/Arizona will be the favorite for the title against whoever wins the first game. We’ve come along way since last April, when the odds were (keeping with the order of teams above) 2.5%, 1.8%, 0.8%, 3.4%.

2026 Masters Champion – As of 3/24/2026 – Top 10

• Scottie Scheffler – 9.4%
• Jon Rahm – 5.6%
• Bryson DeChambeau – 5.6%
• Rory McIlroy – 5.1%
• Xander Schauffele – 3.8%
• Ludvig Aberg – 3.6%
• Matt Fitzpatrick – 2.9%
• Cameron Young – 2.9%
• Tommy Fleetwood – 2.7%
• Collin Morikawa – 2.2%
• Justin Rose – 2.2%
• Patrick Reed – 2.0%

Scheffler’s odds have dropped from once being 16.0% to now being 9.4% with just one more week before Masters week. He was a late WD at the Houston Open (won by Gary Woodland), so his health and overall game is perhaps a question. The split to win by nationality (cup team) is USA 51.4%, EUR 33.9%, INT 14.7%. USA golfers have won 10 of the last 12 majors with EUR winning the other 2. The last INT win was with Cam Smith at the 2022 British Open. Hideki Matsuyama (1.8%) is the best chance for them to win.

We’re looking for a wide open Masters 2026. The winner is just as likely to come from anywhere outside the top 13 of contenders than it is from those top names. Back in January it was 10 players that made up the top half of the win equity.

Looking Back at Recent Champs

CFP 2025 College Football Season (January 2026)

Indiana won the CFP at the end of the 2025 season. Here is a history of their odds.

• Before Quarterfinals: 21.8%
• Before Semifinals: 41.4%
• Before Finals: 73.7%

NCAA Basketball Champions (April 2025)

Florida won the 2025 National Championship. Here is a history of their odds.

• 2/17 – 7.5%
• 3/3 – 7.0%
• 3/17 (Before R64) – 16.2%
• 3/22 (Before R32) – 17.2%
• 3/24 (Before S16) – 17.5%
• 3/29 (Before E8) – 21.0%
• 3/31 (Before F4) – 24.0%
• 4/7 (Before CG) – 51.0%

Florida had the fourth-best odds for much of February into March. By the start of March Madness, this had grown to second-best (behind Duke).

Starting Win Odds for 2025 Major Champions

• 2025 British Open: Scottie Scheffler – 13.6%
• 2025 U.S. Open: J.J. Spaun – 0.5%
• 2025 PGA Championship: Scottie Scheffler – 11.8%
• 2025 Masters: Rory McIlroy – 9.4%

Published 12/27/2024, before the quarterfinal round has begun.

Although the 12-team CFP has already started, here are the championship odds for each team using College Football Insiders data:

1. Oregon – 23.8%
2. Georgia – 16.0%
3. Boise State – 0.7%
4. Arizona State – 3.1%
5. Texas – 14.3%
6. Penn State – 8.2%
7. Notre Dame – 10.3%
8. Ohio State – 10.1%
9. Tennessee – 2.2%
10. Indiana – 3.8%
11. SMU – 5.2%
12. Clemson – 1.8%

A noticeable thing occurs when looking at seeds 3 and 4, Boise State and Arizona State, who earned byes into the quarterfinal round due to their conference championships but are nevertheless the two weakest teams in the playoff. Boise has almost no shot of winning, and Arizona State’s odds before the playoff were worse than two of the first-round road teams.

Because the bracket isn’t balanced like one normally assumes it would be (with the #1 seed being the best followed by the #2 seed and so on), there is a strong argument to make that being the #5 or #6 seed is actually better than being the #1 or #2 seed. Let’s compare the #1 seed with the #5 seed, remembering that we need to consider the playoff scenarios before the CFP started. #1 Oregon will play the winner of #8 Ohio State and #9 Tennessee (this is Ohio State) in the quarterfinals. #5 Texas plays #12 Clemson and, if it wins (it did), faces #4 Arizona State in the quarters. So, Oregon just has to win 1 game and Texas has to win 2 games to get to the semifinal round, but Texas arguably has the easier opponents.

Oregon is given a 59.5% chance of winning its Rose Bowl (quarterfinal) game and making the semis. Texas is given a 52.4% chance of winning both the first round and then the Peach Bowl game to make the semis. But, this was before the tournament began. Since that time, Oregon’s quarterfinal opponent was confirmed to be the tougher Ohio State, giving the Ducks a 55.4% chance of making the semis. This still favors Oregon’s path, but 55.4% vs. 52.4% is not a big difference.

To add further weight to the idea that Texas has an easier path to the CFP semifinals, let’s look at actual odds from the bettors. Texas was 13.5 point favorites vs. Clemson, corresponding to a 86.0% chance of winning. In the Longhorns’ Peach Bowl game against Arizona State, they again are 13.5 point favorites. Taking 0.86 * 0.86 gets us to 0.7396, or a 74.0% chance of Texas making the CFP semifinals. Oregon on the other hand is 2.5 point underdogs to Ohio State in the quarterfinal Rose Bowl game, giving the Ducks a 45.8% chance of advancing to the semifinal bowl game. Now, this isn’t a perfect comparison as we don’t exactly know what Oregon’s odds would be in Texas’ spot in the bracket and vice-versa, but Texas would have to be about 10 point favorites vs. Ohio State for it to be a wash for the Longhorns and Oregon would have to have been only about 7.5-point favorites in both games it would have played (Clemson/Arizona State). It’s doubtful that’s the case.

At the moment, the final 8 teams have the following odds of winning the CFP according to the website’s odds (which do differ noticeably from the market-based odds):

1. Oregon – 20.5%
2. Georgia – 14.2%
3. Boise State – 0.6%
4. Arizona State – 2.4%
5. Texas – 19.5%
6. Penn State – 13.4%
7. Notre Dame – 15.6%
8. Ohio State – 13.8%

FanDuel’s futures odds give the following implied predictions of winners¹:

1. Oregon – 14.8%
2. Georgia – 14.8%
3. Boise State – 0.8%
4. Arizona State – 1.1%
5. Texas – 22.8%
6. Penn State – 14.2%
7. Notre Dame – 10.1%
8. Ohio State – 21.5%

Texas and Ohio State are seen as the strongest teams, and Penn State, the #6 seed, despite not being the strongest team, is nearly equal money with Georgia and Oregon due to the Nittany Lions having an easier road through the quarters.

1. Calculated using the money-equivalent-bet method, where we presume the market is predicting a bet for any team has the same expected average cash out. Or, any of the FanDuel futures would be a comparable bet to all others were the winning percentages to work out in this way. I can think of no better way to derive winning percentages from market odds given that bookmaker’s take cuts (and thus you cannot just add all projected pay-outs and get it to equal 1.0). ↩︎

In the spirit of designing advanced stats with silly acronyms, today we introduce Player’s Own Clutch Wins Against Bubble (POCWAB). What makes POCWAB unique is that it looks at each game individually to see how well someone performed in helping his team win that game. Unlike other methods, which take a player’s season-long value and divide it over the number of games or possessions played, POCWAB weights game played against the better teams more heavily. A player who performs solidly in the games where KU is favored by 20 but doesn’t play well against the tougher teams on the schedule will show up as not being clutch. The player who does show up in the big moments will be recognized even if he doesn’t have the best value metrics for the whole season.

POCWAB is calculated by seeing what a bubble-team’s chances of winning that particular game would be, then taking into account that specific player’s game-specific raw PPG +/-, then recalculating what a bubble team’s chances are of winning given what the specific player did that game. So if Player A is playing in a game where the stakes are even (i.e. against a bubble team) and said team has a 50% chance of winning and said player produces +4 points of raw value, we now calculate how likely it is a team would win if it were 4 point favorites. (Technically we also account for minutes played, so it would be more like if the team were 4.5-to-5 point favorites). If the team now has a 65% chance of winning, this would mean the player contributed 0.15 clutch wins (65%-50%). Clutch wins can be positive or negative.

We can then take that number and sum the total value of clutch wins, or POCWAB.

Since 2022, the furthest year we’ve gone back so far, here are the five best seasons of POCWAB (as well as the leader in 2025):

• Jalen Wilson 2023: 6.09
• Ochai Agbaji 2022: 4.68
• Christian Braun 2022: 4.16
• Dajuan Harris 2023: 2.91
• Jalen Wilson 2022: 2.82

• Hunter Dickinson 2025 (9 games): 0.61

This and That

POCWAB appears to correlate well to WAR and PPGAB +/-¹. This probably shouldn’t be too surprising, but we might expect that, for a few players, someone stands out as especially clutch or un-clutch². For a more in-depth discussion of clutchness, see the note below.³

The season of Remy Martin in 2022 needs to be explored. In Remy’s first 11 games, he had a +0.54 POCWAB, good for third on the team. This was the non-conference portion of the schedule. Remy would miss the 12th non-conference regular season game with injury, and once conference play began was nothing like the player thought it was getting. Martin only played 10 total and 9 conference games (missing 9), producing -0.29 of POCWAB in this span of 19 games⁴. In comparison, Dajuan Harris was still able to add +0.24 POCWAB as a game-manager sophomore point guard during this time. The final stretch, the 9 games which made up KU’s Big 12 Tournament Title and NCAA Tournament Title runs, saw Remy produce a second-best +1.00 POCWAB (only behind Agbaji). Remy’s NCAA Tournament run of +0.67 was the team-lead. Undoubtedly Remy was clutch in the biggest games.

KU’s worst players in POCWAB over the past few seasons have been its ball-handling guards. Joseph Yesufu (-0.93) and Dajuan Harris (-0.81) were the worst in 2022. Bobby Pettiford (-1.66) was easily the worst in 2023. In 2024, it was Elmarko Jackson (-1.97). This season it has been A.J. Storr (-0.51) and Rakease Passmore (-0.41), though David Coit (-0.26) has also struggled given he’s had a decent season. Coit’s expected POCWAB is only -0.08.

The best relatively clutch seasons are from the clutchest players as seen above for the most part. KU’s 2022 team was clutch, unsurprising given how well it did at the end of the season against tournament competition.

Hunter Dickinson has been relatively un-clutch (does better against easier opponents) over his 2 seasons at KU. Kevin McCullar last season was the worst clutch player when comparing his performances against top opponents vs. the easier ones. This might not be Kevin’s fault, as his injury affected his performance during tougher conference games.

For future plans with this, see the notes⁵.

1. In fact it has an R^2 of 0.9123 with PPGAB +/-. ↩︎
2. Perhaps the only one I could find was the comparison between Mitch Lightfoot and Jalen Coleman-Lands from 2022. Coleman-Lands had a better PPGAB +/- (+0.41 to +0.15), Per100 (+2.88 to +0.72), and WAR (+0.91 to +0.85); but the POCWAB was +0.11 to +0.34 in Lightfoot’s favor. ↩︎
3. Clutchness is an oft-debated topic in analytics, with many stating it does not exist. We believe it does, but that it is very tough to measure. Frankly, the best players are the best because they are consistently good. But we can still measure player performances while accounting for high-leverage situations. In our scenario, the high-leverage scenarios are just those games which are likely the closest for a bubble-team. POCWAB could be made even better if we added new caveats (such as the actual final margin) or played around with the hypothetical replacement level (which is bubble-level). ↩︎
4. The Kentucky game (which saw KU humiliated in Allen Fieldhouse) was in the midst of conference play so it is part of the 19-game stretch we discuss. ↩︎
5. What could make POCWAB better is to increase leverage for NCAA Tournament games, account for how a player’s teammates performed in each game, and adjust the hypothetical level from bubble-level to something higher (like a 4-seed). Right now, Remy Martin’s clutch play isn’t accounting for the importance of the Tourney. Right now, performances like Jalen Wilson’s clutch game in the OT loss at K-State in 2023 are being undervalued, as the only reason that game was even close was due to Wilson’s elite play. Right now, games against teams much better than bubble-teams are not seeing as much leverage as games against true bubble-level teams. Using 2025 as an example, a player’s POCWAB can be affected much more based on his performance against Missouri or Creighton than on his performance against Duke. ↩︎

With only conference championship games left to play, here is how the field currently shakes out for the 2024, 12-team College Football Playoff.

1. Oregon
2. Texas
3. SMU (presumed conf. champ.)
4. Boise State (presumed conf. champ.)
5. Penn State
6. Notre Dame
7. Georgia
8. Ohio State
9. Tennessee
10. Indiana
11. Alabama
12. Arizona State (presumed conf. champ.)

The presumed conference champions get their spots in the field due to winning the conference, not due to the CFP’s actual ranking of teams. From there, the at-larges are slotted in order from highest to lowest until the field is filled. The teams on the outside-looking-in, in order, are:

1. Miami
2. Ole Miss
3. South Carolina

Let’s look at certain scenarios to determine how much the bracket can change from here to Sunday when the final rankings are released and actual bracket is generated.

The MWC conference game takes place Friday night, with Boise State hosting UNLV. Obviously Boise wins and it is in. If UNLV wins, Boise will fall outside the top 12 and the Rebels will almost certainly get a bid in the CFP. The reason we use almost is because there is another conference championship being played that night, Army vs. Tulane. In theory, a 1-loss Army team could get the final spot reserved for a conference champion over a 2-loss UNLV. However, given the fact UNLV is currently at #20 and Army is at #24 as well as the fact UNLV is facing a tougher opponent (#10 Boise State) than Army (unranked Tulane), it is very difficult to see Army jumping UNLV and making the field¹.

Regarding seeds, Boise State with a win should hold on to the #4 seed. The only team that could jump over the Broncos would be the Big 12 winner (#16 Iowa State / #15 Arizona State, more on this game later), and while the Big 12 winner will get a solid win to add to its resume, a Boise State defeat of UNLV would boost Boise a bit as well. So it’s very unlikely that Boise falls to below the #4 seed and first-round bye.

On to Saturday, where the Big 12 championship game kicks off first at 11:00 am in Arlington. This is a simple, win-and-you’re-in situation. Either Arizona St. or Iowa St. will represent the Big 12 in the CFP. The only question is whether or not the winner will earn a bye and top 4 seed. They will have a better idea depending on the result of the MWC game the day before, but the ACC result later on will matter as well.

Next, at 3:00 pm CT, is the SEC championship game from Atlanta. Georgia and Texas face in a rematch, with the winner almost certainly getting a top-2 seed and the loser falling somewhere from #5-#10. Texas is probably the only team that can get a #1 seed (if Oregon loses) and would likely only fall to #6 at worst. Georgia winning would get the Bulldogs a #2 seed and losing would give them 3 losses and could make them be the road team in the first round of the playoff. It is highly unlikely a 3 loss Georgia team falls out altogether, given the SOS is great and only getting stronger this week and the fact UGA has 10 wins already. In other words, Georgia will remain in front of the other 3-loss SEC teams.

Saturday evening sees the final two relevant conference championship games, with the ACC championship in Charlotte and Big 10 championship in Indianapolis. Should Oregon knock off Penn State, the Ducks keep the #1 seed. A loss puts them down to the #5 seed regardless. Penn State winning would get the Nittany Lions a #2 seed if Texas won the SEC game and a #1 seed if Georgia won. A Penn State loss would find the Nittany Lions in the #5-#7 range depending on what happened with Texas/Georgia and how the committee decided to order Penn State with Ohio State. In the head-to-head, Ohio State defeated Penn State. However, even with a loss, Penn State would have a better record (11-2 vs. 10-2) than Ohio State. But both teams would still host a first round game.

Onto the ACC, which has the biggest implications for the bubble. SMU is the #3 seed and keeps that with a win, whereas Clemson is only in the field with a win. Let’s start with a Clemson win to see how high the Tigers could likely go. At 10-3, Clemson would have a worse record than 11-2 Big 12 winner. They would also be behind a 12-1 Boise. However, if UNLV beat Boise the night prior, Clemson winning would get them into the top-4 with UNLV getting the final conference spot (and #12 seed). Can Clemson jump the Big 12 winner? Not likely, however it would have a better win (SMU is #8) than either Big 12 team winning, as it is #15 vs. #16 in Arlington.

Clemson winning would knock down SMU, the current #3 seed but #8 ranked team. A loss, albeit not a “bad” one, would drop the Mustangs some. How far is the question. In this scenario, 11 teams would be decided (Oregon, Texas, Penn State, Notre Dame, Georgia, Ohio State, Tennessee, Indiana, ACC champ Clemson, Big 12 champ, MWC champ), with the final spot presumably coming down to SMU, Alabama, and perhaps Miami. It’s tough to see how SMU losing would help Miami, but we will get more into the Miami/Alabama discussion at the end. Were SMU to lose and it came down to SMU/Alabama, would the committee punish SMU for losing a conference championship game (they were required to play in while Alabama sat idle)? Seems like a really poor decision. Then again, the committee seems to be beholden to the wishes of Greg Sankey.

SMU can end all discussion by winning and securing a #3 seed, a spot which would give it a good chance of advancing (vs. the winner of #6 and #11) in the quarterfinal bowl game it plays in. While it may still make the CFP with a loss, the best chance SMU has at going all the way is to win Saturday night in the ACC title game.

Now let’s look at some further scenarios. Notre Dame is effectively locked in as a first-round host, but it would get all the way up to #5 with losses by both Penn State and Georgia. If both Penn State and Georgia win, the Fighting Irish are nearly guaranteed to get the #7 seed. Still a first-round home game, but not as good of a path.

Ohio State can move up 1 or 2 spots (they need Penn State and/or Georgia to lose), but as the current #8 seed they are poised to host a first-round playoff game. What a weird year with the 12-team field. Normally losing on senior day to arch-rival Michigan would be the end of the Buckeyes’ dreams, but not only can they still win a national championship they can do so by winning in their final game at the Horseshoe, thereby helping to erase the embarrassing loss in the Big Game.

Idle Tennessee (at #9) could move in to a host spot were Georgia to lose and lose somewhat convincingly. Again, it’s tough to say how far the committee would ding a 10-2 Georgia if it loses on Saturday to fall to 10-3. Georgia already beat Tennessee this year head-to-head, why should the Vols be rewarded from sitting at home while the Dogs are forced to play Texas? But would the committee keep Tennessee at #9 and drop Georgia to #8, generating a first-round all-SEC rematch? Not sure that’s what anyone really wanted the playoff for.

Idle Indiana (#10) seems stuck in place, with the only potential movement coming in the event of an SMU loss (which would likely move the Hoosiers up to #9). This would still force Indiana to go on the road in the first round, in spite of its 11-1 record. We have Indiana with the 8th best resume (WAB), as does ESPN’s SOR. Tough luck for the Hoosiers, who have a convincing case that they should be hosting a first-round playoff game. Still, Indiana is a lock.

We finally make it to the final playoff spot, which likely comes down to Alabama, Miami, and SMU with a loss. Should the Ponies lose, this gets even more complex than it already is. Let’s assume SMU wins and look just as Alabama/Miami. For Alabama, they can’t do much except trust that the committee won’t change their collective mind. The Tide are in the #11 spot, one ahead of Miami, and both teams are idle. Should be an easy case of “this has already been decided.” For Miami, they arguably have a better resume (ESPN SOR disagrees, but other sources show the Canes), but if the committee doesn’t see it that way they can’t do anything. One thing that would benefit Miami is a Georgia loss. While the Dogs won’t likely fall below Miami in this scenario, it would weaken the resume of Alabama, a team who boasts a big win against Georgia. If this win were diminished somewhat, is that enough to push Miami over ‘Bama? Likely not, but some hope is better than none. Still, I don’t see Alabama falling out to Miami. The far more likely way for them to miss the field is if Clemson beats SMU (in a close game), allowing SMU to remain in the field but nudging the Tide out in the process.

Predictions:

Boise State over UNLV. Boise in the field as #4.

Tulane over Army. Both out, but ending any long-shot of Army sneaking in.

Iowa State over Arizona State. ISU in the field as #12.

Georgia over Texas. UGA as #2, UT as #5.

Oregon over Penn State. UO as #1, PSU as #7.

SMU over Clemson (close game). SMU as #3.

Notre Dame goes to #6. Ohio State #8. Tennessee #9. Indiana #10. Alabama #11.

Predicted First round games:

#12 Iowa State @ #5 Texas

#11 Alabama @ #6 Notre Dame

#10 Indiana @ #7 Penn State

#9 Tennessee @ #8 Ohio State

Bowl games:

#1 Oregon chooses the Rose Bowl.

#2 Georgia chooses the Peach Bowl.

#3 SMU chooses the Fiesta Bowl.

#4 Boise State is slotted the Sugar Bowl.

1. Had UNLV lost to say, Kansas in the non-conference, then a 3-loss UNLV team would likely miss out to a 1-loss Army. Had this occurred, what would have been even more amazing would be the fact Army still has a regular season game to play…its rivalry game against Navy. Navy is not the worst mid-major at 8-3 (#68 in FPI, so near the middle of FBS), and could easily knock off Army in a game that wouldn’t matter to the playoff committee solely because it was played after the selection. Inversely, perhaps Army getting an 11th win vs. Navy before the conference championship would have boosted its resume enough to help it jump over UNLV. The timing of the Army/Navy game almost had huge ramifications on the CFP field, and arguably it still might have had a minor impact this season. ↩︎

Tonight (12/3/2024) will be the second-to-last CFP field reveal of the 2024 season, with the final 12 being announced this Sunday following conference championship Saturday. Even despite the field expanding from 4 teams to 12, controversy and debate have followed the announced rankings each week. This season has seen quite a bit of parity with multiple potential playoff teams losing games as favorites late in the season. Just this past weekend (Thanksgiving weekend), Miami lost to fall to 10-2 and miss the ACC championship game, Clemson lost to South Carolina to fall to 9-3 (but gained entry to the ACC championship game due to Miami’s loss), and of course Ohio State fell to 10-2 with its loss to rival Michigan. Additionally, Georgia needed 8 overtimes to escape its rival Georgia Tech.

Generally the debates about the field are whether or not a 3-loss SEC team (Alabama, South Carolina, Ole Miss) should make the playoff over a 2-loss ACC team like Miami or if a 1-loss Big 10 team with an easier schedule like Indiana has done enough to earn a bid. Much of the controversy is pointless discussion by people who don’t actually go through the exercise of filling out a 12-team bracket. Still, it is very likely that 2 to 3 teams who are left out will claim valid reasons for why they should have been included.

Rather than debating the merits of each particular team, we should determine what the criteria should be beforehand and use this criteria to rank the teams. This helps to take away any bias. From the CFP website, here is how the committee looks at how it selects and ranks the teams:

The selection committee ranks the teams based on the members’ evaluation of the teams’ performance on the field, using conference championships won, strength of schedule, head-to-head results, and comparison of results against common opponents to decide among teams that are comparable.

This criteria was first stated in 2016 when the CFP was 4-teams. Of course the 4-team era had its own controversies, including last season when undefeated Florida State missed out after losing its starting QB to injury and not impressing the committee enough in how it won.

Frankly, last season’s decision to drop FSU to #5 and a slew of other decisions have harmed the committee-model. I’ve defended the basketball selection committee from those who’ve wished for a computer-based selection process, but the basketball system seems to have better criteria and the committee members are more reliant on resume metrics than simply the teams’ brands. Regarding computers picking teams, the BCS computer-based model of years ago had its own flaws (including leaving out AP #1 USC in 2003). Regardless of the method chosen, some of this becomes “pick your own poison.”

But the poison of the current system is that, using the own words of the committee, win/loss results in-and-of-themselves don’t matter. This is somewhat overstated, of course a team’s record does matter, for instance the committee will view 10-2 Tennessee better than 9-3 Alabama, given the two share three common opponents in SEC play and a head-to-head matchup (won by Tennessee). But this is rather easy to do, we hardly need a committee to tell us this. What is more difficult is comparing the performances of 11-1 Indiana in the Big 10 and 10-2 Miami in the ACC. Neither share a common opponent nor were there many games between Big 10 and ACC opponents to draw from. The ACC went 3-2 this season, but this is hardly enough to go off of. In addition, given how large these conferences have become, a team can have a much easier or harder schedule than the median team in a particular conference. So when it comes to deciding, between certain teams, too often the committee goes with its gut instead of what’s fair.

The committee is tasked with something difficult (i.e. comparing Indiana vs. Miami) without many tools at its disposal nor a clear direction on how to get there. This only leads to biased selections, justified in their own minds no doubt, but biased nonetheless.

A Map and a Compass

The CFP Committee needs not just a map, it also needs a compass. The map in this case is a clear description as to the criteria the process is selecting for. The compass is the metric-based tool that helps the committee along the path to ranking 25 teams and selecting 12.

So what is the map? What should the criteria be? This is where the arguments come in. In my opinion, here is what the committee should be tasked with doing:

The selection committee’s job is firstly to rank the teams most deserving of making the Playoff, determined by how strong each team’s record is in comparison to the difficulty of its schedule, using a number of pre-approved analytic metrics. When head-to-head or shared-opponent comparisons exist, they should be used but not without due concern to the prior criterion. Thirdly, conference championships may be considered but with understanding that each conference is unique in terms of number of teams, strength of teams, and games played.

The term “deserving” alongside a “Strength of Record” metric is the crux of what the committee should be tasked with ranking. That metric should be its compass. Currently the term “deserving” isn’t used, and this is where the current bias comes in. Teams with good records who aren’t winning impressively (but are winning nevertheless) are seeing their clutch-play in high leverage situations discounted so a team with arguably more talent but also more losses can make the field. This makes a mockery of the regular season and the idea that winning games matters.

Now ESPN has a Strength of Record metric, one the committee can (and does) use. This is a good thing, however this metric is not without needless flaws. First, the metric is a black box in that it isn’t calculable by outside sources. Second, we don’t know the degree of difference between teams, just the order of teams. In other words, it is a ranking not a rating. If there is a clear gap between the #4 SOR and the #5 SOR, we should be able to see this. Third, it may be the case that this metric, based upon ESPN’s Football Power Index (FPI) includes a preseason weight. Obviously preseason weights cannot exist in determining the playoff field for this season.

What is preferable is an independent SOR, one that can be checked/validated by the public and one which only considers game results from the current year. Interestingly, it isn’t too easy to find this around the web. Power ratings are published in numerous places, although many of these have preseason weights and are thus not acceptable for use in creating a SOR metric. But the scarcity of people attempting to objectively rank college football resumes shows how dire the situation is. The old bowl system was fairer, because at least the smaller (8-10 team) conferences of those days did a decent job of determining a regional champion and the bowl tie-ins featured teams of comparable conferences.

Ranking the Field. Top 20 teams as of 12/3/2024.

Rank	Team	Record	WAB (SOR)
1	Oregon	12-0	2.817
2	Texas	11-1	2.192
3	Penn State	11-1	2.001
4	Notre Dame	11-1	1.994
5	Georgia	10-2	1.949
6	SMU	11-1	1.587
7	Ohio State	10-2	1.421
8	Indiana	11-1	1.367
9	Boise State	11-1	1.037
10	Miami (FL)	10-2	1.032
11	BYU	10-2	0.918
12	Tennessee	10-2	0.864
13	South Carolina	9-3	0.800
14	Iowa State	10-2	0.674
15	Alabama	9-3	0.648
16	Arizona State	10-2	0.591
17	Army	10-1	0.319
18	Illinois	9-3	0.271
19	Ole Miss	9-3	0.153
20	Missouri	9-3	-0.006

The preceding table shows an objective SOR-type metric (Wins Above Bubble or WAB) which allows us to not only rank the teams but also show how close they are to one another. We see Oregon, the only undefeated team left, far and above the others as having the #1 resume. We see how the various 11-1, 10-2, and 9-3 teams are handled. For instance, #11 BYU is nearly a full win above #19 Ole Miss in this scenario¹.

The table was created using College Football Ranking’s Simple Rating System (SRS) numbers as the power ratings. These SRS team ratings allow us to see how likely it is for a “bubble team” (set as an SRS of 15) to have won any particular game on any particular schedule (accounting for home, neutral, or away). When we compare this to a team’s actual results, we arrive at a team’s WAB/SOR. So Oregon, by virtue of going 12-0 against the schedule it faced, did an estimated 2.8 wins better than a “bubble” team.

Admitted Drawbacks

The logic is simple enough as to why an objective, resume-based selection system is ideal. But there are drawbacks. One is that while there are numerous objective metrics we can use, there isn’t one infallible way to determine the order of the resumes. To highlight this, we will take a look at the top teams in ESPN’s SOR.

Oregon remains #1, however #2 isn’t Texas (like we have) but rather Georgia (who is #5 in our rankings). Now while these two teams will determine it on the field in the SEC Championship game, the fact of the matter is sometimes different rating systems will produce different resumes. ESPN appears to show the SEC stronger than CFR does (perhaps ESPN uses preseason weights?), with Tennessee at #7 in SOR not #12. ESPN does see a similar spread between BYU and Ole Miss at #12 and #18.

Another website, one of the few I found which looks at resumes in this way, uses Massey and Sagarin to determine team strength. It has Big 12 teams stronger than other sources, with BYU (#7), Iowa State (#10), and Arizona State (#11) all currently much higher than elsewhere and in the top 12. It has Ohio State at #12, which would technically put the Buckeyes outside the field given the need for a fifth conference champion in the playoff (presumably #13 Boise State)².

The point is, there isn’t a “One True Metric” that will prevent all controversy. Objective systems disagree with one another. Now we could average a series of different computer-based resumes similar to how the BCS system worked, and this would help reduce error. Regardless, a committee should still exist, just with guidelines and a strategy (map and compass).

Closing Arguments

With its map and its compass, the committee should look seriously at a collection of resume-metrics to give it a full picture of which teams deserve consideration as the most “deserving.” From here, other metrics (including Game Control) as well as head-to-head and shared opponents should be considered, particularly among teams which are very close to one another in resume ratings. For instance if SMU lost to get to 2 losses and was right next to BYU and on the bubble, it would be tough to go with the Mustangs over the Cougars given BYU’s win at SMU earlier in the season.

EDIT: Comparing Alabama with Miami

Last night (12/3/2024), the committee’s updated rankings had Alabama over Miami, giving the Crimson Tide the final at-large spot in the playoff and left the Hurricanes outside the top 12. Although Alabama is 9-3 and Miami 10-2, the committee felt Alabama had better wins and that was enough to jump over Miami. Let’s compare the two resumes:

Best wins for Alabama:

• at LSU +.507
• vs Georgia +.490
• vs South Carolina +.478

Best wins for Miami:

• at Louisville +.530
• at Florida +.453
• vs Virginia Tech +.375

Despite what the committee said, Miami’s top wins aren’t that much below Alabama’s. The committee focused on top 25 wins, which Alabama does have in greater excess, but the top 25 they used was the very list they selected! If we go by a top 25 of computer metrics, whether that be College Football Reference’s SRS or ESPN’s FPI, then Miami has top 25 wins against Louisville, Virginia Tech, and Florida at (for FPI, SRS has the Gators at #27) while ‘Bama keeps its 3 listed above. Now, the Tide’s top wins are objectively better than the Canes’, but the gap here isn’t as wide as the committee made it look. Take Miami’s road win at Louisville. Even the SEC-biased FPI has Louisville at #13.

But, good wins are only part of the equation. Michigan arguably has the best win of the season, going into Columbus and beating Ohio State last weekend. But no one has the Wolverines in the CFP, simply because their record is not good enough at 7-5. Too many bad losses in other words. So let’s compare the losses for both Miami and Alabama:

Alabama losses:

• at Vanderbilt -0.664
• at Oklahoma -0.625
• at Tennessee -0.507

Miami losses:

• at Georgia Tech -0.598
• at Syracuse -0.595

Alabama’s 2 worst losses are objectively worse than Miami’s losses, as Georgia Tech and Syracuse aren’t actually all that bad. In fact, losing at Vandy is something a bubble-team should only do 1 in 3 times and losing at Oklahoma about the same. In fact, ‘Bama went 2-3 on the road whereas the U went 4-2. Different schedules, but if you cherry-picked those results it would lead you to favoring Miami over Alabama. Case in point for a new map and compass; when things are close in the standings, the committee can basically pick whatever data points it wants to get the result it wants.

Which brings us to the final point, we need to look at the entirety of the teams’ resumes to determine who is more deserving. Did the committee do this? Likely not. We have Miami as having a 1.032 WAB (#10) and Alabama at 0.648 (#15). The Massey/Sagarin WAB average method had Miami with 0.85 (#14) and Alabama with 0.84 (#16), so closer, and BPI’s SOR does have Alabama (#10) ahead of Miami (#14)³. Using SOR would have been valid justification had the committee done that. Instead, we got cherry-picked portions where Alabama’s resume was better as they ignored areas where Miami’s was superior.

Perhaps no team is more overlooked than BYU. The Cougars are 10-2 with wins against Kansas State, at Baylor, and at SMU. They have the #11 WAB in the SRS model, #7 WAB in the Massey/Sagarin average, and #12 SOR in BPI; yet the committee has them at #17. BYU’s computer (efficiency) numbers are poor–#17 in SRS, #30 in College Football Insiders, and #32 in FPI–but again this shouldn’t matter if we focus on the final results. At 10-2 against a solid schedule, BYU should be very much in consideration for a top 12 spot. Instead, they are very much out of consideration.

1. Ole Miss is an interesting case. The Rebels have great wins against Georgia and at South Carolina, but they also have a home loss against Kentucky alongside two others. Meanwhile, BYU’s true road win against a top-2 ACC team in SMU, which wasn’t necessarily impressive at the time, has actually become the third-best win this season of any team listed in the top 20 above. BYU also got a great road win at Baylor, a team that finished 8-4 and with strong computer metrics. ↩︎
2. Making matters more complex, there are metrics arguably better than WAB at determining which resumes are best, including a multiplicative one which looks at what level of likely team strength would be needed to have a team’s actual record against its actual schedule. ↩︎
3. Again, I found it highly likely that there are preseason weights in FPI’s numbers. The SEC is very strong when compared to other systems. And in researching, I found the Wikipedia article mention: “In college football, each team unit has its own prior. Four of the main inputs for each prior includes data on the last 4 seasons (with an emphasis on the previous season), the number of returning starters on the offense and defense (with the QB counting as more), a binary input on the returning coach, and the strength of the team’s recruiting class (with an input for transfers). College FPI is more reliant on the priors in the model due to the regular occurrences of mismatches each week.” Amazing. FPI, which is owned by ESPN (the same organization which broadcasts the CFP and weekly rankings show), includes preseason metrics in its most important computer ranking system, thereby affecting the rankings for teams/conferences who may not have been historically great but are having good seasons. FPI also affects SOR, thereby inflating or deflating resumes based upon the historic strength of teams, not their proven on-field strength for this season. ↩︎