KenPom Rankings, Game Score, and the Difficulty of Determining Who is Best

KU’s 24-point win against a hapless Oklahoma State team last night boosted its KenPom rating to +22.27, which is good enough to be 14^th overall as of January, 16. There’s been some debate about the accuracy of KenPom’s system, as it appears to reward teams more for blowing out bad teams than beating good ones in close games.

Without getting into a longwinded opinion about what KenPom is “about,” I wanted to explore what I call a team’s “Game Score” and see if, in theory, a good team could improve its KenPom rating by scheduling easier.

First, let’s discuss Game Score. Game Score is the attempt to quantify how good a team’s game is by adjusting the final margin depending on the opponent and location. That is, a 10-point home win against a team ranked 250^th would be much less impressive than a 10-point road win against a team ranked 25^th. Game Scores tie to KenPom’s ratings in a few ways. One, they use KenPom’s Adjusted Efficiency numbers to determine opponent strength. Two, they are linear or rely on a linear system to compare game performances, just like KenPom. If a team is projected to win by 10 and it wins by 15, the team’s KenPom rating will climb. However, the hypothesis is that a good team can have a higher average Game Score (and in turn elevated KenPom rating) by playing an easier schedule, because it is easier to “juice” a winning margin in games already in hand. When playing an overmatched opponent, leaving in the starters just a few extra minutes can be the difference between say, a 25-point win or a 30-point win. The Game Score will increase by 5 points as well, as the opponent adjustment is already baked-in.

To test this, we will use KU games over the past few seasons. Kansas has been consistently good over this period and is a good team to use. My theory is that there will be an inverse relationship between Game Score and opponent quality. Remember, in theory, opponent quality should neutralize the effect of the actual margin, making Game Score independent of opponent quality. Does reality show this to be the case?

KU has played 229 official games since the beginning of the 2018 season (November 2017). These games have been played against a variety of opponents, some good, some not. Will it be easier to produce higher Game Scores against worse opponents than better ones?

Results

There was a small inverse correlation between opponent quality (opponent’s KenPom rating) and Game Score. While this might seem insignificant at first, it can make a difference when we look at the results.

Since 2018, KU is averaging a +8.03 Game Score. This just means that KU is beating its opponents by an average of about 8 points more than a typical bubble-team would. When we break it down into buckets by quality of opponent, we see a clear trend. Against the best teams in the country, those with a +20.00 KenPom rating or higher (roughly your top 15 to 20 teams, depending on year), Kansas is only producing a Game Score of +6.07. Against decent to okay teams, those with a KenPom rating from 0.00 to +20.00 (corresponding to ~ 20 – 180), Kansas is producing a Game Score of +7.72. The best Game Scores are those against inferior opponents, those with a negative KenPom rating or who are worse than the average D1 team. KU is, on average, +13.82 in these games.

What this shows is that, over the past 7 seasons, KU is more likely to climb in KenPom when it plays games against easier opponents. As KenPom is trying to account for opponent strength already, it does give strong credence to the idea that teams who play a weaker schedule are juicing their KenPom scores by doing so. This would apply to teams like Gonzaga in past seasons, but also to the non-conference schedules of Houston and BYU in 2024.

What we’ll next show is the expected KenPom ratings/rankings for a hypothetical Kansas team that is as strong as the average KU team since 2018.

The first column looks at the average opponent KenPom rating, and then projects this hypothetical KU team’s KenPom rating. The next column is the KenPom ranking for additional context. We see that as the average opponent score climbs, the KenPom rating/ranking for hypothetical Kansas worsens. A schedule of 0.00 opponents would allow Kansas to juice its score all the way to a #4 ranking this season. If it played a schedule against top-tier opponents, like it will in stretches of conference play, this ranking drops out of the top 10.

An additional level of data relates to the 374 games played since the 2014 season. KenPom provides pregame point differential estimates as well as winning percentage estimates (effectively odds). With this info, we can make the prediction that KU will “overperform” in easier games compared to games where the margin is expected to be smaller. In the 90 games where KU had a 90% chance or greater of winning, that is the easier games, KU has outperformed its expected KenPom margin by +2.41 points per game. In the 284 other games, the games expected to be closer, KU is outperforming KenPom expectation by only +0.49 points. This accounts for a difference of 1.92 points per game, or roughly 2.74 AdjEm on KP. Again, this is further confirmation that good teams can juice their KenPom ranking by playing an easier schedule.

Relevance to the 2024 season

This is relevant to Houston, a team which has a non-conference average opponent rating of -1.21 (215^th), as well as BYU, a team which has a non-conference average opponent rating of -5.01 (307^th). For Houston, if we look at its 6 games against Top-100 opponents, we note that it has performed where a team with a KenPom score of +27.39 would, or nearly 5 points lower than its current (juiced) +32.31 rating. Only looking at conference-play against Top-100 opponents, a small sample size for sure, its comparable KenPom score is +21.82.

BYU’s 7-game slate against Top-100 opponents has it 4-3, with an estimated KenPom rating of +20.14. This is the difference between 9^th or 20^th. Again, we’re seeing evidence that a team’s KenPom rating is aided by blowing out inferior opponents. For both Houston and BYU, their overall schedule strengths will get better, meaning that what KenPom says about them in March will include more data against strong opponents. As both are good teams, one shouldn’t write either of them off. But what Purdue (best overall schedule) or Wisconsin (surprise team around BYU’s ranking yet has a solid SOS) have done so far should give them the nod ahead of teams similar to them in the KenPom totem.

We can use Bart Torvik’s filter tool to explore this phenomenon more. While Torvik has different inputs than Pomeroy, he is attempting to do the same thing. If we only look at opponents that are Quad 2 or better (using NET), we see that Purdue is best (and better than Houston by almost 7 points per 100 possessions. Houston is slightly better than Purdue accounting for all games). Similarly, BYU is 26^th best when playing top opponents, as opposed to 7^th when all games are included.

Determining the Best Teams

So, who is the best team in the country? How much should advanced metrics be considered? Keen observers will note that, in criticizing KenPom’s rating for Houston or BYU, I still used KenPom numbers. That highlights the inherit difficult of any ranking system. Rankings are relative, they depend on how good others are. KenPom’s value is that the system provides a fairly accurate look at each team’s relative strength compared to any other of D1’s 362 teams. If a system makes adjustments to remove Houston or BYU’s blowout wins, it underestimates their strength relative to a team outside its Top-150. So for any advanced rating system to be most accurate, not just accurate among a certain range of teams, it needs to include all game data.

For the purposes of college basketball, the only important rankings are those of the selection committee preparing to set the field for March Madness. The selection committee gets criticized, sometimes rightly, but those who want to do away with the committee and institute a computer-based ranking system (such as WAB) are completely misguided¹. Even the most well-known advanced rating system has noticeable flaws. The committee’s focus on strength of schedule (dissuading teams from scheduling cupcakes in the non-conference to artificially boost their overall record) appears to be a good thing. It looking at a team’s best wins or worst losses will also filter out noise, allowing a better comparison between teams near each other on the seedline.

Summary

Teams have an easier time improving their advanced metrics by playing a softer schedule that allows for blowout wins. Teams that’ve achieved high rankings by playing a strong schedule should be seen with less skepticism than teams that have mostly blown-out lesser opponents. Still, let’s not be overly critical toward systems like KenPom, which are trying to rank all teams (not just the top 50 or whatever).

The only real ranking that matters is the NCAA Committee’s “S-curve” on Selection Sunday. While the committee does get things wrong, it has avoided errors by not adopting a metric-dependent system like some of its detractors demand.

1- Gasaway states the following in his appeal to do away with the Committee: “Whether your preferred metric is wins above bubble (WAB), strength of record (SOR), or something similar, any team’s record can be evaluated according to how difficult it would be for other teams to achieve the same result against the same opponents in the same venues. Wins are the key metric, as they should be, and teams can excel whether their schedules turn out to be grueling or unexpectedly soft.” (Links in original).

His assumption is faulty. We cannot confidently say that WAB or SOR are accurate enough when its been shown that the advanced metrics themselves understate how easy it is for a good team to blowout a bad one. This isn’t to say that WAB or SOR cannot be tools that an informed committee uses, but a committee-less NCAA Tournament selection process that only looks at a computer metric would reward teams that dominate against a light schedule and punish teams that take some lumps in tough conferences but still finish around 19-12.

Career WAR and PAB by Player (1993-2025)

Player	Career WAR	Seasons
Raef LaFrentz	20.20	4
Frank Mason	19.34	4
Nick Collison	19.22	4
Wayne Simien	18.95	4
Perry Ellis	18.35	4
Sherron Collins	16.72	4
Paul Pierce	14.82	3
Marcus Morris	14.81	3
Mario Chalmers	14.23	3
Marcus Garrett	13.61	4
Devonte’ Graham	13.57	4
Kirk Hinrich	12.99	4
Keith Langford	12.93	4
Jalen Wilson	12.91	4
Cole Aldrich	12.77	3
Drew Gooden	12.74	3
Jacque Vaughn	12.34	4
Devon Dotson	11.21	2
Ochai Agbaji	10.90	4
Scot Pollard	10.76	4
Thomas Robinson	10.76	3
Hunter Dickinson	10.45	2
Brandon Rush	10.31	3
Udoka Azubuike	10.21	4
Jeff Withey	10.16	4
Jerod Haase	9.92	3
Kenny Gregory	9.89	4
Markieff Morris	9.60	3
K.J. Adams	9.39	4
Dajuan Harris	8.88	5
Darnell Jackson	8.75	4
Wayne Selden	8.66	3
Christian Braun	8.57	3
Tyshawn Taylor	8.40	4
Aaron Miles	8.36	4
Ryan Robertson	8.33	4
Eric Chenowith	8.26	4
Steve Woodberry	8.18	2
Darrell Arthur	8.13	2
Travis Releford	7.99	4
Sasha Kaun	7.72	4
Greg Ostertag	7.68	3
David McCormack	7.13	4
Kevin McCullar	6.76	2
Julian Wright	6.33	2
Dedric Lawson	6.18	1
Landen Lucas	5.64	4
Ben McLemore	5.49	1
Russell Robinson	5.32	4
Andrew Wiggins	5.11	1
Billy Thomas	5.07	4
Nick Bradford	4.97	4
Svi Mykhailiuk	4.77	4
Richard Scott	4.64	2
Elijah Johnson	4.43	4
Rex Walters	4.43	1
Jeff Boschee	4.12	4
Josh Jackson	4.04	1
Adonis Jordan	3.82	1
Malik Newman	3.76	1
Xavier Henry	3.74	1
Joel Embiid	3.70	1
Tyrel Reed	3.66	4
Zeke Mayo	3.48	1
Gradey Dick	3.23	1
Kelly Oubre	3.06	1
Jeff Graves	2.96	2
Patrick Richey	2.52	2
Kevin Young	2.50	2
Sean Pearson	2.38	4
Brannen Greene	2.14	3
Eric Pauley	2.11	1
Lagerald Vick	2.03	4
Mario Little	1.99	2
Darrin Hancock	1.92	1
J.R. Giddens	1.90	2
Remy Martin	1.76	1
Lester Earl	1.71	3
Isaiah Moss	1.63	1
Flory Bidunga	1.59	1
C.J. Giles	1.56	2
Greg Gurley	1.46	3
Cliff Alexander	1.37	1
Tarik Black	1.26	1
Naadir Tharpe	1.02	3
Josh Selby	0.90	1
Jalen Coleman-Lands	0.89	1
Johnny Furphy	0.88	1
Silvio De Sousa	0.69	2
David Padgett	0.55	1
Andrew White	0.55	2
Brady Morningstar	0.55	4
K.J. Lawson	0.49	1
Chris Zerbe	0.49	2
Carlton Bragg	0.46	2
Ashante Johnson	0.45	2
Nick Proud	0.43	1
Alex Galindo	0.41	1
Tyon Grant-Foster	0.39	1
Michael Lee	0.38	4
Rodrick Stewart	0.34	3
Dwight Coleby	0.29	1
T.J. Pugh	0.24	4
Luke Axtell	0.21	2
Rylan Griffen	0.20	1
Conner Teahan	0.17	4
Ernest Udeh	0.16	1
David Coit	0.16	1
Micah Downs	0.15	1
Tyrone Appleton	0.12	1
Justin Cross	0.12	1
Joel Branstrom	0.11	2
Jeremy Case	0.08	4
Conner Frankamp	0.05	1
Omar Wilkes	0.05	1
Jeff Carey	0.02	4
Todd Kappelmann	0.02	2
James Sosinski	0.01	1
Will Thengvall	0.01	1
Chris Huey	0.00	1
Hunter Mickelson	0.00	2
C.J. Henry	-0.01	1
Merv Lindsay	-0.02	1
Latrell Jossell	-0.02	1
Chris Carter	-0.04	1
Josh Pollard	-0.04	1
Zuby Ejiofor	-0.04	1
Robert Reed	-0.05	1
Cam Martin	-0.06	1
Wilder Evers	-0.07	2
Scott Novosel	-0.08	1
Quintrell Thomas	-0.10	1
Kyle Cuffe	-0.10	1
Blake Weichbrodt	-0.10	2
Gethro Muscadin	-0.11	1
T.J. Whatley	-0.12	4
Royce Woolridge	-0.13	1
Shakeel Moore	-0.13	1
Sam Cunliffe	-0.13	1
Steve Ransom	-0.13	2
Jordan Juenemann	-0.14	4
Cheick Diallo	-0.15	1
B.J. Williams	-0.16	4
Marlon London	-0.16	2
Patrick Cassidy	-0.16	2
Tucker Vang	-0.17	1
Nicolas Timberlake	-0.17	1
Christian Moody	-0.22	4
Terry Nooner	-0.23	4
Jamari Traylor	-0.24	4
Elijah Elliott	-0.25	1
Bryce Thompson	-0.27	1
Garrett Luinstra	-0.28	1
Clay Young	-0.28	3
Zach Clemence	-0.30	3
Michael Jankovich	-0.30	5
Lewis Harrison	-0.31	2
Brett Olson	-0.31	2
Parker Braun	-0.32	1
Nick Bahe	-0.33	2
Matt Kleinmann	-0.34	4
Dillon Wilhite	-0.34	3
Jamari McDowell	-0.35	1
Brennan Bechard	-0.37	3
John Crider	-0.40	2
Brad Witherspoon	-0.41	2
Chris Teahan	-0.45	5
Chris Martin	-0.47	2
C.B. McGrath	-0.48	4
M.J. Rice	-0.48	1
Rio Adams	-0.49	1
Charlie Moore	-0.49	1
Chase Buford	-0.52	3
Evan Manning	-0.53	4
Niko Roberts	-0.57	4
Mario Kinsey	-0.57	1
Mitch Lightfoot	-0.58	5
A.J. Storr	-0.58	1
Christian Garrett	-0.63	4
Rakease Passmore	-0.70	1
Tristan Enaruna	-0.75	2
Joseph Yesufu	-0.76	2
Jelani Janisse	-0.76	2
Moulaye Niang	-0.77	3
Calvin Rayford	-0.91	3
Stephen Vinson	-0.97	4
Tyler Self	-0.97	4
Quentin Grimes	-1.08	1
Brett Ballard	-1.19	2
Elmarko Jackson	-1.22	1
Bryant Nash	-1.24	4
Bobby Pettiford	-1.25	2
Justin Wesley	-1.46	3
Jeff Hawkins	-1.51	4

Player	Career PAB	Per100 Est
Raef LaFrentz	602.09	9.56
Wayne Simien	562.14	11.40
Nick Collison	560.97	8.24
Frank Mason	479.82	6.31
Perry Ellis	469.44	7.44
Paul Pierce	421.88	7.95
Marcus Morris	403.97	8.84
Sherron Collins	400.72	5.63
Drew Gooden	385.30	8.58
Mario Chalmers	357.30	6.51
Cole Aldrich	345.19	8.52
Marcus Garrett	308.44	4.98
Jalen Wilson	306.66	5.50
Kirk Hinrich	304.84	4.16
Jacque Vaughn	291.66	4.38
Devon Dotson	284.80	7.35
Keith Langford	283.42	4.12
Scot Pollard	279.64	5.80
Thomas Robinson	279.47	8.16
Hunter Dickinson	269.99	7.39
Devonte’ Graham	268.48	3.41
Udoka Azubuike	261.53	7.35
Jerod Haase	246.40	5.12
Markieff Morris	241.41	6.55
Jeff Withey	240.36	5.92
Kenny Gregory	223.38	4.00
Steve Woodberry	216.52	6.15
Greg Ostertag	216.08	7.13
Darnell Jackson	212.28	5.85
Darrell Arthur	206.43	6.90
Brandon Rush	204.46	3.43
Ochai Agbaji	193.47	2.78
K.J. Adams	177.08	3.13
Dedric Lawson	172.46	8.37
Sasha Kaun	168.74	4.34
Christian Braun	163.93	3.25
Eric Chenowith	158.10	2.91
Ryan Robertson	150.49	2.57
Wayne Selden	148.71	2.64
Kevin McCullar	147.54	4.36
Julian Wright	145.37	4.86
David McCormack	142.05	3.48
Travis Releford	133.96	2.57
Ben McLemore	133.87	6.42
Andrew Wiggins	124.87	6.22
Rex Walters	117.23	6.51
Richard Scott	101.61	3.66
Joel Embiid	99.25	8.77
Tyshawn Taylor	97.72	1.38
Landen Lucas	96.69	2.57
Josh Jackson	94.79	5.02
Aaron Miles	93.34	1.24
Adonis Jordan	89.37	4.53
Xavier Henry	86.15	4.97
Billy Thomas	81.85	1.98
Malik Newman	74.52	3.45
Zeke Mayo	71.14	3.77
Nick Bradford	67.86	1.58
Kelly Oubre	67.71	5.12
Dajuan Harris	65.96	0.73
Gradey Dick	58.53	2.84
Jeff Graves	46.60	2.02
Darrin Hancock	39.99	3.25
Eric Pauley	36.86	2.41
Mario Little	35.66	2.80
Remy Martin	31.78	2.86
Russell Robinson	30.95	0.53
Flory Bidunga	29.76	3.07
Patrick Richey	26.92	1.05
Kevin Young	26.41	1.20
Lester Earl	24.22	1.47
Tarik Black	22.96	2.93
Cliff Alexander	22.90	2.66
Svi Mykhailiuk	22.67	0.42
Brannen Greene	21.84	1.16
C.J. Giles	21.18	1.65
Isaiah Moss	21.01	1.63
Jalen Coleman-Lands	16.03	2.84
Chris Zerbe	15.89	11.80
Nick Proud	13.96	8.31
Greg Gurley	11.53	0.70
Tyrel Reed	8.49	0.19
Silvio De Sousa	8.15	1.42
Andrew White	7.60	1.83
Josh Selby	6.29	0.68
Elijah Johnson	5.44	0.10
Tyon Grant-Foster	5.08	1.63
Dwight Coleby	3.88	1.65
Alex Galindo	3.87	0.97
Justin Cross	3.67	24.72
Ashante Johnson	3.58	0.62
K.J. Lawson	2.97	0.49
Tyrone Appleton	1.84	2.24
Joel Branstrom	1.24	1.05
Sean Pearson	0.61	0.02
James Sosinski	0.06	0.40
Will Thengvall	-0.01	-0.10
Chris Huey	-0.02	-1.29
Omar Wilkes	-1.23	-0.96
Chris Carter	-1.47	-81.38
Micah Downs	-1.68	-0.63
Robert Reed	-1.83	-20.92
Latrell Jossell	-1.88	-4.14
Merv Lindsay	-1.94	-3.96
Todd Kappelmann	-1.96	-1.58
Josh Pollard	-2.03	-8.91
Cam Martin	-2.84	-15.49
Scott Novosel	-3.84	-10.44
Kyle Cuffe	-3.98	-36.61
C.J. Henry	-3.99	-3.12
Wilder Evers	-4.34	-7.25
Gethro Muscadin	-5.36	-9.57
Blake Weichbrodt	-5.43	-5.85
David Padgett	-6.22	-0.60
Ernest Udeh	-6.41	-1.46
Royce Woolridge	-6.71	-8.72
Patrick Cassidy	-6.93	-17.73
Tucker Vang	-6.95	-24.81
Johnny Furphy	-7.52	-0.54
Zuby Ejiofor	-7.74	-3.46
Steve Ransom	-8.07	-6.99
Sam Cunliffe	-8.39	-6.48
T.J. Whatley	-8.46	-3.81
Conner Frankamp	-9.16	-2.33
Elijah Elliott	-9.85	-26.80
Jordan Juenemann	-10.16	-5.64
Garrett Luinstra	-10.42	-54.12
Quintrell Thomas	-10.42	-4.25
J.R. Giddens	-13.29	-0.45
Dillon Wilhite	-14.16	-18.38
Brett Olson	-14.17	-11.40
Cheick Diallo	-15.21	-4.30
Clay Young	-15.25	-7.92
Nick Bahe	-15.29	-8.48
Luke Axtell	-15.41	-1.44
Lewis Harrison	-15.68	-10.67
Michael Jankovich	-15.72	-8.46
Brennan Bechard	-16.31	-14.79
Hunter Mickelson	-16.39	-2.81
Brad Witherspoon	-16.44	-22.92
John Crider	-16.57	-16.62
Terry Nooner	-17.34	-4.46
Shakeel Moore	-18.02	-3.71
Rodrick Stewart	-18.30	-1.71
David Coit	-19.95	-2.17
Chris Martin	-20.02	-22.88
Jeremy Case	-20.26	-2.51
Carlton Bragg	-20.96	-1.56
Rio Adams	-21.25	-14.29
Chase Buford	-21.45	-20.43
Chris Teahan	-21.93	-10.15
Matt Kleinmann	-22.07	-6.44
Jamari McDowell	-23.30	-5.95
Evan Manning	-23.76	-12.57
Zach Clemence	-23.88	-5.05
Parker Braun	-23.89	-5.38
Niko Roberts	-23.94	-16.09
Christian Garrett	-25.09	-21.08
Rylan Griffen	-25.32	-2.17
M.J. Rice	-25.78	-8.48
Mario Kinsey	-26.22	-10.70
Bryce Thompson	-26.32	-4.42
Rakease Passmore	-30.60	-15.11
Nicolas Timberlake	-31.71	-3.47
Jelani Janisse	-33.52	-19.95
Jeff Carey	-37.13	-2.62
Tyler Self	-38.61	-23.72
Jeff Boschee	-39.46	-0.55
Charlie Moore	-40.09	-4.99
Conner Teahan	-44.97	-2.48
A.J. Storr	-46.29	-4.95
Moulaye Niang	-46.84	-6.83
C.B. McGrath	-49.94	-4.38
Stephen Vinson	-50.33	-10.27
Tristan Enaruna	-53.77	-5.46
Naadir Tharpe	-55.75	-1.67
Marlon London	-58.95	-3.21
Brett Ballard	-59.02	-11.75
Michael Lee	-62.49	-2.17
Joseph Yesufu	-63.93	-4.81
Christian Moody	-65.36	-3.05
Bobby Pettiford	-68.99	-7.94
Justin Wesley	-70.99	-9.75
Lagerald Vick	-73.99	-1.40
Elmarko Jackson	-74.52	-6.72
Quentin Grimes	-86.43	-5.01
Calvin Rayford	-87.98	-4.65
Bryant Nash	-88.18	-6.00
T.J. Pugh	-94.72	-2.50
B.J. Williams	-108.62	-2.95
Jamari Traylor	-111.52	-3.01
Jeff Hawkins	-113.17	-5.20
Mitch Lightfoot	-114.75	-3.57
Brady Morningstar	-116.05	-2.39

WAR vs. PAB

Wins Above Replacement (WAR) and PAB (Points Against Bubble) have similar concepts and therefore the lists will be similar. Both are attempting to rank the careers of all KU players since 1993 to see which Jayhawks have been the most valuable for the program. The differences stem from the benchmark player upon which value is judged. In WAR, the “replacement player” is basically an average D1 player whereas in PAB, the “bubble player” is a player whose value makes a bubble team no more or less likely to make the tournament. In other words, a bubble player is a much higher standard than a replacement player.

So why the two lists? Which list is more valid?

At the top of the list there isn’t a ton of difference as guys like Raef LaFrentz easily clear the bubble level just as they clear the replacement level. It is further down the list where this distinction matters. Take Jeff Boschee for instance. Boschee was a four-year starter who is currently 57th in career WAR but 168th in PAB. Boschee was better than a replacement player but not better than a bubble-level player for the majority of his career¹.

These are widely different rankings, leading to the follow-up question, which list is better? Well, it depends. Being a better-than-bubble player is crucial for KU to approach the success demanded of it. So we would normally lean with PAB. However, sometimes players who are not quite at bubble-level (but still solid enough to be above replacement level) are required to play heavy minutes due to roster issues or coaching decisions, and it wouldn’t be fully accurate to list these players below low-minutes-played players because of this. Boschee serves as a great example. He was certainly a below-KU-average guard given the minutes he played, particularly as an underclassman. But he was still valuable because he was better than the alternatives at his position.² This is where WAR can be a better metric.

There is a third way to measure that is present, namely Per100 (possession) against bubble value. This is the third column on the second table. This shows the value the player provided while on the court, effectively ignoring playing time. Be aware of the limitations of this measurement. One, if we sorted by this list we’d include walk-ons at the top who graded out very well in very small, unimportant minutes. Two, durability is an important feature inherent to both WAR and PAB but ignored in Per100 value. A player who generates high value per possession but does not stay healthy throughout the year may not help the team as much as someone who can play a full schedule and heavy minutes at a lower value. A four-year player with solid value (like Marcus Garrett) will provide more value to KU than an elite one-and-done (like Joel Embiid).

Both WAR and PAB are useful lists but cannot be considered in isolation. If we are looking toward the top of the list, PAB is the better measurement. It estimates how many points a player gave to Kansas above that of a bubble-player over his career. Toward the middle and bottom of the list, WAR becomes the better mark. Clearly role players with sub-bubble (but above-replacement) value are preferable to the walk-ons. Lastly, Per100 can add some more context but we must be careful to not over-emphasize this measure as it is total value, not per possession value, that is most important when determining this ranking.

Boschee’s only above-bubble season was his senior year in 2002. ↩︎
Marlon London and Jelani Janisse are examples of other backcourt players on the roster in the Boschee-era. KU’s backcourt talent depth was quite thin during Boschee’s first three seasons at Kansas. ↩︎

A Tale of Two Juans

Dajuan Harris’s last 15 games of 2023 vs. Dajuan Harris’s first 15 games of 2024:

Stat	2023 – Last 15 Gms	2024 – First 15 Gms
PPG	11.3	6.9
eFG%	62.8%	48.5%
TS%	61.5%	471.%
APG	5.8	7.1
A/TO	3.0	2.6
SPG	2.6	1.2
RPG	2.9	2.2
ORtg	109.6	102.4
Pts AB +/-	+48.39	-44.98
PPG +/-	+3.23	-3.00
Per 100 +/-	+5.14	-4.84

The numbers speak for themselves. Another notable thing is that KU’s average opponent had a KenPom rating of +17.10 over last 15 games in 2023 compared to +4.77 over KU’s first 15 games in 2024. Juan’s drop in production and efficiency has come against weaker foes.

The last three numbers are the value scores, first in total points against bubble, and then rated in per game and per 100 possession bases. These numbers account for opponent strength as well as deeper defensive metrics such as points allowed (Harris is allowing 12.4 points per game in 2024; it was 10.0 over the last 15 in 2023).

Aside from assists, Harris has gotten worse in every facet of the game. But even with assists, his increase in assists has come with a greater increase in turnovers, meaning his overall “ball-handling” value has worsened.

This and That

The following bits of info relate to the 2024 season through KU’s first 15 games.

Johnny Furphy’s defense has been better than his offense. Per 100 possessions, Furphy is about -1.03 points to a bubble player on offense and 0.00 to a bubble player on defense. Furphy is shooting well, but he relies on assists to score and turns it over far more than he creates offense for others. Defensively, his coverage score is not terrible and he rebounds at an acceptable clip.
Elmarko Jackson has posted 3 above-bubble offensive games, or 20% of the 15 he’s played so far in his KU career. And 2 of these came in KU’s first 2 games. What’s giving him any playing time at the moment is his defense. Jackson has produced 10 positive defensive outings this season (67% of games).
K.J. Adams is KU’s best defender right now, posting a +2.23 average defensive game score. This is far better than his 2023 season, which was negative (-0.10). Adams’ improvement on defense is in large part due to him defending less in the post as an undersized 5-man, his role last season. Offensively, Adams has seen a slight decline from ’23 to ’24 (+0.60 to +0.44).
Kevin McCullar’s value-score improvement from last season to this season, on a per game basis, is +3.98. This is better than Jalen Wilson’s improvement from ’22 to ’23 (+3.34), Ochai Agbaji’s improvement from ’21 to ’22 (+3.09), and Christian Braun’s improvement from ’21 to ’22 (+3.83). The largest season-to-season jump on record is Frank Mason’s ’16 to ’17 improvement (+4.52). Thomas Robinson from ’11 to ’12 was +3.67. Tyshawn Taylor from ’11 to ’12 was +3.65. Other large jumps in the past include Raef LaFrentz from ’95 to ’96 (+3.67), Keith Langford from ’02 to ’03 (+3.59), and Drew Gooden from ’01 to ’02 (+3.41). There have certainly been other great career developments of steadier growth, but in terms of having such a large leap in value production in consecutive years, what McCullar is doing this season is very special.
Hunter Dickinson’s two worst value-score games (-10.40 vs. Marquette, -6.23 at UCF) have coincided with both of KU’s losses. Through 15 games, Hunter is currently KU’s second-most valuable player this season, but is neck-and-neck with Kevin for this distinction. If both can finish the season above +5.00 per game, they’d be the first duo since the Morris twins in 2011 to do this for a Kansas team.
Jamari McDowell has not proven to be the answer to the team’s poor wing play. The freshman has produced the worst Per 100 value of any scholarship player, at -7.91. After some initial stingy defense, Jamari has gotten scored on quite frequently in limited minutes. At best, McDowell is only going to be a role player and defense-first guy this season.
Parker Braun has been KU’s 5th-best player, and might warrant more court time. He is athletic and experienced. His value scores through 15 games have been 7 positive, 7 negative, and 1 right at 0. While he is at Kansas primarily to back up Hunter, there have been a few moments where Self has gone “small” and played Adams as the 5 when Hunter is sitting. But this leads to more minutes for guys (Jackson, Harris, Timberlake) that aren’t playing as well as Braun has, and haven’t shown enough offensive firepower to compensate the loss of not having a true center inside. Parker isn’t necessarily someone who will win you the game, but he is someone who can keep you in it until Dickinson can return.

Wrapping Up the Non-Con

KU's McCullar earns Big 12 award after second triple-double | KSNT 27 News

The 2023 portion of the 2024 regular season is wrapped up. KU went 12-1 during this non-conference stretch, a good result given the quality of opponents it faced. Using the NCAA’s Net Rankings, KU went 3-1 in Quad 1 games and 1-0 in Quad 2 games. Extrapolate that type of performance out over the rest of the season, and the Jayhawks will cruise into March as a 1-seed. But, this may not be that likely given a few concerning trends.

Best Game, Worse Game

Using a very similar concept to how Ken Pomeroy rates his teams, Charting the Hawks using a point margin difference to rate individual players or games. For CtH, a comparison level of a bubble-team is used to judge how well a player or the Kansas team itself is performing. This is in point differential (or margin) in comparison to a bubble-level mark. For instance, if we’d expect a bubble-level team to beat KU’s current opponent by 10 points (after accounting for location), and KU wins by 18, we’d say that KU had a “game score” of +8.00. This +8.00 score would also equal the net of all Kansas players’ individual game scores, as the system is breaking down each player’s value as a portion of the total team score.

This system is not perfect, but it does have the benefit of being easy(ish) to calculate and understand. In the non-conference, KU’s best game (relative to opponent) was its first one against North Carolina Central. KU’s game score was +24.04, meaning it won by 24 more points than a bubble-team would have expected to. On the flip side, KU’s 8-point win against Eastern Illinois garnished a game score of -15.43, indicating that the actual single-digit margin Kansas won by was about 15 points worse than what a bubble-team would have expected to achieve.

Looking at individuals, KU’s best performance in a game was Hunter Dickinson’s +17.63 margin against Tennessee. Dickinson scored 17 points in that game, but defended great (only allowing 3 points) and rebounded at an elite level, coming down with 20 official rebounds. The interior was owned by Kansas, forcing Tennessee to jack up 33 3-point shots, only to make 9. KU scored 20 more points than the Vols inside the arc that game.

Hunter also has KU’s worst individual performance. It occurred the night before the Tennessee game, against Marquette. Hunt’s score was -10.16 points, indicating a bubble-level player (think average player on a bubble-team) would be expected to play this many more points better against that opponent. In Dickinson’s case, it was the play of Oso Ighodaro which contributed to such a poor game score. Hunter had his worst defensive performance as Ighodaro scored 21 points that night, much of it against HD. In total, Dickinson would give up 26 points to Marquette while grabbing a season-low 8 rebounds.

While this was Dickinson’s worst game, on the season Hunter has been tremendous. Through 13 games, he is adding an estimated 5.35 points per game above bubble, second only to Kevin McCullar at +5.89. The team, as a whole, is averaging only +5.30 points per game above bubble. While the Hawks are 12-1, their point margins haven’t been as good as we’d expect.

Grading Projections

Before the season, KU was projected to have an average game score of +8.51 (vs. +5.30 in reality). This 3.21 point per game difference could be the difference of a win and loss in multiple conference games. In fact, KenPom’s predicted scores for KU’s conference games show 14 games out of 18 to finish within single-digits. KU having underachieving its desired margins so far is a sign of concern, as point margin has predicative implications. This doesn’t mean Kansas can’t improve. To see how, let’s break down KU’s performance to the player level.

This table shows each player’s 2024 projection and actual play through 13 games in points per game.

Player	Pre-Season Proj	Current Actual
Dajuan Harris	+2.50	-2.79
Elmarko Jackson	+0.10	-0.89
Kevin McCullar	+1.71	+5.89
K.J. Adams	+0.93	+2.33
Hunter Dickinson	+5.18	+5.35
Nicolas Timberlake	+0.32	-1.82
Johnny Furphy	-0.84	-0.12
Parker Braun	-0.39	-0.37
Jamari McDowell	-0.72	-1.07
TEAM	+5.30	+8.51

In PPG, individual scores won’t add up to TEAM due to walk-on scores missing

While there is still a lot of season left, there have been quite a few players with far different scores than their preseason predictions. Dajuan Harris has been the worst, performing over 5 points worse per game than his projection. Nicolas Timberlake and Elmarko Jackson have also been worse than expected, although Jackson has performed to his preseason expectation over his last 8 games (thanks to his defense). But KU’s guards are what’s holding the team back.

On the flip side, Kevin McCullar has overshot his preseason forecast by more than 4 points a game. McCullar is the Jayhawks’ leading scorer, having hit double-figures in every game this year. His low output was 12 against Kentucky, but this came in his first triple-double performance of the season (he’d add another against Chaminade). Fellow returnee K.J. Adams is defending at a conference first-team level after making the switch back to the defensive perimeter this season. His value score has easily exceeded his value score last year, as he’s also finding new ways to score. Newcomer Hunter Dickinson has hit his lofty preseason expectation of over 5 points of value per game. His backup, Parker Braun, is right at his incoming projection. The other wings, Johnny Furphy and Jamari McDowell, are within range of their projections, but Furphy has certainly played the better of the two.

If we look at where KU can get more value, it’s clear that the wings and interior players are tapped out. Not much more can be expected from Hunter or Kevin. KU needs its guards to start playing better on a consistent basis. This starts with Dajuan Harris. After reaching a season low -3.64 points per game against bubble mark after the Mizzou game, an unprecedented mark for a starter, Harris has responded with 2 positive games over his last 3. Against Wichita State, Harris had his best performance mostly due to a solid defensive game. It’s on this end where Harris has been especially disappointing. Dajuan was the conference defensive player of the year last season; in the 2024 season he’s worse than 1.08 points per game compared to a bubble-level defender. Had he been generating a bunch of offense to compensate, this would be more understandable. But his offense has been the worst it’s ever been, due to limited scoring and poor shooting rates on his floaters and runners.

With KU performing around 3.21 points worse per game than expected, and some of that due to the play of the walk-ons, we can essentially single out one single culprit as to why KU’s margins aren’t as strong as they were expected to be. This culprit is Dajuan Harris. While Timberlake and Jackson have been less valuable than expected, their poor play has been covered by the strong play of McCullar and Adams. Had Harris only played at a bubble-player level, or around 0.00, KU would be hitting its preseason expectations and be about fifth on KenPom (instead of 13^th). Furthermore, Harris is KU’s point guard. He is the only one with the pace and ball-handling to run the team effectively at this point. KU can mix and match on the wings, using Furphy and McDowell when Jackson or Timberlake struggle. It doesn’t really have a Dajuan replacement and thus needs him to perform.

New Projections

The new projections use the actual play over the first 13 games along with the preseason projections in a weighted fashion. We should expect a player to trend back toward his preseason projection. These numbers are just a math equation; there’s been no new analysis involved at deriving them.

Player	Pre-season Proj	Current Projection
Dajuan Harris	+2.50	-0.63
Elmarko Jackson	+0.10	-0.49
Kevin McCullar	+1.71	+4.18
K.J. Adams	+0.93	+1.76
Hunter Dickinson	+5.18	+5.28
Nicolas Timberlake	+0.32	-0.95
Johnny Furphy	-0.84	-0.36
Parker Braun	-0.39	-0.38
Jamari McDowell	-0.72	-0.88
TEAM	+8.51	+6.61

In PPG, individual scores won’t add up to TEAM due to walk-on scores missing

Worst Case, Median Case, Best Case Scenario

These scenarios are updated from the preseason ones. Also included after each scenario is a look at what that team’s Final 4 chances would be, using historic F4 percentages by seed-line.

The new team scenarios are as follows. The actual worst case is of course an injury to Kevin or Hunter, but barring that improbability, a worst-case scenario that sees KU maintaining its full roster would be that KU’s guards never develop and teams continue to double on Hunter to make the others beat them. In this scenario, teams also focus their defensive energies on denying the ball to McCullar. While I can’t see KU failing to win all but a few of its home games, it could hit a tough spell during conference play on the road and rack up multiple losses in a row. For seeding purposes, KU could fall to a 5 or 6-seed if it can’t get necessary plays from its back-court. F4 chances: ~5%.

The most-likely scenario, or median case, would be that Jackson and Harris pick things up, building off of recent good performances, and start to produce for the Jayhawks during conference play. Harris doesn’t seem far off, and Jackson has been a legitimately solid defender whose offensive game has started to come around (had a career high 12 points vs. Wichita State). In this scenario, KU continues to win close games, loses a few close games, but also gets a bit better on the margin front which helps it out. KU earns a 2 or 3-seed and has a good chance to make the second weekend given its experience and talent. F4 chances: ~15% (Bart Torvik puts KU’s F4 chances at 10.4% as of 1/2/2024)

The best-case scenario is that KU gets the Dajuan Harris of last season to go along with the excellent play of its wings and front-court. Jackson fills his role nicely as a solid transition player and defender, Furphy and Timberlake come off the bench to knock in 3’s, and Braun and McDowell continue providing solid energy so the team can play 9 and stay fresh. In this scenario, KU establishes its dominance during conference play and fends off the new teams with strong efficiency marks in non-conference play. KU then goes on to earn a 1-seed in the NCAA’s and puts itself in a good position to make a Final Four run. F4 chances: ~35%

Future Major Projections – 2024

The following table projects the career majors for 100 top and notable golfers. This projection was made as of January 1, 2024. The ages listed reflect the golfer as of this date.

Player	Age	Current Majors	Projected Majors	Est’d Total Majors
Tom Kim	21	0	2.23	2.23
Scottie Scheffler	27	1	2.17	3.17
Collin Morikawa	26	2	1.77	3.77
Gordon Sargent	20	0	1.60	1.60
Viktor Hovland	26	0	1.55	1.55
Jon Rahm	29	2	1.54	3.54
Jordan Spieth	30	3	1.43	4.43
Sungjae Im	25	0	1.42	1.42
Rory McIlroy	34	4	1.08	5.08
Joaquin Niemann	25	0	0.90	0.90
Rasmus Hojgaard	22	0	0.80	0.80
Justin Thomas	30	2	0.72	2.72
Xander Schauffele	30	0	0.69	0.69
Patrick Cantlay	31	0	0.69	0.69
Cameron Smith	30	1	0.61	1.61
Cameron Young	26	0	0.60	0.60
Brooks Koepka	33	5	0.56	5.56
Matt Fitzpatrick	29	1	0.55	1.55
Sam Burns	27	0	0.54	0.54
Nicolai Hojgaard	22	0	0.49	0.49
Will Zalatoris	27	0	0.44	0.44
Hideki Matsuyama	31	1	0.43	1.43
Bryson DeChambeau	30	1	0.41	1.41
Ludvig Aberg	24	0	0.37	0.37
Ryo Hisatsune	21	0	0.31	0.31
Tommy Fleetwood	32	0	0.30	0.30
Min Woo Lee	25	0	0.29	0.29
Sahith Theegala	26	0	0.27	0.27
Tyrrell Hatton	32	0	0.27	0.27
Si Woo Kim	28	0	0.21	0.21
Jason Day	36	1	0.21	1.21
Patrick Reed	33	1	0.19	1.19
Mito Pereira	28	0	0.19	0.19
Corey Conners	31	0	0.18	0.18
Wyndham Clark	30	1	0.18	1.18
Tony Finau	34	0	0.16	0.16
Cameron Davis	28	0	0.16	0.16
Beau Hossler	28	0	0.15	0.15
Denny McCarthy	30	0	0.14	0.14
Rickie Fowler	35	0	0.14	0.14
Max Homa	33	0	0.13	0.13
Russell Henley	34	0	0.12	0.12
Aaron Rai	28	0	0.11	0.11
Christiaan Bezuidenhout	29	0	0.11	0.11
Justin Suh	27	0	0.11	0.11
Emiliano Grillo	31	0	0.11	0.11
Shane Lowry	36	1	0.10	1.10
Dustin Johnson	39	2	0.10	2.10
Talor Gooch	32	0	0.10	0.10
Patrick Rodgers	31	0	0.09	0.09
J.T. Poston	30	0	0.09	0.09
Brian Harman	36	1	0.09	1.09
Taylor Montgomery	28	0	0.08	0.08
Abraham Ancer	32	0	0.08	0.08
Lee Hodges	28	0	0.08	0.08
Adam Svensson	30	0	0.06	0.06
Sepp Straka	30	0	0.06	0.06
Harris English	34	0	0.06	0.06
Vincent Norrman	26	0	0.06	0.06
Adrian Meronk	30	0	0.06	0.06
Keegan Bradley	37	1	0.06	1.06
Harold Varner III	33	0	0.05	0.05
Jordan Smith	31	0	0.05	0.05
Byeong Hun An	32	0	0.04	0.04
Adam Schenk	31	0	0.04	0.04
Adam Hadwin	36	0	0.04	0.04
Andrew Putnam	34	0	0.04	0.04
Cameron Tringale	36	0	0.03	0.03
Mackenzie Hughes	33	0	0.03	0.03
Billy Horschel	37	0	0.03	0.03
Yannik Paul	29	0	0.03	0.03
Adam Scott	43	1	0.03	1.03
Thorbjorn Olesen	34	0	0.03	0.03
Gary Woodland	39	1	0.03	1.03
J.J. Spaun	33	0	0.03	0.03
Nick Taylor	35	0	0.03	0.03
Alexander Bjork	33	0	0.03	0.03
Justin Rose	43	1	0.03	1.03
Mark Hubbard	34	0	0.02	0.02
Jason Kokrak	38	0	0.02	0.02
Dean Burmester	34	0	0.02	0.02
Chris Kirk	38	0	0.02	0.02
Stephan Jaeger	34	0	0.02	0.02
Alex Noren	41	0	0.02	0.02
Ben Griffin	27	0	0.02	0.02
Brendon Todd	38	0	0.02	0.02
Matthieu Pavon	31	0	0.02	0.02
Matt Wallace	33	0	0.02	0.02
Ben Kohles	33	0	0.02	0.02
Ryan Fox	36	0	0.02	0.02
Marc Leishman	40	0	0.02	0.02
Sam Ryder	34	0	0.02	0.02
Luke List	38	0	0.02	0.02
Matt Kuchar	45	0	0.01	0.01
Tiger Woods	48	15	0.01	15.01
Chesson Hadley	36	0	0.01	0.01
Eric Cole	35	0	0.01	0.01
Nicholas Lindheim	39	0	0.01	0.01
Lucas Glover	44	1	0.01	1.01
Phil Mickelson	53	6	0.00	6.00

The list is sorted in terms of projected majors from 1/1/2024 on. Tom Kim leads, with a projected 2.23 majors.

The Process for Determining Future Major Champions

Major championships are the defining feature of professional golf, defining a player’s career across generations. Debate about who the greatest golfer of all time generally ends once Jack’s 18 is brought up. When a golfer wins multiple majors, his career is almost always compared to others with the same major total. Brooks Koepka, once he became the 2023 PGA Champion, suddenly joined the echelon of Seve Ballesteros as 5-time major winners. Pundits discuss who the best players are to have not won a major, indicating that this accomplishment is what truly makes a golfer great.

In addition, young players’ careers are often forecast in terms of major championship potential. When we hear of a young new potential star, the question emerges naturally. How good is this new guy gonna be? Just good enough to win a handful of times on Tour and enjoy a profitable career good? Or multiple majors good?

What hasn’t been done, to my knowledge, is the creation of a systematic process meant to identify which golfers have the best potential to win majors. The goal of this process is to quantify a player’s major championship potential. This is no easy task, but there are five key factors that determine a player’s expected future majors.

Skill level
Age
Past major performance
Health
Major Qualifying Status

Skill Level. As Data Golf has shown, the average major winner has been a player who was playing about 1.5 strokes per round better than an average PGA Tour player leading up to the event. In fact, only 6% of the last 113 major winners were playing worse than an average PGA pro in the onset of those majors. Additionally, pre-tournament models and betting odds reflect that the better the player is, the better his chance is at winning a major.

Age. When projecting out a career, age is also important. The vast majority of major champions have been between 23 and 39 years old. While there are a handful of 40+-year-olds who have won majors, only once as a golfer in his 50’s won a major (Phil Mickelson). As players age, their career windows for winning majors shrink. Historically, the prime spot in a career for golfers has been during their early-30’s (30-32). Lately, there are indications this number has moved toward younger golfers, i.e. those around 28-29. Whatever the case, age is a huge factor.

Past Major Performance. There is a tendency for some guys to play better in majors (than their record week-to-week would otherwise suggest), and for some to play worse in majors. Brooks Koepka is a paradigm example of this, having won 5 majors at this point.

Health. Health is similar to age, in that it determines the amount of opportunities left for a golfer to win a(nother) major. This is difficult to project, but the factor is implicit when we look at historic data.

Qualifying Status. This factor has grown in importance with the onset of LIV and the OWGR refusing to award points to these LIV events. Numerous LIV players without exempt status have lost and will continue to lose major opportunities unless something changes. Talor Gooch is a prime example. As a player in his 30’s, the next couple of years will be his best chance to win a major. Currently, he is not exempt in a single major for 2024 despite winning the 2023 LIV Golf individual title.

The five factors are accounted for in the model, which uses a player’s historic skill-level (using Data Golf public strokes-gained data), age (easy to get), and past major performance (using an internal model which projects estimated true majors won). Implicit in this data will be health data, with qualifying status being important mostly to the names on LIV that don’t have exemptions.

The most time-consuming part of the model was the creation of a player/age skill curve (PASC), which measures the normal career of a player as he ages. Regardless of relative skill level, the average player is at the top of his career during a period between age 26 and 35, normally peaking around 30-32. As mentioned earlier, the trend has been for players to peak younger.

The PASC allows us to project out a player’s remaining career. It is by no means perfect, but it does help to compare a player who is very good for 21 (like Tom Kim) vs. one who is in his prime years.

Independent of the PASC is the major performance chart, which looks at how a player has performed in majors so far using an in-house performance model. This is weighted to be important, but not overriding.

Analyzing the Results

Tom Kim is projected to win 2.23 majors in his career according to this model, the most of any golfer moving forward. The factors in Kim’s favorites are numerous. He is young yet still very skilled. Additionally, he has a T2 finish at a major (2023 British Open). Him being healthy and exempt in 2024 to the majors is also a plus.

Contrast Kim (2.23) vs. Ludvig Aberg (0.37). Aberg burst onto the scene with a solid rookie season, but there are some things that the model dislikes about Aberg’s chances. First, his skill-level has actually been worse than Tom Kim’s in each of the last 4 years. Kim has been better, in terms of strokes gained. Adding context to this is that Kim is three years younger than Aberg. Kim’s age 24 season is projecting out to be better than Aberg’s very good age 24 season. Aberg has also not had a top major finish. This leads to more uncertainty regarding his ability to contend in the biggest events. One might assume that Ludvig will contend in majors without problem, but this isn’t always the case. Max Homa didn’t earn his first Top 10 in a major until last year, during his age 33 season.

Gordon Sargent is also projecting out very high, as the model assumes he should win 1.60 majors in his career. At age 20, Sargent has shown elite potential. Collin Morikawa, who has won 2 majors already, is still only 26. He is just entering his prime. Don’t sleep on him. Of all players currently 30 or over, the model still loves Jordan Spieth to win another major or more. Spieth’s performance in majors is underrated. People forget his 2019 PGA and 2021 British Open battles despite not playing great during this point of his career. Even if Spieth has already peaked, he will have a chance to rack up another grand slam event somewhere along the way.

The LIV guy hurt the most by the current OWGR system is Joaquin Niemann. The model has him winning 0.90 majors, but if he can’t receive exemptions over the next couple of years it will hurt his chances (he has qualified for the 2024 British already). So, adjust this score down somewhat.

Lastly, the model is likely underrating Koepka’s major potential. The problem with any model is that it attempts to apply general factors to specific players, and Brooks is anything but general. Koepka’s career is special. He plays at an elite level in majors, and the major performance factor should likely be weighted more heavily for him. But coming into his age 34 season, he has fewer chances to add to his career than one might otherwise think.

Phil Mickelson is projected to win 0.002 majors moving forward. While I wouldn’t write him off completely (he did finish T2 at last year’s Masters), his 6 majors is a great career and likely the end result. However, his new nemesis, Rory McIlroy, has been sitting on 4 majors for 9 seasons. Rory, a consistent top player with strong major results is expected to win 1.08 more majors, which would be behind Mickelson’s career total. This is an interesting dynamic for sure.

Ideas for the Future

The plan is to do this projection yearly and eventually see how accurate these forecasts are. With time, it would also be enlightening to run this model from some point in the past (say 2000 or 2010) and see how accurate the model was.

Some intriguing bets for most career majors (which include majors already won) are:

Scottie Scheffler (1) vs. Justin Thomas (2). Scheffler is projected to win 2.17 more; Thomas to win 0.72 more. Scheffler leads at the moment, but JT currently has a 1 major lead and there is a famous saying about 1 in the hand being worth more than 2 in the bush.
Matt Fitzpatrick (1) vs. Viktor Hovland (0). Each is projected to finish with 1.55 career majors. Viktor looks to have more potential, but when can he get that first major?
Bryson DeChambeau (1) vs. Sungjae Im (0). Sungjae is only 25, just a year older than Ludvig Aberg. Sungjae has a T2 in a major (2020 Masters). Sungjae has been a consistent performer on the PGA Tour. The model is high on Sungjae Im. Bryson, in contrast, has recently battled injury and is already 30. Right now, the model says Sungjae gets 1.42 majors and Bryson gets 1.41.
Tom Kim (0) vs. Dustin Johnson (2). The model has Kim leading. Tom Kim has many years of prime golf ahead of him; Dustin Johnson has already started to slip. Dustin turns 40 in 2024. He might have one more left in him. But the model thinks Kim has a better chance to win more majors (2.23 vs. 2.10).

Closing Thoughts

Current LIV Golfers are projected to win 0.62 majors in 2024, and this works about to about a 50/50 chance that any current LIV golfer wins at least one major. This may seem small, but the PGA Tour has the larger quantity of top players. I would better the over (i.e., at least 1), in part because I think the model is overlooking Bryson DeChambeau’s recent improvement.

Not all golfers are included on the list above, just the top 100 (or top 98 + Phil & Tiger). There is still a chance a winner comes from outside this list. This model isn’t meant to downplay the chances of anyone left off, it’s just that for purposes of time and space we only included 100 names. If any 2024 major winner does emerge outside this list, we can backward forecast his chances (by looking at his career as of 1/1/2024) and update the table to reflect what his projected chances were coming into 2024.

The New Big 12

Starting in 2024, the Big 12 will have 16 teams. Or even more. With Florida St. jostling to leave the ACC, thus making things shaky over there, and commissioner Brett Yormark looking to expand the Big 12’s footprint, this could change. But for now, the plan is 16 teams.

However, there aren’t a ton of historically great football programs in the Big 12. Since 1869, the first year college football emerged, teams have been playing and building up a profile of their program. Once we combine all end of season poll rankings, bowl appearances and victories, and weight the recent BCS and CFP championships, we get a defensible list of programs rated from best to worst. For the new Big 12, here’s the list (and associated ranks).

29. TCU

37. West Virginia

39. Colorado

42. Oklahoma State

44. Arizona State

46. Baylor

51. Brigham Young

52. Kansas State

55. Houston

57. Texas Tech

58. Utah

68. Kansas

71. Cincinnati

72. Arizona

78. Iowa State

82. Central Florida

This collection of programs also has 2.8 National Championship Shares. See here for an explanation of NCS. Of this group, only 2 programs have made the CFP (TCU/Cincinnati), and only one program (TCU) has made a national championship game appearance since the beginning of the BCS/CFP era (1998).

The average ranking is 55.1, and the median ranking is 53.5 So, historically, these teams haven’t dominated. But let’s just look at how these teams fare from the beginning of the BCS/CFP era (1998-on).

20. TCU

24. Oklahoma State

28. Kansas State

29. Utah

30. West Virginia

33. Cincinnati

35. Baylor

43. Central Florida

44. Brigham Young

49. Texas Tech

50. Arizona State

53. Arizona

58. Iowa State

59. Colorado

60. Houston

72. Kansas

The average ranking here is 42.9, with the median ranking at 43.5. And a couple programs creep into the top 25, something that the conference failed to get when looking at all-time rankings. Looking at some of the teams that left the Big 12 semi-recently; Nebraska ranks 23, Texas A&M 32, and Missouri 37.

So, these programs have been better recently than their long-term history suggests. Now let’s rank these teams just from the beginning of the CFP era, which was 2014 (10 seasons).

12. TCU

16. Oklahoma State

20. Baylor

22. Utah

23. Cincinnati

29. Central Florida

31. Kansas State

39. Iowa State

41. West Virginia

43. Arizona

44. Houston

48. Brigham Young

53. Arizona State

65. Colorado

81. Texas Tech

114. Kansas

The average rating is 42.6, with the median at 40. The top teams are better, with 5 programs in the top 25 over the last 10 seasons. Of those who left, Nebraska is 85, Texas A&M is 30, and Missouri is 35 in this span. Just being included as a member of the Big Ten or SEC does nothing to help your program necessarily.

Who knows how things will play out, but the Big 12 will get a guaranteed team to the 12-team CFP next season, and depending on how things work out, might get 2 or even 3 with 6 available at-large berths.

Let’s look at the other new conferences. First, the new SEC:

1. Alabama

3. Oklahoma

8. Texas

11. LSU

12. Georgia

14. Tennessee

15. Florida

17. Auburn

25. Arkansas

26. Texas A&M

28. Mississippi

36. Missouri

60. Mississippi State

66. Kentucky

67. South Carolina

93. Vanderbilt

Average rating is 30.1; median is 21. The total NCS is 37.5, which is considerably more than the Big 12. We won’t list ratings from 1998 or 2014-on, but since 1998-on the new SEC has 6 top 10 teams. It has won 6 of the 9 CFP games with a chance to win 7/10 if ‘Bama can pull it off this season.

The new Big 10 will add four teams from the West Coast, and it will have the following rankings:

2. Ohio State

4. Michigan

5. Nebraska

6. Southern California

9. Penn State

18. Washington

22. Oregon

23. UCLA

24. Wisconsin

27. Michigan State

30. Iowa

32. Minnesota

38. Illinois

48. Maryland

49. Purdue

53. Northwestern

79. Indiana

100. Rutgers

Average rating is 31.6; median is 25.5. Again, this is better than the new Big 12 to be sure. The total NCS is 35.3, just slightly below the SEC’s. Ironically, the oldest of these programs, Rutgers (which played in the first college football game ever), is also the worst at #100. Rutger’s only national championship share (of 0.5) came in this first season, where it is recognized as co-champs alongside Princeton. Rutgers has never won a bowl game above Tier III (bowls are tiered into four categories based on strength), being one of only a few power conference teams with this distinction.

Last, we will look at the current ACC. We will include Florida State and Notre Dame since ND plays around 5 games against ACC opponents each year.

7. Notre Dame

10. Florida State

13. Miami (Fla.)

16. Clemson

19. Georgia Tech

20. Pittsburgh

21. Stanford

33. Virginia Tech

34. California

35. Syracuse

45. North Carolina

54. Southern Methodist

56. North Carolina State

59. Duke

62. Louisville

64. Boston College

74. Virginia

88. Wake Forest

Average rating is 39.4; median is 34.5. NCS is 29.7. Again, the Big 12 is the outlier in terms of national championship shares. Since 1998, these 18 ACC schools have two top-10 teams (Florida State at 5 and Clemson at 6). However, the average ranking is 43.2 and median is 39.5. Those average and median

rankings are comparable to the new Big 12 programs during this time.

The “others” are teams that don’t have a spot in one of the current big four conferences. These include Oregon State and Washington State. Of these “others,” there are a total of 46.8 NCS. Most of these came from Ivy schools early on in the history of the sport. Other schools such as Boise State and Tulane are hoping to improve their conference affiliation and boost their program’s profile should more rounds of realignment see them added somewhere. But there’s not much these teams can do at the moment.

Should I stay or should I go?

Soon after being excluded from the final four-team CFP, Florida State made public its intentions of leaving the ACC. This will be difficult considering that it signed into a GOR agreement with the other ACC schools, but only the future knows what will happen and how long it will take for things to change. The SEC doesn’t seem to want Florida State, and the Big 10 is also hesitant according to reports. The Big 12 would love to add FSU (alongside teams like Clemson and North Carolina) as a way of jockeying itself as a strong third conference to challenge the emerging hegemony of the two others. Where this would leave the handful of other ACC teams remains to be seen.

There are so many issues at play here, but let’s start with the playoff formats as they’ve evolved. Florida State has begun to feel that the ACC was beginning to not be competitive enough for it to compete for national championships, so when the committee essentially agreed by leaving the Seminoles out, this was the straw that broke the proverbial back.

But, is this the case moving forward? With a 12-team playoff, FSU would have been hosting the 12 seed Liberty Flames and then gotten a chance to take on the 4-seed Alabama Crimson Tide. Furthermore, had things gone differently in the SEC (such as Auburn holding on to win the Iron Bowl), FSU would have likely remained in. The 12-team playoff should take care of the problems of the BCS/4-team CFP era, which was the oddities and vagaries of the college football season leading to controversial selections. A large enough playoff at least gets all good teams into the final tournament, although the seeding controversies (i.e. who gets a bye) will likely remain. The point of this segue is to argue that Florida State might be best to remain as the top dogs of the ACC. With Clemson slipping, and Notre Dame not technically playing for a conference bid, FSU is primed to finish with a strong record each year and have a good chance to win the league on a consistent basis. No ACC league champion will get left out moving forward.

Three’s Company

Eleven games into the 2024 season, KU’s been carried by its “Big 3” of Hunter Dickinson, Kevin McCullar, and K.J. Adams. The trio has combined for 64.7% of KU’s points scored this season while playing 48.4% of available minutes. This production works out to points per game averages of 19.2, 19.2 and 13.0. No one else on the team averages even 7.0.

Additionally, these three have been the best defenders on the team according to the charting. Per total per game value; Dickinson (+6.64), McCullar (+5.14), and Adams (+2.31) are playing well beyond that of a bubble-player, while all 6 of the other scholarship guys are below 0.00. KU is heavily reliant on this trio to win games.

For a Kansas team to be so reliant on just a few players seemed odd, so I explored a way to quantify this and compare it to other KU seasons. The best way was to use WAR, which is additive, and sort each season by that year’s team’s most valuable player to its worst.

Here are the numerous ways the 2024 team stands out:

Of the 2024 team’s total WAR, each of KU’s Big 3 has collected at least 25% of the team’s total WAR (note that a player can be negative if he has negative WAR). Since 1994 (31 seasons), only once has this happened over the course of an entire year (2017 with Frank Mason, Josh Jackson, Devonte’ Graham).
Through 11 games, the trio of HD/KM/KJA has produced 5.94 WAR, which if multiplied out to a 36-game season, would be 19.43. This would be the best out of any KU team’s best three players, with the 2012 team earning 19.07 during a 38-game schedule. (If we compared apples to apples, this year’s Big 3 is on pace to earn 20.51 WAR over 38 games).
It’s not only that this year’s top trio is playing well; it’s also that no one else is doing much. Of the team’s total WAR, the HD/KM/KJA three have earned 124% of the WAR, indicating that the sum of everyone else is below replacement-level. At no point since 1994 have players 4 on down collectively generated negative Wins Above Replacement score for the Kansas Jayhawks. The closest was in 2005, when all players save Wayne Simien, Keith Langford, and Aaron Miles produced a meager 0.53 WAR. The current 2024 non-Big 3 is at -1.15 WAR through 11 games.

Note how the orange dot (Top 3 combined WAR) is always contained within the blue bar (Total Team WAR) except for the 2024 season. For the 2024 team, KU’s needed everything it has gotten from its Big 3 due to the rest of the roster struggling to play at a high level. Projecting forward, one assumes that Dajuan Harris will pick things up. There might also be some reversion down, particularly with Kevin McCullar. McCullar has vastly over-performed his projected marks coming into the season.

Dajuan, Dajuan, What is Wrong?

(Long article. Scroll down to bottom for the summary)

During the midst of the 2023 season, KU had a 3-game losing streak and a date at Rupp Arena against a talented Kentucky team. Dajuan Harris was coming off poor showings in his last 2 games, and questions surrounded a team that had no true center in the starting rotation and a pass-first point guard who wasn’t a natural scorer.

Kansas went on to beat Kentucky that game, bolstered by great play from Jalen Wilson, but also a good outing from the rest of its starters, including its point guard Dajuan Harris. From that game forward, KU would play 16 games to close out the season. Harris produced a +3.22 per game value score, indicating that he was worth over 3 points per game to Kansas than a “bubble” level player would be. During this stretch, Harris was playing his best ball, comparable to a junior-year Frank Mason (2016 season) or senior-year Tyshawn Taylor (2012 season). Harris was doing it in different ways than these scoring guards; certainly through defense first, but he was also adding value on offense thanks to both shot-making and assisting.

In fact, during the closing stretch of 2023—KU’s final 10 games, Harris produced offensive value of +1.06 per game. This was third on the team behind Wilson and Gradey Dick over that period. While KU getting bounced in the Round of 32 wasn’t fun, no one can blame the play of Dajuan Harris. He was solid against the Razorbacks on both ends, adding about a point-and-a-half both ways and +3.30 points overall. For the season, Harris contributed +2.06 points per game above bubble. Roughly, based on his play, Harris was worth 2 points a night when compared to an average player on a bubble-team.

The elements for Dajuan Harris having a successful 2024 were all in place. He was coming in as a 5^th-year-in-the-program player (having redshirted), 2-year starter who saw his game improve each season. Always a defense-first, pass-first player, Harris had improved during the back-half of his junior season and started to be a net contributor to the offensive scheme. This development is common, but not guaranteed, among Bill Self program guys. Harris was making the same strides in his game that others had before him. Some of these strides were unique to him, but he was playing better and making a positive mark on a program that has had numerous talented players come through.

Coming in to the 2024 season, Harris was projected to be a +2.50 player on the season, a conservative improvement on his 2023 value scores. Harris was expected to see a slight bump on both offense and defense. It wasn’t that his expectations were too high. If anything, they were somewhat low. By all appearances, Harris had figured things out. His defense had been consistent throughout his career, and now his offense had finally come around. With Hunter Dickinson coming in, his assist numbers would reach career highs. His shooting and scoring would still be there, but he wouldn’t need to do too much.

But through 10 games, this hasn’t been the case. Not only is Harris not performing to his projections, he isn’t performing anywhere near what he is capable of. He is playing worse than in 2023, by a long shot. He is also playing worse than he did in 2022, when he was a role player on the national championship team. But not only that, Harris is worse than he was in 2021 as a red-shirt freshman who rarely shot and got taken advantage of by stronger players.

Looking at Per 100 numbers, that is the value a player adds (in points) to his team over the course of 100 possessions, Harris’s development looks like this:

2021: -5.18

2022: -1.72

2023: +3.46

Players tend to improve as they age through their college careers, although again not everyone develops in this straight-forward of a manner. Some players have drops in value or flat-line after they reach certain points. So, if Harris was merely on pace to produce a similar or slightly worse season than last year, this wouldn’t be out of the ordinary. Even if Harris was noticeably worse, say close to 0.00, it would be disappointing but not unprecedented. Unfortunately, this isn’t the case, either. In fact, Harris is playing the worst basketball of his career at Kansas.

2024: -5.80

Again, this is a per possession number, so it takes into account the fact Harris is playing more than he has in past seasons. On a per possession basis, Harris is worse than a bubble-player by 5.80 points, which is even worse than his value during his freshman season of 2021.

If we look at it from a per-game mark, it looks even worse. At -3.52, Harris is producing the worse per-game mark of any Jayhawk rotation player by over a full point per game (2019 Quentin Grimes -2.40). Harris is also worse than his prior season at over 5.50 points per game. Never has there been such a drop-off, year-over-year. KU has had players unexpectedly decline, Eric Chenowith’s junior year (2000) being one example (he went from a +2.63 player to a +0.59 player), but it has never been this dramatic.

Breaking down Harris’s play so far, we’ll first look at offense, comparing his 2023 and 2024 seasons.

Going through each line, we see that Harris’s efficiency has slipped from 1.11 to 1.03. What makes this worse is that KU hasn’t faced its meaty conference schedule yet. Not only is Harris less efficient, he is less efficient against an easier schedule. Harris is accounting for nearly 2 fewer points per game on offense than he did last year, despite playing more minutes. Harris is shooting worse overall than he did last season (49.5% vs. 45.2%). He is also shooting less, despite the team needing him for more production. In turn, his impact is less this season. Once we adjust for opponent, we see that Harris is 2 points worse (in terms of overall value) on this end that he was last season.

Breaking down into offensive categories on a Per 100 possession basis, we see (2023 vs. 2024):

Not only is Harris’s scoring down, his handling (turnovers/assists) is also down. Only his boarding, or offensive rebounding value, has changed for the better. But in terms of overall value, this increase is negligible.

Defensively, Harris has declined even more.

Harris went from a stingy defender, giving up 9.2 points per 60 possessions to allowing 11.8 per 60. The other important category, possession winners (PW), went from 5.3 to 4.2 per 60. A player needs to win possessions to add value. Harris is getting fewer steals, forcing fewer turnovers, and not rebounding any better while giving up more points to his man. All despite playing an easier schedule when compared to 2023 numbers. This leads to a player who is worse on the defensive end by more than 3 points a game.

His Per 100 numbers on defense, broken down into categories:

Harris went from KU’s best defender in 2023 to its worse in 2024. How? Well, mostly by giving up too many points to his opponent. A stinginess score in the red means he has to add value from generating turnovers (which he hasn’t done well at in 2024) or rebounding (which is marginally better, but as a point guard, not ever going to be the area where he adds defensive value). This is the biggest disappointment in Dajuan’s decline. He was the Big 12 defensive Player of the Year last season. Now, he’s a liability most games.

2023 vs. 2024. Harris is over 5.50 points per game worse in 2024. He is over 9.20 points worse Per 100. He has also gone from a season where he added 3.87 Wins Above Replacement to one where he has lost 0.52 WAR.

Should Harris be benched?

Dajuan is putting up historically-bad numbers in a season which has him playing historically-high minutes. Clearly, something isn’t working with him. But if KU went to the bench, who would replace his minutes? We can’t consider Adams, McCullar, or Dickinson/Braun, players whose minutes are already maximized or aren’t suitable replacements for a guard. Let’s look at KU’s other four guards/wings in terms of player value. Remember, based on the roster, KU needs to have at least 1 of these guys on the court at all times right now. If Juan gets benched, you’ll need 2 of these 4 to play at once.

There’s a bunch of red here. For one, Nicolas Timberlake is playing worse than Harris on a per possession basis. Next, we can look at the play of Jamari McDowell and Elmarko Jackson. Neither has been much better than Harris. McDowell in particular has struggled recently after a hot start. Of these four, only Furphy has produced near bubble-level value and positive WAR.

So, let’s grant that Furphy has been better than Harris. Who else do you go with? Elmarko has had his poor moments. McDowell is very limited on offense, and his defense won’t likely become elite anytime this season. Timberlake is an even worse option. Another consideration is that, when you take Juan out, you need Kevin McCullar to run more point/generate more offense. Sure, you still have Elmarko and maybe part of the solution is giving him the keys from time-to-time. But if he’s been struggling as an off-guard with less pressure, what chance does he have being the guy to get everyone organized and the offense started?

KU’s best chances are still with Harris. He cannot, surely cannot, continue to play as poorly as he has. As a 3-year starter, Harris put up back-to-back seasons that were better than anyone else on the list above. If he could just match the 25% percentile of his play over the past two years, he’ll be much more valuable on the court than more minutes for the other four guards/wings.

Silver Lining

The good news is that Harris will not continue to play as poorly as he has. Yes, it’s been a disappointment. Here are many reasons to consider that his play will tick up.

He will get exploitable matchups as teams game plan around stopping Hunter/Kevin/K.J. He will get open layups, open 3’s, and other opportunities.
He will have a few defensive shut-down games to boost his defensive score.
As schedule gets harder, Harris will play up to his opponent. While he hasn’t done well against KU’s better opponents, he’s still better against KU’s 5 power-conference foes (-2.87 per game) than the 5 lower-tier opponents (-4.17 per game).
Harris’s score can naturally climb faster given how poor he’s been. Just a few positive games can be enough to change the narrative.
The team is currently 9-1 despite him playing poorly. Juan hasn’t been responsible for a loss, as the 14-point Marquette loss was the fault of many poor showings.
Precedence of Tyshawn Taylor in 2011. Taylor was -3.89 per game during 17 conference regular season/tournament play yet still finished the season with a solid NCAA Tournament of +3.35 per game (even his play during the VCU game wasn’t terrible). So other good players have gone through disastrous stretches but still picked things up later on.
Precedence of Remy Martin in 2022. Harris’s former teammate Martin had a disappointing regular season (-0.05 per game). But in his 9 tournament games (Big 12 + NCAA’s), Martin put up +3.66 per game in value, hitting huge shots in key moments during KU’s National Title run. Again, like Taylor, we see improvement when it matters most.

If Harris can get back to the level of play he had in 2023, his tough start to 2024 won’t matter at all.

Conclusion

Here are the key points. Dajuan’s performance through 10 games this season has been his career worst. Compared to other KU starters, Harris has been worse than any other Jayhawk in 30 years. He has seen decline in his game across all categories, but the primary problems have been with his scoring and defensive coverage. He isn’t scoring enough but is letting his man score too easily.

However, despite his poor play, Harris is a necessary component to this team. His would-be replacements are either worse or wholly unequipped to carry the load as a Big 12 point guard. Not only does he need to play, he needs to play substantial minutes for this team.

The expectation should be that Harris improves and is back to being a positive contributor by season’s end. There is precedent for good Kansas guards figuring it out by NCAA Tourney time. As KU is winning despite his poor play, a solid Dajuan Harris can make this team a true national title contender.

The Battle for Net Extra Possessions

KenPom has Kansas as the 6^th best eFG% offense and the 14^th best eFG% defense in nation, counting only D-1 matchups. This spread, of 18.7%, is the best in the nation and nearly 2.5% better than the second-best of BYU. To reference, the difference from #2 BYU to the #10 team is smaller than the gap between Kansas and BYU. Which is to say, KU’s shot quality is far superior to its opponents in the brief number of games played so far.

While KU is winning the shot quality battle, it is losing the other battle, that is the battle for net extra possessions. A net extra possession means offensive rebounds minus turnovers. Teams that turn it over too much lose out on chances to shoot. Teams that hit the offensive glass add shooting chances. We want to find the net of these numbers.

Collectively, KU’s opponents have had -1 net extra possessions, meaning KU has forced 1 additional turnover than it has allowed offensive rebounds. This doesn’t sound all that bad on the defense’s behalf, until you see what KU’s offense has done. In its 6 games, KU has turned it over 41 more times than it has got an offensive board, meaning the team is -41 in this metric. If we find the difference between these numbers, we see that KU’s opponents have had 40 more chances to score than KU has this year, solely due to turnover and offensive rebounding differences. This works out to -6.7 per game. KU is effectively getting 7 fewer scoring chances than its opponent on average. If we only consider the top opponents, who to this point are Kentucky, Marquette, and Tennessee; KU is -11.3 per game. Knowing this, its amazing the Jayhawks were able to take 2 of those 3 games.

Opponent	KU NEP	Opp NEP	Diff
North Carolina Central	-6	0	-6
Manhattan	-3	-6	+3
Kentucky	-6	+7	-13
Chaminade	-8	-5	-3
Marquette	-8	0	-8
Tennessee	-10	+3	-13
TOTAL	-41	-1	-40

NEP means Net Extra Possessions

The silver lining is that KU should improve on this possession battle. Much of this can be attributed to effort and newcomers learning how they need to play. Valuing the basketball and hitting the glass is something that will be expected for those looking to earn minutes alongside the “big 4” of Harris, McCullar, Adams, and Dickinson (KU also needs more from Harris and Adams on this front). As long as KU continues to get good looks, and having Hunter is a large reason for that, they should be fine. In tonight’s tune-up against Eastern Illinois, KU needs to dominate the glass and turnover margin in order to prepare for Connecticut and other tough games that will come later in the year.

Kansas 89, Kentucky 84

The 2023 version of the Champions Classic proved to be a classic, with a high-action, fast-paced game between the two winningest programs in college basketball history. In the end, our beloved Jayhawks prevailed, but not before giving up a 9-0 lead and being down as much as 14 in the second half.

First things, first. Here are the PPGAB +/- scores from last night, and these will be referenced below. This was quite a game, as so much happened.

Game Notes:

Hunter Dickinson (27 points, 21 rebounds) became Kansas’ first 20/20 player since Thomas Robinson (2012 season vs. North Dakota) and the first to record 20 and 20 against a power conference opponent since Nick Collison (2003 season vs. Texas).
This milestone featuring traditional stats is paralleled by a team-best +13.12 value score. This value score is Dickinson’s second +10 game of the season (NCCU) and his second team-best game (Manhattan).
Dickinson’s value score was quite bad to begin the game, as the charting indicated he was responsible for 16 of Kentucky’s first 24 points due to sagging defense and over-helps (i.e. initiating late double-teams that allow for an easy escape pass). Over the rest of the game, he was not responsible for any more points allowed. It’s estimated that Dickinson’s opponent-adjusted value score over the final 26 minutes was better than +20.
As rare as Dickinson’s 20/20 milestone was, an even rarer event occurred with Kevin McCullar recording the program’s third official triple-double in its history. (This excludes the Chamberlain era, when certain stats weren’t officially kept). McCullar was the first Jayhawk to get the triple-double by registering 10+ assists, as the prior two (Cole Aldrich, Jeff Withey) did so with points, rebounds, and blocks.
McCullar’s 12 point, 10 rebound, 10 assist night was also bolstered by a stingy defensive effort. McCullar’s defensive PPGAB +/- of +5.41 is the most valuable defensive performance of any player in any game so far this season.
McCullar’s triple-double came in the last second of the game, literally, as he grabbed his 10th board following a missed 3-point shot from Kentucky.
The Kansas/Kentucky game was a high-paced game, with 81 recorded possessions from Charting the Hawks. KenPom’s box score shows 83, and his line score shows 80. Either way, the fast tempo allowed more possessions which in turn allowed more opportunities for Dickinson and McCullar to record their milestone nights.
KU is currently the fastest-paced team in KenPom, with an adjusted tempo score of 77.2. In the Self-era, KU’s highest rank on the tempo metric is 40th (2004), and the Jayhawks have only been above 70.0 in terms of adjusted tempo 2 times in 20 seasons under Self.
Not to be overlooked, Dajuan Harris scored a career high 23 points on 5/6 shooting from 3 (with his only miss being a desperation heave following a broken play with the shot clock expiring). Harris graded out as KU’s second-best offensive player against Kentucky.
This is in stark contrast to Harris’ point output in the team’s prior 2 games. Against North Carolina Central, Harris didn’t take a shot from the field or line, and against Manhattan he scored only 2 points on 1-4 shooting. Yet when Kentucky went under screens or put help defenders on the likes of Hunter Dickinson, Harris was there to make them pay by his ability to score.
Defensively, Harris’ game was not his sharpest. He went under multiple screens leading to easy Kentucky 3-point opportunities and gave the Wildcats some run-out layups by careless live-ball turnovers. In all, he was responsible for 19 Kentucky points and graded out as the second-worst defender for Kansas in the game, slightly ahead of K.J. Adams.
Speaking of K.J. Adams, the junior continued his explosive scoring with a 16 point outing on 8-11 shooting. While not a shooter, he scores off of cuts, lobs, and put backs better than almost anyone I’ve seen. Adams also added 3 assists.
Adams’ defense was the team’s worst, and he fouled out with under 4 minutes to play from a careless loose-ball foul, gifting Kentucky 2 free throws.
Freshman Jamari McDowell was Adams’ replacement, despite not playing during the first 36:14 of the game. McDowell would play the final 3:46, during which KU would go on a 14-3 run to win.
McDowell didn’t allow a point to an explosive Kentucky offense, continuing his streak this season. In nearly 17 1/2 minutes of play, McDowell has not allowed his opponent to score.
McDowell also put the finishing touches on the win, grabbing a rebound following a 3-point miss that, had it gone in, would have tied the game for Kentucky. After being immediately fouled, McDowell calmly made both free throws to set the final margin of 5.
In the first 3 minutes and final 3 minutes of the game, KU outscored UK 20-1. The Cats were 14 points better during the middle 34.
Kentucky shot 38 3 point shots, equaling the total of FG attempts they had inside the arc. The team from Lexington was 9-18 to begin the game, allowing them to first come back from 9-0 and then take the lead. However, they would cool off considerably, hitting only 3 3-pointers in the second half (none after the 8:00 mark), and finish 12-38 for the game. This works out to 31.6%, which is worse than the 33.3% the Jayhawks shot (6-18).
Freshmen reserve guards Shepperd and Dillingham went 7-9 from 3 for the Wildcats. The rest of the team, including Antonio Reeves, went 5-29 (Reeves 3-17). When Kentucky was making its 3’s, it looked unbeatable. It was running, hustling for loose balls, and scoring in spurts. But this pace, which helped Kentucky build up a 14 point lead, allowed Kansas to come back once the shots stopped falling.