Remy Martin slams down 2 points, and earns his second team-MVP of the season
Game Summary:
Cook, Remy, cook. After earning his first team-MVP of the season in the 34th game on the schedule in the Big 12 Championship, Remy Martin got his second team-MVP right away. Scoring, assisting, stealing, and playing with a contagious energy; he was a spark that allowed KU to put away 16-seed Texas Southern early in the game. If he continues to play like this, KU is undervalued when it comes to their chances.
Other strong outings were from Christian Braun, Mitch Lightfoot, Jalen Wilson, Dejuan Harris, and David McCormack. Focusing on Mitch, the sixth-year super-senior has had positive value-performances in 5 of his last 6 games. For the season he is nearly adding more value than a bubble-player, showing he has improved tremendously.
With a TEAM score of +16.38, KU has played four games in a row where their opponent-adjusted value score was +10 or higher. This matches their longest streak of the season, which occurred in the season’s first four games. It may be tough to remember that streak now, but it was when KU was playing its best and didn’t look to have many flaws. Although that period was short-lived, injury and slumping play from some its regulars would plague much of the season, it seems to have returned at the right time.
Coach Self said that Martin and Lightfoot essentially give KU 7 starters, an assessment that the numbers show to be accurate. If anything, it is understated; Jalen Coleman-Lands has been playing well on the year, also. With the team strongly favored in its next two potential games, KU is rolling when you want to be.
Since 1985, (before 2022 Tourney) here are how seed-lines have performed in the NCAA Tournament:
There have been 36 tournaments (1985-2019, 2021). With 4 of each seed-line during each tournament, there have been 144 total teams for each seed line over this time period. This gives us a healthy sample from which to make observations.
Let’s begin by tracking overall tournament wins per seed line.
Seed
Wins
1
3.46
2
2.42
3
1.90
4
1.56
5
1.14
6
1.10
7
0.93
8
0.73
9
0.61
10
0.64
11
0.66
12
0.54
13
0.26
14
0.17
15
0.08
16
0.01
The 1-seed averages 3.46 wins per tournament, the 2-seed 2.42 and so on.
Round 64
The table below is fairly self-explanatory. FavTP is the average total points for the favorite (better seed), and the UdTP is the underdog (worse seed) average total points for each seed matchup. Point differential and winning percentage (for better seed) is then shown.
One thing to note is that only the top 3 seed lines are expected to win their games by double-digits. The 4-seed wins by 10+ less than half the time. Another notable factor is that the 5/12, 6/11, and 7/10 games are fairly close to one another in terms of point differential and favorite win percentage. The 8/9 is almost a true coin flip, but the 9 seeds have actually had slightly more success to this point.
There is a clear drop-off from the 1/16 to the 2/15 games. It is actually the starkest drop-off in terms of winning success. 1-seeds are 143-1 in Round 64 NCAA Tourney games; it is almost a bye game to the next round for the top seeded teams. To illustrate the relative performance by seed line, observe the following table. This table compares a favorite seed (seeds 1-8) against the next seed line down in terms of winning success. So we compare 1 v 2, 2 v 3, etc. and see how much more successful the better seed-line is than the next line down.
This table shows that a 1-seed is 10.1 times more likely to win its R64 matchup than a 2-seed. To be certain, 2-seeds are relatively safe to advance this round. But 1-seeds are just this much likelier to advance. As we go down the chart, we see that difference between seed lines shrinks. 5-seeds are barely better than 6-seeds when it comes to getting out of the First Round. If your team is projected to be near one of these seed lines or the other, getting that extra bump up a seed line can matter.
Round 32
When we get to this round, there become diverging possibilities with regards to which seeds play which seeds. For instance, a 1-seed can play either an 8 or a 9. A 4-seed which wins will play a 5 or a 12. This means that additional context is needed to understand how the bracket has historically played out.
Let’s start with the 1-seeds. Of the 143 top seeds which made the R32, 123 have gone on to win in this round (86%). Of the 20 which lost, interestingly 14 of these have lost to 8-seeds and only 6 have lost to 9-seeds. With the only 16-seed to reach this round falling to a 9-seed, this means that the 1/16/8/9 pod has had this many advance out of the R32 (to the S16):
1: 123 (85%)
16: 0 (0%)
8: 14 (10%)
9: 7 (5%)
Continuing on, here are how the 2/15/7/10 pods have gone:
2: 91 (63%)
15: 2 (1%)
7: 28 (19%)
10: 23 (16%)
Here are how the 3/14/6/11 pods have gone:
3: 75 (52%)
14: 2 (1%)
6: 43 (30%)
11: 24 (17%)
Here are how the 4/13/5/12 pods have gone:
4: 67 (47%)
13: 6 (4%)
5: 49 (34%)
12: 22 (15%)
The first thing that stands out is how good the 1-seeds are at getting out of the first weekend. At 85%, this is far more than the 63% of 2-seeds that make the S16 or beyond. Next, notice this effect on the 8 and 9-seeds. 10, 11, and even 12-seeds are more likely to make the S16 than the 8 or 9-seed because these worse seed lines avoid the 1-seed in the region. In fact, a 10, 11, or 12 is more likely to make the S16 than the 8 and the 9 combined.
The worse seed to make the S16 is a 15-seed, and this has been done twice. Two 14-seeds have also made the S16. We will focus on that round next.
Sweet 16
With another round in the books, the bracket possibilities can become even more complex. By this point, usually the entire country has seen its perfect bracket busted. If the bracket is chalk in a region, this is where the 1-seed faces the 4-seed and the 2-seed faces the 3-seed. Note that a 1-seed has faced a 4-seed 55 times (38.2%), and a 2-seed has faced a 3-seed 45 times in this round (31.3%). A Sweet 16 which has all four top seeds still alive should occur about 11.9% of the time. Since there are four regions (four occurrences), we expect a Sweet 16 with seeds 1,2,3,4 in any one of the same region to only happen in 2/5 tournaments. This just shows how crazy the bracket can get given the possible permutations and upset possibilities.
When we look at success, we see that 1-seeds have won 100/123 games in this round (81%). After accounting for those 1-seeds that have already lost prior to this round, we discover that 69% of 1-seeds make it through the S16. Below is the table showing how many of each seed-line makes it through, from the S16, to the E8:
1: 100 (69%)
2: 65 (45%)
3: 37 (26%)
4: 21 (15%)
5: 9 (6%)
6: 15 (10%)
7: 10 (7%)
8: 8 (6%)
9: 4 (3%)
10: 8 (6%)
11: 9 (6%)
12: 2 (1%)
No seed worse than a 12 has made it to the E8. We see the 1-seed power once again in how the results are skewed, as 5-seed is now less likely to advance than either the 6 or 7-seeds (and has the same number of E8 appearances as the 11-seed). By this point, the only seed expected to make it past the S16 more often than not is that top line. Even the 2-seed is batting less than 50%.
Elite 8
The Elite 8 round has with it additional pressure, as teams can taste a Final 4 berth. Interestingly, in this round the 1-seed does not have the best winning percentage. In fact, the 4,5, and 8-lines are all better than the 1-seeds. 5-seeds are 7-2 in this round, showing that if they can make it through, they do quite well in the later rounds.
Nevertheless, the 1-seeds get the most teams through this round due to the fact they get so many teams to this round. Of the 100 1-seeds to make the Elite 8, 59 have made the Final 4 (59%). Below are the teams to make it past the E8 and into the F4 by seed-line:
1: 59 (41%)
2: 30 (21%)
3: 17 (12%)
4: 13 (9%)
5: 7 (5%)
6: 3 (2%)
7: 3 (2%)
8: 5 (3%)
9: 1 (1%)
10: 1 (1%)
11: 5 (3%)
This handy graph helps you visualize the above:
The 1-seed is nearly twice as likely to make the Final 4 as the 2-seed. There seems to be a steady decline in F4 frequency by seed-line, until you reach the 8-seed. It drops back down, until, surprisingly, the 11-seed pops back up with 5 teams making a Final 4. A curious case of how the bracket has unfolded, to be sure. 59% of the Final 4 features teams in the top half of the bracket (seeds 1,4,5,8,9,12,13,16); 41% of the Final 4 features teams in the bottom half of the bracket (seeds 2,3,6,7,10,11,14,15). This is, once again, due to the skew of the 1-seed.
Final 4
The Final 4 round, or National Semifinals, is the first round that can feature teams of the same seed-line facing off against one another (ignoring the First Four/play-in games). This has happened in 18 out of the 72 times (25%). In 13 instances, it has been two 1-seeds. In 3 instances, it has been a matchup of 2-seeds. Once it has been between 4-seeds, and once more it has been between 5-seeds. No 3-seeds have ever faced off against each other in this round.
Using the same idea as before, we track the teams which won in this round and made it to the next. Of the 72 teams which won a Final 4 game to make it to the National Championship game, this is how the result are broken down by seed-line:
1: 36 (50%)
2: 13 (18%)
3: 11 (15%)
4: 3 (4%)
5: 3 (4%)
6: 2 (3%)
7: 1 (1%)
8: 3 (4%)
A point of clarity may be in order. These percentages add up to 100%, which is to say that exactly half of all teams to make the national championship round have been 1-seeds. But this does NOT mean that a 1-seed has a 50% chance of making the National Championship game. To get those odds, you’d have to divide each percentage above by 2. So, a 1-seed has a 25% chance of making the NC game, a 2-seed a 9% chance, etc. given the historical precedent.
The 1-seed dominates this round again, showing that in a battle of attrition (which is the NCAA Tournament’s loser-go-home system), the best teams tend to last the longest. The final NCAA tournament “round” (the National Championship game) will be the final round examined.
National Championship Game
Even though 1-seeds occasionally face each other in this round, (7 times out of 36), the 1-seed has a winning percentage of 64%, and captures 64% of all NCAA Tournaments. This means that a 1-seed is more likely to win the NC game than the rest of the seed-lines combined. When a 1-seed has faced a team of a different seed in the NC game since 1985, it is 15-5 (.750). For whatever reason, the best seed-line seems to outperform in this round. In fact, 4 of the losses that the 1-seed had against non-1-seeds were before 1998…since that year only 2-seed Villanova in 2016 has knocked off a 1-seed in the NCAA title game.
Below are the proportion of national champions by seed-line. Once again, these aren’t the odds of a team on that seed line winning the NC (you’d have to divide by 4 to get the odds of any one team winning), but rather how many national champions have that associated seed.
1: 23 (64%)
2: 5 (14%)
3: 4 (11%)
4: 1 (3%)
5: 0 (0%)
6: 1 (3%)
7: 1 (3%)
8: 1 (3%)
Once again, this is only data from 1985 on. Before the tournament expanded to 64 teams in 1985, there were lower-seeded teams like 1983’s N.C. State (6-seed) who won. But since ’85 the title has been mostly claimed by 1-seeds with some 2-seeds.
If anything, this trend is moving against underdogs. The 80’s saw multiple Cinderella runs, including 1985 Villanova (8-seed) and 1988 Kansas (6-seed). Since 2007, the only team that has won a National Championship outside a 1 or 2-seed is UConn (2011 as a 3-seed, 2014 as a 7-seed). There have been upsets and even Final 4 runs by double-digits seeds, but in the end it tends to be a 1-seed that wins it all.
538 Data
Since 2014, 538 has released March Madness models which project each team’s chances after the bracket is released and throughout the tournament. Using the pre-tournament data (but following the play-in First Four games), we will use these models to compare against the actual results. The reason to do this is to see if the historic results are close to what is predicted, or if the models think the seed-line success to follow a different distribution.
Starting with expected wins by seed, here is what 538 has projected in the 7 tournaments from 2014 – 2021 (with no tourney in 2020):
1: 3.12
2: 2.52
3: 1.83
4: 1.70
5: 1.29
6: 1.08
7: 0.86
8: 0.69
9: 0.64
10: 0.60
11: 0.59
12: 0.38
13: 0.21
14: 0.16
15: 0.06
16: 0.02
With more data, these numbers should become an even more accurate “predictor” of what seed-lines “should” win. Let’s now compare what 538 is saying seed-lines “should” win with what the wins the seed-lines have actually produced:
Seed Actual Wins Expected Wins Difference
1: 3.46 3.12 +0.34
2: 2.42 2.51 -0.09
3: 1.90 1.83 -0.07
4: 1.56 1.70 -0.14
5: 1.14 1.29 -0.15
6: 1.10 1.08 +0.02
7: 0.93 0.86 +0.07
8: 0.73 0.69 +0.04
9: 0.61 0.64 -0.03
10: 0.64 0.60 +0.04
11: 0.66 0.59 +0.07
12: 0.54 0.38 +0.16
13: 0.26 0.21 +0.05
14: 0.17 0.16 +0.01
15: 0.08 0.06 +0.02
16: 0.01 0.02 -0.01
The 1-seed has the biggest differential between “expected” wins and actual wins. 538 models have 1-seeds winning 0.34 fewer games per team per tournament, which isn’t a small number. On the flip side, 5-seeds are expected to win more often at 0.15 wins per team per tournament. 4-seeds are similar to 5-seeds. Clearly the 81% winning for 1-seeds in that Sweet 16 round is showing up in these differences.
We’ll next compare the Round of 64 games, the only games with set seed matchups (1 v 16, 2 v 15, etc.).
Matchup ActWin% ExpWin% Diff
1 v 16 99.3% 98.0% +1.3%
2 v 15 93.8% 95.3% -1.5%
3 v 14 84.7% 87.8% -3.1%
4 v 13 84.1% 78.5% +5.6%
5 v 12 64.6% 73.0% -8.4%
6 v 11 62.5% 61.8% +0.7%
7 v 10 60.4% 56.7% +3.7%
8 v 9 49.3% 51.2% -1.9%
As far as favorites go; 1-seeds, 4-seeds, 6-seeds, and 7-seeds have outperformed in the R64. For underdog seeds; 9-seeds, 12-seeds, 14-seeds, and 15-seeds have been better than expected. The biggest thing that jumps out is the 5/12 matchup, a trend many filling out their NCAA brackets have picked up on. The % difference can be misleading. We’ll now look at the expected number of wins (out of 144) for the better seed compared to what has actually occurred:
Better Seed in R64 Act Wins Exp Wins Diff
1 143 141.2 +1.8
2 135 137.3 -2.3
3 122 126.3 -4.3
4 113 121.1 -8.1
5 92 105.1 -13.1
6 90 89.0 +1.0
7 87 81.6 +5.4
8 71 73.8 -2.8
In terms of numbers, the 12-seed over the 5-seed is the biggest historic surprise. However, there is one better way to judge the performance of seeds against expectation.
Seed in R64 Multiple
1 2.887
2 0.736
3 0.775
4 0.688
5 0.655
6 1.030
7 1.168
8 0.926
9 1.080
10 0.856
11 0.971
12 1.526
13 1.453
14 1.289
15 1.358
16 0.346
What this shows is that the 1-seed has actually performed the best in the R64 compared to expectation. To best explain this, 1-seeds are expected to win 49.5 times more often than they lose in the Round of 64. In reality they’ve won 143 times more often than they’ve lost (143-1). This generates a 2.887 multiple (143/49.5). Moving down the line, 2-seeds are expected to win 20.4 times more often than they lose. In reality they’ve won 15 times more often, for a multiple of 0.736 (15/20.4).
Instead of comparing each round, we’ll move on to the Final 4 and see how seed-lines have done against expectation. Below shows the seed, actual frequency of the F4, expected frequency, and difference.
Seed ActF4% ExpF4% Diff
1 41% 39% +2%
2 21% 22% -1%
3 12% 10% +2%
4 9% 10% -1%
5 5% 6% -1%
6 2% 4% -2%
7 2% 3% -1%
8 3% 2% +1%
9 1% 1% 0%
10 1% 1% 0%
11 3% 1% +2%
12 0% 0% 0%
There isn’t a ton to say here. The numbers are fairly close to what is expected, with the 11-seed (unsurprisingly) overperforming.
Last, we’ll look at National Championships. This is similar to the F4 table above, but includes total NC’s by seed (since 1985) alongside percentage of national champions.
Seed ActNC ExpNC Diff
1 23 (64%) 18.6 (52%) +4.4
2 5 (14%) 7.8 (22%) -2.8
3 4 (12%) 2.7 (7%) +1.3
4 1 (3%) 2.9 (8%) -1.9
5 0 (0%) 1.3 (4%) -1.0
6 1 (3%) 1.0 (3%) 0.0
7 1 (3%) 0.5 (2%) +0.5
8 1 (3%) 0.4 (1%) +0.6
9 0 (0%) 0.3 (1%) -0.3
10 0 (0%) 0.2 (1%) -0.2
11 0 (0%) 0.3 (1%) -0.3
12 0 (0%) 0.0 (0%) 0.0
First, 1-seeds have clearly overachieved according to this table. They’re “expected” to win an NC about half the time, but in reality account for nearly 2/3 of all titles in the 64+ team era. Next, 4-seeds are “expected” to win more often than 3-seeds. This reveals an anomaly in the model data. Apparently since 2014, 4-seeds have graded out as slightly stronger than 3-seeds from an efficiency perspective. Over a longer time horizon, we’d expect this to revert to normal. We see that seeds below have a slight chance per the model, until you reach the 12-seed where it becomes extremely rare.
We can also look at how likely it is for a seed-line to have won over a certain time period. In other words, given the 36 tournaments since 1985, how likely is it that an 8 seed has won at least one National Championship?
It isn’t too surprising that it is very likely that at least 1-seed has won a NC since 1985. With 36 years of play, it is guaranteed that a top seed will be able to cut down the nets. This remains true for all of the top four seeds. It is somewhat of a surprise that a 5-seed hasn’t won a NC yet, since this should happen 72.9% of the time over a 36-year period. But the 7 and 8-lines make up for this.
We’ll close this out by showing how many tournaments are expected for a seed-line to win a NC. For a 1-seed, you’d need 1.93 tournaments, which reflects that a 1-seed “should” win a NC about every other year. Obviously, at some points a 1-seed will win multiple years in a row (currently at 4 in a row), and at other points it will be a few years when a 1-seed doesn’t win (they’ve been shut out in consecutive years multiple times, including 85/86, 88/89, 97/98, 03/04).
Once again, the “expected” data needs more years to smooth out. A 4-seed shouldn’t be more likely to win than a 3-seed, the same goes for a 14-seed over a 13-seed. If you combined seeds 10-16, we run into a calculation of a double-digit seed winning the National Championship once every 67 years. This may seem to be too generous to double-digit seeds, but remember that 4 11-seeds and 1 10-seed have made a Final 4. Although none of these have won a national semifinal game, UCLA came very close last season in 2021. Had they knocked off Gonzaga, they’d have been one win away from doing just this (and would have had about a 20% chance of winning). With more time, the unthinkable might happen. But it’d almost certainly be a 10, 11, or under-seeded 12 which won.
Remy Martin had his first team-MVP of the season, sparking KU to a Big 12 Tournament Championship
Game Summary:
Kansas took home the Big 12 Tournament with three great team outings. On Saturday, it was Remy Martin off the bench (12 points, 3 assists, 4 rebounds) who led the team in value. For the tournament, Ochai Agbaji was the best Jayhawk (+6.16 per game average). Mitch Lightfoot was also very good over the course of the three games (+4.64 per game).
Ochai’s emphatic dunk was part of a team-high 11th MVP game of the season.
Game Summary:
KU took the rubber match against TCU thanks to outstanding efforts from Ochai Agbaji and Mitch Lightfoot. Remy Martin had his best game in a few months, finishing with 10 points and 3 assists. Joe Yesufu got back on the positive side of the ledger with 3 points and 2 assists (with no misses or turnovers). Christian Braun struggled to finish but had a solid defensive outing to add net value.
KU will play for the Big 12 championship on Saturday.
CB earned his 9th team-MVP of the season, thanks to a balanced performance.
Game Summary:
KU won its quarterfinal match-up with solid performances from Christian Braun, Mitch Lightfoot, Ochai Agbaji, Jalen Coleman-Lands, Jalen Wilson, and Dejuan Harris. The team will play again tomorrow night against TCU.
Christian Braun about to finish on an and-one. He earned his 8th team-MVP thanks to excellent defense on Saturday.
Game Summary:
Kansas won a defensive slugfest on Senior Day, and it was Christian Braun who played the best defense of anyone. He only gave up 3 points, while filling up the box score with a steal, a block, and multiple rebounds. The best offensive players for Kansas were David McCormack (1.34 PPP_af) and Jalen Wilson (1.80 PPP_af). They each got big buckets and made clutch free throws when points were limited.
Seniors Jalen Coleman-Lands and Mitch Lightfoot each had positive performances as well. Although there were only 4 points between the two of them, they each defended well. K.J. Adams had an offensive rebound and 1/2 FT to add nearly a point of value after adjustments. Neither Zach Clemence nor Chris Teahan recorded a stat in their few minutes of play.
Dejuan Harris had a neutral game characterized by excellent defensive plays (blocks, steals), some great passing (had 5 of KU’s 10 charted assists with 0 turnovers); on the other hand he only scored 3 points and did give up some easy baskets due to his slight frame. Remy Martin had good activity on offense to start, but lost his man a few times and gave up Texas’ first 5 points. Joe Yesufu didn’t do much in his few minutes of play.
Ochai Agbaji is officially slumping, as he recorded his third consecutive negative-value game. He seems to be pressing, forcing bad shots on offense while reverting to forgetful moments on D. He’s now down to 41.1% from 3 on the year. On the plus side, he is rebounding better than he did to start the year. This shows he is playing hard. Once the shots start to fall again, he will be someone who can carry this team far in the Tournament.
The TEAM score was helped by KU getting to overtime. But its still indicative of the Agbaji slump. Thankfully; Braun, Wilson, and McCormack all had solid outings to help get KU a win for the 39th consecutive Senior Day/Night game.
There are multiple ways a player can add value to his team in a basketball game. Attempting to capture these different ways quantitatively can be difficult. If you look at an advanced season box score, you’ll notice that most of the stats are listed in rates instead of totals or per game.
College Basketball Reference, 2022 Kansas Jayhawks through 30 games
So let’s take two players and compare them, Ochai Agbaji and Christian Braun. They have similar minutes played, PER, and TS%. As we continue to move left to right, we see that Och’s 3-pt. attempt rate is much higher, although CB gets to the line more (FT rate). Agbaji is credited with more points produced, CB is a better rebounder on both ends, a better assister, and also gets a higher proportion of steals and blocks. Ochai has a lower turnover percentage, and a higher usage.
These numbers are all wonderful to have, and do tell us quite a bit. They are also a mess to organize and make sense of. For instance, PER (Player Efficiency Rating) rates how efficient players are, with higher been better. It takes into account all offensive stats.
Notice we see that David McCormack is leading in this category. This may be surprising, but remember he shoots FT’s quite well and also grabs a ton of offensive rebounds. His turnover rate is also fairly low. Still, we see he is shooting worse than players like Agbaji and Braun, and has a far lower assist rate than the guards.
What if we wanted to compare D-Mac’s offensive rebounding rate (which is very high) to a guard’s assist rate? In other words, is he adding more or less value through his rebounds than Dejuan Harris is through his assist? You can’t look at the raw numbers, because they are measuring two different skills. But certainly there is a way to see this.
Represented below is the attempt to do just that. Breaking down offense, we’ve organized all offensive plays into one of three categories: Finishing, Handling, and Boarding.
Finishing – This measures how well and frequently a player scores. It takes into account his shooting percentage and volume, as well as if the play was assisted or not. It includes measurements of both production and efficiency.
Handling – This measures how well a player takes care of the ball and distributes it. It uses assists and turnovers to drive the result.
Boarding – This looks primarily at offensive rebounding, the main driver in a team getting second-chance opportunities. It also includes the occasional “save” which rescues a team’s offensive possession following a teammate’s poor pass that should have resulted in a turnover.
This chart is represented in Points Above Bubble, the value currency most frequently used on this website. This chart is showing PAB per 100 possessions, so it ignores any value related to endurance/stamina/etc. and purely rates each player on the same possession basis. There are a few interesting results:
The best offensive skill set for KU this season has been Agbaji’s ability to score (Finishing). He is shooting 42.2% from 3, and also has a 61.3% TS% (using Sports Reference’s calculations). In all, he is adding 7.29 Points above bubble over the course of 100 possessions (PAB/100) due to his ability to score.
The second-best offensive skill set for KU this season is David McCormack’s offensive rebounding. He is adding 5.17 PAB/100 just from his ability to get second and third-chance opportunities. And while some may wish to discount his rebounding ability by claiming it is due to poor shooting (“he just gets his own misses”), this doesn’t look to be too true as he has a Finishing score of +1.59. When D-Mac is out there, he is the team’s second-best offensive weapon.
The best at “Handling,” which is basically maximizing assists and minimizing turnovers, is Dejuan Harris. At +3.06, is far better at the true point guard skills than other guards. But notice even despite his strong Handling score, his lack of Finishing and Boarding makes his offensive value below bubble-player level on offense.
These numbers are not position-adjusted. So we expect to see the guards with positive Handling numbers but negative Boarding numbers, and the posts to have negative Handling numbers but positive Boarding numbers. But take a look at K.J. Adams. A solid offensive rebounder, K.J. has also done a decent job handling the ball for a 4/5 man. It is his scoring (Finishing) which has made his score so low.
In summary, different players will bring different things to the table. For KU’s offense in 2022, they are getting great scoring marks from Agbaji, Braun, and even Coleman-Lands. Harris is the best ball-handler, although the other point guards add some value here. And inside, David McCormack and K.J. Adams are beasts at winning additional opportunities. Jalen Wilson is positive in all features, being balanced at finishing, handling, and boarding.
Jalen Wilson played fine defense to earn his 5th team-MVP game of the season
Game Summary:
KU struggled to put TCU away, but ultimately prevailed thanks to a late defensive stop (Dejuan Harris block). Jalen Wilson was the team’s best player, only allowing 7 points on defense while grabbing 9 defensive rebounds and forcing 3 TCU turnovers (1 live-ball steal, 2 dead-ball). This defensive effort overshadowed a quiet offensive night, as he only took 12.6% of his team’s shots while in the game. He did produce over 10 points of offense on an efficient 1.94 points per possession used.
Dejuan Harris had his best game in some time, scoring 11 with 4 assists on 1.30 points per possession used. He also played solid defense, giving up 9 points but also coming away with a key block on TCU’s Miles with 4 seconds to play. Had TCU converted it would have been a 1 point game, but the block sealed KU’s win. Christian Braun was efficient on offense (1.95 points per possession) to earn a quiet, but positive, value score. Mitch Lightfoot and Zach Clemence were both basically neutral players.
On the negative side of the ledger, Ochai Agbaji turned a solid first-half into another high-volume shot-output game which harmed his team’s chances. The 22 points were good, but he had 11 misses and 3 turnovers to get those points. His 0.84 points per possession used was well below the team’s 1.07 for the game. Defensively, he allowed 13 points, the most of any Jayhawk last night.
David McCormack didn’t shoot well from the field, but was solid again from the FT line. He did give up some baskets inside, which cost him. Joseph Yesufu had a poor shooting outing, as did Remy Martin in limited playing time. Jalen Coleman-Lands had a few nice hustle rebounds, but gave up one basket while missing his only shot attempt. K.J. Adams’ only impact was fouling a 3-point shooter to allow TCU to cut a 6-point deficit to 3.
The TEAM score of +1.17 was KU’s third-consecutive game under +3. Based on opponent, location, and pace; KU’s offense did slightly better than its defense did; but both sides of the ball showed a mediocre performance for a team of KU’s caliber.
D-Mac had a solid game on the offensive glass, and earned his 5th team-MVP of the season
Game Summary:
In an ugly game, David McCormack was actually a very solid contributor for Kansas, earning 8 second-chance opportunities for Kansas on the offensive end (although he had trouble finishing just as his teammates did). He also prevented his opponent from doing much damage scoring the ball, but failed to earn as many defensive rebounds as he should have. In all, despite the awkwardness and missed layups, D-Mac’s energy and presence helped KU last night.
Similarly, Jalen Wilson had a solid defensive game and added enough on offense to be on the plus side of the value-ledger. It wasn’t his man who was scoring the points for TCU. The other Jalen, JCL, played very well in his 6:32 of game time. Finishing with 2 3-pointers on 2/4 shooting, he also grabbed 3 rebounds (2 defensive, 1 offensive) without giving up points on defense.
The only other positive Jayhawk was Zach Clemence, who blocked a shot and then finished the possession ripping down a rebound as TCU kept getting second-chance looks. He had to leave the game due to a cut above his eye.
Hovering around 0 value were Christian Braun and Joseph Yesufu. CB had trouble finishing inside, but he played solid defense for the most part. Yesufu had success getting to the cup (2/2 FT’s getting fouled on a layup to go along with a made FG on a drive), but also gave up 5 points to offset what he scored.
Mitch Lightfoot started strong, scoring KU’s first 5 points, but his poor defensive positioning and rebounding soon cost KU. One of his replacements as a bench big-man, K.J. Adams, also had a rough night on defense. Guards Remy Martin and Dejuan Harris had mediocre games, with Harris doing a good job spreading the ball around (7 assists, 1 turnover) but little else (0 points scored, 10 points allowed). Martin hit a midrange jumper and gave up a 3, along with a missed shot and turnover. Overall, not a great game but not terrible.
The player who easily had his worst outing was Ochai Agbaji. Although he finished with 13 points, this was done on 18 shot attempts. He finished with a 0.56 Off PPP_af (this website’s version of ORating). His defense was even worse, as he gave up 23 points. Some of this was losing his man in the half-court, some was due to good defense that scored on, and some was due to his poor transition recovery. In all, the energy he has been expending on offense has cost him on the other end. The last two games Ochai has taken 38.7% and 28.8% of the team’s shots while in the game, upping his shot frequency to 27.4% on the season. This number doesn’t taken into account the type of shot, meaning it doesn’t account for how much more work Ochai is doing to get these shots up, many of which are unassisted opportunities. Simply put, Agbaji needs more help on offense, and needs to be more patient when he does have the ball.
For the team, this was their second-worst game of the season and the 7th game below bubble-level for KU. The Hawks got crushed on the glass and inside the paint, losing the rebounding battle 47-35 and shooting 35% from inside the arc while TCU shot 48.1%. The wings weren’t great at rebounding, but it was the guards (1 total rebound between Harris, Martin, and Yesufu in 47 combined minutes) that didn’t help out at all.
KU gets TCU tomorrow (March 3rd). Expect a far better effort on the glass, and a better game plan at getting Ochai better looks.
CB had his 7th team-MVP of the season at Waco in a loss
Game Summary:
The road Jayhawks got off to a nice start, before allowing Baylor to get back into the game at halftime. The second half was back-and-forth until Baylor took control down the stretch. Kansas had trouble limiting the pick-n-roll options of Baylor and didn’t finish well at the rim on offense.
The wings were great again. Christian Braun’s 17 points and 4 assists produced far more than the 9 points he gave up on defense. Ochai was even more productive with 27 points and 4 assists, with 11 allowed on defense. CB gets the slight advantage in overall value this game due to his far higher offensive and slightly better defensive efficiencies.
On the other end, the two main posts had dreadful defensive outings. David McCormack gave up 25 points due to poor positioning on ball screens, slow recoveries, overhelping, and fouling. Mitch Lightfoot finished with 4 fouls in 3 1/2 minutes, giving up 7 points without even registering a shot on the offensive side. Dejuan Harris had a questionable defensive outing, but it was his offensive game that was worse. Given 7 assists in the official box score, upon closer examination it was clear that most of these were too generous, leaving him with even less of an offensive impact than he might have had otherwise.
The return of Remy Martin was accompanied by nice offense (5 points on 3 shots) and poor defense (5 points allowed). He drew a charge and almost a second (that was called a flop warning instead).
Fatigue seemed to set in down the stretch, as all 5 KU starters played 30+ minutes. Remy was the only bench player with more than 10 minutes, at 11. Despite solid performances either in this game or in recent weeks; Yesufu, Coleman-Lands, Adams, and Clemence combined for only 12 minutes total. The attrition battle showed. KU finished 7-28 from 3 and 17-42 (barely 40%) from inside the arc. Kansas had a 1.27 offensive rating at the 5:30 mark in the 1st half, and then went 0.89 the rest of the way.
The TEAM performance of 2.59 was lower than it otherwise would have been aside from the fouling in the final minute. Still, KU had a chance to keep the game close in the final 4 minutes and could not. Once again, fatigue is a plausible factor. With Martin back and KU at 11 healthy scholarship players, the bench is something that needs to be used.
Charting the Hawks 