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Players In NCAA With Biggest Jumps In Points Per Game

In today’s column, I examine college basketball players who saw the biggest jumps in PPG production in 2013-14. I’m going to exclude seniors. A number of seniors, like Temple’s Dalton Pepper (who improved his PPG from 2.9 to 17.5 last year) probably deserve more acknowledgment. But today I want to focus on players who still have college eligibility remaining. I’m also going to exclude players who saw big jumps in PPG production because they changed teams, since it is not that unusual for a player to score at a higher rate after transferring. In the tables below, I list players with jumps of five or more PPG last season while playing for the same team.

I’m going to focus on players that were rated as a 3-star recruits or higher by at least two scouting services, since these players tend to be more interesting to most readers. Nonetheless, as my last table shows, these are not the only players who broke out last year. Finally, I am going to drop players that only played a few games due to an injury or other issues, since very small samples can skew the PPG production.

PPG LY = Points Per Game Last Year in 2013-14

Increase in PPG = Increase in PPG from 2012-13 to 2013-14

ORtg Diff = Change in ORtg, points scored per 100 possessions

Pct Poss Diff = Change in aggressiveness, percentage of possessions used

Pct Min Diff = Change in playing time on full season. In a few cases due to injuries or other factors, players saw a decrease in playing time on the full season but saw their minutes per game increase. When the numbers don’t seem to add up, this is usually the explanation.

Pace Diff = Change in raw pace. I show raw pace instead of opponent-adjusted pace since PPG is based on the raw number of possessions in a game.

I said I was going to focus on players with eligibility remaining, but I wanted to discuss another group in the first table. This table lists players with huge jumps in PPG production who either declared for the NBA draft or who have elected to transfer this off-season. Sometimes when a player breaks out, he also decides to move on.

Player

Last Year’s Team

PPG

LY

Increase in PPG

ORtg Diff

Pct Poss Diff

Pct Min Diff

Pace Diff

T.J. Warren

NC State

24.9

12.8

-11.4

15%

18%

-3.7

Jabari Brown

Missouri

19.9

6.2

6.2

6%

32%

-2.4

Byron Wesley

USC

17.8

7.6

10.5

6%

2%

3.5

Nik Stauskas

Michigan

17.5

6.5

1.4

8%

10%

-1.5

Eron Harris

West Virginia

17.2

7.4

6.8

3%

25%

3

K.J. McDaniels

Clemson

17.1

6.2

9.3

6%

20%

-2.3

LaQuinton Ross

Ohio St.

15.2

6.9

6.5

1%

30%

0.8

Seth Allen

Maryland

13.4

5.6

15.0

1%

-6%

0.4

Jerami Grant

Syracuse

12.1

8.2

12.2

5%

38%

-4.7 

Everyone in the first table declared for the NBA draft except for

-Byron Wesley who announced he was transferring to Gonzaga and who should be eligible next season

-Seth Allen who announced he was transferring to Virginia Tech and who will sit out next year

-Eron Harris who should make his transfer decision soon

Sometimes players see their PPG improve despite a drop in their ORtg. TJ Warren in the last table, and Kellen Dunham in the next table, saw their efficiency plummet as they took a much larger number of shots. In neither case were they being selfish; both teams lost a lot of scoring and needed someone to fill the void. But it is worth emphasizing that while Warren and Dunham scored a lot more, they also missed a lot more shots last year.

Player

Team

PPG LY

Increase in PPG

ORtg Diff

Pct Poss Diff

Pct Min Diff

Pace Diff

Daniel Bejarano

Colorado St.

16.3

10

-1.4

11%

29%

1.2

Cameron Wright

Pittsburgh

10.5

6.2

-3.5

3%

42%

2.2

Kellen Dunham

Butler

16.4

6.9

-12.4

7%

23%

-0.8

On the flip side, a few players saw their PPG scoring jump despite becoming less aggressive on the court. In these cases, their jump in PPG production was almost entirely driven by an increase in playing time, but the improved shot selection also increased their efficiency.

Player

Team

PPG LY

Increase in PPG

ORtg Diff

Pct Poss Diff

Pct Min Diff

Pace Diff

Alex Hamilton

Louis. Tech

14.5

6.7

7.4

-1%

26%

1.8

Maurice Walker

Minnesota

7.8

5.6

14.9

-1%

24%

0.9

Perry Ellis

Kansas

13.5

7.7

9.6

-1%

36%

0.8

Landry Nnoko

Clemson

6.5

5.5

31.9

-1%

50%

-2.3

Kenny Gaines

Georgia

13

9.3

22.4

-1%

42%

2.7

J.P. Tokoto

N. Carolina

9.3

6.7

14.8

-2%

50%

-1.5

Stefan Nastic

Stanford

7.4

5.4

32.7

-5%

37%

-1.2

Most of the players in the next table played substantially better last year.  Still, I’m pulling this next group out to emphasize something. All of the teams in the next table saw substantial decreases in the quality of defense they faced on the full season. For example, the MWC was much worse last year, and San Diego St. faced far fewer great defenses than the year before. While the AAC had some great teams at the top, Louisville and Rutgers clearly had an easier schedule than the previous season. A few teams on this list are a surprise. I was frankly a little surprised to see that Syracuse’s opponents defenses plummeted from 8th in 2013 to 84th in 2014.

That said, when you improve your ORtg by double digits or increase your aggressiveness by 5% or more, that’s extremely impressive even if the defenses you faced were a little worse. I was particularly pleased to see Rutgers’ Kadeem Jack finally play to his potential. He enrolled early at Rutgers, struggled with some injuries, but responded well to new head coach Eddie Jordan.

Player

Team

PPG LY

Increase in PPG

ORtg Diff

Pct Poss Diff

Pct Min Diff

Pace Diff

Yogi Ferrell

Indiana

17.3

9.7

9.4

7%

14%

1.5

Winston Shepard

San Diego St.

11.7

6

11.2

7%

18%

-3.2

Dwayne Polee II

San Diego St.

8.5

5.7

18.5

5%

23%

-3.2

Kadeem Jack

Rutgers

14.3

8.6

8.3

6%

26%

4.3

Fred Van Vleet

Wichita St.

11.6

7.3

31.0

1%

39%

-0.6

Anton Wilson

Detroit

7

5.2

17.9

0%

38%

-2

Trevor Cooney

Syracuse

12.1

8.7

25.4

0%

53%

-4.7

Montrezl Harrell

Louisville

14

8.3

2.6

5%

33%

2.5

Rashawn Rembert

E. Tenn. St.

16.8

7.9

23.9

1%

23%

4.9

Todd Mayo

Marquette

11.3

6

6.5

3%

31%

2.9 

Many players who saw their PPG production jump benefitted from the fact that their teams played at a faster pace last year. This includes many of the players listed above, as well as the players in the next table. But keep in mind the extra possessions are not a big contributor to production. Even though Oklahoma had about 5 more possessions per game, given his role in the offense and playing time, that only translated to about 1 more PPG for Buddy Hield.

Player

Team

PPG LY

Increase in PPG

ORtg Diff

Pct Poss Diff

Pct Min Diff

Pace Diff

D. Smith-Rivera

Georgetown

17.6

8.7

16.4

4%

26%

2.6

Jonathan Holmes

Texas

12.8

6.4

20.7

5%

13%

2.7

Cameron Ridley

Texas

11.2

7.1

34.1

2%

25%

2.7

Charles Mann

Georgia

13.9

7.2

10.5

3%

18%

2.7

Juwan Staten

West Virginia

18.1

10.5

17.2

7%

26%

3

Marvelle Harris

Fresno St.

14.3

6.9

9.9

2%

25%

4.2

Kyan Anderson

TCU

17

5

14.3

3%

2%

4.9

Buddy Hield

Oklahoma

16.5

8.7

16.7

3%

27%

5

Isaiah Cousins

Oklahoma

11

8.3

39.9

2%

34%

5

Defenses got worse across the board last year (thanks to the rule changes), so we saw more than our normal share of big jumps in PPG production. But I still think it is important to emphasize that sometimes even playing against relatively strong defenses again, with little help from pace, players simply improved in every area.

It’s easy to look at the summer as a chance to earn money, play video games, and catch your breath. But for a select few players every year, the time they put into the gym results in huge gains in every measurable category.

I was frankly shocked last year that Michigan’s Caris Levert shot 6% more than the year before, saw his ORtg jump 18.4 points, and his percentage of minutes jump 62%.  But that’s the kind of development that can substantially improve the outlook for any team.

Player

Team

PPG LY

Increase in PPG

ORtg Diff

Pct Poss Diff

Pct Min Diff

Pace Diff

Q. DeCosey

Temple

15.4

13.4

17.0

3%

75%

0.8

Will Cummings

Temple

16.8

11

18.2

9%

21%

0.8

Caris LeVert

Michigan

12.9

10.6

18.4

6%

62%

-1.5

Frank Kaminsky

Wisconsin

13.9

9.7

1.9

4%

44%

1.9

Marcus Paige

North Carolina

17.5

9.3

23.9

3%

18%

-1.5

DaVonté Lacy

Washington St.

19.4

8.9

7.9

7%

6%

0.6

Aaron Thomas

Florida St.

14.5

8.5

16.4

3%

32%

-0.2

Ky Madden

Arkansas

12.7

8.5

7.9

8%

23%

1.8

Jarvis Summers

Mississippi

17.3

8.2

12.6

7%

13%

-1.2

Michael Qualls

Arkansas

11.6

7

12.0

6%

22%

1.8

Michael Frazier

Florida

12.4

6.8

4.1

2%

33%

-0.2

Jake Layman

Maryland

11.7

6.2

7.5

3%

29%

0.4

Anthony Beane

S. Illinois

14.7

5.6

18.6

2%

13%

0.2

Anthony Perez

Mississippi

7.1

5.4

7.8

2%

34%

-1.2

Norman Powell

UCLA

11.4

5.3

23.3

5%

9%

0.5

Chasson Randle

Stanford

18.8

5.2

9.9

1%

10%

-1.2

Toddrick Gotcher

Texas Tech

7.3

5.1

23.9

3%

35%

-4.6

Kevin Bailey

Portland

16.5

5.1

16.5

1%

0%

1.9 

While everyone listed above was rated relatively high by at least two of the scouting services, that’s not a requirement for a breakout year. Here were a few lower ranked players with huge jumps in PPG production last year.

Detroit’s Juwan Howard, Jr. maintained his ORtg despite using 10% more possessions last season. Eastern Washington’s Tyler Harvey started his career as a walk-on, and yet he became one of the highest scoring players in the nation last year. Air Force’s Tre’ Coggins and Youngstown St.’s Ryan Weber are transferring. Weber has landed at Ball St. Finally, Tulane head coach Ed Conroy was just given an extension, and his ability to develop multiple quality pieces at a time is surely one reason why.

Player

Team

PPG LY

Increase in PPG

ORtg Diff

Pct Poss Diff

Pct Min Diff

Pace Diff

D.J. Balentine

Evansville

22.8

14.7

13.9

10%

38%

1.3

Tyler Harvey

E. Wash.

21.8

14.7

9.1

1%

69%

1.3

Tre' Coggins

Air Force

16

13.6

11.5

6%

55%

0.9

Craig Bradshaw

Belmont

15.7

13.5

1.1

12%

52%

1.4

Louis Dabney

Tulane

15.2

13

6.6

5%

63%

-1.8

Jay Hook

Tulane

13.9

12

31.3

2%

66%

-1.8

Spencer Parker

B. Green

12.5

10.9

9.9

-3%

80%

2.5

J. Howard, Jr.

Detroit

18.3

10.7

4.0

10%

24%

-2

Max Yon

Air Force

13

10.3

2.7

4%

68%

0.9

Ryan Weber

Youngst. St.

12.2

10.2

20.5

3%

56%

0.9

Should We Ignore The Components Of Defense That Teams Can't Control?

Given the small sample of games in a college basketball season, even if every player returns, a team’s defense is surprisingly unpredictable. As an anecdotal illustration of that point, in last week’s column I noted that North Dakota St. brought back essentially its entire roster in 2014, and yet NDSU’s defense was much worse than in 2013. But brilliant South Carolina Gamecock writer @chickenhoops emailed me and pointed out that North Dakota St. wasn’t necessarily a worse defensive team last year, they were just unlucky. The Bison’s collapse on defense was almost entirely driven by factors outside their control. Notably, North Dakota St.’s opponents hit an incredibly high percentage of three pointers, and made an extremely high percentage of free throws on the season. I had noted that North Dakota St.’s defense fell from 59th to 131st. But ChickenHoops noted that if opponents had made the same percentage of threes or the same percentage of free throws as the average D1 team, North Dakota St.’s defense would have ranked 70th in 2013 and 74th in 2014.

Most people would agree that teams have little control over their opponent’s free throw percentage. And Ken Pomeroy has argued that an opponent’s three point percentage is something teams also have little control over. (Ken argues that teams have control over whether opponents take threes, but have substantially less control over whether those shots fall.)

And as ChickenHoops proposes, we can easily recalculate each team’s points allowed per 100 possessions, assuming their opponents hit the D1 average percentage of free throws and threes. This new measure of defense will be the points allowed per 100 possessions, assuming more typical luck. In the next table I show how each team’s defense would have looked last season using this new measure.

In the major conferences, a team like Vanderbilt stands out as fortunate last year. While the average team made roughly 70% of its free throws last year, Vanderbilt’s opponents made only 65% of their free throws. And while the average team made 35% of its threes last year, Vanderbilt opponents made only 30% of their threes.

On the flip side, a team like Notre Dame was probably very unfortunate to have such a bad defensive season last year. Opponents made 75% of their free throws and 39% of their threes against Notre Dame last year, far above the national averages.

Team

Conf

Def

New Def

Change

Vanderbilt

SEC

99.0

101.3

2.3

Clemson

ACC

95.0

97.2

2.2

Memphis

AAC

98.1

100.0

1.9

Texas A&M

SEC

96.3

98.1

1.8

Louisville

AAC

90.0

91.8

1.8

Arizona St.

P12

97.6

99.4

1.8

Northwestern

B10

94.2

95.9

1.7

Kansas St.

B12

94.8

96.4

1.6

North Carolina

ACC

95.4

96.9

1.5

Virginia

ACC

90.1

91.6

1.5

Virginia Tech

ACC

100.5

101.9

1.4

Nebraska

B10

96.1

97.4

1.3

Connecticut

AAC

91.8

93.1

1.3

Cincinnati

AAC

91.3

92.5

1.2

Utah

P12

96.5

97.6

1.1

USC

P12

102.2

103.2

1.0

Ohio St.

B10

89.6

90.6

1.0

Butler

BE

99.6

100.6

1.0

Wake Forest

ACC

101.9

102.8

0.9

South Carolina

SEC

102.3

103.2

0.9

Syracuse

ACC

93.6

94.4

0.8

Duke

ACC

102.3

103.1

0.8

SMU

AAC

94.7

95.5

0.8

UCF

AAC

106.1

106.9

0.8

Missouri

SEC

104.4

105.2

0.8

Oklahoma St.

B12

96.6

97.4

0.8

Kentucky

SEC

96.9

97.7

0.8

Maryland

ACC

95.5

96.2

0.7

Indiana

B10

97.5

98.2

0.7

Washington

P12

104.5

105.2

0.7

Xavier

BE

100.6

101.3

0.7

West Virginia

B12

104.2

104.8

0.6

UCLA

P12

97.3

97.9

0.6

Miami FL

ACC

100.0

100.6

0.6

Providence

BE

102.2

102.8

0.6

Florida St.

ACC

98.8

99.4

0.6

Iowa

B10

102.7

103.3

0.6

Florida

SEC

89.3

89.8

0.5

Baylor

B12

100.0

100.4

0.4

Georgia

SEC

99.1

99.5

0.4

Kansas

B12

96.3

96.7

0.4

Georgetown

BE

102.1

102.5

0.4

Pittsburgh

ACC

96.2

96.5

0.3

Mississippi

SEC

102.7

102.9

0.2

Marquette

BE

100.2

100.4

0.2

Creighton

BE

104.1

104.3

0.2

Temple

AAC

109.1

109.3

0.2

Mississippi St.

SEC

103.7

103.9

0.2

Georgia Tech

ACC

99.8

99.9

0.1

Oregon

P12

100.6

100.7

0.1

Purdue

B10

101.2

101.3

0.1

NC State

ACC

102.9

103.0

0.1

Arizona

P12

88.5

88.6

0.1

Arkansas

SEC

98.1

98.1

0.0

Illinois

B10

93.3

93.3

0.0

Texas

B12

98.4

98.4

0.0

Michigan

B10

102.1

102.1

0.0

TCU

B12

103.1

103.0

-0.1

Houston

AAC

108.0

107.9

-0.1

Tennessee

SEC

94.8

94.7

-0.1

St. John's

BE

96.8

96.6

-0.2

Washington St.

P12

103.5

103.2

-0.3

LSU

SEC

99.4

99.1

-0.3

Oklahoma

B12

100.6

100.2

-0.4

Colorado

P12

96.9

96.5

-0.4

Wisconsin

B10

97.6

97.2

-0.4

Villanova

BE

94.5

94.1

-0.4

Oregon St.

P12

107.1

106.6

-0.5

Seton Hall

BE

101.2

100.7

-0.5

Alabama

SEC

100.0

99.5

-0.5

Michigan St.

B10

96.2

95.7

-0.5

Stanford

P12

97.0

96.4

-0.6

Penn St.

B10

100.8

100.2

-0.6

Iowa St.

B12

99.9

99.2

-0.7

DePaul

BE

107.3

106.5

-0.8

Texas Tech

B12

102.2

101.4

-0.8

Auburn

SEC

106.5

105.7

-0.8

Minnesota

B10

100.4

99.6

-0.8

South Florida

AAC

104.1

103.2

-0.9

California

P12

100.6

99.6

-1.0

Rutgers

AAC

106.3

104.9

-1.4

Boston College

ACC

111.4

109.9

-1.5

Notre Dame

ACC

106.4

104.1

-2.3

Though not listed in the above table, Wichita St. was also extremely fortunate last year. The Shockers opponents made only 65% of their free throws and 31% of their threes last year. Wichita St.’s opponents rarely had scorching shooting nights, and when they did, (Evansville making 5 of 11 threes and 15 of 16 free throws), the game was enough of a mismatch that it didn’t matter. The Kentucky game was arguably the first time on the season that one of Wichita St.’s quality opponents had an unusually good shooting night.

This is worth revisiting in the middle of next season, to see if the margin-of-victory rankings are misleading us about the top teams. But it can make a meaningful difference. With this alternative metric, Notre Dame was not the 99th best team in the nation last year, but they were the 82nd best team.

Another way to think about this is to think about performance between seasons. A team’s adjusted defense is correlated year-to-year. But if I use this new measure of defense, the year-to-year correlation is meaningfully higher. The improvement in the unexplained variation is a bit difficult to put into words, but perhaps a comparison will help. One of the factors that matters in my model is the height of each team’s center. Using this new measure of defense improves the performance of my prediction model four times as much as adding the height of each team’s center to the model.

Since my prediction model is a bit complicated, for now let’s think about the simplest possible prediction model which accounts for returning minutes (but which does not account for individual player stats, player heights, recruiting rankings, or coaching). I’m discussing results based on the last 10 seasons of data. In this simple prediction model, if a team brings back the average number of minutes, having a defense that is 1 point better predicts that the defense will be 0.67 points better the following season. But when we use this new measure of defense, a defense that is 1 point better predicts that the defense will be 0.72 points better the following season. For a team on the unfortunate end of the scale, like Notre Dame, using the new measure of defense essentially moves them up about 12 spots in my projections for next year.

It is not fair to say that teams have no control over their opponent’s free throw percentage. FT defense depends on which players a team fouls. And Pomeroy has admitted that three point percentage includes some information. But college basketball analysts should be thinking of this a bit like Batting Average on Balls in Play in baseball. If a team is way too high or way too low in free throw defense or three point percentage defense, that probably is a bit about luck. Both within seasons and between seasons, we shouldn’t necessarily expect that component of defensive performance to persist.

Will Duke Or Kansas Have A Better Defense In 14-15?

After I presented my early Top 25 last week, Kansas PG Naadir Tharpe left the team for personal reasons, and PG recruit Devonte Graham committed to the Jayhawks. I’m not going to fully re-run the projections again until after the Spring signing period, but based on the early numbers, my model seems to be a tad higher on Kansas than most experts. My model’s confidence is based on Bill Self’s per-possession track record (as reflected in 10 straight conference titles.) In particular, the model expects Kansas’ defense to bounce back substantially this year. But the more I thought about it, the more I realized there was plenty of nuance to the defensive projection.

Before I get to the defensive discussion, let me say that there is a high probability that Duke will have a better offense than Kansas next year. Duke’s returning players (Quinn Cook, Rasheed Sulaimon and Amile Jefferson) were more efficient than Kansas’ returning players. Perry Ellis was very efficient, but Wayne Selden was not a natural scorer last year, and the Kansas PG situation is still a little unsettled. Both teams bring in multiple impact freshmen, but based on the entire roster, Duke has more offensive weapons. The more interesting question is whether Kansas or Duke will have the better defense in 14-15.

Unfortunately, our current methods for predicting defense are not great. Individual defense is extremely poorly measured with current statistics. I can tell you that Duke’s Matt Jones had a higher steal rate than Rodney Hood. But that doesn’t mean Jones was a better defender. Hood’s size might have altered more shots. We also don’t know who Jones was matched-up against defensively. We don’t have a statistical measure of whether Jones took more chances and gave up more drives to get those steals. And we don’t know if Jones got a few more deflections because he played sparingly and didn’t need to conserve his energy for the offensive end of the floor. Perhaps if Jones played starters’ minutes, his defense wouldn’t be nearly as good.

Team defense is also astonishingly unpredictable. North Dakota St. brought everyone back last season, and yet NDSU’s defense fell from 59th to 131st. NDSU’s offense was good enough that Saul Phillips’ squad won the Summit League and defeated Oklahoma in the NCAA tournament. But even for teams with little roster turnover, the small sample of college basketball games in a season means we do not always have a true barometer of a team’s defense.

The larger sample of games for each coach may be more reliable, and that’s why my model includes coach effects on defense. The next table shows the national rank of Bill Self and Mike Krzyzewski’s defense in each season over the last 10 years, measured on a per-possession basis, adjusting for opponent and venue.  The table makes a pretty compelling case that Bill Self has been a better defensive coach over the last 10 years, and that Kansas will have a better defense in 14-15.

Def Rank

Bill Self

Mike Krzyzewski

2013-14

31st

116th

2012-13

5th

31st

2011-12

3rd

81st

2010-11

11th

21st

2009-10

9th

8th

2008-09

9th

36th

2007-08

1st

8th

2006-07

1st

7th

2005-06

3rd

18th

2004-05

25th

3rd 

But the personnel situation for Duke makes it a little less certain. Duke’s biggest problem on defense last year was that they did not play a true low post defender. Duke essentially played Jabari Parker out of position at the center spot for much of the season, rather than give Marshall Plumlee a chance to develop in the paint. Mike Krzyzewski clearly thought he had a better chance to outscore teams with that small lineup, but the defense was bad all year. This year with Jahlil Okafor in the paint, and a more mature Marshall Plumlee playing more minutes, Duke will almost certainly improve its interior defense. An argument can be made that Okafor is a uniquely talented center, and Duke’s defense will look more like it did in 2010, when Duke had Brian Zoubek in the middle. Meanwhile Kansas, which has often featured 7 foot shot blockers in the middle, may end up playing a slightly smaller front line of Cliff Alexander and Perry Ellis next year.

The above table also raises another issue. 13-14 was the worst defensive season for both Bill Self and Mike Krzyzewski since Ken Pomeroy began tracking the per-possession stats. And there was something else that changed in 2013-14 besides each team’s personnel. The NCAA also changed its rules about hand-checking perimeter players and drawing charges. And as the next table shows, Bill Self and Mike Krzyzewski’s teams both struggled with sending players to the free throw line after this change. Bill Self’s team particularly struggled in this area. While the average NCAA team sent players to the line 4 to 5 more times per 100 shots, Kansas sent players to the line 13 more times per 100 shots last year.

And this may not have been the only impact of the rule changes. If Duke’s defenders could no longer step in front and draw charges as they did in previous seasons, that may be more of a permanent concern for Krzyzewski’s team. Both teams were worse at creating turnovers in 2013-14, but both showed similar drops to the national average.

Def Free Throw Rate

Bill Self

Mike Krzyzewski

Bo Ryan

National Average

2013-14

45

41

27

41

2012-13

32

33

26

36

2011-12

33

33

30

37

2010-11

32

30

33

38

2009-10

31

34

36

38

2008-09

35

31

34

37

2007-08

31

32

25

37

2006-07

34

30

28

37

2005-06

32

28

31

36

2004-05

38

32

28

37 

It is obviously too soon to tell whether the rule changes have a unique effect on these two coaches. A strong argument can be made that the primary reason Kansas fouled so much last year was because of the team’s excessive youth. 2013-14 was the youngest team of Bill Self’s career.

But it is worth asking whether coaches that are particularly good at teaching their teams to play defense without fouling (Bo Ryan), coaches that play zone defense (Jim Boeheim), or coaches that play pack-line defense (Tony Bennett) will be better prepared to play elite defense going forward. Bill Self is clearly one of the best teachers of physical man-to-man defense in the nation, but it is harder to bank on his historic track record in an environment where the rules have changed.

Overall, I remain optimistic about both team’s defense next year. If you have watched any of Duke or Kansas’ recruits play, or read the scouting reports, it is clear they have the type of athletes coming in to be a real threat defensively.

Paul Biancardi says Duke’s Justise Winslow might be the best defensive player in the country. Duke’s Tyus Jones has such an impressive ability to drive and create using his low-center of gravity, I will be shocked if he doesn’t have the ability to keep opposing guards out of the lane. Kansas’ Cliff Alexander is an aggressive defensive rebounder. And Kansas’s Kelly Oubre has an impressive wingspan, the kind of arms that should cause a lot of deflections this year. Certainly, the incoming personnel are capable of playing elite defense.

But the changes in the rules make me nervous about including defensive coach effects in my model. An unusual number of great defensive coaches had mediocre defensive seasons in 13-14. In next fall’s projections, I may need to lower the impact of coach effects on the predictions. I’m projecting Duke’s offense to be better than Kansas’ offense, and I’m quite confident in that prediction. I’m also projecting Kansas’ defense to be better than Duke’s defense, but that is far from guaranteed.

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