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Which Types of Players Benefited The Most From Change In Way Fouls Called? (Part 1)

There has been a lot of discussion of what happened when the NCAA changed its foul enforcement rules in 2013-14. Three trends seemed clear. Points per possession were higher, free throw attempts were up, and turnovers were down. But I have not seen any discussion about how this impacted different types of players.

Let me classify players into five groups. First, I break out all big men, essentially all players over 6’8” tall. For teams that do not have at least three rotation players 6’8” or taller, I will also classify some shorter players as forwards, based on stats such as rebounding.

I next break out the point guards based on Verbal Commits recruiting classifications. Then I adjust based on a few measured stats. If a guard has an assist rate over 20%, I reclassify him as a PG, even if he was a SG out of high school. If a guard has an assist rate under 8%, I remove him from the PG group, even if he was classified as a PG out of high school.

That leaves me with a large group of off-guards and wings. I classify these players into three groups based on how often they shoot threes. For players that take over 66% of their shots from three point range, I classify them as three point specialists. For players that take under 33% of their shots from three point range, I classify them as non-shooters. (These are your typical wing players.) The remaining players that take 33-66% of their shots from three point range are your typical perimeter players that can drive and shoot.

Obviously not every player fits into one category, but for now this is how I grouped the various types of players. I am going to look at all D1 players who played at least 30% of their teams minutes in 2012-13 (before the rule change) and 2013-14 (after the rule change).

Number of Observations

2012-13

2013-14

Point Guard

634

639

Three Point Specialists

143

133

Drive and Shoot

514

522

Non-Shooters

315

316

Big Men

974

981

The next table shows that not every position on the floor is equally efficient. Three point specialists are typically the most efficient, but that is partly because they shoot less. On average, three point specialists use only 16% of the possessions when on the floor. All the other position types average 20-21% of the possessions used when on the floor.

The non-shooters tend to have some of the lowest efficiency ratings, but keep in mind that I have broken out this group based on their tendency not to shoot threes, so it isn’t a surprise that they are less efficient. The more interesting fact is that PGs tend to be a little bit less efficient. Part of this may be the fact that teams feel obligated to have a PG on the floor at all times, even if he is less talented. A team can get by without a true SG (see North Carolina last year), but no team can really run its offense without a true PG. And thus you get some less effective PGs who play major minutes.

Another thing to keep in mind is that ORtg isn’t a perfect measure of player value. When Dean Oliver developed the metric, he wanted to assign some credit for made shots to the assisting player and players that got offensive rebounds that led to the basket. But even though he had a strong basis for his formula, nothing says that his weight for assists is accurate for every team in every situation. If a PG drives into the lane and collapses a defense, and there are two passes for the wide open shot, he might not get any credit for creating the opportunity. Some PGs are more valuable than the measured stats indicate, and some PGs are less valuable.

Moreover, non-shooters at the wing position are typically some of the better defensive players. These are typically tall athletic players who help stop opposing scorers. Thus just because PGs and non-shooters are showing up as less efficient here, doesn’t mean that teams are making a mistake by putting these players on the floor.

ORtg in 2013-14

10th percentile

Median

90th percentile

Point Guard

88

103

116

Three Point Specialists

97

110

126

Drive and Shoot

93

106

119

Non-Shooters

87

101

113

Big Men

93

106

119 

Next, I want to look at how each type of player was impacted by the rule changes. My expectation was that the impact of the new foul rules would not be uniform. For example, I would expect a rule limiting hand-checks or impacting block/charge calls to benefit PGs more than three point specialists.

On the other hand, there tends to be a bit of an equilibrium situation in team defense. Even if a rule change has a smaller direct impact on a three point specialist, when devising a game plan, teams still have to weigh costs and benefits. And if an opposing PG is now more dangerous because of the new block/charge and hand-checking rules, that might result in the best defender spending less time on a good three point shooter and more time on the PG. That might still benefit the SG indirectly.

Regardless of whether the effects are direct or indirect, here is how the ORtgs changed for these groups from 2012-13 to 2013-14.

Difference in ORtg

10th percentile

Median

90th percentile

Point Guard

+3

+4

+3

Three Point Specialists

+6

+3

+5

Drive and Shoot

+5

+4

+4

Non-Shooters

+5

+4

+3

Big Men

+5

+3

+4 

The overall trend shouldn’t be a surprise given the higher points per possession across D1. If you run a t-test, the difference in the means of the two distributions is statistically significantly different, meaning that on average players were clearly more efficient in 2013-14, after the rule changes.

But I was shocked to see that the rule changes tended to impact all positions fairly equivalently. Spot up shooters gained just as much as big men and point guards.

There seems to be some evidence that the new rules helped bad players more, as the 10th percentile generally shows a larger improvement. Turnover prone players tend to have the worse efficiency ratings, and the worst players had fewer turnovers last season. But for the most part, the new rules benefited players with all sorts of variation in skills.

But even if the raw ORtg changes were equivalent, the changes in foul calls and turnovers were not identical. Next week I will discuss how different types of players benefited in different ways from the rule changes last year.

College Basketball Greatness Is Always Fleeting

Sriram Hathwar was a co-winner of the National Spelling Bee last week. This is more remarkable when you consider that this was his fifth appearance, and he continued to improve with each appearance at nationals:

Sriram Hathwar

Finish

1st Appearance

91st

2nd Appearance

37th

3rd Appearance

6th

4th Appearance

3rd

5th Appearance

1st

Everyone loves the story of a student who keeps working hard and gets better every year. But if they gave you a movie script with that kind of improvement trajectory, it would probably be rejected as unrealistic.

The movie even has the perfect climax. Hathwar actually got a word wrong. But it came when there were just two spellers left. And in the Spelling Bee, when the last two spellers make a mistake, they both stay in the competition. Hathwar faced the agony of defeat for the fifth time, but he received a last chance for redemption, and he made the most of it.

Unfortunately in sports, things often don’t have this same perfect ending. When Kevin Durant and Russell Westbrook started winning in Oklahoma City, it sure seemed like it was only a matter of time until the Thunder won an NBA title. But after making it to The Finals in 2012, they haven’t been able to take that next step. If the Thunder never win a title, they won’t be the first team to get better but fall short of the ultimate goal.

College rosters face this issue too. A few years ago, the remarkable story was how Mick Cronin had improved the fate of Cincinnati every season. Here is the Bearcats margin-of-victory rank in his first five seasons.

Cincinnati

MOV

2007

144th

2008

92nd

2009

90th

2010

73rd

2011

21st

But since that time Cincinnati has never broken into the Top 20 in terms of margin-of-victory. Sometimes, no matter how much progress you make, reaching that next level of success remains elusive. Right now only three NCAA teams have improved their margin-of-victory five years in a row:

Year

NC Central

Southern Miss

Fordham

2009

343rd

154th

323rd

2010

338th

98th

305th

2011

316th

77th

292nd

2012

204th

72nd

253rd

2013

168th

66th

244th

2014

81st

56th

204th

NC Central is the perfect story of improvement right now. They joined D1 as an independent in 2008 and after a couple of painful seasons, LeVelle Moton has guided the team all the way to the NCAA tournament. Many of the recent entrants to D1 would dream to have an improvement trajectory like that.

Southern Miss has also been interesting. Larry Eustachy took the team to the tournament in 2012 (when they probably were not that good), but Donnie Tyndall has taken over and continued the upper momentum. Unfortunately Southern Miss loses four senior starters, and last year’s shared CUSA title may have been the pinnacle.

Finally, it is amusing to find Fordham on this list as the Rams finished just 2-14 in the A10 last year. They are still far from being competitive in their league, but when you look at the underlying performance, head coach Tom Pecora has made a difference. And if one of the highest scoring freshmen in the nation, Jon Severe, sticks around and fulfills hs promise, the future may be even brighter.

In the major conferences, no team has improved more than three years in a row right now. Iowa St., Oklahoma, Houston, Wake Forest, and Virginia have all made improvements for three straight years.

Of course, for every winner there is a loser. Temple and Virginia Tech are among four teams that have posted worse margin-of-victory numbers for four years in a row. Temple has fallen from 15th nationally in margin-of-victory in 2010 to 159th last year, while Virginia Tech fell from 43rd all the way down to 192nd. And the Citadel has gotten worse for five straight years. Ed Conroy had the team at 20-13 and 171st nationally in margin-of-victory, but since he left the Citadel has slowly fallen to 346th last year.

Realistically, in the college world, players don’t stick around long enough for teams to build around them and slowly get better over time. And perhaps that is both the curse and the blessing of college athletics. You don’t have to worry about your team being “blocked” from a title by LeBron James or Michael Jordan. But when you must constantly re-stock the cabinet with new players and new recruits, it can be hard to consistently get better.

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?

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?

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.

NCAA Top 25 Projections (Post NBA Draft Declaration Deadline)

I explore the stats that make my model more skeptical of Texas, SMU, San Diego St., and Oklahoma, and I show that Syracuse, Ohio St., and UCLA still have plenty of elite high school talent.

Way Too Early Top 25 Projections

I break out my lineup-based projections model to predict the 2014-15 season.

Counting Down To Four

Why Bo Ryan deserved a Final Four trip, Michigan St.'s poor half-court offense, and other thoughts as we set the field for the Final Four.

Sweet Sixteen Day 2

A comeback, classic announcers, Michigan St.'s new closer, and Alex Poythress highlight Day 2 of the Sweet Sixteen.

Sweet Sixteen Day 1

What it means to have a Cole Aldrich moment, Scott Drew's enigmatic coaching, UCLA's three point defense, and Aaron Gordon's promotional video highlight Day 1 of the Sweet Sixteen.

NCAA Tournament Day 4

North Carolina is never predictable, Stanford's perfect tournament lineup, UK vs Wichita St., and Joe Harris' sleep habits highlight Day 4 of the NCAA Tournament.

NCAA Tournament Day 3

Saturday wasn't basketball, it was art.

NCAA Tournament Day 2

Baylor's late season surge continues, why this year's UCLA team is not last year's UCLA team, and other Day 2 observations.

NCAA Tournament Day 1

Aaron Craft, NC State's missed FTs, the irony of Cameron Ridley, and important facts like the worst graphic of the day.

Stats To Pick Apart The Bracket

The right way to measure hot teams, a different way to measure March coaches, and how teams have performed against the rest of tournament field.

Major Conference Tournaments Underway

How good would Duke, Kentucky, Kansas, and Arizona be if their freshmen stuck around? I also check in on some seniors and the first day of the major conference tournaments.

The RPI Organizational Tool, Conference Tournament Nitty Gritty

Who would gain the most if we used the Kenpom rankings to organize opponents instead of the RPI rankings?

Injury Splits - March Edition

How well has Arizona played without Brandon Ashley? What about Pittsburgh without Durand Johnson? What about Colorado without Spencer Dinwiddie?

Year Four to Six (The Hot Seat Years)

Today I present the probability a D1 college basketball head coach survives in his job for six years and I show the efficiency numbers for 4th through 6th year head coaches.

Experience Is Not A Guarantee

Oklahoma St. (89% of minutes back), Boise St. (89% of minutes back), and Boston College (95% of minutes back) have struggled, but experience does not guarantee that a team will win.

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