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Where College Coaches Matter

The tempo free rankings I released last week include a coaching component. It tends to bring down teams like Michigan (based on John Beilein’s historical struggles on defense), lowers Baylor slightly, and causes a few other peculiar rankings. One question we might ask is, "How would the rankings look if we dropped the coaching effect?" If we assumed every team had an average D1 coach, what would my Top 345 look like?

Note that this column isn't about ranking coaches. For example, today's column isn't going to talk about recruiting. This column takes all the players on the team as given, and doesn't give the coach any credit for bringing elite talent to campus.

And this isn't about in-season coaching either. In the case of Indiana, if everyone had an average coach, the Hoosiers would still be #1. That's because the Hoosiers bring back a slew of talented players including national player of the year candidate Tyler Zeller. If everyone in the nation had an average D1 coach, the Hoosiers would still look like the best team on paper.

In fact, because of the large correlation between talent-on-hand and coaching, discrepancies usually only exist when a coaches ability doesn’t match up with who he has in the lineup. With that in mind, here are some teams whose rankings would change meaningfully if I ignored coaching effects in my model.

#2 Kentucky would be #5 if my model ignored coaching.

Kentucky has Top 10 talent no matter how you slice it. But only John Calipari has proven he can win big instantly with such a large group of freshmen.

#6 Florida would be #14 if my model ignored coaching.

I don't think we give Billy Donovan enough credit for developing dominant offensive teams. The Gators have plenty of strong pieces and Donovan will fit them together.

#7 Duke would be #22 if my model ignored coaching.

Offense or defense, any model you estimate says Mike Krzyzewski gets the most out of his players. If not for him, we'd be spending a lot more time scrutinizing whether point guard Quinn Cook could lead a team to an ACC championship. And we’d spend hours documenting how terrible Duke’s defense was last year and wondering whether Ryan Kelly will ever be an effective defender after returning from injury. But in 10 years, Mike Krzyzewski has only had an adjusted defense worse than 90.8 once. Faith in his ability to improve Duke’s defense is more than warranted.

#14 UNLV would be #7 if my model ignored coaching.

Dave Rice has put together a scary collection of talent this season. Once we reach the mid-semester break, UNLV will have eight players who were consensus Top 100 recruits out of high school, including Top 10 freshman Anthony Bennett. And that list doesn’t even count returning players Justin Hawkins and Carlos Lopez who were not elite recruits out of high school, but who were incredibly efficient last season. The reason the Running Rebels aren’t higher in my model is because Dave Rice doesn’t yet have a track record as an elite defensive coach. Coaching dominant defense at a fast pace is a special skill. While Roy Williams has proven his teams can run and dominate at preventing points, Dave Rice’s teams have only shown flashes of defensive strength.

#113 SC Upstate would be #61 if my model ignored coaching.

Since SC Upstate may not be a familiar name, I think it is worth emphasizing that SC Upstate’s head coach is Eddie Payne. Payne coached Oregon St. in the late 1990’s and failed to achieve a winning record with the Beavers.

In my last column, I talked a lot about how you should not expect the loss of key players to impact all teams equally. Instead you need to look at who is inserted into the lineup. Similarly, you should not expect player development to be equivalent for all types of players. BCS caliber players can have different trajectories than players in smaller conferences. The coaching effect helps proxy for this.

As an example, South Carolina Upstate returns a remarkable 93% of its possessions from last season. And my model picks the Spartans to improve from 132nd in the nation to 113th, narrowly edging Mercer for the A-Sun title. But if I assumed Clemson and SC Upstate players developed at the same rate, my model would project something breathtakingly high for the Spartans.

Instead, my model notes how head coach Eddie Payne’s teams have had a remarkably flat path in their first four years in D1. Payne was not typically able to get his players to make significant jumps in offensive efficiency, or improve substantially on defense from year to year. Last year, behind a class of extremely impressive sophomores, Upstate did improve substantially, but the model is cautious to expect that every season.

The differences listed above were some of the larger differences I found when I removed the coaching effect, and they hardly seem controversial. But other coaching adjustments have made me scratch my head a little. For example:

#85 South Carolina would be #178 if my model ignored coaching.

This offseason, the Gamecocks hired Frank Martin as their new head coach. As I emphasized in the introduction, when returning talent and coaching talent do not match, the coaching effects tend to have the biggest kick. The way South Carolina’s players performed last season would lead you to believe the team has almost no SEC-caliber talent. And that is far below what we would expect from a Frank Martin coached team. Thus, we get a big contrast in expectations with and without Martin.

My model expects Frank Martin to emphasize defensive rebounding and physical play and improve South Carolina’s adjusted defense from 101.0 last season to 96.8 this season. I think most people can sort of visualize that happening. The change may seem a little extreme, but it isn’t that implausible. What may be a little harder to imagine is Frank Martin developing South Carolina’s offense from 101.6 to 105.8. But keep in mind that since Michael Beasley left, Martin has had limited talent at Kansas St. He has developed strong offenses without rosters filled with Top 100 players. The key is that Martin teaches players to crash the offensive boards and get to the free throw line at an incredible rate. Those are very real skills, and skills that can be taught. 

This expected improvement from Martin may be overly optimistic, especially until Bruce Ellington becomes available after the football season. But I was at least somewhat reassured by Frank Martin’s comments to Jeff Goodman last week. Martin went so far as to say he doesn’t understand why his team cannot compete in the SEC in year one.

#74 UMass would be #52 if my model ignored coaching.

My model is currently skeptical of Derek Kellogg. Derek Kellogg took over at UMass for the 2008-09 season:

Year

UMass Offense

2011-12

105.4

2010-11

95.4

2009-10

101.6

2008-09

104.2

2007-08

110.1

In his first three seasons, Kellogg’s team got worse offensively. I’m skipping over some key details about returning players and minutes here, but UMass was not hit unusually hard by player defections over this time period. You could also probably make an argument that all that happened in 2012 is that Kellogg got lucky and found Chaz Williams as a transfer. Chaz Williams’ incredible ability to distribute the ball suddenly transformed UMass into an elite team, not Kellogg’s ability to develop players.  (That isn’t actually what the model thinks. Williams was a good, but not dominant player at Hofstra, and Kellogg does get meaningful credit for improving Williams’ game.) But the basic point is there. The model is very concerned about whether players consistently get better under Kellogg. 2011-12 was also Kellogg’s best defensive season by far, and the model is nervous about whether Sampson Carter can return from injury and maintain that level of play.

I go back and forth about whether these coaching adjustments are appropriate. The reality is that you cannot rank teams without including some consideration for the head coach. Nobody takes a vote in the AP Poll without thinking about who is on the sideline for each team. But determining where and how to include these coaching effects can make a major difference in the rankings.

(For the record, Ken Pomeroy’s model doesn’t explicitly use 10 years of coaching data, but his model isn’t blind to coaching either. By using multiple years of historic offense to predict this year’s offense, and multiple years of historic defense to predict this year’s defense, Pomeroy is essentially including a coaching effect too.)

Season Kick-Off

The season kicks off on Friday with a full slate of Aircraft Carrier games. Ohio St. takes on Marquette aboard the USS Yorktown, Georgetown takes on Florida aboard the USS Bataan, and Syracuse takes on San Diego St. aboard the USS Midway on Sunday. (And don’t forget Michigan St.’s game against Connecticut from Ramstein Air Force base in Germany.) The multi-game military salute makes this one of the better season tip-offs in recent years.

But despite the backdrop of the games listed above, the most highly anticipated matchup might be Kentucky vs Maryland from the Barclays Center in Brooklyn. Many people want to tune in to see Kentucky’s exciting new freshmen in action. Others want to tune in to see if the defending national champions get knocked off early in the season. But this game became much more interesting on Wednesday morning when we learned that Xavier transfer Dezmine Wells was declared immediately eligible at Maryland.

With the addition of Wells, Maryland improves from the 111th rated team in the nation in my rankings to the 96th ranked team. I think a lot of experts are going to think that is low. My model is projecting Maryland’s defense to improve by nearly 4 points, but the offense continues to have some questions which holds my model back from endorsing Maryland as an NCAA tournament team.

My model’s expectations for Maryland’s lineup are listed below. First, Dez Wells was expected to be Xavier’s best player this season, and he projects to be the best player on an improving Maryland squad. I think my model is also relatively optimistic about Nick Faust. Faust was horrible last year. With a 86.8 ORtg, Faust’s shooting percentage was terrible and his turnover rate was equally bad. Still, Mark Turgeon gave him a lot of minutes and that helps confirm what the high school scouts saw when they rated him 43rd in the nation. If Turgeon trusted him that much with such bad numbers, Faust must have a lot of potential. That is why he is expected to mature into an effective team leader this season and improve his ORtg to a much more respectable 101.6.

James Padgett is obviously a solid rock in the lineup. But then we come to the models first difficult conclusion. Who plays in the post alongside Padgett? Alex Len is the natural choice, but my model sees his middling numbers last season, 98.9 ORtg, and likes freshmen Shaquille Cleare to challenge Len for that playing time. While I agree that Cleare’s upside is pretty high, I feel like that may be a little pessimistic about Len. Len didn’t become eligible until mid-season last year, and his middling offensive numbers last year may not be representative of what he can do when he has a full slate of fall games to integrate with his team. If you are looking for an argument for why Maryland’s offense will be better than what is listed here, the best explanation would be a breakout season from Len.

My model is also very concerned about Pe’Shon Howard’s two year stats. Howard posted a 99.6 ORtg two years ago and an 81.0 ORtg last year before going down with an ACL tear. Howard was never fully healthy last year after missing the beginning of the season with a foot injury. But the model is skeptical of an oft-injured inefficient player who was not an elite recruit out of high school. Howard’s ball-handling and point-guard skills may earn him a starting nod, but his production needs to be better than it was last season for Maryland to be an NCAA contender.

Looking at the bench, with Wells and Faust playing major minutes, Albany transfer Logan Aronhalt may have trouble earning major playing time, but his three point shooting will earn him a key role in the rotation. And Freshmen Jake Layman also gets a nod for major minutes as a more athletic and versatile wing.

What Maryland really needs to become an NCAA team is for someone to emerge as an efficient high volume scorer.  There are some possibilities, but this is still a young team with a lot of new pieces. 

Maryland Projected Lineup

Ht Ft

In

RSCI

Class

Pred Min

Pred Poss

Pred ORtg

Dezmine Wells

6

5

54

So

71%

20%

109.5

Nick Faust

6

6

43

So

70%

23%

102.1

James Padgett

6

8

 

Sr

60%

22%

108.5

Shaquille Cleare

6

9

34

Fr

51%

20%

104.0

Pe`Shon Howard

6

3

 

Jr

50%

18%

96.0

Logan Aronhalt

6

3

 

Sr

50%

23%

103.0

Jake Layman

6

8

67

Fr

43%

19%

101.7

Alex Len

7

0

 

So

42%

18%

103.4

Seth Allen

6

1

 

Fr

33%

18%

99.6

Charles Mitchell

6

8

 

Fr

29%

18%

99.5

           

SOSmod

1.023

           

Pred Off

105.8

 

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