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More On Kentucky's Downside

I have received a number of Twitter questions asking how to interpret the Best Case and Worst Case Scenarios in my projections. If you think of a normal bell curve, while most of the mass is in the middle, the tails can stretch out for some distance. I don’t think there is much value in trying to present the full tail for each team when projecting the season. If I reported the true outliers for every team, every team’s range would be ridiculously large. I tried to settle on cut-offs that communicate the relative riskiness of teams.

The real question is how often teams fall within my Best Case/Worst Case range. I have an idea based on past seasons, but since I used those seasons to fit the model, I’m not quite willing to make a definitive statement on that question yet. For now, let me present a couple of outliers from last year.

- What would my new simulation model have projected for Kentucky and Michigan for 2012-2013?

Kentucky 2012-13

Median Simulation : 16th

Best Case: 4th

Worst Case: 43rd

While most of us fell in love with the upside for Kentucky’s starting lineup last year, what we were not accounting for was the fact that Kentucky had very little depth. If Kentucky’s starters were injured or struggled, the downside simulations were quite weak. In fact, based on the number of available at-large bids, Kentucky’s worst case scenario was that of a borderline NCAA/NIT team last year.

And as we saw, the worst case scenario came to fruition. According to Sagarin’s margin-of-victory-based “Predictor”, Kentucky finished 38th last season. According to Ken Pomeroy’s old MOV formula, Kentucky finished 48th. And according to Ken Pomeroy’s new capped MOV formula, Kentucky finished 67th last season.

Michigan 2012-13

Median Simulation: 23rd

Best Case: 6th

Worst Case: 57th

As with Kentucky, Michigan had a relatively large range for a Top 25 team. And the reason for those large ranges is because both teams were relying a ton on freshmen last year. The performance of freshmen is extremely unpredictable. In the end, Michigan finished above my best case scenario at fourth or fifth depending on your preferred MOV system.

While these finishes were just outside my projected range, I am comfortable with both of these. That is because I believe in both cases those were true outlier seasons, far out in the tail.

Despite having what the experts labeled as the 8th-12th best recruiting class in the country, Michigan’s freshmen class was by far the most productive in the country last year. To have freshman Nik Stauskas come in and make 80 threes, to have Mitch McGary play like a superstar in the NCAA tournament, and to have players like Spike Albrecht come out of nowhere and play mistake-free basketball was incredible.

Meanwhile, for Kentucky, just about everything that could go wrong, did go wrong. From the injury to Nerlens Noel, to the disappointing play of a highly touted transfer PG, to John Calipari’s rare failure to get the Wildcats to buy-in on defense, everything broke the wrong way.

If these are the type of seasons that fall just outside my projected range, I feel fairly confident in the accuracy of my system.

- What does this mean for 2013-2014?

While I am not guaranteeing that Kentucky will finish in the Top 13 this year, my model is essentially saying that this is extremely likely.

Kentucky simply has too much depth for things to completely fall apart this year. As I noted last week, Julius Randle could be a massive underachiever and Will-Cauley Stein could get hurt, and Kentucky would not miss a beat. The only possible weakness on the Wildcats is the lack of depth at the guard positions.

But with a downside of 13th this year instead of 43rd last year, Kentucky fans can be confident that even if things go wrong, the team will still be relevant in March.

- Didn’t I have Michigan rated lower than 23rd in last year’s preseason projections?

Yes, absolutely. Michigan is a huge reason that I added the simulation to the model. What I wanted to be able to do was more effectively emphasize the importance of star players. It is much easier for the winner of a competition to be a role player. And because Michigan had Trey Burke (and to a lesser extent Tim Hardaway), they already had their stars last year. They only had to find role players to fill in around them. I agree that my old model was too pessimistic, and Michigan is a large reason I added a simulation to my model this year.

This also explains why Michigan’s upside remains extremely strong this year. Again, Michigan is going to be relying a lot on unproven players. But with Mitch McGary and Glenn Robinson leading the way, if this year’s guards click, the upside for Michigan remains that of a Top 5 team. (The Wolverines also needs McGary to get over his lingering back issues.)

But the real importance of the simulation is the earlier note about depth. This year Maryland, Alabama, NC State, Temple and Vanderbilt have very short benches. Those teams might have competitive rotations, but the lack of scholarship players is a risk. Do not be surprised if injuries derail the season for at least one of these teams.

Is Youth An Excuse?

College basketball can be sloppy in November thanks to the large amount of roster turnover every year. Luckily, over the course of the season almost all teams learn to play better basketball and the level of play is elevated.

That has a pretty important implication at the team level.  If you want your team to have a great season, it isn’t enough to simply get better. You have to get better at a faster rate than your rivals. One thing I have said in the past is that teams that play a lot of freshmen have the potential to improve the most. This is based on the statistical sophomore leap. I.e. rising sophomores improve more than rising juniors or seniors. This doesn’t necessarily mean that freshmen are more likely to add a facet to their game. Freshmen are no more likely to become dominant shooters or drivers. But what freshmen do is make a lot of mistakes, and those can be corrected.

But should we expect teams that give a lot of minutes to freshmen to improve more within a season? In the following table, I took the 50 youngest teams from high major conferences over the last 10 years. I compared their offensive and defensive efficiency over the course of the season, adjusting for opponent and location as on kenpom.com:

Young Teams Last 10 Years

Avg Adj Off

Avg Adj Def

Avg Pyth

Games 1-8

105.6

93.0

0.7861

Games 9-16

107.1

94.6

0.7801

Games 17-24

107.4

94.4

0.7907

Games 25+

108.9

96.6

0.7738

Young offenses improve from 105.6 on average in the first eight games of the season to 108.9 at the end of the season. By correcting mistakes, making sure the right players take the right shot, and simply learning to play together, young teams improve substantially at putting the ball in the basket.

(Keep in mind that the magnitude of these effects may be slightly less meaningful than what you see here. I haven’t done a comprehensive study, but I believe the average D1 offense improves around one point over the course of the season as coaches begin to use tighter rotations. Regardless, the data suggest freshmen-led teams improve more.)

While improved offensive play seems obvious to anyone who watches the game, there was no guarantee we would see this in the data because there are mitigating factors. First, it often takes until the middle of the season for teams to develop scouting reports for freshmen. A freshman with a great first-step who was dominant at getting to the line in November and December, may suddenly get challenged to take three pointers in January and that may hurt his performance. The change in the quality of competition may also matter. The forward who looked great against low-major competition to open the season, now plays taller competition and gets the ball blocked back in his face. So certainly, the offensive improvements we see in this table were predictable, but perhaps not completely expected.

But as the Pythagorean standings show, late in the season young teams tend to play their worst basketball. Do the freshmen hit a wall? Is there a point in the season where they just run out of gas? Offensively things continue to get better. But there is a trend on the defensive end, and I think that trend deserves more explanation. Let’s look at a team like Indiana in 2009:

Indiana 2009

Adj Off

Adj Def

Pyth

W

L

Games 1-8

90.2

94.4

0.3856

4

4

Games 9-16

90.7

100.9

0.2500

0

8

Games 17-24

103.8

102.5

0.5319

1

7

Games 25+

103.2

105.8

0.4369

0

6

I think the thing we have to keep in mind with young teams is that losing takes a toll on the players. Notice how the Hoosiers defense falls off a cliff at the end of the season? The reality is that playing strong defense takes a lot of effort. And at a certain point, if you continue to put in a bunch of effort and not see a result from that effort, it is human nature to not try as hard.

It sort of becomes a cliché in February when a bad team hangs with a good team, that the coach deserves credit because his team doesn’t quit. But certainly for young teams, maintaining a level of defensive efficiency throughout a losing season can be very difficult.

But John Calipari’s young teams have not only been able to win, they’ve been able to improve their level of defensive play throughout the season. Look at the Kentucky splits from the 2010 season:

Kentucky 2010

Adj Off

Adj Def

Pyth

W

L

Games 1-8

112.5

89.3

0.9150

8

0

Games 9-16

119.0

89.8

0.9474

8

0

Games 17-24

116.1

87.3

0.9491

7

1

Games 25+

117.4

84.7

0.9660

12

2

Despite one of the youngest teams in the nation, Calipari was actually able to improve his team’s defense late in the year. (His team’s offensive struggles early in the season were also corrected.) Talent had a lot to do with that season-long improvement. And there are plenty of folks who question whether John Calipari’s team has the talent to duplicate those splits this season.

But the data suggest we should not be too concerned if Kentucky struggles in a few early games. Almost all young teams improve offensively over time. And if a young team can maintain or improve its defense, it can get better at a faster rate than its rivals.

Opening Weekend Notes

Alabama’s Trevor Lacey opened the season with a bang, knocking down a game-winning three as time expired to beat South Dakota St. But I enjoyed the novelty of the Purdue-Bucknell ending even more. With Purdue trailing Bucknell by just 3 points with 10 seconds left, everyone in the stadium knew that DJ Byrd was going to get the ball. (Byrd was an outstanding three point shooter for the Boilermakers last season and is expected to step into a lead role for the team this season.) And the Bucknell players knew he was going to get the ball too. But in the final seconds two Bucknell players collided while trying to get in Byrd’s way, and collapsed to the floor. That left Byrd wide open from deep. Sadly for Purdue, his shot rimmed off. Even non-buzzer beaters can be filled with drama.

As much as I’d love to point out the things my model has gotten right and justify the things my model has wrong at this point in the season, we are definitely in the world of small sample sizes. Anything can happen in one game, and that is particularly true early in the season. I have major plans to evaluate my predictions, but not until at least January.  St. Peter’s upset Rutgers in the season opener, but we’ll have a better sense in January whether I was wrong about Rutgers, wrong about St. Peter’s, or whether this was a one-game fluke.

More than results, at this point in this season, I am most fascinated to see which players are breaking out and which players are treading water. Finding breakout players may be obvious. Everyone saw Alex Len breakout against Kentucky and Len’s dominant performance against an athletic front line may be a reason to substantially increase our expectations for the Terrapins this year.

Finding players who are treading water may not be as straightforward. But if you had to ask me what I’m looking for, I think UCLA’s Josh Smith personified it in a possession in the first half last Friday. Smith rebounded the ball, lackadaisically threw it to an Indiana St. player instead of his teammate, stepped into the lane to try to cut off the drive and was called for a foul. The look on Smith’s face said everything you need to know about why he still isn’t playing starters’ minutes despite being one of the most gifted players in the Pac-12.

Comparing The Conferences

The Pac-12 has been suffering through a long dark period. The Big Ten has been dominant (at least in the pre-conference schedule) for the last few years. Should we expect a change this year? Is the Pac-12’s slump over? Is the Big Ten’s boom about to come to an end? Let’s take a quick look at some basic roster data and see if we can uncover any trends.

Part of predicting the season is noting the number of elite high school prospects on each roster. Not only are these players more likely to play well as freshmen, but they are also more likely to breakout later in their career. Recall, for example, Michael Snaer of Florida St. Snaer was a former Top 20 recruit, and while it took him three seasons, he broke out in a big way in 2011-12. After adding up the numbers…

- The Big East has the most former RSCI Top 100 prospects on rosters heading into the season with 58.

- But the Big East has more teams, and the Big East has only 3.9 elite recruits per team. The ACC has the most former Top 100 recruits per team with 4.6 per team.

- But James McAdoo is the only former Top 10 prospect in the ACC this season. That seems like an unprecedented lack of super-elite talent for the conference. If you want super elite talent, you probably want to watch the SEC, assuming everyone is declared academically eligible. John Calipari never lets us down on the recruiting trail.

- The SEC, however, is only welcoming ten Top 100 freshmen this year as a whole. Even the Big Ten, the land of typically poor recruiting, is welcoming more Top 100 freshmen than the SEC this season. And yes, the slumping Pac-12 brings in quite a few elite recruits this year.

Conf

T10

T100

T100 Fr

ACC

1

55

22

BE

1

58

17

SEC

4

49

10

B10

1

40

15

B12

3

33

11

P12

3

37

15

MWC

1

15

5

A10

0

11

3

The next table isn’t really roster data, but it does reflect some of my preliminary projections about playing time.

- The ACC is going to be the youngest conference in the nation this year, according to my projections.

- The Big East has a startlingly low number of key seniors on rosters this year.

- As usual, the MWC and A10 have more mature rosters. They lose fewer players to the NBA and that helps the top MWC and A10 teams compete, even without a plethora of blue chip talent.

Class

Sr%

Jr%

So%

Fr%

MWC

35%

30%

17%

17%

A10

33%

27%

19%

21%

P12

28%

32%

18%

22%

B12

32%

19%

26%

23%

BE

22%

32%

27%

19%

B10

27%

26%

23%

24%

SEC

25%

28%

24%

22%

ACC

25%

22%

23%

31%

The Pac-12 is getting older in a hurry, thanks in no small part to an influx of transfers. Note that your transfer numbers may vary slightly. I’m excluding transfer walk-ons and a few JUCOs who seem unlikely to play in the next table.

Incoming Transfers

D1

JUCO+

P12

15

8

SEC

10

11

BE

14

6

MWC

7

5

B12

7

5

A10

8

3

ACC

3

3

B10

5

1

The transfer table doesn’t mean the Pac-12 has suddenly become the conference of transfers. This is all a natural consequence of recent league history. The Pac-12 teams have struggled the last few years making those teams particularly attractive places for transfers to matriculate. If you want to transfer and PLAY in an elite league, you would have chosen the Pac-12 too.  On the other hand, the Big Ten has been on an upswing and few coaches have needed to dip into the JUCO ranks as a quick fix. Deverell Biggs of Nebraska is currently the only incoming JUCO player projected for the Big Ten this year.

Overall, the Pac-12 was a depleted league, but it is adding a number of impact freshmen and key transfers this year. The days of the league failing to field a Top 25 team are over. As for the Big Ten, the jury is still out. The teams at the top still have plenty of talent, but programs like Purdue could be in for a bit of a slip without an influx of can’t miss players coming in.

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