Category Archives: Stats


Minnesota Gophers: Net Steals by Player (1st 9 B1G games)

February 5, 2013
Plenty of work going on behind the scenes, but we’re not ready to share much of it. However, some may find the information below interesting.

Steals in each of the Gophers’ Big Ten games so far this season have been broken down by player. The first table shows steals against (i.e., Minnesota player charged with a turnover when an opponent stole the ball) and the second table details steals by Minnesota when on defense.

Finally, a total net steals for/(against) by player schedule summarizes the plus/minus for each Gopher.

Steals against MSU NW at ILL at IND MICH at NW at WISC NEB IOWA TOTAL
Dre Hollins 1 2 1 4 1 4 1 1 1 16
Mbakwe 4 3 1 1 2 1 1 13
Williams 1 1 2 3 2 9
Coleman 1 1 2 1 1 3 9
Ahanmisi 1 2 1 1 2 1 8
Welch 3 2 1 6
Au. Hollins 1 2 2 5
Eliason 1 1 2
Osenieks 1 1 2
Ingram 2 2
Walker 1 1
Ellenson 0
TOTAL 10 11 7 9 13 9 4 2 8 73
Au. Hollins 1 2 1 4 1 1 3 1 14
Coleman 3 3 2 1 2 1 12
Dre Hollins 1 1 4 2 1 2 11
Williams 3 2 1 2 8
Mbakwe 1 1 2 1 5
Ahanmisi 1 1 1 1 4
Eliason 1 1 2 4
Ingram 2 1 1 4
Welch 1 2 3
Osenieks 1 1 2
Walker 0
Ellenson 0
TOTAL 9 8 5 9 10 4 5 10 7 67
Player Plus/Minus
Austin Hollins                    9
Joe Coleman                     3
Elliot Eliason                     2
Andre Ingram                     2
Oto Osenieks                    –
Wally Ellenson                    –
Rodney Williams                   (1)
Mo Walker                   (1)
Julian Welch                   (3)
Maverick Ahanmisi                   (4)
Andre Hollins                   (5)
Trevor Mbakwe                   (8)
NET STEALS                   (6)

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Doing the Splits: Andre Hollins

January 24, 2013

In early December we wrote Andre Hollins: A Tale of Two Games? which looked at Dre’s stats for all games excluding Memphis and South Dakota State games. Today, we’ll do the same.

As discussed in the earlier article, Joe Jackson of Memphis played just seven minutes against the Gophers and was on the floor at the same time as Andre for less than five (4:38). Also, SDSU star Nate Wolters did not play at all due to injury. In his place the Jackrabbits started a true freshman who turned the ball over seven times.

Dre has had several high-scoring games other than the two that are excluded below. However, the splits of (1) the Memphis & SDSU games compared to (2) all other games are interesting.

  PPG eFG% 2FG% 3FG% FT%
MEM & SDSU 31.5 102.0% 69.2% 91.7% 92.3%
All Other 11.8 47.2% 43.2% 34.2% 74.2%
TOTAL 13.8 54.6% 46.8% 41.8% 77.2%

Now, let’s look at this season’s “All Other” performance compared to Dre’s freshman year:

  Pts/40 eFG% 2FG% 3FG% FT%
Soph, All Other 16.9 47.2% 43.2% 34.2% 74.2%
Freshman 16.5 48.2% 40.5% 37.9% 90.4%

Even after 19 games, it’s apparent what a big factor the Memphis and South Dakota State games have on Dre’s 2012-13 statistics.

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Big Ten Efficiencies: Raw vs. Adjusted

January 20, 2013
Numbers alone rarely tell a complete story and that can be a blessing or a curse, depending on who is using them and how they are doing so. We’re not going to deep into explanations here, but below is a bit of food for thought. If you have specific questions or thoughts, please feel free to contact us.

OPENING COMMENTS offers a wealth of data and we strongly recommend everyone in the world visit that site. Understanding the methodology and assumptions used by Pomeroy and other advanced ranking systems, at least with some degree of clarity, can greatly enhance the usefulness of the data and predictions.

The overall predictive rankings of teams is based largely on their respective offensive and defensive efficiency figures. Although there are often immaterial differences in methodologies used to calculate actual efficiency figures when comparing one system to another, most should approximate one another.

The focus of most users (and rightfully so) of Kenpom and other advanced rating systems is squarely on the adjusted figures. Most people are fine with accepting limitations of different systems without understanding the impact and sensitivity of those limitations on the figures they’re using.

As you’ll see below, it might be best that people don’t get too into the details because you could spend hours making small adjustments to the detail only to come up with answers that aren’t all that different from what you started with. Still, considering the methodologies and assumptions of different ranking systems can be enlightening and useful for some.

BIG TEN DATA (through D-I games of 1/19/2013)

The tables below lists Kenpom offensive and defensive efficiency data for Big Ten teams (all D-I games including nonconference through 1/19/2013).

Offense Example: Minnesota’s OffEff of 4.5 means that their Adjusted Offensive Efficiency is 4.5 better (higher) than their Raw Offensive Efficiency (Note: the average D-I Adjusted Efficiency figure is 99.5). Minnesota’s OffEffRank of 3 means that their Adjusted Offensive Efficiency rank among 347 D-I teams is 3 spots higher as compared to their Raw Offensive Efficiency rank.

 OFFENSE OffEff OffEffRank
Michigan St. 4.9 47
Minnesota 4.5 3
Michigan 4.4 0
Illinois 3 28
Nebraska 2 39
Purdue 2 27
Ohio St. 1.9 4
Penn St. 1.5 31
Wisconsin 0.7 -5
Northwestern 0.5 -4
Indiana 0.2 -2
Iowa -0.1 -12

Defense Example: Minnesota’s DefEff of 5.1 means that their Adjusted Defensive Efficiency is 5.1 better (lower) than their Raw Defensive Efficiency (Note: the average D-I Adjusted Efficiency figure is 99.5). Minnesota’s DefEffRank of 38 means that their Adjusted Offensive Efficiency rank among 347 D-I teams is 38 spots higher as compared to their Raw Offensive Efficiency rank.

 DEFENSE DefEff DefEffRank
Minnesota 5.1 38
Nebraska 4.2 63
Illinois 2.8 39
Iowa 2.6 11
Wisconsin 2.5 7
Ohio St. 2 5
Michigan 1.9 14
Penn St. 1.8 38
Michigan St. 1.3 -2
Northwestern 1.2 9
Purdue 0.6 1
Indiana -0.2 -3

A high-level answer is that a team’s performance each game is adjusted for the level of their competition (and there are other factors to a team’s adjusted efficiency figures including preseason rankings and higher weighting of more recent games). Minnesota, relative to other Big Ten teams, has played a strong nonconference schedule and therefore it’s not surprising they would have some of the larger adjustments.

One thing Kenpom doesn’t take into account is whether a key player was out for a particular game. Thus, when the Gophers hosted South Dakota State and Nate Wolters was out with an injury, Kenpom effectively assumes that Wolters played about 35 minutes.

Minnesota’s defensive efficiency against SDSU was 93.6 and this was one of the Jackrabbits’ worst offensive performances of the season. However, because SDSU’s adjusted offensive efficiency for the season is a strong 108.2, Kenpom adjusts Minnesota’s defensive efficiency for the game down to a much better 85.8.

Logically, the size of this adjustment doesn’t make sense because Wolters was injured and didn’t play in the game. We have estimated the impact of the Wolters’ injury on Minnesota’s overall adjusted defensive efficiency for the season, but that’s too much detail for this article.

However, something to be cognizant of is that there are many games across the world of college basketball for which adjustments, both positive and negative, may not be warranted (logically that is, although under the ranking system they make perfect sense).

In the case of Minnesota there are few examples with more than an insignificant impact, including the SDSU and Memphis games (Joe Jackson sat after playing just 7 minutes; Geron Johnson’s first game back, etc.). At the same time one can point to Trevor Mbakwe having played in less than 55% of the team’s minutes so far this season. As it’s reasonable to assume he’ll play more than that throughout the remainder of this season, an additional adjustment to the predictive adjusted efficiency figures is warranted.

Now, you could spend days running through adjustment exercises for all D-I teams and there probably wouldn’t be many earth-shattering changes in rankings. However, understanding unusual and significant factors in reaching predictive rankings can be worthwhile.

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Doing The Splits: Jordan Hulls & Yogi Ferrell

Below we take a quick look at some statistical splits for Indiana’s talented backcourt duo of Jordan Hulls and Kevin “Yogi” Ferrell.

JORDAN HULLS – His 3-point percentage of 52.1% so far this season is tremendous and we could look at his numbers from a million different perspectives and still would come to the same conclusion: he’s an excellent shooter.

Nonetheless, it’s true that (a) his 3FG% dropped significantly in conference play last season and (b) his 3FG% in nonconference games this year is lower than a year ago.

3FGA 3FGM % made
2012-13 38 73 52.1%
2011-12 72 146 49.3%
3FGA 3FGM % made
2012-13 36 66 54.5%
2011-12 40 70 57.1%
2011-12 BREAKDOWN    
3FGA 3FGM % made
NonConf 40 70 57.1%
B1G 32 76 42.1%
Total 72 146 49.3%

YOGI FERRELL – His future is bright, but the freshman has struggled at times this season, especially against better teams. How well he continues to progress is an important factor in Indiana’s Big Ten.

To have 17 assists and no turnovers combined against two teams is impressive, but it would be far more amazing if Ferrell had done it against teams better than Sam Houston St. and Jacksonville.

Ferrell has played 400 minutes this year. Below are statistics split by the top 7 opponents (per overall rating) and the bottom 8 opponents.

  Min.       A to TO
Competition Played eFG% Assists Turnovers ratio
Top 7 196 31.9% 26 18 1.4
Bottom 8 204 41.0% 50 13 3.8


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Indiana Hoosiers Cardinal adidas Originals BTC Hooded Sweatshirt

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Gophers’ 2FG% in Nonconference Play Down From 2011-12

Over at, you can read More Than a Number: Minnesota’s 2-Point Field Goal Shooting.

The article looks at the Gophers’ 2-point field goal shooting and how they can and need to improve their eFG% simply by deferring some lower percentage shots.

Last night Northwestern allowed Michigan to shoot 71% eFG, including 60% 2FG, in a 94-66 Wolverine win. Minnesota hosts the Wildcats Sunday evening at 6pm.

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Crazy Correlations & Minnesota’s O-Reb Dominance

Now available at, More Than a Number: Minnesota’s Offensive Rebounding Dominance by J.B. Bauer explores some odd correlations between the Gophers’ four factors and their offensive efficiency and discusses the team’s offensive rebounding achievements to date and some reasons to believe they won’t be as dominant going forward.

A couple of other random insights on the topic… some might think that an offensive rebound usually results in an easy two.  A layup, dunk or tip in does often follow, but in analyzing data here a couple of things we found:

1)  Approximately 15% of Minnesota’s offensive rebounds were team rebounds. Therefore, they took the ball out of bounce and therefore there can’t be a quick put back.

2) Of the remaining ~85%, about 35% of those offensive rebounds were followed by another shot at the rim (excluding shot attempts that don’t show up in scoresheet because the shooter was fouled). Of these shots, Minnesota made 74% which is approximately the same FG% as all of their other shot attempts at the (i.e., in transition or in the half court  offense).

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Andre Hollins: A Tale of Two… Games?

Heavy praise is being heaped on Minnesota second-year guard Andre Hollins after he posted big scoring totals against Memphis (41) and South Dakota State (22).

We believe Dre is an excellent young player with a promising future and today we’re simply offering a bit of food for thought. Our analysis and thoughts on Hollins go far deeper than what’s here, but we think the basic information below is plenty interesting.

Selected statistics for Andre Hollins (Sophomore season through December 7, 2012):

  PPG eFG% 2FG% 3FG% FT%
Soph 13.7 52.6% 45.3% 40.9% 79.5%
Fresh 8.7 48.2% 40.5% 37.9% 90.4%

Andre’s points per game average is up 57%, with 20% of the increase explained by more minutes played. The other significant factors are improved shooting from the field and more frequent trips to the free throw line.

Now, let’s separate the two big games from the other eight and see what we’re left with.

Against both Memphis and South Dakota State, Dre was in a zone and any team might have had trouble stopping him. However, you may want to consider that Minnesota didn’t have to see much of the opposing teams’ normal point guards, both of whom are veterans and talented scorers themselves.

Joe Jackson of Memphis played just seven minutes against the Gophers and was on the floor at the same time as Andre for less than five (4:38).

SDSU star Nate Wolters did not play at all due to injury. In his place the Jackrabbits started a true freshman who turned the ball over seven times.

Selected statistics for Andre Hollins – excluding the Memphis & SDSU games:

  PPG eFG% 2FG% 3FG% FT%
Soph 9.3 35.4% 37.5% 21.9% 74.1%
Fresh 8.7 48.2% 40.5% 37.9% 90.4%

Other notes about these eight games:

  • Has not scored above full season average of 14 in any of these games
  • Has posted an Offensive Rating of less than 94 in half of these games
  • In his four roughest shooting outings, Dre’s eFG% was 17.2% (5/29 FG; 0/13 3FG)
  • Of those four, two have occurred in the Gophers’ last four games played


  • Other than Duke, the best defense Minnesota has faced in their last 11 games (10 this season and last year’s NIT final) has been Stanford twice
  • In two games against Stanford, Hollins has shot 2/13 FG (0/4 3FG) with 2 assists and 7 turnovers
Fellas, get her this for the Holidays:

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Minnesota Rebounding: Gophers Only Cleaning One Side of the Glass

In recent years, Minnesota teams have excelled at blocking shots and offensive rebounding. Contrary to what you might expect, however, the defensive rebounding of the Gophers has ranged from mediocre to poor when compared to the rest of the Big Ten and other D1 teams. So far in 2012-13, these trends have continued.

According to, Minnesota’s offensive rebounding percentage this year is second best in the nation and tops in the Big Ten. On the defensive boards, the Gophers are dead last in the Big Ten and one of the worst in the nation at 329.

Minnesota’s performance on the defensive glass isn’t skewed by a game or two. In fact, the team’s defensive rebounding percentage has been better than the national average in only one game this year (Toledo).

As illustrated in the tables below, during Tubby’s years as head coach the team’s offensive rebounding percentage has been impressive while their defensive rebounding percentage has been relatively poor.

5-Year conference rankings for Minnesota’s offensive & defensive rebounding percentages:

Conference OR% & DR%










5-Year national rankings for Minnesota’s offensive & defensive rebounding percentages:

Overall OR% & DR%










A renewed focus on defensive rebounding could be directed by the coaching staff, but how much might stressing this area hurt the team’s transition into their offense? Minnesota has been particularly successful this year when they strike quickly after a defensive rebound. According to, the Gophers have an effective field goal percentage (eFG%) of 73% this season when they shoot within 10 seconds of a defensive rebound. Last year, that figure was 54%.

The figure of 73% eFG% is going to fall as the season goes on as it’s been boosted by an unsustainable 59% 3-point conversion rate. Still, for a team that is often stale in half court sets, anything that detracts from their ability to score in transition could do more harm than good.

We see a few things that could be done, including:

  • Better utilization of the roster, especially Trevor Mbakwe. He’s one of the better rebounders in the college game and should be playing 30+ minutes per game, not fewer than 20.
  • Centers Elliot Eliason (15.8 mpg) and especially Maurice Walker (7.8 mpg) could be given more minutes. At times, we believe it will be appropriate to play one of these two along with Trevor Mbakwe and Rodney Williams.
  • Push the others to improve their tenacity and consistency. Rodney Williams (who has been sensational in most aspects of his game), Andre Ingram (8% DR%) and Joe Coleman all need to improve their contribution on the defensive glass. In the last four games, Minnesota’s opponents have pulled down offensive rebounds at a 39% rate in the first half, but the percentage jumps to 52% in the second half.
What do you think? CONTACT US with your thoughts on this and other articles, as well as any suggestions, comments and critiques.


Additional Data for Consideration
Using data through games of November 29, 2012, we looked at who makes up the top 48 defensive rebounders in the Big Ten. Only those who have played in at least 40% of their team’s minutes were included.

If all teams were created equal, we’d expect four players from each of the 12 Big Ten teams to be listed. All but two teams are within one of that expectation (i.e., 10 teams have three, four or five players in the top 48). The outliers are Penn State (six) and Minnesota (one).

For the Gophers, Maurice Walker would be in the Top 10, but has played in less than 20% of the team’s minutes. Elliot Eliason is just short of 40% in minutes and ranked at #30.

Details are listed below:

Rank Name Team DR% Min%
9 Jermaine Marshall Penn St. 19.3 81.2
15 Ross Travis Penn St. 17.9 84.5
29 D.J. Newbill Penn St. 15.3 87.3
36 Brandon Taylor Penn St. 14.4 40.4
38 Sasa Borovnjak Penn St. 13.7 42.2
41 Jon Graham Penn St. 12.6 50.6
17 Eric May Iowa 17.4 48.9
19 Adam Woodbury Iowa 16.8 41.8
32 Zach McCabe Iowa 14.9 44.6
35 Aaron White Iowa 14.5 73.9
40 Melsahn Basabe Iowa 12.9 48.9
1 Adreian Payne Michigan St. 28.2 28.2
7 Derrick Nix Michigan St. 20.3 68.2
21 Denzel Valentine Michigan St. 16 61.1
33 Branden Dawson Michigan St. 14.7 69.3
46 Keith Appling Michigan St. 12.3 89.3
4 Alex Olah Northwestern 23.8 45.3
16 Jared Swopshire Northwestern 17.7 67.4
28 Mike Turner Northwestern 15.4 45.3
43 Reggie Hearn Northwestern 12.5 73.7
45 Drew Crawford Northwestern 12.4 74.7
11 Sam Thompson Ohio St. 19 61
22 Deshaun Thomas Ohio St. 16 80
30 Lenzelle Smith Ohio St. 15.1 76.5
44 Evan Ravenel Ohio St. 12.4 49
48 LaQuinton Ross Ohio St. 11.9 41
10 Tim Hardaway Michigan 19.1 82.5
26 Glenn Robinson Michigan 15.7 80.8
42 Jordan Morgan Michigan 12.6 48.8
47 Nik Stauskas Michigan 12 65.8
6 Andre Almeida Nebraska 20.5 51.7
12 Brandon Ubel Nebraska 18.9 75.4
18 David Rivers Nebraska 17.4 60.8
20 Dylan Talley Nebraska 16.7 88.8
2 Ben Brust Wisconsin 24.6 75.4
8 Ryan Evans Wisconsin 19.4 72.5
24 Jared Berggren Wisconsin 15.8 66.8
39 Mike Bruesewitz Wisconsin 13.7 54.6
23 Joseph Bertrand Illinois 16 52.9
25 D.J. Richardson Illinois 15.8 78.2
27 Brandon Paul Illinois 15.6 77.2
3 Christian Watford Indiana 24.1 62.1
13 Cody Zeller Indiana 18 67.4
37 Will Sheehey Indiana 14.3 52.3
14 A.J. Hammons Purdue 18 43.7
31 Anthony Johnson Purdue 15 66.9
34 Ronnie Johnson Purdue 14.7 65.3
5 Trevor Mbakwe Minnesota 22.5 45.6
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