Category Archives: Stats

Statistics

Another Compelling Win Probability Chart

After Minnesota beat Michigan State on New Year’s Eve, we examined substitution patterns and the Ken Pomeroy (www.kenpom.com) win probability chart for the game.

After last night’s 61-50 loss by the Gophers, we did the same.

Tubby Smith’s substitution patterns were more odd than usual in East Lansing, but our summary is the same as it was in the first game: Minnesota’s starters can handle Michigan State.

Minnesota’s bench usage began with 15:26 left to play in the first half and the Gophers trailing 5-2. The purple rectangle in the win probability chart below highlights a long, successful period of time when all five starters were on the court together. Other than this period, Minnesota usually had some reserves on the floor.

Now, there are certainly some strange player groupings to point to during the game. For example, down 7 with 6:05 to play, Smith called on Mo Walker, Oto Osenieks and Elliot Eliason to make something happen.

But without getting into the specific combinations of players, the point is that when Minnesota has their best players on the court they can be a very good team. The problem is that they’re often playing with multiple reserves.

This is by choice and isn’t new to the 2012-13. That said, things may not change no matter how compelling the facts may be.

WinProb Chart MSU 61, MINN 50

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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
Steals for MSU NW at ILL at IND MICH at NW at WISC NEB IOWA TOTAL
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
Kenpom.com 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

WHY ARE THERE DIFFERENCES? DO THEY MAKE SENSE?
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.

FULL SEASON COMPARISON  
3FGA 3FGM % made
2012-13 38 73 52.1%
2011-12 72 146 49.3%
NONCONFERENCE COMPARISON  
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 KenPom.com 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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Gophers’ 2FG% in Nonconference Play Down From 2011-12

Over at GopherHole.com, 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 GopherHole.com, 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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