Some Interesting Facts About Big Ten Scoring Offenses: 2000-Present

Some Interesting Facts About Big Ten Scoring Offenses: 2000-Present

Submitted by LSAClassOf2000 on March 5th, 2013 at 11:26 AM

SOME INTERESTING FACTS ABOUT BIG TEN SCORING OFFENSES: 2000-PRESENT

I find the things I am uncovering to be interesting (hopefully, you do as well), I am extending this series another week, and this time, we are going to poke around at some of the historic stats on scoring offense in the Big Ten. I even included Nebraska this time.

Since 2000, the conference’s football teams have scored 49,281 points over a stretch of 692,579 yards of total offense, or roughly the distance from Detroit to Springfield, Illinois. The conference has also amassed 5657 PATs to go with 6,168 touchdowns, as well as 2,076 field goals and 4,776 total yards per year.  Actually, here’s a small chart with the four most common scoring types and their relative occurrence:

 

SCORING EVENT

POINTS

% TOTAL

TOUCHDOWN

37008

75.1%

EXTRA POINT

5657

11.5%

FIELD GOAL

6228

12.6%

SAFETY

148

0.3%

 

In the 1,792 games that all this encompasses, the Big Ten has managed to maintain an average rate of 27.5 points per game and 386.5 yards per game, which is not the West Virginia-Baylor game of recent note but is also not bad. It still means an average ranking nationally in the mid-50s, which essentially means there have been about as many terrible offenses in this stretch as there have been good ones, but slightly fewer bad ones. Yes, very technical statement there.

It doesn’t look like it on the field sometimes, but take thirteen years of data and do a table of “percent of total” for a few things and you get this:

 

TEAM

Yards

TDs

Points

Extra Poins

Field Goals

Safties

Ohio St.

8.79%

9.73%

9.91%

9.93%

11.08%

12.16%

Nebraska

1.64%

1.73%

1.76%

1.82%

1.88%

1.35%

Northwestern

9.39%

8.80%

8.63%

8.63%

7.66%

8.11%

Indiana

8.27%

7.70%

7.62%

7.37%

7.18%

13.51%

Michigan

9.15%

9.94%

9.72%

9.97%

8.29%

6.76%

Wisconsin

9.66%

10.62%

10.44%

10.84%

9.10%

9.46%

Penn St.

8.75%

8.54%

8.69%

8.52%

9.59%

13.51%

Purdue

9.23%

9.03%

8.98%

9.16%

8.72%

8.11%

Minnesota

8.98%

8.98%

8.87%

8.75%

8.19%

2.70%

Michigan St.

9.20%

8.90%

9.03%

8.82%

9.87%

9.46%

Iowa

8.43%

8.48%

8.68%

8.70%

10.07%

8.11%

Illinois

8.51%

7.54%

7.66%

7.50%

8.38%

6.76%

 

Nebraska is, of course, the anomaly here. Illinois and Indiana show their protracted stretches of relative ineptitude even here, as the difference between Wisconsin and Illinois, for example, means a veritable sh*t ton on scoring over 13 years even if the percentage is small. For the most part, things are more even than I would have thought, but again, fractions of a percent here hide off seasons.

Here are the totals / averages by team from 2000 to the present:

 

TEAM

Games

Yards

Avg. Yards Per Game

Avg. Points Per Game

Points

TDs

Extra Points

Field Goals

Safties

Win

Loss

Win Pct.

Avg. National Rank

Nebraska

27

11390

421.9

32.1

866

107

103

39

1

19

8

0.704

19

Wisconsin

169

66875

395.7

30.4

5146

655

613

189

7

115

54

0.680

44

Ohio St.

163

60893

373.6

30.0

4884

600

562

230

9

132

31

0.810

44

Michigan

161

63402

393.8

29.8

4790

613

564

172

5

106

55

0.658

42

Purdue

160

63897

399.4

27.7

4425

557

518

181

6

84

76

0.525

54

Michigan St.

161

63688

395.6

27.7

4452

549

499

205

7

88

73

0.547

52

Minnesota

160

62215

388.8

27.3

4373

554

495

170

2

74

86

0.463

57

Penn St.

161

60632

376.6

26.6

4283

527

482

199

10

101

60

0.627

64

Northwestern

160

65033

406.5

26.6

4255

543

488

159

6

84

76

0.525

61

Iowa

162

58366

360.3

26.4

4277

523

492

209

6

98

64

0.605

60

Indiana

153

57268

374.3

24.5

3756

475

417

149

10

49

104

0.320

72

Illinois

155

58920

380.1

24.3

3774

465

424

174

5

61

94

0.394

71

 

It may or may not be the variation you would expect. I sorted the table by average points per game and was not entirely shocked by the order of the teams myself. All things considered, maintaining an average ranking of 42, in our case, which would be the upper reaches of the second quartile of teams, is not that bad at all when compared to the grand mean of 56.

So, similar to the other two diaries that I did recently, I asked myself the question – which of these nearly 150 teams in this spreadsheet were very good at scoring, in relative terms? Using a similar method, I decided to create from the excessively large table a small table of teams which were above average in at least four of the following: Total yards, TDs, FGs, PATs, and Points.

You get 63 teams that compare as follows:

 

 

ALL TEAMS

TEAMS ABOVE AVG. IN AT LEAST FOUR METRICS

AVG. TOTAL YARDS

4776.4

5329.8

AVG. YARDS / GAME

386.5

416.1

AVG. NO. OF TDs

43

52

AVG. NO. OF PATs

39

49

AVG. NO. OF FGs

14

15

AVG. NO. OF POINTS

340

407

 

Here, from a historic average of 27.5 points per game, you jump to 31.8 points per game for the teams that fit the criteria for this table. I then did the same thing with the remaining teams, and you see the following from the remaining 23 teams:

 

 

ALL REMAINING FROM FIRST ELIMINATION

TEAMS ABOVE AVG. IN AT LEAST FOUR METRICS

AVG. TOTAL YARDS

5329.8

5622.7

AVG. YARDS / GAME

416.1

431.2

AVG. NO. OF TDs

52

59

AVG. NO. OF PATs

49

56

AVG. NO. OF FGs

15

15

AVG. NO. OF POINTS

407

459

 

These teams were scoring at an average rate of 35.2 points per game, or slightly more than 1 TD per game more than the Big Ten grand mean in this time period.

Not shockingly, being able to actually get the ball across the plane or through the uprights on a consistent basis makes a considerable difference. The Big Ten’s cumulative winning percentage since 2000 has been 0.564, but when I did the first elimination, that jumped to 0.686, and then on the second one, it leapt to 0.753. Essentially, it is the difference, in scoring terms, between 7 and 9 wins in a season based on historic numbers.

TL;DR CONCLUSION:

Once again, this was an exercise conducted under an admittedly arbitrary set of assumptions, but it is interesting to see  the improvements that mere points will bring in numerical terms and give an added dimension – hopefully – to what occurs on the field and how much it means to, well, score.

 

Some Characteristics Of Highly Rated Passing Offenses In The Big Ten: 2000-Present

Some Characteristics Of Highly Rated Passing Offenses In The Big Ten: 2000-Present

Submitted by LSAClassOf2000 on March 1st, 2013 at 1:13 PM

“SOME CHARACTERISTICS OF HIGHLY RATED PASSING OFFENSES IN THE BIG TEN: 2000-PRESENT”

In a companion diary to my last entry, I took a similar dive into the passing statistics of the Big Ten since 2000 to see what some of the characteristics of the highly rated passing offenses were. In an attempt to be a little more thoughtful as well, I also looked at the passing efficiency data, particularly since TD and INT percentages are part of the calculation of efficiency ratings. I like to believe that these two percentages really matter more than the average total yards in that they provide insight into what a team does with the yards they managed to accumulate. This is also the reason that the selection process for this exercise varies a little from the method employed when looking at rushing offenses.

Some high-level trivia:

  • Since 2000, a Big Ten quarterback has thrown the ball to someone else 53,905 times, and on 31,128 of those occasions, someone caught it. That’s good for a 57.7% completion percentage and 381,792 total yards.
  • When the ball was caught, teams averaged 12.27 yards per completion. When it was not, it was thrown an average of 7.08 yards.
  • For all that passing, 2,640 touchdowns were produced, or an average of 1.5 passing TDs per game. There were also 1,646 interceptions thrown, or 0.93 INTs per game.
  • The touchdown percentage of the Big Ten in that space was 4.06%. The interception percentage was 3.09%. Michigan fell slightly above the average in both cases, incidentally.
  • The cumulative passer efficiency rating of the Big Ten in this timeframe is 127.59
  • The average yards per game passing in this time turns out to be 216.69 yards
  • The cumulative winning percentage of the conference? 0.562

So, once again, I laid all this out in an egregiously large spreadsheet and then put it aside to do some actual work at work. I came back to this later and decided to pay particular attention to four factors which are considered in the efficiency statistics. In this case, I thought it would be interesting to use the following – average yards per game, touchdown percentage, interception percentage and completion percentage.

As it turns out, there were only 27 passing attacks in the group which were above average in all four areas, but here is what those teams were typically capable of doing:

  • Average completion percentage: 61.02%
  • Average interception percentage: 2.22%
  • Average touchdown percentage: 6.30%
  • Average yards per game: 248.16
  • Average passer rating: 142.65
  • Average yards per attempt: 7.68
  • Average yards per completion: 12.59

These are noticably better than the grand means in each category. Another interesting improvement is in total years for the season. For the entire sample, it was 2,669 yards, but for this statistically elite group, it was 3,102 yards. Further, the cumulative winning percentage of this group is 0.653, so having an efficient passing game gets perhaps one more win each year in the Big Ten.

I eased the restrictions a little for the next sort just to see if I could squeeze out a list of the best of the best, if you will. For the next step, I took teams from the smaller sample that were above average in at least two of the four statistics and managed to get a group of 13 teams. Their means are:

  • Average completion percentage: 62.40%
  • Average interception percentage: 1.97%
  • Average touchdown percentage: 6.65%
  • Average yards per game: 262.17
  • Average passer rating: 148.89
  • Average yards per attempt: 8.00
  • Average yards per completion: 12.82

Those teams that made the final cut under these assumptions are:

 

Year

Team

National Rank

COMP. %

Int. Pct.

TD Pct.

Avg. Yards / Game

2011

Wisconsin

2

71.04

1.52

10.37

234.29

2011

Northwestern

13

71.01

2.21

6.39

254.23

2005

Ohio St.

6

64.90

1.66

5.96

225.67

2011

Michigan St.

28

63.86

2.22

5.76

252.50

2010

Iowa

11

63.31

1.68

7.28

234.54

2007

Purdue

48

62.12

2.19

5.05

307.15

2004

Purdue

10

61.11

1.65

7.82

321.17

2005

Iowa

26

60.64

1.98

5.69

257.75

2003

Michigan

36

59.66

2.10

5.46

270.77

2012

Penn St.

59

59.65

1.10

5.26

273.58

2009

Michigan St.

17

59.34

2.84

6.62

269.38

2001

Michigan St.

8

58.59

2.82

6.76

284.91

2000

Michigan

4

58.33

1.67

8.00

225.27

 

TL;DR CONCLUSION:

Like the rushing version of this from earlier in the week, the point of this was to simply run through a short exercise on finding a potential way to discover from a large set of data which teams stood out among their peers in the conference in a specific set of statistics. I chose to go with statistics that I thought pointed towards an efficient passing attack, not necessarily the most prolific, although the two do in fact overlap somewhat. There are probably better ways to think through this, but I was working with easily available data.

It is also rather intriguing that, at least under my own assumptions in doing these two diaries, having an efficient passing attack and an effective rushing game produce the same typical bump in winning percentage, at least when looked at separately like this.

RANDOM ENTERTAINMENT:

Because I was missing "Animalympics" earlier...

Our Journey Through Big Ten Basketball To Date

Our Journey Through Big Ten Basketball To Date

Submitted by LSAClassOf2000 on February 18th, 2013 at 1:15 PM

“CHARTING THE JOURNEY THROUGH CONFERENCE PLAY”

Now that there is some respite from the meat grinder that seems to be the Big Ten basketball schedule (at least until this weekend), I felt it might be an appropriate moment to step back and look at some of the basic numbers and breakdowns for our Wolverines. Much has been said in the postgame threads over the last stretch of games, and indeed, some of it bears itself out in the trends that you will see here. The caveat here is that the conference schedule is not yet complete and these numbers are not final.

I had wanted to do something like this since the Indiana game, but I held off because there simply was not enough data regarding conference play to make much of a determination about where the areas of focus should be at that point. Now, I think you can see some definite trends. I also compiled our statistics in a Michigan win versus Michigan loss format so you can easily see just how stark some of the differences are in some cases.

TABLE 1 – “Michigan Win Vs. Michigan Loss”

Photobucket

The one thing that leapt out immediately, at least to me, is that in conference play, we are shooting about 11% when we win as opposed to when we lose, which is significant considering that our four losses have been at the hands of some of the most defensively efficient teams in Division I basketball, not just the conference. The difference is small for our opponents, who shoot only about 6% better in wins as opposed to losses. It’s a fairly similar story for three-pointers – we are down about 13% in losses compared to wins, whereas our opponents again show a difference of only 6% between the two scenarios.

Here is the shooting data broken out into individual games:

Photobucket Photobucket

Many of the findings aren’t entirely unexpected – we have fewer assists when we lose, we rebound less, and so on, but there are actually sustained trends that are worth noting at this point. Below are trends for point totals and the running average of points:

Photobucket Photobucket

In both of these, you can see an overall decline in our own production and a gradual increase in the production of those we have played. Since Indiana, in fact, we are giving up three more points per game on average, which may not seem like much, but when you consider that the fewest we have given up since then is 65, it is noteworthy. Our average in the same period has declined about two points, but our average is bolstered some by some of our early performances in conference play.

Tied somewhat to that would be offensive and defensive efficiency, shown below. This is the running number, cumulative as of each game:

Photobucket Photobucket

The trends are obviously not favorable, but overall, the efficiency numbers have not slid too much, as you will note. In both case, it is less than a 10% slide. It is enough, however, to say that there are items to address soon on both sides of the ball.

Rebounds and assists have also tailed off somewhat, but turnovers show one notable aberration:

Photobucket Photobucket Photobucket

TL;DR CONCLUSION:

This is here for your perusal. The discussion which hopefully follows will become the conclusion of the board, or at least that is my intention. If there are other statistics that you would like to see charted, let me know and I will insert the data as time permits. I thought I might just get the discussion going with what I did here.

 

OBLIGATORY:

Seeking Relationships In The Big Ten Rushing Game

Seeking Relationships In The Big Ten Rushing Game

Submitted by LSAClassOf2000 on December 26th, 2012 at 9:30 AM

“SEEKING RELATIONSHIPS IN THE RUSHING GAME”

In a similar fashion to a diary about correlations in the passing game that I presented a little over a week ago, I decided to do a quick analysis of all 144 regular season Big Ten games and their rushing statistics. Once again, this is to see if the relationships that we believe we see do in fact present themselves mathematically in some fashion.

One of the driving forces behind this particular analysis, as with the passing correlations, is to probe certain (perhaps insignificant admittedly) aspects of the so-called “eye test”, which to me has always been a somewhat nebulous term that people used to encompass broad perceptions of games, regardless of the actual relationships which may exist within the game.

I managed to collect the total carries, total yards and yards per carry for each of the 144 regular season games for the 2012 season, so this should be a sufficient sample size to find some evidence of the three tested relationships that I chose to look at. These are “Carries / Yards”, “Carries / YPC” and “Yards / YPC”.

RESULTS:

 

Relationship

R

Carries / Yards

0.768

Carries / YPC

0.442

Yards / YPC

0.884

 

DISCUSSION:

On two of the above relationships, there is a rather strong correlation. In the case of carries being positively correlated to total rushing yards, we know that this is generally true unless your team either has a substandard rushing attack which they wield anyway, or alternatively when a team runs often against a superior rushing defense. Both scenarios are evident in the individual game statistics of the Big Ten this season. Yards and Yards Per Carry have the strongest relationship, and in my own opinion, one of the major contributors to that are games where good rushing teams have gone against substandard rushing defenses, accumulation prolific yardage and, by extension, longer runs.

So, as was true with the passing diary a week or so ago, you aren’t necessarily being told something you didn’t already know implicitly. The idea here was to explore the possibility that the relationship you believe you see does in fact exist in the numbers, and in the case of the rushing game, at least for this season, it would seem to be the case.

SUMMARY STATS:

 

  CARRIES YARDS YPC
AVERAGE 40 176 4.2
STD. DEV 10 96 1.7
MAX 67 564 9.3
MIN 19 4 0.2
MEDIAN 40 162 4.1
MODE 41 74 3.0

 

ONE OF MY FAVORITE MOMENTS FROM "THE CRITIC", JUST BECAUSE:

Regarding early-game sloppiness in bowls

Regarding early-game sloppiness in bowls

Submitted by erik_t on January 12th, 2011 at 4:43 PM

For a number of years now, it has been a popular position that the time off between the end of the regular season and the beginning of bowl season is excessively long, and leads to poor play early in games. Certainly I have found this to be an attractive explaination for the prodigous quantities of DERP we saw this bowl season.

But DERP is subjective, and the plural of anecdote is not data. Is there any substantial quantitative support to the notion that bowl games start off unusually sloppy? There are a great many factors at play; I chose to look at relevant statistics on a quarter-by-quarter basis. I don't have the massive database that seems popular around these here parts, and the typical box score doesn't give a per-quarter breakdown of anything but score. Bummer. Well, maybe if we go ahead and look anyway at a

Chart?

chart of percent of total points scored per quarter, we can find something elucidating. All bowl games are included. Each team's per-quarter scoring is normalized by their total score in the game. Averages and standard deviations are then computed, based on the entire bowl team population. It seems plausible that excessive pre-bowl layoff will result in a substantially higher standard deviation in the early part of the game, when either offense or defense might be DERPerrific.

 

Team 1Q 2Q 3Q 4Q Total Points
BYU 32.69 26.92 26.92 13.46 52
NIU 12.24 34.69 38.78 14.29 49
Ohio 33.33 0.00 33.33 33.33 21
SMiss 50.00 25.00 0.00 25.00 28
Utah 100.00 0.00 0.00 0.00 3
Navy 0.00 100.00 0.00 0.00 14
Hawaii 0.00 40.00 40.00 20.00 35
FIU 0.00 20.59 41.18 38.24 34
USAFA 21.43 21.43 0.00 57.14 14
WfVU 0.00 100.00 0.00 0.00 7
Mizzou 12.50 29.17 58.33 0.00 24
ECU 0.00 15.00 50.00 35.00 20
UIUC 15.79 26.32 21.05 36.84 38
OkSU 47.22 16.67 27.78 8.33 36
Army 81.25 18.75 0.00 0.00 16
K State 20.59 20.59 20.59 38.24 34
UNC 35.00 50.00 0.00 15.00 20
Nebraska 0.00 100.00 0.00 0.00 7
USF 22.58 32.26 22.58 22.58 31
ND 42.42 39.39 9.09 9.09 33
Georgia 50.00 0.00 50.00 0.00 6
SCar 0.00 17.65 41.18 41.18 17
NW'ern 0.00 15.79 47.37 36.84 38
Alabama 14.29 42.86 28.57 14.29 49
Florida 0.00 37.84 16.22 45.95 37
MissState 19.23 40.38 26.92 13.46 52
Wiscy 52.63 15.79 0.00 31.58 19
UConn 0.00 50.00 50.00 0.00 20
Stanford 17.50 15.00 32.50 35.00 40
tOSU 45.16 45.16 9.68 0.00 31
MTSU 66.67 0.00 33.33 0.00 21
LSU 17.07 51.22 17.07 14.63 41
Pitt 0.00 48.15 25.93 25.93 27
UNR 70.00 15.00 15.00 0.00 20
Auburn 0.00 72.73 13.64 13.64 22
   
UTEP 12.50 29.17 29.17 29.17 24
Fresno 41.18 17.65 0.00 41.18 17
Troy 29.17 50.00 20.83 0.00 48
L'Ville 0.00 67.74 0.00 32.26 31
BSU 0.00 61.54 26.92 11.54 26
SDSU 40.00 20.00 0.00 40.00 35
Tulsa 16.13 27.42 33.87 22.58 62
Toledo 21.88 43.75 9.38 25.00 32
GT 100.00 0.00 0.00 0.00 7
NCSU 30.43 13.04 26.09 30.43 23
Iowa 25.93 37.04 11.11 25.93 27
Maryland 11.76 19.61 41.18 27.45 51
Baylor 0.00 0.00 50.00 50.00 14
U of A 70.00 0.00 30.00 0.00 10
SMU 0.00 0.00 50.00 50.00 14
Syracuse 19.44 19.44 36.11 25.00 36
Tennessee 35.00 35.00 0.00 30.00 20
Washington 52.63 0.00 36.84 10.53 19
Clemson 11.54 38.46 0.00 50.00 26
Miami (YTM) 0.00 17.65 0.00 82.35 17
UCF 0.00 30.00 0.00 70.00 10
FSU 23.08 26.92 23.08 26.92 26
TTech 22.22 31.11 31.11 15.56 45
MSU 0.00 0.00 0.00 100.00 7
Penn State 29.17 41.67 29.17 0.00 24
Mich 100.00 0.00 0.00 0.00 14
TCU 66.67 0.00 33.33 0.00 21
OU 29.17 12.50 29.17 29.17 48
VT 16.67 83.33 0.00 0.00 12
Arky 26.92 11.54 42.31 19.23 26
Miami (NTM) 20.00 20.00 40.00 20.00 35
TAMU 41.67 29.17 0.00 29.17 24
Kentucky 30.00 0.00 70.00 0.00 10
BC 53.85 0.00 23.08 23.08 13
Oregon 0.00 57.89 0.00 42.11 19
 
Average 26.52 28.94 21.71 22.82 26.13
StdDev 26.20 24.62 18.49 20.83 13.32

 

Well, hmm. We could weight the per-quarter score fractions by the total number of points scored per team (0-3-3-3 is less telling than 0-21-21-21), but we find that this makes little difference. In the unweighted case, we find that the first half standard deviation is 29% higher than the second half standard deviation. The first quarter standard deviation is about 16% higher than the average quarter.

This seems to lend mild support to the idea that bowl games start off unusually sloppy. How does this compare to regular-season results? I compared to games from weeks 12-15, but only if the game involved two teams that ended up bowl-eligible (I counted Arizona State, because I graduated from there and it was Wisconsin's/SJSU's fault anyway and if you don't like it then tough). I toyed with the idea of removing rivalry games, because rivalry games are weird, but I did not.

 

Team 1Q 2Q 3Q 4Q Total Points
Navy 32.26 45.16 0.00 22.58 31
ASU 15.00 15.00 0.00 70.00 20
NIU 66.67 0.00 0.00 33.33 21
UIUC 0.00 43.48 30.43 26.09 23
SMU 0.00 0.00 0.00 100.00 7
Auburn 37.50 12.50 25.00 25.00 56
MTSU 25.00 25.00 25.00 25.00 28
FSU 30.30 21.21 21.21 27.27 33
UConn 15.79 36.84 15.79 31.58 19
OU 0.00 73.91 13.04 13.04 23
Temple 100.00 0.00 0.00 0.00 3
WfVU 20.00 20.00 40.00 20.00 35
SMU 0.00 26.32 55.26 18.42 38
Auburn 0.00 25.00 50.00 25.00 28
SMiss 20.41 26.53 20.41 32.65 49
U of A 48.28 17.24 10.34 24.14 29
BSU 9.68 67.74 0.00 22.58 31
BC 18.75 18.75 43.75 18.75 16
Mich 0.00 100.00 0.00 0.00 7
Sparty 25.00 25.00 25.00 25.00 28
USF 0.00 17.65 41.18 41.18 17
Kentucky 50.00 0.00 50.00 0.00 14
LSU 0.00 60.87 26.09 13.04 23
Florida 100.00 0.00 0.00 0.00 7
NCSU 45.16 0.00 9.68 45.16 31
BYU 18.75 18.75 43.75 18.75 16
NW'ern 13.04 60.87 26.09 0.00 23
SCar 31.03 34.48 34.48 0.00 29
GT 0.00 41.18 20.59 38.24 34
ND 0.00 65.00 0.00 35.00 20
OU 14.89 36.17 0.00 48.94 47
Ohio 22.58 32.26 0.00 45.16 31
Fresno thanks for breaking the chart Fresno 0
Pitt 17.65 0.00 41.18 41.18 17
WfVU 41.18 41.18 17.65 0.00 17
Wiscy 14.58 35.42 14.58 35.42 48
NCSU 0.00 34.48 24.14 41.38 29
Troy 0.00 29.17 41.67 29.17 24
UTEP 25.00 50.00 25.00 0.00 28
UIUC 43.75 12.50 14.58 29.17 48
tOSU 0.00 15.00 35.00 50.00 20
VT 22.58 9.68 22.58 45.16 31
Army 100.00 0.00 0.00 0.00 3
UConn 30.43 13.04 30.43 26.09 23
Arky 45.16 9.68 22.58 22.58 31
FSU 33.33 10.00 23.33 33.33 30
OU 39.62 24.53 35.85 0.00 53
Nebraska 50.00 0.00 0.00 50.00 6
Utah 7.89 55.26 0.00 36.84 38
 
Army 0.00 41.18 17.65 41.18 17
U of A 0.00 0.00 70.00 30.00 20
Miami (NTM) 50.00 0.00 26.92 23.08 26
Fresno 64.00 12.00 24.00 0.00 25
UCF 41.18 17.65 41.18 0.00 17
SCar 41.18 41.18 0.00 17.65 17
FIU 51.85 11.11 11.11 25.93 27
VT 31.82 15.91 31.82 20.45 44
USF 18.75 0.00 18.75 62.50 16
Nebraska 50.00 50.00 0.00 0.00 20
Miami (NTM) 26.09 30.43 13.04 30.43 23
Pitt 70.00 0.00 30.00 0.00 10
ECU 36.84 0.00 7.89 55.26 38
Alabama 77.78 11.11 11.11 0.00 27
Tula 37.50 12.50 12.50 37.50 56
Oregon 14.58 14.58 41.67 29.17 48
UNR 0.00 22.58 22.58 54.84 31
Syracuse 0.00 0.00 100.00 0.00 7
tOSU 0.00 64.86 35.14 0.00 37
Penn State 13.64 0.00 0.00 86.36 22
Miami (YTM) 0.00 0.00 41.18 58.82 17
Tennesseee 0.00 58.33 29.17 12.50 24
Arky 22.58 45.16 0.00 32.26 31
FSU 9.68 67.74 22.58 0.00 31
Maryland 0.00 44.74 18.42 36.84 38
Utah 0.00 0.00 0.00 100.00 17
Wiscy 20.00 50.00 30.00 0.00 70
Clemson 100.00 0.00 0.00 0.00 7
Georgia 33.33 16.67 33.33 16.67 42
USC 18.75 0.00 62.50 18.75 16
OkSU 7.32 34.15 17.07 41.46 41
Temple 0.00 43.48 0.00 56.52 23
BSU 5.88 33.33 33.33 27.45 51
USF 0.00 30.00 70.00 0.00 10
L'Ville 30.00 70.00 0.00 0.00 10
Mich 0.00 0.00 75.00 25.00 28
UNC 28.00 24.00 24.00 24.00 25
SCar 40.58 40.58 4.35 14.49 69
Tulsa 22.58 45.16 32.26 0.00 31
NW'ern 51.85 37.04 0.00 11.11 27
Iowa 41.18 0.00 17.65 41.18 17
Miami (YTM) 41.18 17.65 41.18 0.00 17
ND 0.00 62.96 37.04 0.00 27
Syracuse 50.00 0.00 50.00 0.00 6
MissState 22.58 45.16 0.00 32.26 31
Maryland 18.75 62.50 18.75 0.00 16
Baylor 0.00 29.17 12.50 58.33 24
TAMU 0.00 33.33 0.00 66.67 9
SDSU 41.18 38.24 20.59 0.00 34
 
Average 25.38 26.61 22.49 25.51 26.33
StDev 24.95 22.41 19.90 22.76 13.87

Stupid Fresno. Anyway, we continue to see elevated stdev for the first quarter of regular season games between bowl eligible teams, but by a lesser degree. For these regular season games, the first half standard deviation is 10.8% higher than that for the second half. The first quarter standard deviation is about 11% higher than the average quarter.

There are certainly some serious issues with this methodology. Does a sloppy defense give up more scores to a sloppy offense than when both are playing carefully? I don't know the best answer, but I'm sure it varies on a case-by-case basis. There are also many late-game effects for which I have not accounted - a prevent version of a dominant defense might give up the only score of the game in the 4th quarter when the game is out of reach (hi there Sparty!). This would give a large standard deviation value to the 4th quarter, erroneously implying sloppiness. I do not know how to account for these sorts of errors with the data set I have available. Further, I do not suggest that this is an all-inclusive list of methodological problems.

Still, 29% vs 10.8% seems vaguely compelling, give 70 bowl and 98 regular season scores. My statistical background has faded badly since undergrad, so I'm going to refrain from a hilariously misguided attempt at error bars. The sample size is large, but boy those data are noisy. Any time my standard deviation is as big as the average, I start to feel a little woozy...

Run Affinity: An Analysis - The Basics (Thru Purdue)

Run Affinity: An Analysis - The Basics (Thru Purdue)

Submitted by Noleverine on November 20th, 2010 at 2:43 AM

So, I had some spare time at work and decided to look into something that I have been wondering for a while: does it seem like Rich Rod sticks to the run too much, even in games where we are losing?  At this point I’m sure you all know RR likes to run the ball—duh.  But how often, exactly, does he run the ball?

This analysis is just a basic overview of my dataset.  I will follow up with more in-depth looks at point margins and down and distance, but I thought you all might be interested to see basic percentages for our offense through Purdue.  If anyone has anything specific regarding playcalling vs. score margin vs. down and distance, let me know and I will see what I can do.

A few notes and stipulations on the dataset: 

1.) All data is taken from Brian’s UFRs for games this season. 

2.) Analysis stopped at end of UFR, so if Brian didn’t include it in his UFR (i.e. blowouts), it is not in my analysis.  If Brian doesn’t think its worth looking at, well, neither do I.

3.) Plays in which either team got a penalty are included ONLY IF the ball was snapped, since if it didn’t, we can’t know what play was called.

4.) Every snap weighted the same regardless of time left in half/game, because my thought process, we are almost as likely to run the ball in a 2 minute drill as the rest of the game (almost, though not quite).

5.)2 pt. conversions left out.

Disclaimer: 4th down numbers are not very accurate due to low sample size.

Now, for a chart:

 

Overview
  Run Pass % Run
1st Down 230 103 69.1%
2nd Down 150 108 58.1%
3rd Down 44 92 32.4%
4th Down 6 7 46.2%
Total 430 310 58.1%

A few things here are obvious.  First, RR likes to run on 1st down (69.1% of the time).  His affinity for running decreases every down, with the exception of 4th down.  Most of the runs on 4th down are 4th and short, and the passes 4th and long.

Next, we will take a look at the distribution when the game is tied:

Tie Game
  Run Pass % Run
1st Down 51 21 70.8%
2nd Down 33 18 64.7%
3rd Down 8 16 33.3%
4th Down 1 2 33.3%
Total 93 57 62.0%

What we see here is that RR is more likely to run the ball on every down when the game is tied than his average, except fourth down.  

Now, we look at when we are winning the game:

Winning Game
  Run Pass % Run
1st Down 115 31 78.8%
2nd Down 78 43 64.5%
3rd Down 23 45 33.8%
4th Down 2 1 66.7%
Total 218 120 64.5%

So, RR is more likely to run when we are winning, though not to a statistically significant level except on 1st down.

And when we are losing:

Losing Game
  Run Pass % Run
1st Down 50 51 49.5%
2nd Down 39 47 45.3%
3rd Down 11 31 26.2%
4th Down 3 4 42.9%
Total 103 133 43.6%

So, RR is much less likely to run on all downs (except 4th) when we are losing.  

This data all flushes out pretty much as expected, but I thought I'd share anyway.  In the next edition, I will analyze run-calling affinity per down based on score margin.  Stay tuned, folks for all upcoming editions of 2010 Playcalling so far: An Analysis.

P.S. If anyone is interested in seeing my whole dataset for validation or to do your own analysis, please leave a comment/message me (can you message people on here?) and I will be pleased to share!

EDIT: Title and Tags edited to be more informative

Our Defense Their Offense - tipping point!

Our Defense Their Offense - tipping point!

Submitted by mistersuits on October 6th, 2010 at 6:10 PM

Each week I trundle off to my favorite sports bar in Brooklyn, pumped up with expectations that far exceed what could ever possibly happen, promising myself I will behave in front of the other patrons, and that I'll remain calm when inevitably the other team scores or when Michigan goofs up.

And yet I was compelled to blurt out "Don't fumble it! Don't fumble it!" when Cam Gordon picked off Ben Chappell. The singular defensive highlight of the day and I respond as if Michigan had been the team committing a turnover on that play. I got some strange looks for sure.

That's how you know you're traumatized.

We still live under the spectre of the 2009 season and the reality that our defense is likely not going to stop any team we play.

The statistics tell me (Mathlete) that Michigan is absolutely going to win at least another couple Big10 games this year. I am resolutely impatient, however, and cannot wait until Illinois week in November to finally claim "improvement" from 2009. In fact if we lose the next three games - games we had already written off (Brian) no less - we'll be 5-3 and in crisis mode*.

A record of 7-5 was always the most likely outcome. But at 5-0, none of us could stand finishing the season 2-5, for so many reasons. So we reach a tipping point.

Win, and no one can ever claim again that this is 2009 all over again. Reclaim bowl eligibility, set the stage for a run at the Big10 title, and silence one of our most loathed rivals in one fell swoop.

Lose, and face the reality that our defense is going to limit us from getting over .500 in conference play, no matter how amazing and awesome Denard Robinson is.

*It's not really crisis mode when that's what we had as a baseline expectation, but it is the undeniable flaw of reaching 5-3 from 5-0 instead of 2-3.

But what do the numbers say?

Indiana Post-mortem

Last week I laid out a chart of our opponents and what kind of offensive output we can expect from each.

The numbers predicted a 36.3 (15.7% under) to 32.2 (8.7% under) Michigan victory - the margin of victory (4.1) was exactly correct. I extrapolated those considering likely real football scores and came up with a 42-31 prediction.

We had a turnover neutral game and special teams played no special role, so that levelled out any scoring variance, making these prediction about as accurate as they could be. Not bad for a first time, by the numbers prediction, all things considered.

Notes:

  • Michigan's offense exceeded expectations, netting 80 yards over predicted.
  • Michigan's turnover was crucial. It's safe to say that we will lose every game in which we lose the turnover battle.
  • I predicted Indiana would kick a field goal. Bill Lynch, however, after losing by three points while kicking four field goals in 2009, decided he was never ever going to only go for three. That attitude was the difference between Indiana's 31 and actual total of 35.
  • Michigan's defense lived up to its bad expectations, yielding 175% of expected yardage.
  • While Michigan gave up almost double the expected yardage, it yielded precisely 100% of the expected points. This, my friends, is how you would define a bend-not-break defense.
  • Prediction wise, Michigan should have had an offensive multiplier greater than 100% against a defense as bad as Indiana.
  • Prediction wise, Indiana's multiplier was slightly too low at 125% (actual was 136%). Hard to determine if it was our defense or Ben Chappell that made up that difference. I will assume it was quality play by a senior QB until he proves me very wrong this week @osu.

How about the rest of our opponents?

Chart of Offensive Expectations (through 5 weeks)

Rank Opponent N-PPG N-YPG SoS
1 Michigan 37.1 506 67.89
2 osu 36.8 386 63.18
3 Connecticut 30.1 337 65.94
4 Iowa 29.8 378 67.32
5 Wisconsin 29.6 363 61.70
6 MSU 28.1 357 58.82
7 Indiana 27.3 312 51.95
8 BGSU 25.2 271 69.16
9 Notre Dame 24.6 404 75.82
10 UMass 21.7 327 55.48
11 Illinois 20.9 311 71.12
12 Penn State 18.1 337 71.62
13 Purdue 18.1 311 62.92

Metrics

N-PPG or Normalized Points-per-game is taken from the teams average PPG with a SoS multiplier factored in to deflate numbers from playing bad competition and inflate numbers based on playing good competition.

N-YPG or Normalized Yards-per-game is calculated using the same SoS multiplier as N-PPG but using this metric will help us determine a less variant guess as to how offenses will perform (PPG is subject to wild variance based on turnovers and special teams).

Strength of Schedule is taken from Sagarin rankings.

Usage: The chart doesn't predict that #3 would beat #5. Instead it tries to predict with the most accuracy how many points/yards on average each of these teams would score against a common opponent.

Results

Michigan's N-PPG jumps into lead this week after a suspect outing by osu versus Illinois, and further expands their staggering lead in N-YPG, eclipsing 500 yards expected even after it has strength of schedule reducing it to normalized amounts, a full 100 yards more than anyone else on the schedule (120 yards more than anyone else on the Big10 slate).

  • There is a full touchdown gap of production between the top two teams and the next five on the list, suggesting a competitive plateau of Iowa-Wisconsin-MSU-Indiana, all shadowed by The Denard Show.
  • Indiana makes a leap with their outburst against Michigan. If they can even put up half of those numbers against osu expect their rank to continue to climb upwards as their SoS will jump way up after this week.
  • Iowa made modest gains this week after a fairly conservative game against Penn State, which they were in control of the whole way.
  • Wisconsin struggled big time against MSU. They are at best the 4th place team in the Big10 behind osu, Iowa and MSU.
  • Illinois had as good an opportunity as they were going to get to make a run at an upset (at home, injured opposing QB), but couldn't produce.
  • Penn State has been absolutely shut down now by two really good defensive teams (Iowa/Alabama).
  • UConn continues to perform decently after two letdowns in their first three games.
  • BGSU and UMass fall with their strength of schedule. The rest of the Big10 saw their SoS jump higher this week (duh!).

Conclusions Based on Almost Enough Data

Until given reason to expect otherwise, I am giving our opponents 125% of their N-PPG and 150% of their N-YPG for predictions vs Michigan.

However!!! The elephant in the room is not Michigan's defense. Our defense remains a constant, an ugly constant. The biggest factor remaining is whether or not Michigan can sustain it's offensive play into the Big10 schedule.

Last year, this is where Michigan's offense fell off a cliff. The last seven Big10 games they averaged 20.1 PPG. They did not outgain any of their opponents and they lost the turnover battle nearly every time. Michigan's 2010 unit, however, is light years ahead of where they were last year and, more importantly, healthy (knock on wood).

Best Case

A week ago had a Big10 best case scenario of 6-2. That remains the outlook this week except instead of our second loss coming from Wisconsin it comes from Iowa (We will beat Wisconsin 37.1 to 37.0!).

Worst Case

In a worst case scenario, wherein our offense drops off to 75-80% of current production and we still yield 125% to our opponents, Michigan will go 3-4 the rest of the way with wins over Purdue, Penn St, and Illinois. This is the same from last week (3-5) except we scratch off Indiana from the possible loss column.

The Truth!

Our new outlook ranges between 8-4 and 10-2!

Bottom line: our record improves with a sustainable, explosive offense. Even with a loss saturday, if our offense still shows up to expectation, we have much to be happy about. If our offense takes a dive, however, run for the hills.

Prediction for Michigan State:

Michigan lost a close game at East Lansing due to primarily yakety sax, snapping issues, and botched fake punts. This year sets up much more favorably for Michigan despite having serious defensive issues.

Here are a couple of statistics that might give us hope:

Rank Team Sacks Allowed Sack Yards
10 MSU 11 82
Rank Team Turnovers Fumbles Interceptions
10 MSU 9 5 4
Rank Team Third Down Conversion %
11 MSU 23/62 37.1%

MSU is 10th in the Big10 in sacks allowed, turnovers coughed up and last in the Big10 in 3rd Down conversions, all of which will play a part in getting our defense off the field. Sparty is also the most penalized team in the Big10 (41 penalties for 362 yards).

In addition MSU, unlike Indiana, will kick field goals - they are 7 for 7 on the year.

NSFMF! MSU has a more experienced QB and a better rushing game than last year. What would you call a Chappellbomb that happens mostly on the ground? A Bakerrush? A Bellringing? A Capernickledandy? Whatever it's called, that's the likeliest of outcomes.

But based strictly on the numbers:

Team PPG vs Mich YPG vs Mich
MSU 35.1 536

It's sobering to see 35 points and 500+ yards as an expected value. Yet there is reasonable hope we will maintain yardage parity with such ridiculous numbers.

I sincerely doubt MSU will hold Michigan to their defense's season average of 101 yards rushing. If they do, it will be a blow-out for Sparty. Last year Michigan gained 28 rushing yards on 28 rushing attempts. You can bet the house that won't happen again.

Even if you believe the assertion that "Michigan hasn't played any real defense yet", you can't argue with the fact that all five opposing defenses have yielded their largest yardage totals on the season (tpilews).

The numbers say 37.1 - 35.1 in favor of Michigan but I can't help but feeling this is a game where special teams is finally going to cost us. Yet after all of this analysis, everything is evenly divided, so I'm not going to pick against Dilithium at home.

Michigan 42
Michigan State 38

/By Saturday at 3:30 I will have convinced myself Michigan is going to win 49-14.

GO BLUE!

Our Defense, Their Offense - numbers offer hope!

Our Defense, Their Offense - numbers offer hope!

Submitted by mistersuits on September 30th, 2010 at 4:07 PM

[Ed.: Bump. As the OP notes, this data is still very shaky four games in, but the amount of improvement in the offense is so great it can hardly be a mirage.]

In my post the other day, Why should 2010 not be another 2009?, I looked at what our offense has accomplished in 2010 relative to what it had accomplished at this point in the season in 2009. It had two meaningful results:

1) This years' offense draws its potency from highly reproduceable, base set offensive plays, unlike the high variance scrambles and special teams play of 2009.

2) This year's offense is putting up far superior numbers to what they did a year ago (up 28%!!) against as-good or slightly-better competition (77th strength-of-schedule in 2010 vs 114th in 2009).

The Conclusion From the Former:

Our offense will come back to earth from meteoric numbers in out-of-conference play, BUT we have statistically significant evidence to believe that our offense will be far more reliable than last year due to depth, experience, and dilithium.

The Worry:

Our defense cannot stop any team that is executing, whether it's UMass or that-team-down-south. In other words, our wins and losses are going to be determined by how good an offense we face each week, and how well they execute.

Examples: UConn played bad (dropped passes, poor throws) and we stopped them. On the flip side UMass played well (good schemes, good execution) and they had their way with us.

Each and every Big10 offense we play is going to put up at least or slightly better numbers than their normalized offensive output.

So let's find out how bad it's going to be against us with a--

Chart of Infinite Defensive Gloom (after 4 weeks)

Rank Opponent N-PPG N-YPG SoS
1 osu 39.5 409.4 61.38
2 Wisconsin 31.2 381.7 59.93
3 Iowa 28.7 355.1 60.53
4 Connecticut 28.2 333.7 64.34
5 MSU 27.2 343.2 56.11
6 BGSU 26.6 310.7 72.20
7 Indiana 25.7 260.1 47.36
8 UMass 23.1 351.4 57.92
9 Notre Dame 23.0 426.3 75.99
10 Penn St 20.9 330.0 68.00
11 Illinois 20.7 294.0 62.24
12 Purdue 17.3 297.9 60.47
 
2009 Chart (requested by commentors)
 
2009 Rank 2009 Opponent Expected N-PPG Expected N-YPG Actual PPG Actual YPG
1 MSU 32.5 404.7 26 417
2 Wisconsin 30.7 402.8 45 469
3 Notre Dame 30.0 455.0 34 490
4 osu 28.8 366.0 21 318
5 Penn St 27.4 387.7 35 396
6 Purdue 27.2 383.1 38 494
7 Illinois 24.1 391.7 38 500
8 Iowa 23.2 336.3 30 367
9 Indiana 22.7 352.8 33 467

 


Metrics

Normalized Offensive Output - The important thing we're doing here is not looking at the raw PPG and YPG of these teams because it does not account for how good of competition they have played. Four weeks in, the SoS data is far from reliable, but it is at least forming.

Our opponent with the strongest SoS serves as the baseline (Notre Dame with 3 Big10 teams and Stanford). In other words, these numbers estimate what all of these teams' offenses would have generated if they had all played Notre Dame's schedule thus far (Purdue, Michigan, MSU, and Stanford).

Strength of Schedule is taken from Sagarin rankings. (BGSU and UMass are going to have way-inflated numbers at this time, but I included them on the chart anyway as a reminder this is not a perfect analysis and as an interesting couple of data points to track as the season progresses.)

N-PPG or Normalized Points-per-game is taken from the teams average PPG with a SoS multiplier factored in to deflate numbers from playing bad competition and inflate numbers based on playing good competition.

N-YPG or Normalized Yards-per-game is calculated using the same SoS multiplier as N-PPG but using this metric will help us determine a less variant guess as to how offenses will perform (PPG is subject to wild variance based on turnovers and special teams).

I am only tracking our 12 opponents because the only thing that matters is the twelve games Michigan plays and I don't want to get depressed that we are playing Wisconsin and Iowa instead of NW and Minnesota.

Results

This chart pans out as expected. That-team-down-south is the clearcut leader. (Michigan is actually second in N-PPG with 36.3 but FIRST in N-YPG with a staggering 494.5).

We see a clearly defined pecking order in the Big10 that matches very closely the general consensus: clear-cut leaders in OSU-Wisconsin, a muddled middle of Iowa-MSU-Indiana, and a struggling bottom of offenses PSU-Illinois-Purdue.

The exceptions are Indiana, which is trending higher up the rankings due to its offense, and Penn St, which was generally considered a top-4 team in the Big10 going into the season (but is clearly not the case with their offense).

UMass and BGSU will continue to fall down this chart as their SoS gets watered down with conference and 1-AA play.

Conclusions Based on Not Enough Data

NSFMF! Teams always seem to play their lights out when they play Michigan. Michigan's defense has a way of making teams look better than they are. Notre Dame for instance had their highest offensive output of the year against Michigan, operating at 125% of their average YPG.

If we take the MOST pessimistic view and give our opponents 125% of their offensive AND scoring outputs against us and only give ourselves 80% (assumption our offense slows down entering league play) of our average going into the Big10, Michigan ends the season 7-5 with wins over PSU, Illinois, and Purdue.

But remember:

Rank Team N-PPG N-YPG SoS
-- Michigan 36.3 494.5 66.77

If instead we give ourselves just our average offensive production going into this weekend - our Big10 expected record jumps to 6-2... 10-2 overall!! - with losses coming from Wisconsin and that-team-down-south.

Where does the truth lie? Probably somewhere in between 6-2 and 3-5. Would you take that outcome at the start of the season? In a heartbeat? I know I would.

It is going to be tremendous to watch this Michigan team storm into the Big10 season knowing that our offense only needs to hold serve and our defense can surrender season-best performances from every single opponent and we still have a fighting chance in all of those games!  And lest we forget... DILITIHIUM!

For now, I think we can look at this and add one more reason to the growing pile of why 2010 is NOT 2009! Get excited! Indiana here we come!

Prediction for Indiana:

Efficiency Team N-PPG N-YPG
125% Indiana 32.2 325

Michigan's ground game operates at MINIMUM of 100% our normalized average and puts up above-average PPG, but since we only score touchdowns we go to the next closest number after 36! Indiana plays their lights out and operates at 125% of their normalized efficiency, mostly through the air.

Michigan 42
Indiana 31

GO BLUE!

Offensive Team Stats after 4 games

Offensive Team Stats after 4 games

Submitted by ldoublee on September 26th, 2010 at 2:02 PM

4 games does not a season make, but after 4 games...

 

Total Offense: 1st  2,251 yards

Offense/Game: 2nd - 562.8 (OK St. 1st, only played 3 games)

Rushing Yards: 2nd - 1,325 (Air Force 1st with 1,576)

Rushing Yards/Game: 2nd - 331.3  (Air Force 1st with 394)

Points/Game: 12th - 40.8 (Oregon 1st with 57.8)

Passing Yards: 51st - 934 (Arkansas 1st with 1482)

Passing Yards/Game: 58th - 233.5 (OK St. 1st with 408)

 

Not too shabby....didn't see topic below as I had this window open for awhile.  Sorry to OP on topic below.