Diaries

I have what I think is a super-awesome-ncaa-fixing idea which I have mentioned to a few friends in conversation and most seem to think it is a pretty good one.  I'd be curious if y'all have any thoughts on it: 

The obvious problem is that many NCAA athletes contribute way more to a school than they are compensated by way of an athletic scholarship.  Of course, any NCAA commercial will tell you that the value of the student athlete's education is beyond measure.  Meanwhile, for every Denard Robinson that seems to squeeze every ounce of value out of his college experience, there are 12 [insert one and done from Kentucky here]'s who have no interest in what a college education has to offer. 

A degree from USC, or even a year of free education from USC, had no value at all to OJ Mayo, I'm sure.  At Michigan, I remember knowing of several classes which were specifically known to be 'football classes,' (at risk of pissing someone off from the Ojibwe department, I won't mention that Ojibwa was definitely one such class).  So here's the thing: let's not force athletes to rack up 120 credits in Ojibwe to complete their degree.  It's a joke, and it does no one any good.  Another issue with the degree is some kids come from such worthless high school backgrounds, that they are completely unprepared for college level courses, so they don't get as much out of the free education as they should.

Let's make the Michigan football experience what it actually is: a serious education in multi-million dollar industry which has just as many career opportunities as linguistics, history, medicine, or engineering (ok, maybe not engineering/medicine).  Make athletics a major.  Film study and off-season workouts?  Make them classes.  Are they not learning how to be players or coaches or fitness experts or nutritionists?  Is there any less opportunity in these fields than there are in traditional college majors?  Also, it's a cool way for coaches to enforce attendance rules on what used to be 'optional' workouts.  If the kid doesn't do summer workouts, they fail the class, and then their grades are not good enough to participate in the sport.

All sorts of majors have to satisfy basic requirements, so I am not suggesting they take no English or history or math.  I am only asking that they be given course credit for the 40 hours a week they put into mastering their craft, just like a music performance major might.  And for those kids I mentioned previously who come to college unprepared, let's allow the 'school of football/baseball/whatever' to offer some *truly* remedial courses.  I am *not* suggesting watering down the degree.  I am suggesting that we make players receive fantastic, personalized education that meets their needs.  Some athletes are crazy smart and have a strong high school background.  I am not suggesting that they have to get 120 credits of remedial reading, I can think of all sorts of cool/advanced courses.  How sweet would it be to get to teach a game theory course, coaching 423-Expected Value and the Punt? 

Also, since this 'school of athletics' (or whatever better name someone comes up with), is a bit of  a special case, I would say that athletes should be allowed to dual enroll in another school if they choose.  So, speaking of the crazy smart athletes above, (like Jordan Morgan and Devin Gardner) let's still let them enroll in social work, or engineering if they choose.  Honestly, Jordan Morgan has been working his ass off for 4.5 years at basketball and school, he totally deserves to have 2 degrees.  Or a volleyball player or a swimmer might wisely choose to dual enroll in athletics and education, for example, since she knows her field has a few less opportunities than football or basketball.  But still, she is learning a lot of the same fitness/nutrition/competition/management skills the football players are, and she should receive a degree that reflects that.

Of course, this does not entirely solve the fact that Universities are making millions of dollars and the kids don't see any (much) of it.  But I think less people would poo-poo the idea that a degree is a special and important thing if we made this clear distinction.  "I'm coming to Michigan to play football and learn football.  If I master my craft, I could get drafted and make millions.  That sounds like a worthwhile degree to pursue."  And again, I think if this school of athletics could offer some really dynamic classes to serve the needs of those that really have weak backgrounds, then this education could be so valuable.

I think it would be really cool if a few schools pioneered an idea like this.  "Come to Michigan, the first University to ever have a school of football.  Lloyd Carr teaches handling the media, and Mike Barwis teaches how to get paralyzed people to walk again."

Obviously, this does not address every issue with the amount of money that there is in NCAA sports.  But at the same time, I feel most pay-for-play options being considered have a lot more drawbacks than my idea.  Instead of rehash them all, I will simply say that to me the most compelling anti pay for play argument is in a quick comparison of attendance at college football games vs. attendance at arena football league games (or whatever your minor league system of choice is).  People love cheering for these kids that lived in the same dorms, went to the same classes, dealt with the same ridiculous weather and long walks, etc.  I love Michigan football because I had the best time of my life there.  As soon as athletes are legit superstar millionaires walking around campus, those kids have *nothing* in common with me, and my love/association with Michigan football will definitely be diminished.  There is a reason college sports are the only ones who approach the professional leagues in terms of popularity, let's try not to mess with that.

Sorry, this got really long, but I would love some feedback on why this idea won't work, as I feel it's pretty unique and the best way to deal with the problem that I have heard.

 

 

After the past two games, much discussion has centered around a rapid transition from guarded optimism to total panic in regards to our offense.  The relative merits of our interior lineman, in particular, have been debated widely in platitudes as well as UFR minutiae.  While Miller is facing a bit of a talent and size deficiency, we all return to the inexperience of these (and other young) Wolverines as a large factor in our offensive struggles.  Though not speaking exclusively on the OL, ST3 hammers the matter of "youth" home in his most recent Inside the Box Score.

It’s widely accepted that an experienced line correlates with a successful offense.  I didn’t expect to have to dig deep into an MGoSearch to find some statistical evidence accompanied by glorious charts, but the hunt turned up empty other than a 2009 Unverified Voracity linking to a WSJ article confirming the strong correlation.  This particular evaluation used combined OL starts as a metric, determining that “offensive-line experience is one of the telltale predictors of success in college football.”  I sought out to see how this correlation might look for Michigan and its immediate cohorts: the Big Ten member teams, Notre Dame, and next year’s new kids Rutgers/Maryland.  I’ve dubbed this the B1G+.

So how would a lurking, stat-friendly but non-mathletic blog poster make some evaluations?  Without data on career starts, I used eligibility year (per rivals depth charts as of 9/26/13) as a metric for experience of an offensive line.  True freshman are a 1, redshirt seniors a 5.  Herein lies an obvious limitation: "age” and “experience” can be quite different in matters of football.

Given that I’m interested in the effect of a young OL, my metric for success was an offense's yards per play; see Ron Utah's recent diary for another breakdown of how our offense stacks up based on yards per play.  The WSJ study used AP poll result to measure success; see LSAClassOf2000 question the legitimacy of this measure.  I included data on team RPI to give some sense of overall team strength.

Scientists: I got a B.A. in Psych from LSA and something called “arts and ideas” from the Residential College, so forgive me for my sins.   If I understand your process, I'm testing the hypothesis that offensive lines with a greater average age will produce more yards per play.  If I understand your caveats, it’s unlikely that my data set is a large enough sample to draw significant conclusions.  But I've got a nifty heat map:

(Green = 1+ standard deviation above average, Orange = within 1 standard deviation either way, Red = 1+ standard deviation below average)

 

The hypothesis would suggest we see a lot more green on the top half of the map (other than SOS, which is mostly for reference).  Of teams with older than average B1G+ OLs, Ohio State fits the hypothesis best with Wisconsin a close second.  To be fair, 3.8 years into eligibility per OL in Madison is probably closer to 4+ anywhere else.  What do they put in the cheese up there?

MSU and Purdue are extreme outliers against expectation.  Michigan St. may be explained by the effect of the "age does not equal experience" limitation.  If I recall correctly, they have shuffled guys to the line from other positions out of necessity.  Purdue... I don't know anything about the makeup of that line, but to be fair their SOS is tops in the B1G+.  Note that Michigan is the epitome of an average team across the entire row, including SOS.

On the bottom half of the map, there are several overperforming young offensive lines.  Maryland is cranking out more yards per play than anyone but Wisconsin despite having the youngest OL in the sample.  Indiana is having no problem moving the ball against a schedule more difficult than MIchigan.  Same for Illinois, though the rush numbers are right on the fringe of going "red," leaving them an average overall offense.  Notre Dame's rushing attack is a minor anomaly.  How about a scatter plot?

 

 

At a glance, the hypothesis is bogus through four weeks of B1G+ action.  That's clearly a negative correlation, both across and within quadrants.  On the other hand, the trend line looks about right if you throw out Purdue, MSU and Maryland.  Michigan is to the B1G+ as David is to man, but Minnesota will be out to prove they are the more perfectly mediocre offense from the most perfectly mediocre conference next weekend.

The tone of the blog after UConn has shifted towards acceptance of our averageness rather than extreme panic or outdated optimism.  If nothing else, these cute visuals may lend credence to that MGoStageOfGrieving.  Sure, we're not that "young," but we're not that bad either, independently or relative to age/competition.

"I'm unhappy because we sucked." - Al Borges did not say this, but was thinking it.

As we continue our transition to "MANBALL," I was curious to see, statistically, how that transition is going.  The questions I'm trying to answer are: "What is this team good at?  What are they bad at?  What is the logic behind the play-calling?  Are we ready to be a MANBALL team?"

What follows is a chart (based on Brian's UFR) of all the formations used against UConn, the type of plays that were run, and the averages.  It's a big chart.  It's also copied from my post in the UFR thread, as are most of my comments below it.  A few notes:

• Plays that had a pre-snap penalty or penalty other than pass interference are not counted.
• Pass interference is counted, since it is assumed the play was successful enough to draw a penalty
• Sacks are rightfully categorized as passing yards
• Yes, I'm aware that this analysis has limited variables and misses important data points.  If you want to add something, please do.

Chart?  Chart!

Formation	Run	Yds	Avg	Pass	Yds	Avg	Plays	Yds	Avg
Ace	1	3	3.0	2	2	1.0	3	5	1.7
Ace 3-wide	1	10	10.0	1	0	0.0	2	10	5.0
Ace H	1	0	0.0	1	6	6.0	2	6	3.0
Ace H twins	1	0	0.0				1	0	0.0
Ace twin TE	2	17	8.5	1	0	0.0	3	17	5.7
Ace twins	4	14	3.5				4	14	3.5
Ace twins stack				1	0	0.0	1	0	0.0
Ace twins twin TE	2	16	8.0	1	-16	-16.0	3	0	0.0
ACE (Totals)	12	60	5.0	7	-8	-1.1	19	52	2.7
Goal line	3	20	6.7				3	20	6.7
I-Form	3	8	2.7	1	0	0.0	4	8	2.0
I-Form Big	4	-5	-1.3	1	12	12.0	5	7	1.4
I-Form twins	1	4	4.0				1	4	4.0
I-Form twins stack				1	2	2.0	1	2	2.0
I-FORM (Totals)	8	7	0.9	3	14	4.7	11	21	1.9
Pistol 3-wide	1	7	7.0	1	10	10.0	2	17	8.5
Pistol FB twins	1	-1	-1.0				1	-1	-1.0
Pistol trips	1	-2	-2.0				1	-2	-2.0
PISTOL (Totals)	3	4	1.3	1	10	10.0	4	14	3.5
Shotgun 2TE twins				1	9	9.0	1	9	9.0
Shotgun 3-wide	5	51	10.2	5	17	3.4	10	68	6.8
Shotgun 3-wide jet	2	14	7.0				2	14	7.0
Shotgun 4-wide				1	0	0.0	1	0	0.0
Shotgun 4-wide tight				2	14	7.0	2	14	7.0
Shotgun double stacks				2	20	10.0	2	20	10.0
Shotgun empty TE				1	6	6.0	1	6	6.0
Shotgun trips	2	19	9.5	4	21	5.3	6	40	6.7
Shotgun trips TE				5	27	5.4	5	27	5.4
Shotgun twin TE				1	0	0.0	1	0	0.0
SHOTGUN (Totals)	9	84	9.3	22	114	5.2	31	198	6.4
TOTALS	35	175	5.0	33	130	3.9	68	305	4.5

 

While it doesn't take into account some easy missed plays and some heroic efforts to make something out of nothing, the chart does show that we seem to be much more successful when we're not under center.  We ran 35 of our 68 plays from the pistol or shotgun, and the shotgun was our best bet.

I agree with Brian's conclusions that this team benefits greatly from being in the gun.  I'd love to see more MANBALL out of the Pistol, but the under center stuff didn't work for most of the game.

That said, the Ace formation gave us critical rushing yards during our comeback.  I believe it was effective because UConn feared we might actually pass when we were behind in the 4th quarter.  When they know we're going to run, the under center stuff just doesn't work.

For those of you calling for more simplicity--you have a point.  We used 26 different formations for 68 plays.

Some interesting data points:

Since it is a bye week, I took the opportunity to expand a little on the AP poll analysis that I do and look at how the distribution of the votes we have received to date stacks up against two other teams – in this case, Ohio State and Northwestern. I chose them mainly because they are also consistently ranked so far in the season, but also to see if there was anything meaningful we could extract from the numbers themselves about how teams are treated.  So, the question becomes this – is that treatment quantifiable in some weird way beyond the simple fact that it is a poll?

There are two distinct measurements – the actual rank in the poll itself based on the points received and the average position based on the individual ballots. They are different numbers, and I am finding in my first foray into this realm of discussion that they can be different by quite a bit. For example, here is Northwestern’s summary data:

 

TOTAL VOTES	274
AVERAGE RANK (ALL VOTES)	18.942
MEDIAN	19
MODE	17
STD. DEV.	2.744
VARIANCE	7.529
HIGHEST VOTE	13
LOWEST VOTE	25

 

Here is want that looks like in graphic form:

Here is the average rank graphed against the actual rank, but there is an additional statistic here: the differential between average and actual, which I am going to unscientifically term “The Hater Index”.

I suppose that, if I had to define it, "The Hater Index" would be a measure of the overall attitude that the voters express towards a program's performance through their votes.

So, you’ll note here that Northwestern was getting rather a lot of love for two weeks in September with an actual rank that was over one place above the average vote it was getting.  I see this as a measure of a few things perhaps, but mostly the positive perception of Northwestern and the trend of its program.

For some contrast, here are the summary statistics for Ohio State:

 

TOTAL VOTES	300
AVERAGE RANK (ALL VOTES)	3.760
MEDIAN	3
MODE	3
STD. DEV.	1.735
VARIANCE	3.009
HIGHEST VOTE	1
LOWEST VOTE	10

 

You might see right away that the number of votes is higher than for Northwestern. That’s entirely possible in the AP polling, and perhaps it skews the results for some teams. In any case, you can see how the distribution is fantastically skewed below:

Here is the graph with the differential (average minus actual). Lots of preseason positivity, then a general trailing to “zero” or slightly negative, which I take to be a way of saying that the pollsters might think that Ohio State is on par with expectations somehow. Again, this could be a terrible theory.

At last, here is Michigan’s summary statistics:

 

TOTAL VOTES	289
AVERAGE RANK (ALL VOTES)	15.277
MEDIAN	16
MODE	16
STD. DEV.	3.409
VARIANCE	11.619
HIGHEST VOTE	3
LOWEST VOTE	25

 

For all we’ve seen in the last month from our team, the distribution of votes somewhat (key word) resembles a normal distribution, albeit you’d have to ignore the left side of the chart mostly.

Michigan’s different is fairly erratic, as you can see – it shows that we didn’t get much credit for CMU, but that people were reasonably high on us after ND. The last two weeks have been negative, but only slightly so after UConn.

TL;DR CONCLUSION:

Again, this is not at all meant to be a scientific measure of attitude, but I do find the fluctuations versus game results to be interesting. One thing I should have done perhaps is use a team with a loss, but that will definitely be part of another iteration of this.

In any event, suggestions for renfinement are always welcome.

This may or may not have been one of the top six swing plays (it was) via Mgoblue.com

1. The Six Factors

	Exp Score	Early Conv	Bonus Yds	Avg 3rd Dist	Adj 3rd Conv	Red Zone
Offense	29.8	57%	95	7.0	-22%	5.7
Defense	35.1	45%	52	11.7	+4%	7.0

Thanks to going –1 in the short (or no) field turnover department, Michigan had another game on the wrong side of the field position numbers. On a down by down basis, Michigan really dominated. A large spread on early conversions, a solid lead on bonus yards although 95 isn’t an overwhelming number for the offense. The defense bounced back against the big plays after an uncharacteristic performance against Akron. Michigan also forced UConn into some awful third downs, but the Michigan offense was not good on third downs. Michigan’s game winning field goal was the lone red zone deficiency for either team.

So aside from the turnovers, Mrs. Lincoln, how was the game? The turnovers made the game awful and the offense put forward its least dynamic showing of the season, but the defense looks to be back on track after a bad second half at Akron. As has been noted everywhere, if Devin Gardner can his head back on straight, this could still be an excellent season. The defense should limit the floor and a continuation of this level of turnovers would be unprecedented. The ceiling looks less certain but its definitely not as bad as the last two weeks have felt.

Legend

Exp Score: A team’s expected points based on where a team started its drives

Early Conversion: The percentage of first downs' that are converted prior to a third down play

Bonus Yards: All yards gained after the first down marker

Average 3rd Down Distance: Average yards to go on third down

Adjusted 3rd Down Conversion: Rate of conversion for a team on third down, adjusted for the standard conversion rate based on yards to go, 0% is average

Red Zone: Points per red zone trip (TD’s counted as 7 regardless of PAT)

All categories except field position are based solely on plays in competitive situations (all first half plays and any second half plays where the drive begins or ends within two scores).

2. Individual Performances

Devin Gardner: +5.3 +13%

Fitzgerald Toussaint: +4.1, +16%

Lyle McCombs: +0.1, +1%

Chandler Whitmer: –0.7, –34%

Despite all the horrible, awful, no good plays from Gardner he still manages to put up enough good plays to more than offset them. It will be another week or two before I kick in the opponent adjustments, but this will almost certainly be his worst rated game to date.

In terms of overall win contribution, the offense contributed about 60% of the win and defense about 40%. This is actually a pretty high number for the defense.

3. Game Chart & Swing Plays

+11.6% Toussaint scores form 12 yards out to tie it up at 21

-12.2% The punts hits off of D’Mario Jones’ leg and is recovered by UConn

-13.4% Gardner stuffed on 4th down with Michigan trailing by 7

+15.0% Foxx loses 3 on the screen to set up 3rd and 13 on Uconn’s final drive

-15.6% Gardner fumbles on the sneak (-8.4%)  and UConn returns it for a TD (-7.2%)

+19.6% Whitmer picked by Morgan (+11.4%) and returned for 29 yards (+8.2%)

So that’s two bad offensive plays, one good offensive play, a bad special teams play and two good defensive plays. Echoes the feeling from the game of the offense being a disaster in general. But after Morgan picked off Whitmer and got a great return, things turned in Michigan’s favor quickly.

The third quarter was just a mess but other than the turnover and some initial yards at the beginning of UConn’s final drive the fourth quarter saw almost every play move the odds in Michigan’s favor.

4. Ron Zook Dumb Punt of the Week

I had a request on twitter to award this to Dantonio for putting in an ice cold Andrew Maxwell for the final drive against Notre Dame. While it probably wouldn’t have mattered either way, it was probably over thinking things a bit. The good news for Michigan State is at least it didn’t add any confusion to a stable quarterback depth chart.

Clemson and NC State kicked the week off on Thursday night with three punts from the opponent’s half of the field and 3 or fewer yards to go.

But I am going to go ahead and give it to Michigan State anyway. Between the Maxwell move and two punts while trailing in the final minutes of the Notre Dame game, the worst being a 4th and 5 from the 45.

Mark Dantonio is your Week 4 Dumb Punter of the Week

Bonus Futile Field Goal of the Week. Austin Peay just wanted to avoid the shut out against the OHIO Bobcats but kicker Walter Spears would have none of it. Spears missed chip shot field goals of 32 and 26 yards and had a 39 yarder blocked on consecutive possessions in the second half as the Governors fell 31-0.

5. Prediction State of the Stats

	Exp Score	Early Conv	Bonus Yds	Avg 3rd Dist	Adj 3rd Conv	Red Zone
Offense	27.6 (48)	52.9% (28)	156 (40)	7.3 (96)	-1% (71)	5.8 (30)
Defense	31.1 (106)	44.6 (27)	102 (27)	7.2 (25)	+9% (97)	3.6 (23)

Devin Gardner: +14 (unadjusted), 22nd in the country/4th in B1G

Fitzgerald Toussaint: +1.4 (unadjusted), 76/10

Michigan Offense: 39th/8

Michigan Defense: 38th/5

The numbers are still pretty loose at this point, most schedules are soft and opponent adjustments haven’t kicked in yet. If Gardner pulls out of his slump, there is plenty of opportunity for him to contend with the top QBs in the country. Currently he trails Taylor Martinez and Nate Sudfeld! and Nathan Scheelhaase! I am as surprised as you. Toussaint is still a long way from the Melvin Gordon’s of the world but is at least in the middle of the pack as opposed to last year.

Although Michigan’s offense is 8th in the conference, the top 8 are pretty packed. With Penn St, Iowa and Purdue in a second tier and then Michigan State way back.

On the six factors the defense has been really solid across all metrics but third downs. The offense has been more sporadic. Good as early conversions and red zone, decent at big plays and below average on third down distance and third downs. The turnovers have created a field position gap of half a TD per game.

6. Bye Week Bonus

Completely unrelated to anything else I do here, but I have been working on the side to design Michigan Stadium  out LEGOs. I recently finished the design and have ordered the parts. Hoping to have this one a kind set built and displayed in the next couples. I will try and post some pictures to twitter when it’s done. The final structure will be about 2.5 feet square and nearly a foot tall. For now, here is the final design.

Note: Several people have expressed interest in doing this, email me at themathletebt at gmail if you are one of them