Mike Lantry, 1972
In football the QB position is the lynchpin for the whole offense. They touch the ball on every play, read the defense, and choose the best course of action based on what they see in the moment. So, naturally, the outlook of an offense depends in large part on the outlook of the QB who will be flying the plane. The goal of this diary is to see if there are any reliable trends in how a generic QB progresses from one year to the next and to investigate if there are factors that can be identified and quantified that will aid or hinder his on field success. I'm actually very surprised about how clear the data is.
To do this I have accumulated information for 226 quarterbacks that have played in BCS conferences since 2003. The pool was restricted to BCS schools so that some level of control was applied to the level of talent surrounding and opposing the quarterback; the presumption being that players in BCS conferences will be playing with and against talent that is on par with their own.
If a player did not average at least 10 passing attempts per game he played in a given year, the data point was not considered because the number is highly unreliable (small sample size). This shuts out some interesting pieces of data (Tim Tebow 2006) but improves the overall conclusions significantly. In Tebow’s case, his second year as a regular player was his first year as a regular passer so his sophomore season was placed in the Year 1 group. There are a few other, more obscure anomalies that were given the same treatment. The large number of data points make the impact of those anomalies negligible.
The metric I used for this study is NCAA Passer Rating. Unfortunately, Passer Rating isn’t perfect when it comes to evaluating QBs; there are many disses available on that topic (Advanced NFL Stats, Football Outsiders, Fifth Down). I leave the detailed explanation to the articles I’ve linked. However, though it’s imperfect, passer rating is still a familiar number for most football fans and it does provide significant and reasonable insight into the relative performance of QBs. On with the show.
The following chart shows the average NCAA QB Rating by year of experience for all QBs included in this study.The chart includes the standard error of the averages for those that know what that means (or are good guessers). The chart shows a couple of interesting things: more experience is better, which…duh, and the average QB rating seems to improve by approximately equal amounts going into year 2 and into year 3 but then tails off a little going into year 4.
Now, the second point goes against conventional wisdom somewhat; QBs are supposed to improve a lot more after their first year than after subsequent years. The fly in the ointment is that, in order to track improvement, the data should be evaluated as matched pairs. This means that we should take each specific QB’s improvement over the preceding year and then average the deltas to understand the average improvement from one year to the next. Doing that yields this chart.
This chart shows what we expect to see, the change after the year 1 is much bigger than the change after years 2 and 3. But, now there’s the apparent negative improvement between years 3 and 4. What’s up with that?
Need … more … charts …
What I did here is plot average improvement versus the previous year’s rating. To clear out the inherent noise in the data, I lumped QB Ratings near each other together (i.e: ratings from 115.0 to 124.9 treated as 120 and so on). The trends are clear and strong, and they demonstrate that mean reversion is in full effect—the higher a QB’s rating is in a given year, the more likely he is to have a lower score in the next year and vice versa. It’s very difficult to have 2 really good or really bad years in a row (unless the QB is awesome or terrible).
We know from the first chart in the series that ratings go up as your years of experience goes up, hence, by the fourth year as starter, the net expected change is negative. The guys above 130 are likely to fall back and the guys below 130 are likely to move up. This effect allows us to infer that there is an expected upper bound for a seasoned QB, probably in the 130 to 140 range. One possible explanation for this phenomenon, is that a QB is unlikely to have the same group of players around him for all four years. The team around him might be out of phase with his development and that will have an effect on the numbers he puts up.
The familiar example around here is Chad Henne. Chad had Braylon Edwards and a veteran offensive line in his first year. So any improvement he may have developed in between 2004 and 2005 was partially offset by the loss of Edwards and other changes around him. However, as the team around him developed and he continued to develop, he saw a big jump in performance in his third year. Then, going into 2007, there were many losses on the offensive line in addition to Steve Breaston, and Henne’s numbers fell back to the 130-ish level. Overall it looks like Henne never really improved, but the reality is that his development made up for and was masked by the changes in the team around him in all likelihood. I think this is a more plausible explanation than “he was always sweet and he never got better.”
Finally, it’s worth taking a look at the dependency of first year performance vs. Seniority. The question being: is it better to have a redshirt junior making his first start instead of a true freshman?
Once again I’ve plotted the averages and their corresponding standard error and included sample size along the axis for reference. The responsible conclusion is that seniority is not a significant factor in first year success for Redshirt Sophomores or younger. Players older than that seem to perform better. However, you could just as easily conclude that since the averages overlap so much, especially in non-adjacent points, the trend is pretty weak and that no trend exists. It seems that other factors, such as supporting cast and the overall talent of the player, matter more than the age of the QB when he makes his first collegiate start. The team thing is difficult to assess but talent is easy; Rivals.com, be my guide.
Same thing as before, lumped averages with standard error and sample sizes shown. This time, I think the trend is real because: A) it makes sense and B) there is no overlap between 2-stars and 5-stars. Also, a 5-star QB is more likely to have a good team around him than a 2-star player is. All of these things support the trend despite the uncertainty in the data. There’s another reason, let’s zoom in on 5-stars; this time with a table.
|Reggie McNeal||Texas A&M||2003||124.5|
|Trent Edwards||Stanford||2003||79.5||4 new OL; 2 new WR; new RB|
|Kyle Wright||Miami (FL)||2005||137.2|
|Marcus Vick||Virginia Tech||2005||143.3|
|Anthony Morelli||Penn State||2006||111.9||4 new OL|
|Matthew Stafford||Georgia||2006||109||3 new OL; 2 new WR;|
|Xavier Lee||Florida State||2006||123.5|
|Jimmy Clausen||Notre Dame||2007||103.9||3 new OL; 1 new WR|
|Tyrod Taylor||Virginia Tech||2007||119.7|
|Terrelle Pryor||Ohio State||2008||146.5|
When you strip out the four guys that had extenuating circumstances (Mallett stays in), the average is about 131. That’s approaching the theoretical upper limit right away, on average.
I’m currently working an a project that tries to use this information to see what we can expect out of the QBs on our upcoming schedule. I’ll also try to use the dataset to try and tease out what we can expect out of our guys based on QBs similar to themselves.
[ed: bumped despite deployment of worst photo of yrs truly ever.]
“…if you gaze into the abyss, the abyss gazes also into you.” –Nietzsche
Son of a bitch. It’s happening. Quit lookin’ at me, abyss.
In 1960 JFK referred to himself as “…a graduate of the Michigan of the East, Harvard University.” I’ve always thought of that as a high compliment towards Michigan. Now, however, I also recognize the glint of the hex that was unwittingly cast upon us on that day. Upon uttering those words, the 35th P.O.T.U.S not only saluted the University of Michigan’s academic excellence, but he also set our crown jewel onto a collision course with gridiron suckitude. Allow me to explain.
I recently came across an article from 1949 in The Harvard Crimson. The article is over 3000 words long, but the first shiver slithered up my spine before I had even reached the byline: “Alumni, Doing Nothing, Scream for Blood After Worst Season Ever.” By the time I had finished reading the article—A Clockwork Orange style—my spine was a full blown electric eel in the throes of a nervous breakdown.
It’s hard to resist full verse recitation of the whole article but, here some highlights:
There are references to Harvard’s recruiting footprint and the need for MOAR
- The authors bitch about being charged “$4.20 or even $3.60 to see Harvard play an obviously poor football opponent.” Oh, the humanity! It’s weird though because throughout the article they vacillate their complaints between playing cannon fodder and actually being cannon fodder.
- There are reports of familiar alumni complaints a la, “Hurr, hurr…get off my lawn with that new fangled, Crisler-style, single-wing offense and two-platoon system; you…you Michigander, you. Gimme back my Power T.”
And two choice excerpts from the article
“[Current coach] ought to adapt his system to fit his players, the way ‘good old [retired coach] used to do.’ "
But the crucial concern right now is that no attempt to change the situation involve the firing of [Current coach], who may not be the genius he was hailed as last year but who is certainly doing his best--and a definitely competent best--with what material he has.
Go ahead and do whatever it takes to rid yourself of those heebie-jeebies; I’ll wait. Seriously, it’s like Brian himself had been warped to 1949 via the way back machine to write that article. It’s impossibly eerie; much like this ancient statue of
Michael Jackson an anonymous Egyptian woman and this authentic picture of Brian some bespectacled dude with long hair in a block M tee shirt.
[ed: more disturbing parallels between Harvard circa 1950 and present-day M after the jump.]
My first diary ever was a look at the hypothesis that option QBs get hurt with a higher frequency due to the extra hits the are exposed to. I followed that one up with an expanded data set to include 5 seasons instead of 1. This update adds 2009 to the set and gives the helihat a spin that I had not gone through the trouble of in the previous diaries.
For the forcier-like detail on what was done, please refer to those diaries and discussions. But here’s a high level summary:
- The scope was all FBS schools and all QBs with significant playing time. Significant playing time is defined as players who average more than 17 plays (passes + runs) per game they played in. This level is based on the median of the data (50-50 split on either side of the line).
- QBs were binned into 4 groups according to their run-to-pass ratio.
- Injuries and games lost (quarter game resolution) were tracked and statistic-ated.
Here’s the data. Sorry for the table not being copy&paste-able, Windows Live Writer was not cooperating with me.
Last season was a bad year for non-zero threat level QBs with levels 1-3 coming in above 30%. It’s important to note here that sample size is a factor in this. For example, each level 3 QB accounts for about 5% of that population so one injury sways the percentage by quite a bit. This is a big reason why there is so much variation in the injury rates for levels 1-3 from year to year. The aggregate totals are much more reliable because, at this point, each category has plenty of observations with which to make conclusions.
The graph above shows the overall range (black line), average (red circles), and the standard error of the average (red hashes) for each category. At first blush it looks like there’s a difference in the injury rates of level 1, 2, and 0/3 but the fact of the matter is that there is insufficient evidence to support this. I actually ran hypothesis tests this time and that was the outcome (failure to reject the null hypothesis that A=B=C=D). Note that this does not mean that no difference exists, simply that there is no reason to conclude that a difference does exist. The differences observed are statistically insignificant.
People who believe that option QBs get injured more often do so because that’s what they want to believe.
Those that know me for realz know that, these days, it won’t be too long before a generic conversation with me turns to Michigan Football or Jay Z; especially if that conversation has been libated. It’s a fairly recent phenomenon, too. Say, oh I don’t know, 960 days or so. Or maybe it’s been 1248 days…Anyway, I used to be obliviously secure in knowing that, no matter what, Michigan would be fine: we’d win a bunch of games and go to a good bowl. Disappointed as I might be that we were usually out of the National Championship picture by the middle of October or, as was way too often the case, the second week in September—ahem—I could always say: “Well, at least we’re not Nebraska or Notre Lame. Hyuk, yuk, yuk. It’ll be a blue moon before Meechigan has to worry about making a bowl. Speakin-a-wich … Z, pass me another! Salud.” Being a Michigan fan was like living in the Shire.
And then I woke up one morning with a blue moon in my eye*. What’s more it was like I walked out of the bedroom to an empty house. The wife, the kids, the dog, the furniture, even my dirty and beaten toy football; any and every anchor of my life—my identity—gone. Gone. All that remained were unfaded rectangles on the walls, where our family pictures once hung, and an infestation of dust bunnies. No warning. No note. Just nothing. [Screw] you, cheese ball. [Screw] you.
Uh, right. Back to football.
Though I didn’t see it at the time, looking back, it almost seems predictable that the team struggled in Rodriguez’s first two seasons. The changes in culture and strategy were just enormous; more, I think, than anyone could have possibly anticipated. Add on top of that the structural problems in the makeup of the team that Misopogon articulated so well and, yeah, we might have been able to see it coming. Maybe not 8-16 but, we might have had a clue.
So, as a new season approaches, I’ve tried to see if I could gauge how many games Michigan can win next season. Not for some silly reason like setting an ultimatum I have no agency in setting but because I want to properly cup my soul dong this time. Well, I woke up this morning and I got myself a gun. By got I mean built, and by gun I mean multivariate least squares linear regression model. (Yeah, I like gun better too). Anyway, my gun says Michigan has a reasonably legitimate shot at, (gulp), 9 wins.
Lock, Stock, and Barrel
The data I used to put the model together were end of year offensive yards gained per game, defensive yards allowed per game, and win percentage for all FBS schools from the 2003 – 2009 seasons. I went for yards per game over points per game for two reasons. The first reason is pragmatic; it would be prohibitive to remove special teams and defensive scoring from the scores of the over 800 seasons (about 10,000 games) in the data set. Even if I were prepared to do it Mathletically, the necessary data don’t exist for all the games I used(2003-2005).
The second reason is that the better team doesn’t always outscore the opponent. Take Wisconsin v. Michigan 2008 as a case in point. In that game Wisconsin outgained Michigan 384 to 268 but Michigan won 27 – 25 thanks to an interception returned for touchdown. Even then, Wisconsin was a failed 2-point conversion from forcing overtime and probably winning the game in the extra period. While one example will never prove a point, I trust that reasonable people would agree that this is not an isolated situation.
As for the data, no modifications were made. Again, stripping out FCS schools poses issues of practicality and necessity. The practical aspect has already been mentioned. As to necessity, the point of the model is to tease out winning percentage and an extreme mismatch is a valid data point for this purpose. Actually, it's really interesting to leave them in because doing so captures behavior that is rarely seen. A game like Delaware State v. Michigan in 2009 should be a virtual lock for a team like Michigan, and though it’s not entertaining for the fans, it is a valid demonstration of the phenomenon that is being modeled. No adjustment for strength of schedule is made either because it is a retrospective model. This will be further explained in two paragraphs.
After doing the math, the results were better than I expected. The regression was able to account for about 67% of the variation of the data just by using Offensive and Defensive yards per game averages. The remaining 33% would be accounted for by other factors like turnover margin, non-offensive scoring, kicking game reliability, and so on. For my fellow nerds out there, the p-value for each coefficient of the model has at least 50 zeroes in front of the first significant digit. For my non-nerd brothers and sisters: that ish be money, yo. Just don’t go gambling with it; you’ll lose.
It’s important to note what this model actually does. It takes retrospective data and uses it to explain what happened. It says, if you actually achieve X-level of offense and Y-level of defense you can expect to win Z-percent of your games. Even then, 33% of the outcome is determined by things that aren’t dependably predictable such as recovering a fumble, housing a punt, and your kicker’s frost resistance. For projection purposes, the only thing that needs to be assumed is the likelihood that a team can achieve the necessary levels of offensive and defensive production. Wuh woh, we all know what happens when you make assumptions; you make an ass out of u and…mptions.
One last comment about the modeling phase; turn-overs are a huge factor. I was able to dig up turnover margin data for the 2009 season and adding that to the model and rerunning with only 2009 data doesn’t really boost the significance of the model that much but it allows us to see just how big a knob TOM is. A turnover margin of +1 per game is good for about a 12% boost in win percentage all else being equal. That’s about 1.5 games over the course of a season. Friggin yuge.
Ultimately, I lumped turnover margin back into the general mayhem bin because visualizing things in 4 dimensions is either impossible or complicated, depending on your major. Also, the NCAA doesn’t track turn-over margin and I was unable to find more than 1 season’s worth of data (2009). The reliability of this model depends on the large number of data points used to generate it so that’s what I wanted to protect the most.
Cheney-Whittington Memorial Chart (aka – Scattershot, in yo’ face!)
Okay. I trust no explanation is necessary. Let's move on.
Psych, there are a few things going on here:
- The galaxy of points is there for decoration, and to show that this is real data.
- The bell curves outside each axis are there to help get a sense the distribution of offensive and defensive production. Also for stat nerds, they show that the assumptions needed for linear regressions are verified.
- The cross hairs show the average offense and defense over the course of the 7 seasons that were used for the project.
- The two colored groups of points are Michigan in yellow and the BCS Champions in green over the period covered.
- The three diagonal lines are generated by the model. They are iso-win lines drawn at the bowl eligibility threshold (.500), Michigan’s Traditional Average (.737), and ,for fun, Predictably Undefeated (1.000; aka the momma said knock you out line). If you are to the left of one of these lines you have achieved that milestone.
A couple of notes:
- It is not necessary to be a “predictably undefeated” team. In fact, only 1 of the 7 BCS champions captured were anywhere near that line.
- There is some nice internal validation shown here in that a team that plays average defense and average offense is expected to win about 50% of its games.
- Teams of equal power line up at levels parallel to the green, red and blue lines. Notice how the BCS Champions line up nicely just below the green line.
- News Flash: Defense wins championships. It’s sooo cliché because it’s sooo true. All of the BCS Champions on this chart played much better than average defense but only slightly better than average offense. That is not to say that offense doesn’t matter (they all played above average offense too), but with out a great defense you have no shot at the title.
- Michigan had only one team in the period captured (2003) that had the look of a national champion if they had earned the opportunity. Incidentally, Florida 2006 is the lowest championship team shown.
Last season Michigan’s year-end averages were 393.3 ypg on defense and 384.5 ypg on offense for a prediction 47% or 5.6 wins out of 12. Michigan actually won 5. So, how does the model spit out a number anywhere near 9 wins for next year?
Let’s make an ass out of umptions.
That Shotgun Shine: Offensive Expectations
In this third year, Michigan should be approaching spread-n-shred steady state. If not, we’re in trouble and, in all likelihood, RichRod is gone. I figure the most important difference between this year’s offensive outlook and last year’s is the fact that we wont be forced to have a freshman QB starting. So, for what that level of production (spread option with experienced QB) looks like we should look toward our nearest approximations: Rodriguez era West Virginia and Meyer era Florida. The following chart shows how each offense progressed in executing the spread option. I’ve split the WVU data into two groups: with Pat White and without Pat White since, you know, we don’t know if we have a QB as prolific as Pat White.
In both White’s and Tebow’s sophomore seasons, their respective offenses gained an average of about 450 yards per game and leveled off there. Both improved 50-75 yds per game from their freshman campaigns. Without a White/Tebow Level player it looks like WVU was capable of about 410-425 ypg at steady state with again a 75 ypg improvement from year 1 to year 2.
The effect shows up at other programs, too. Penn State improved 40 yards per game in Darryl Clark’s second year as starter. Notre Dame gained 100 yards more per game (!!!) in Jimmy Clausen’s second year as starter. Even Tressel ball gained an additional 30 ypg in Terrelle Pryor’s second season and he was neither a polished passer nor used as a consistent running threat.
It’s not unreasonable to think that Michigan can produce 425 – 450 ypg of offense this year. There are really no excuses on offense this year. The offensive roster is stacked with a normal level of talent and experience at all positions, and they’re all familiar with the system. We’ll see if the spread option works in the Big Ten or not, homies.
Roulette Anyone?: Defensive Hopespectations
Ugh, I don’t want to do this but…I’ve got a gun to my head. (Ha!) I look through split fingers…
Ok, this is much more difficult an exercise. Here, the most drastic change is that we lost our three best players off a team that gave up 400 ypg. No biggie. Generalizing, the concerns are about the roster. The benchmarks I’ll use here are: Minnesota, Northwestern, and Purdue all from 2009. None of those teams had Brandon Graham, Donovan Warren, or Steve Brown either. Yet, they all played a current Big Ten Schedule, they’d all trade their defensive rosters for ours in a heart beat, and they were all able to beat teams we couldn’t. The range of performance for those 3 goes from 344.3 to 376.7 ypg. That represents an improvement vs. Michigan 2009 of 25-50 ypg.
Incidentally, 375 ypg is what Michigan allowed in 2008. So, yeah, that’s not a stretch of the imagination by any means. While the peaks of Michigan’s defense might be lower this year, the valleys should be higher. There were a lot more valleys than peaks last year.
350 – 375 ypg it is.
Pull the Trigger&Sights on the Future
So, low expectations: OYds = 425; Dyds = 375. Weed Smoked expectations: Oyds = 450; Dyds = 350. Beep, Bop, Bip, Bope, Boop…err, I mean, ka-BOOM! Projected Win % = 0.593 – 0.700. That means Bowl Eligibility, which means 13 games, which means 7.7 – 9.1 wins.
If that scenario indeed plays out, is it that hard to envision a scenario in 2011 where Michigan is in contention for the MNC? The offense is already within striking distance of steady state Florida and West Virginia and it’s not crazy to think that the defense would have the capacity to improve another 50-75 ypg with a full squad of talented and experienced returning players. 2011: Offense = 450; Defense = 300.
Here’s another Cheney-Whittington Chart:
This time I’ve separated the Michigan cluster into two groups: pre-Rodriguez and post-Rodriguez. Then I added what I think are reasonable projections for the 2010 and 2011 seasons.
When Rodriguez was hired I think the majority of people thought that Michigan would be in contention for the national championship by year 3 or 4 depending on their level of optimism. The drastic step back the team took in 2008 blew those expectations out of the water. But after doing this analysis, it doesn’t seem that we’re as far off track as I thought when I started this project.
Going forward, I’d like to investigate if championship teams share common characteristics in terms of roster depth and experience, turn-over margin, kicking game reliability, and so on. I’ll also take a look at the teams we have coming up on our schedule to see what direction they are moving in and to gauge how much general mayhem we’ll need to go in our favor next season to meet these win projections.
* The inspiration for the theme of this diary was a result of listening to a song I’d never actually listened to before: the theme of the Sopranos. I prefer the Detroit Mix on iTunes because it’s performed in first person. Like I said, most things with me come back to Michigan Football.
I woke up this morning / Got myself a gun / Mama always said I'd be / The Chosen One.
One in a million / I believe you've got to burn to shine / I was born under a bad sign / With a blue moon in my eyes.
Woke up this morning / All that love has gone / Papa never told me / About right and wrong.
I’m looking good, baby / I believe I’m feeling fine / I was born under a bad sign / With a blue moon in my eyes.
I woke up this morning / The world turned upside down / Thing's ain't been the same / Since the Blues walked into town.
But I’m one in a million / I've got that shotgun shine / Born under a bad sign / With a blue moon in my eyes.
When I woke up this morning everything was gone. By half past ten my head was going ding-dong. Ringing like a bell from my head down to my toes; like a voice telling me there was something I should know.
Last night I was flying but today I’m so low. It’s times like these that make me wonder if I'll ever know the meaning of things as they appear to the others: wives, husbands, mothers, fathers, sisters and brothers.
I wish I didn't function—wish I didn't think—beyond the next paycheck and the next little drink. Well, I do so I made up my mind to go on. ‘Cause when I woke up this morning everything I had was gone.
Author Note: Jump to Ecclesiastes 1:5 to roll right into more analytical thinking. No, no preaching here…well maybe a little.
Author Note 2: Additional data for historical context of Michigan achievement patterns added in Back to the Future section before the Penn State recovery discussion.
In 9th grade, I had just arrived in Ann Arbor after spending two years abroad. It was January, right before mid-term exams or some other teapot tempest of youth, and I sat there in 9th grade English class watching a lesson plan, ahem, about a story the class had apparently read and discussed a few days before. Skeletor, as the other kids called her due to her impossibly gaunt physique as well as the fact that her face had no skin or muscle on it, was at her desk doing whatever, looking up only when the conversations got loud enough to distract her.
I think I was the only one watching the movie, maybe that mousy girl was too, because I had no friends to talk to since I had just arrived in Ann Arbor ad I had to pass the time somehow. Either that or I was a hopeless social outcast, which, absurd. Anyway, besides being bored out of my effing mind, all that I remember was thinking, "Damn, that kid's pretty tough."
The movie was a film adaptation of Ernest J. Gaines's short story "The Sky is Gray". The story is set deep in the segregated south of the '30s or '40s and was first published in 1963 and so it is dense with racial and social themes, but the reason the story stuck with me had nothing to do with any of that. I remember it because of the dignity and poise Gaines's characters displayed in that story. Especially Octavia the mother of the 8 year old protagonist, James.
There are many episodes from which to draw but the most vivid for me comes towards the end of the story as Octavia and James are headed home. They're standing outside and a cold gust of wind causes James to flip his collar in an vain attempt to keep the wind's bite off his neck. I still have never been able to reconcile the fact the story was set in a cold winter in Louisiana, but whatever, poetic license, suspended disbelief, and all that. Anyway, Octavia tells him to put his collar down by saying something like "...only bums do that. And you're not a bum. You're a man." Then she stands there stoic, eyes up, shoulders back pinching the brim of her hat so it wouldn’t blow off.
From time to time, I'll see or hear something that reminds of that story and this time it was a Ohio State v. Michigan hype video of all things. In the video, chunkums opens with Rudyard Kipling's poem “If” set to music and images. The whole poem is dead on point for Michigan's situation right now, both the fan base and Rich Rodriguez. Frankly I can’t help but relate that poem to other things around me: the Big Three, the World Economy, Detroit. These lines are particularly compelling:
If you can force your heart and nerve and sinew
To serve your turn long after they are gone,
And so hold on when there is nothing in you
Except the Will which says to them: "Hold on";
Tales from the Dorkside: Guernica in Maize
[Editor's note: bumped. At this rate I'm going to be a spectator around these parts soon.]
Herein lies data. For those readers who prefer to skip my right brained musings in a tenacious fit to resist all culture and proceed directly to the left-brained portion of the show proceed to the So, How Goes It? section. Ahem…
The fallout from Michigan’s catastrophic failure against Illinois has left in its wake a fan base wretched in suffering. And anger. And chaos. And despair. A veritable Guernica in Maize. Pablo Picasso’s renowned painting might as well have been painted in the aftermath of last Saturday’s loss. The centerpiece of the painting features Michigan’s Defense (the horse) in the throes of death complete with Juice Williams as javelin gashing it right up the middle, exposing the gaping wound that is Michigan’s defensive barracks.
All of the major players are shown:
- Terrorized souls engulfed in the inferno of buyer’s remorse (far right).
- Horrified and confused onlookers (center right).
- Dismembered soldiers , also known as The Legend of Tate Forcier: Heisman Freshman ;complete with shattered sword (bottom).
- Grieving mother clutching the lifeless corpse of her child (read.: hope; far left).
Even the Eye of Mordor (read: FreeP) is represented (top). Not to mention that weird looking bull thing with fire coming out of it’s butt (left). I guess that’s Brian?
Anyway, Such a scene makes the reasonable observer wonder—what is up the suck? Misopogon has thoroughly sifted through the immediately obvious symptoms of poor defensive play and walk-on starters to provide tremendous insight into the plight of the defense. He has emphatically demonstrated the task Rich Rodriguez and his man Greg Robinson have in front of them if they are to their save their jobs and save Ann Arbor from burning: fix the defense. Accomplishing this will not be easy and it will test Rodriguez’s mettle as a head coach. And it will take time.
So how goes it?
I think reasonable people would agree that it’s not yet time to render a final verdict…at least as far as the defense is concerned. So let’s focus on what is reasonable to evaluate Rodriguez on at this point in time: offensive production. He’s had ample time to demonstrate core competencies in his area of expertise. He’s recruited his guys, has a reasonable amount of talent depth (inexperienced or not), and has had a reasonable amount of time to install his system.
The prototype I’m using as the model of what the performance of what a good offense should be will be the unit RR replaced, 2007 Michigan. That team had the requisite talent and experience at every single position: an offensive line that featured two three time lettermen (Jake Long- RS Sr. and Adam Kraus-RS Sr.), a three time letterman at QB (Chad Henne, Sr), a three time letterman at RB (Mike Hart, Sr), and three 2-time letterman at WR (Mario Manningham, Jr; Adrian Arrington, Sr; Greg Mathews, So). That’s as good a squad that a coach can ask for.
While the schemes employed by that offense are drastically different from what is currently being used at Michigan, the differences are irrelevant. Either is suitable for executing the mission: move the ball down the field and score points.
For the sake of thoroughness, I’ll stack them up against 2006 Michigan as well. Largely the same cast of characters but with fewer injuries. Reasonable or not, this level of production is what all Michigan fans desire or expect.
To evaluate the units I’m turning to very basic and universal categories.
Plays per Drive
This is a tempo-neutral possession metric. Evaluating Rich Rodriguez’s offense by time of possession is misleading since his philosophy is explicitly unconcerned with that metric. However, all offensive schemes seek to run as many plays as they can until they score. So, this metric also allows us to evaluate execution at a base level as well. Plays-per-drive allows us to compare different schemes to each other.
The calculation of average and standard deviation for this metric omits the highest (yellow) and lowest (red) game averages since yards per drive are highly correlated with the strength of the opposing defense. The presumption here is that one good or bad game is a fluke. Games against markedly inferior competition (blue) have been omitted regardless of game outcome. Ahem.
What we see here is that Michigan 2009 has in fact improved over 2008 in this particular metric both in average plays per drive as well as in the standard deviation of this metric. However, 2009 lags 2006 and 2007 a little in regards to average but matches the 2006 campaign in terms of consistency. The average part is not very surprising.
The benchmarks have significant advantages over 2009 in terms of personnel and experience. However, the consistency part is a bit of a surprise. This year’s team, freshmen QBs, botched snaps, and miscellaneous turnovers included is as consistent as the 2006 unit and more consistent than the 2007 unit. Anyone who has had to improve a process knows that you get rid of deviation first, and then you shift the mean. In this case, there is the good fortune of the mean shifting on its own via player maturity.
Yards per PlayThis is a category of raw production. This is more in line with offensive strategic objectives such as controlling field position, getting into scoring position, and so on. Again, the high, low, and inapplicable data points have been omitted from the calculations of average and standard deviation.
Through the games played so far, the 2009 offense has improved significantly over the 2008 team and matches the production of the 2007 team. It is also the most consistent offense captured.
Points per Drive
The bottom line. Is the offense pulling its weight in the “outscore your opponent” equation? Again, the high, low, and inapplicable data points have been omitted from the calculations of average and standard deviation.
Once again, through the games played so far, the 2009 offense has improved significantly over the 2008 team, which was consistently bad, and beats the production of the 2007 team in terms of drive average and consistency. 2009 lags 2006 in terms of average but again, 2006 is a stout benchmark.
The TakeawayDespite its glaring and soul dong punching deficiencies, the 2009 offense stacks up surprisingly well to arguably the best offensive unit Michigan has seen in approximately two decades, probably more like four, and maybe even six. DECADES(!). And significant low hanging fruit remains (turnovers).
Regardless, after games like last Saturday’s we are right to break out the compasses and maps and graphing calculators to reevaluate just where the heck are we, exactly?
Here's where we are:
- Tate Forcier is a FRESHMAN who has played in EIGHT games.
- The rest of the offense are de facto true sophomores who have only shown signs of effectiveness in about 14 games.
- The defense does not have the breadth or depth of personnel necessary to meet the Michigan standard.
Recognizing that we have a major vulnerability in defensive personnel is in no way a slight against the Lloyd Carr stewardship. It is simple root cause diagnosis. And, maybe RichRod can tweak a thing or two or three, here and there and over there. But, to suggest that the team has made no progress is simple ignorance at best and dubious ignorance at worst.
There is a big difference between excusing and explaining…that difference is responsibility. RichRod is responsible for his record, but its only fair to give him more time to hold him accountable as well. Forging the program into a consistent winner requires Rodriguez to demonstrate the full gamut of the requisite core competencies needed to be a successful chief executive in an elite college football program: excellent recruiting, excellent motivating, and excellent personnel evaluation(coaches and players), and excellent focus. If he succeeds, he will have vindicated Bill Martin decision and earned the respect of many. If he wins it all, he will be the next Bo Schembechler.
Godspeed, RichRod. Godspeed.
[Editorial take: I don't think things are quite as sunny as the numbers suggest; in the comments it's noted that adjustments were not made for outliers like turnovers and special teams items. Michigan's gotten great production out of Olesnavage and Stonum this year. Also, Michigan has yet to face the #65, #21, and #6 defenses so far this year and will likely see their to-date respectable metrics continue to dip below the okay production of the 2006 and 2007 teams. The 2006 team was pretty good but only 38th in total offense and 26th in scoring. It may have been arguably the best collection of talent at Michigan, but it wasn't exactly set free to roam the plains, its majestic rippling muscles trampling over mascots that dare oppose it. Michigan is approaching the mediocre numbers put up by Mike DeBord.
Even considering that the progress made from year one to year two is obvious.]