“He was on the other side of the court, screaming: ‘Good shot, Kev!’” Durant said, shaking his head in delight. “I’m thinking, this guy’s an All-American type of teammate right there.”
Everytime the moon shines I become alive. I’m a beast in the night. I’m on the prowl and I hope to find some light.
-Kid Cudi, Alive (Nightmare)
We all have our on favorite canons, don't we? Once upon a diary in a dark and distant era I made reference to one of mine, that one by Pachelbel; You know the one. It is inescapable. I mean, et tu Rap? Eminem, Jay Z, Tupac. But more favorite to me than the original is Blues Traveler’s riff on it, Hook. The song itself is kind of a slap in the face to the general public who’s taste in music is, apparently, so trite and unsophisticated that we don't realize that “when I’m stuck and need a buck, I don’t rely on luck” meaning musicians can just hijack Pachelbel and we’ll gobble it up anyway, even if we can’t put a finger on just why we like it.
“Hey pizza guy! Dough, sauce, cheese, and toppings? What kind of cookie cutter bullshit is this???” Relax, Popper, that [stuff] is delicious so what exactly is the problem here?
(Unfortunately for you all the combination of deep night, data induced madness, and alcohol overcomes the “this only fascinates me” levees in my mind and the inmates overrun the asylum so all apologies and thank you for indulging me.)
So why do I like the song even though Popper is being kind of a dick? Because it’s a brilliant monument of knowledge, understanding, wit, and self awareness. And irony. The song is a musical trope wrapped around lyrics consisting of lyrical analysis, mocks its performers, its industry, and its audience and STILL made hella loot. Kharma(n)* is a bitch though: Blues Traveler hasn’t had a song that popular ever since and it was this close to being their most popular single ever.If Penn & Teller did music, they would would do this.
For me, peeking behind the curtain piques my curiosity rather than diminishes my interest. It makes me think “hey, I can do that” for a couple days until I remember “oh yeah, I lack skillz and talent.” And then I settle into true appreciation and fascination in watching people do things I cannot. The hook brings you back, man. Again and again.
My point in all that was that worthy synthesis is derivative of rigorous analysis. That is, a good place to begin creating something worthy of creation is by understanding things that are worthy of understandation. Follow? UFR, Mathletics, other stuff, they’re all the same—they break things down in order to build other things up. Even if the thing that is being built up is just context, that’s a thing worth building. If you know a bit about something and look at it upside down, sideways, and in a mirror, you might just see something kind of cool…in a nerdy sort of way. ‘Tis the Canon of MGoBlog.
*MCalidagger said “that’s Carmen!” to Lady of the Lake one time when he was three just seconds after she had stubbed her toe while placing him in the purgatory of time-out. He was mad, she was mad, it was hilarious. Then I got in trouble for laughing. How is that my fault? Troof is troof, yo.
Here We Glow Again
Another of my favorite cannons (remember: gun == Multivariate Least Squares Linear Regression Model) is what I call the Blue Moon Model. It really began as a basic assessment technique with which to project my team’s prospects with a few simple lower level assumptions. Three years on, I think its worth a remix. Previous foundations are laid here and here.
As a refresh the model takes a team’s Offensive Yards per Game, Defensive Yards per Game, and Turnover Margin per Game and converts that to an expected Win Percentage. IT IS A RETRODICTIVE MODEL so, it’s predictive value relies upon the validity and accuracy of the assumptions that are made. Even when you’re dead nuts on with those assumptions, you’ll be off by more than 1 win about 26% of the time. So, good luck guessing, then good luck winning. That’s the betrayal part of the name Belewe. This is not a problem though, in the world of inductive logic even though conclusions are only probable, they are useful nonetheless. Also, being able to lay 3 to 1 odds is pretty good. And, guessing aint that hard when you know what you’re doing (upside down, sideways, mirrors…did I mention incense?).
It turns out, the model can be boiled down even further without sacrificing it’s accuracy by collapsing OYDS and DYDS into Net Yards per Game (NYDS). Voila, a 2 factor model with killer statistical significance. The intercept makes more sense too because it is unbiased. Say your team is average (OYDS = 375, DYD = 375, TOM = 0); why should you expect to win an extra 3% of your games? Trick question; You shouldn’t. The best application of this math is to make your assumptions about offense and defense, turn them into an average yardage differential, set TOM to 0* and proceed with your projection.
*For the last time (yeah, right) predicting TOM is a fool’s errand and that's coming from a guy that LOVES trying to predict stuff. Go ahead and try but you’re wasting precious time that could be used to make more worthwhile assumptions.
|3 Factor Model||2 Factor Model|
I know this model is simple but that’s part of it charm: you can do this math in your head. Take your yardage differential, round by 5, divide by 5, move the decimal two spots to the left, add 50% and ADJUST BY 10 PERCENT (will never get over that) for each net turnover. I appreciate the sophistication of college level analysis but I was way smarter in elementary school. Arithmetic is where its at, homies.
I think there are two main applications of the model: expectation setting and benchmarking. This diary is long to I’ll split the benchmarking bit off into a different diary.
All fans want to know the same thing: how good are we going to be this year? Sensibly, we start at the end of last year then plug any holes left behind by attrition and arrive at an expectation of X because, naturally. I have no beef with that process because its a whole lot of fun, but you need to have the right starting point. BMM is handy for this. Here’s how local schools of interest did last year:
|OYDS||DYDS||TOM||NYDS||2012 Wins||BMM Expect||Delta Wins|
This year’s prize for Most Dissonant Record goes to: Ohio State. Plus 4, folks. Thirteen years of data has only seen that feat accomplished 8 times out of over 1500 total observations. Fun Fact: that is the third time OSU has managed to post a +4 during that period: 2002, 2003, 2012. In 2004 they posted a +3 followed by +2 in 2005 and 2006…wtf, man? Tresselball, that’s wtf. Ball Control offense, good to great defense, low risk play calling. Jim Tressel hates math, Q.E.D.
I submit that the extended deviation is the offense’s “fault” because when you have good/great defense, you generate yardage differential by racking up yards on offense. What are you going to do, allow 0 yards per game? So I think the Tresselball offensive philosophy explains why Ohio State consistently defied the math for so long. Once the Buckeyes started stock piling national level talent and opened up their offense to leverage it a little more, their performance lined up with the model just fine. Until last year.
Look, I expect Ohio State to be a formidable opponent as usual but, #2 in the country they ain’t; at least not right now. Well, they are a deuce just a different kind of deuce, nameen? Shout out, to my local head start program. Anyway, Urban Meyer’s Florida teams leveled off at 450 ypg and I think OSU offense will be there this year. Yeah, yeah ESS EEE SEE defenses (!) but OSU’s roster isn’t Florida circa 2008 either. The typical B1G defense is OK all things considered, not great but not necessarily a pushover either. Braxton Miller is good but he has some work to do in his passing game, I need to see it first. I’m sticking at 450 OYDS. 475 is on the table but, show me.
Defensively, nothing has really changed at Ohio State. Coaches, recruiting, philosophy, nothing. Well, tatgate happened. During the tatgate era Ohio State’s defense was insane: 300 DYDS or better, often much better (275 or lower), every year between 2005 and 2010. Then, oops, back to typical (about 325 for them). I hereby grant them reasonable improvement on defense from last year out of the goodness of my heart and they end up at NYDS = 100. That’s 9 wins, with a shot at 10. I’d hate to see them get unlucky, truly.
Meanwhile, in Michigan
I’ll take the more straight forward part first, the defense. Not that its clear or easy just that, because of the reactive nature of defense, I think the best policy is to look at a program’s track record, give consideration to any systemic and roster issues that might exist, and call it a verse.
Rich Rodriguez era notwithstanding, Michigan’s Defense has been pretty consistent by the singular measure of DYDS. With competent coaching and a Michigan caliber roster, we typically hang out in the 300 – 350 zone; last couple of years we were at 325. Now Greg Mattison is pretty good but to start breaking through to the next level of defensive prowess and start heading toward elite, I think Michigan needs more experience and maybe a touch more raw talent. Jordan Kovacs will be missed but Heininger Certainty Principal, jack. I’m sticking with a base expectation of 350 – 325 for that side of the ball. Anything better than that would be kind of amazing.
Offense is trickier, especially with the loss of Darboh. Its no revelation to say that Michigan’s offense should take a step forward this year with more harmony between conductor and orchestra so it’s correct to expect more than average OYDS this year, but how much more? Since 2000, Al Borges has never called an offense better than about 425 (Auburn 2004). Indiana put up a 450-ish in the B1G last year so it’s possible and Michigan has better talent on its roster right this minute than Indiana does, but I don’t think we have an offensive philosophy like Kevin Wilson’s either. And we don't have the talent / experience overall to simply out-execute everybody like Alabama does(450 last year). Let’s build it up from one more level down just to make sure.
I’m on record for Devin to pass for 225 to 250 ypg this season. The loss of Darboh gives pause, but I’m not backing off on that. So, getting to 425 means we need 175-200 ypg rushing from the backfield. That’s where we were last year with Denard featuring heavily in the run game. Fitz was a different back seemingly reverting back to 2011 form with Devin under center but then there was that leg thing. I’m going to forget about the leg thing and the questions re: the interior line (thou shalt not accuse me of not being generous) and give Fitz 1000 yds on the year leaving about a 100 - 125 ypg gap to get to the desired rushing target. Y’all think I’m crazy but I think we need to get about 50 ypg out of Devin on the ground to get to an offensive performance level that will keep us from freaking out unless one of the other RBs emerge to provide 600 – 700 yards on the year.
I can’t convince myself to go over Auburn 2004 and that’s being generous. What’s the Borges version of HCP? Even without the questions vis-a-vis the running game, going over 425 probably demands Devin the Monster and a Adrian Peterson level recovery from Fitz and Al Borges’s best offense ever. Again, things happen but I’d be kind of amazed if that happened. You can’t outrun your canons; you acquire new ones. That’s possible, but humans are some stubborn mofos. 400 – 425.
BMM says: 8 or 9 wins with a shot to win 10. If you think Michigan can get to a TOM of +0.7, shift your expectations up by 1 win, then go take your meds.
The Road to Indy
Legends division looks pretty tough this year. Nebraska has a lot coming back and has TOM mean reversion working for them. MSU got unlucky in close games and stands to see at least modest improvement on offense to compliment an elite defense returning virtually intact. Northwestern doesn’t really look as good as last year’s record to me and they had a nice TOM working for them last year; they’re on reversion watch. And their schedule is brutal. Still, the Wildcats are pesky.
The two most important games on the schedule occur November 2 and 9 (duh). Win both and we’re probably in the B1G title game. If we split those, we’re likely to be in a tie with Michigan State or Nebraska possibly both going into The Game which we will have to go all out to win.
So my wife and I just returned from halftime at Highland's first game. There was some ominous lightning in the area and it had just started raining.
At any rate, Bryan Mone looked pretty good to my untrained eye. Through the half he had 4 tackles (there was a facemask penalty on one of those) and 1 sack. He played both ways but got breathers when he was on defense. He played most of the game at DT and RG but got a few reps in at DE. The sack came when he was playing DE on a 3 man line. It looked like he used a shoulder slap to knock the tackle off balance and then just blew by him and threw the QB down.
Most (if not all) of the tackles came at DT where he just blew through the line and ate the RB. Surprisingly the guys he was going against on the other team weren't that much smaller than him (the tackle in particular was larger than him) but they didn't look nearly as athletic. He also chased to the sidelines full bore so I'm not worried about his motor.
At guard he looked pretty good from what I could tell. But the defense he was going against was one of the stranger ones I've seen. It looked like 5 or 6 man lines with the DT's split wide and a LB in between them at the LOS. Most of the time Bryan was just destroying that poor LB... I laughed several times and my wife asked what I was cracking up about. So I pointed it out to her and she started laughing as well. Not sure what they were going for there but it wasn't working.
Lastly, the article posted today mentioned how he describes himself as mean and nasty (I think anyway) and I can verify that. He got into it a few times with one of the other team's DT's and watching him just mercilessly destroy that LB over and over was enough evidence for me.
Wish I could have watched the whole game but as I said it was getting nasty out. Thankfully my wife loves high school football so I'm sure I'll make it to another game or two. We might try to go see the DE that Michigan is recruiting next year who's name escapes me at the moment as well.
How long should we wait for this guy?
There is constant chatter on this board and in the media about how freshmen RBs should be able to contribute right away. The basic tenet of this belief is that if a RB is athletic and is any good, he'll be able to produce right away. Sure, he might not have the nuances of pass protection and route running down, but he should at least be able to pick-up some yards on running downs as a true freshman. Guys like T.J. Yeldon make this easy to believe.
So, I decided to find out how true this is. If you suck as a freshman RB, are you likely to be any good at any point in your career? If Derrick Green doesn't contribute significantly this season, should we ? Going even further, is Rawls a lost cause at this point? Hayes?
Having a little less time than I'd like to do a thorough examination of the data, I used a somewhat limited sample: the top 40 RBs in terms of yards/game from 2012. I broke seasons into three categories: Primary starter (PS), significant back-up (SB), and insignificant season (IS).
These categories are actually surprisingly simple to define: Primary starters are obvious, and guys that are significant contributors at the position are equally easy to separate from the dudes that get trash-time and spot carries. Insignificant seasons also include redshirts, but not medical redshirts. I also took out JUCOs.
Here are the top 40 RBs from 2012 (NOT in order of production):
|2||1||0||Le'Veon Bell||Mich St||JR|
|2||1||0||Joseph Randle||Okla St||JR|
|2||0||0||Jahwan Edwards||Ball State||SO|
|1||0||0||Kenneth Dixon||La Tech||FR|
|2||0||1||Giovani Bernard||N Carolina||SO|
|1||2||1||Kerwynn Williams||Utah State||SR|
|3||0||1||Robbie Rouse||Fresno St||SR|
|1||1||1||Dri Archer||Kent State||JR|
|1||1||1||Carlos Hyde||Ohio State||JR|
|1||0||2||Antonio Andrews||Western Ky||JR|
|1||1||2||Kasey Carrier||New Mexico||JR|
|1||1||2||D.J. Harper||Boise St||SR|
|1||0||3||Zurlon Tipton||C Mich||JR|
|1||0||3||Cody Getz||Air Force||SR|
I have to admit, I was pretty surprised. Only 15 (37.5%) avoided having insignificant or redshirt seasons their first year on campus. And only six (15%) were the primary starters as true freshman, leaving nine (22.5%) as back-ups. That means the vast majority, 25 players (62.5%) spent at least one year doing nothing or next-to-nothing. Of those 25, only four (10%) went from insignificance to starting in one season. The rest (21, 52.5%) spent at least two years developing before becoming starters. And nearly as many (14, 35%) spent multiple years doing almost nothing as jumped right in as contributors (PS or SB) in their true freshmen campaigns. Heck, even Eddie Lacy redshirted.
This is admittedly a small sample size, but it's enough to draw some basic conclusisons:
- Plenty of talented RBs have insignificant seasons; many have more than one
- RARELY does a freshman RB burst onto the scene as a primary starter
- About half of these guys spend at least two years developing before they start
- The experts are idiots (of course, I must admit that I believed the "if they're any good they'll contribute as true freshmen stuff before I looked at it)
And some Michigan-specific conclusions:
- If Green and/or Smith doesn't contribute significantly this year, he's unlikely to start next year
- We shouldn't worry if Green and/or Smith doesn't contribute significantly this year
- Hope is not lost for Hayes, Johnson, or even Rawls.
It's worth noting that a few of the guys that spent multiple seasons developing turned out to be pretty darn good players. Guys like Eddie Lacy, Venric Mark, Carlos Hyde, Kenjon Barner, and Stefphon Jefferson all spent at least a couple seasons as insignificant contributors. On the flipside of that coin, lots of the best talent contributed early: Ka'Deem Carey, Le'Veon Bell, Montee Ball, Johnathan Franklin, and Todd Gurley.
Basically, we don't need to worry if Green and Smith don't contribute this year. It's definitely a good sign if they do, but there are much better things to be concerned about (S, OG, OC, and now WR) in 2013.
Rivals is my favorite recruiting system. Not my favorite site, and not my favorite rankings (ESPN is winning that title this year), but my favorite system. In addition to stars, they have a relatively simple system for ranking recruits:
The ranking system ranks prospects on a numerical scale from 6.1-4.9.
6.1 Franchise Player; considered one of the elite prospects in the country, generally among the nation's top 25 players overall; deemed to have excellent pro potential; high-major prospect
6.0-5.8 All-American Candidate; high-major prospect; considered one of the nation's top 300 prospects; deemed to have pro potential and ability to make an impact on college team
5.7-5.5 All-Region Selection; considered among the region's top prospects and among the top 750 or so prospects in the country; high-to-mid-major prospect; deemed to have pro potential and ability to make an impact on college team
5.4-5.0 Division I prospect; considered a mid-major prospect; deemed to have limited pro potential but definite Division I prospect; may be more of a role player
4.9 Sleeper; no Rivals.com expert knew much, if anything, about this player; a prospect that only a college coach really knew about
A 6.1 player is basically top 35; 6.0 = 35-85; 5.9 = 85-160; 5.8 = 160-300.
To put it in NFL terms, a 6.1 is a 1st or early 2nd-round NFL draft pick. A 6.0 is a 2nd-3rd rounder. A 5.9 is a middle-round pick. A 5.8 is a late round or undrafted FA type. A 5.7 is a player with fringe NFL potential, a 5.6 is an NFL longshot, a 5.5 isn't going to make it. 5.4 and below are guys that are unlikely to see snaps at U-M.
Keep in mind that the standard at Michigan is high. Jeff Backus keeps a picture of a Michigan huddle on his wall. Why? Because everyone in that huddle would go on to play in the NFL. While that's not typical, the majority of our starters on both sides of the ball should at least find themselves on NFL rosters for a season or two.
That said, I have taken the Rivals Rankings and re-ranked our players according to my current expectations. This is based on the evidence I have, which is obviously flimsy for the guys that haven't played yet. It's a combination of what I've seen on the field, practice buzz, and my gut. Using Derrick Green as an example, I don't think we've seen or heard anything at this point that would suggest he is a 1st-round NFL pick (PLEASE remember that I haven't seen him play an actual down of college football yet). The flipside is that Dymonte Thomas is already showing signs of an impact player, justifying his 5.9 ranking, while Gardner appears on his way to being a solid early-round NFL draft choice.
I have ONLY ranked the players I believe are likely to contribute this season.
|James Ross III||WLB||5.8||5.9|
No, I'm not going to explain the rankings one-by-one. What I will say is that I believe our average needs to be closer to 5.83 before we are considered "elite."
Also note that the rankings should be slightly inflated. Why? Because these are the guys that are projected to contribute to our team this season. They have gone from recruits to players, and have either demonstrated performance on the field or generated significant buzz.
You'll also notice that higher-ranked players are likely to see rankings revised downward. This is part common sense, part timing: a top-ranked player has nowhere to go but down and most of our higher-ranked players are young and not yet fully-developed.
Finally, you'll notice a few grades below 5.7 in the re-rank. If we are to be an elite team, we should not have any (other than kickers) players below 5.7 pushing for playing time.
Here are the rankings, with my projected starters only:
|James Ross III||LB||5.8||5.9|
This includes a slot WR, nickel CB, KR, and extra LB (JMFR). If we're looking to be a dominant team, I think we need an average closer to 5.88.
I will revisit these rankings after the season, and perhaps once in the middle.
While everyone is busy breaking down the scrimmage film with a Jim Garrison-like passion, I thought I would sneak a little preseason preview of some concepts I have been thinking about for how to measure success on a down by down basis. If you want to avoid the nerdy details, skip down for some pretty charts.
Looking at down by down success is a tricky thing and right now there are only limited tools for how to evaluate how an offense is utilizing its most precious resource. The only mainstream tool is third down conversion percentage. This tool’s simplicity is both its weakness and a hidden strength.
Third down conversion rate does not take into consideration how hard your third downs are to convert. Two teams could have identical conversion percentages but if one team has a lot of third and shorts and the other doesn’t the team that doesn’t is accomplishing a much tougher job than the first team. That absence of context is also the hidden strength. Third down percentage isn’t a great predictor of how good your team performs on third down as much as it is an all-encompassing look at how good your team is at getting to manageable third downs and then converting them.
The newer stat that looks at all downs is the Success Rate metric, one I have been on record as not being a huge fan of. Success Rate is a more nuanced look at each down and assigns them a binary pass fail grade depending on whether they meet certain threshold criteria. A binary makes some sense on third down and more sense over the collection of downs, but there is too much opportunity for other value to come and go for the binary to be of major use.
A third way is an expected value (EV). How much value is each team adding or subtracting on given downs. This is a literal value look at ranking teams by what they are accomplishing on a given downs. I have traditionally used this metric but again, it lacks the detail of what is really going on behind the numbers. An EV look tends to lend a lot of value to big play teams and punish consistent gainers. There is evidence to support the rankings coming out that way, but again, I don’t think the numbers tell a good football story in one dimension.
The Early Downs Breakthrough
As I began digging into this I pulled all kinds of numbers looking at each of the three downs separately before it dawned on me, first and second down are really a package deal. They are the offense’s opportunity to either do something big or maximize their chances of a third down conversion, first and second downs and typically on the offense’s terms. You can only create big plays so often and even being good at getting in great third downs all the time still means you are having a lot of plays with a chance for the defense to get off of the field. 3rd and 1’s are converted 72% of the time by the offense, so if you get in three of those situations the odds are nearly two to one that you get stuffed on one of them. Being good at avoiding third downs is a better skill for an offense than getting in manageable ones (although both are obviously preferred).
So to that end, I put together three key metrics for an offense for 1st and 2nd downs:
Early Conversion %: Percent of first downs that are created prior to third down. An average team will convert at about 50% with the best offenses closing in on 60%, like the 2011 Oregon offense.
Bonus Yards: This is a big play metric. For the plays that create a conversion, how many yards beyond the sticks does the average play go. Average teams are around 6.5. Mike Leach’s 2005 Texas Tech team was one of the best ever at 9 yards beyond the stick.
Average 3rd Down Distance: The first two metrics are about the successes, historically, most football coaches are more about minimizing the negative. This metric is for them. For the 50% of the time that the average team faces a third down, how many yards are they typically facing. The average team still has 6.5 yards to go on an average third down. Last year’s Air Force team that Michigan faced was the best of the last 10 years with an average distance faced of 4.0 yards for the season.
Now that early downs have hopefully been understood a little better, it’s time to look at third down and focus on a true measure of the down itself. One option that’s sometimes used is to break down the conversion rates into yardage buckets representing short yardage, medium, etc. This isn’t the worst way to go about it, but still isn’t great. Unless its over a large portion of time, sample size problems are likely and you still potentially have problems, although much smaller now, of where do the actuals trials fall into the buckets. Too many buckets and the splits become hard differentiate, too few and there is little continuity to what you are measuring.
To try and solve these issues, here is my suggested stat:
Adjusted 3rd Down Conversion Percentage: Each third down distance has an average conversion rate that looks like this:
1 yard to go converts at 72%, 10 yards to go at 28%. If an offense converts a third and 1, they get +28% for that play. Fail and it’s –72%. Average up all the third downs for a period and you are left with a single number to reflect how a team has done on third downs, that isn’t weighted by being better at first and second down. The other nice thing is that it is naturally anchored to zero. An average team is at +0%. 2011 Wisconsin with Russell Wilson and Montee Ball was the best Big Ten third down team at +16%. 2011 Alabama was the best third down defense at –15%.
Taking all the above analysis, I pulled the results for last season and put them together in a fancy new Tableau table (click to control the view [ed-S: we know; we're working on the links]).
Circle size represents average third down distance
So, Michigan was pretty good on a down by down basis, last year. Only Clemson and A&M where better at third downs when accounting for yards to go. Michigan was also one of the best teams at avoiding third downs altogether, converting on first or second down about 54% of the time.
The other big take away from this is that there are a lot of Big Ten teams at the left hand side of the chart. It’s a bit hard to tell from this view, but Big Ten teams are some of the best at managing third down distance but some of the worst at everything else. Fully half of the teams in the conference are in the lower left quadrant of teams that are bad at both. An offense whose goal is to get into manageable 3rd downs is an offense that is set up to fail.
Michigan lands pretty average across the Big Five conference landscape in both early downs and third downs on the defensive side. The strength of Michigan State’s defense really shows up here, as they only allowed teams to convert before 3rd down about 2 out of 5 times.
I am trying to put together a package of weekly reports and rankings that I can publish online. If anyone has any thoughts as to what you want to see that aren’t otherwise available, I am open for suggestions.
I think these charts do a good job of reflecting what’s happening on a down by down basis. What they don’t show are the impact of big plays and high leverage plays like turnovers and red zone plays.
Hey hey, I’m back with the first 2013 installment of “Reading the Tea Leaves,” the now annual pseudo-scientific prediction fest that comes in bushels of three (2012 editions here, here and here; 2011 editions here, here and here). And there’s a bonus for all you fantasy nerds out there—this is the A Song of Ice and Fire edition. To avoid nerd rage related controversy, or perhaps to court it, I’ve used this handy guide for my rankings of George R. R. Martin’s 5 aSoIaF novels.
In order to predict our regular-season record, I look at the two main “on paper” factors: who we are and who we play (which including circumstances, such as where we play them). Luck, of course, also plays a role—a bad fall here means Bellomy is your QB for most of the Nebraska game; a good bounce there and you recover a fumble instead of watching them kick the game-winning field goal. In lieu of a good way to quantify luck, I then utilize a probability algorithm to predict the number of wins, which is based on the aggregate of individual game assessments. It’s not complicated, but last year it correctly predicted 8 wins. This year I use 2—a conservative version and one that rests on more moderately bullish individual game assessments. Then I move on to the various scenarios and assign probabilities to each.
Our 2013 Roster/We Who Are
As far as our team goes, there are some question marks, but I’m oddly confident. After all, our DC got former super-bust BWC and ex-walk-on Kovacs into the NFL. We’ll miss Jake Ryan early on, but we only have one marquee game before he returns. And the secondary should be significantly better than it was last year (even considering the lack of Kovacs). Though we traded in experience for more, albeit green, talent, I trust Mattison. I don’t expect us to be dominant on defense, especially early on, but I think we’ll do fine and should improve as the season progresses.
On the offensive side, it’s largely going to come down to how quickly the new starters on the line acclimate. I expect growing pains early on, but unlike 2012, those pains won’t have to come in road games against either Alabama’s terra cotta army of 4 and 5 stars or a Manti Teo-led Notre Dame defense in South Bend. By the time we roll into East Lansing, I expect Kalis and, to a lesser degree, Braden and Miller, to be playing with enough confidence and poise to offset their lack of experience. There should be a lot of continuity from the late-2012 passing offense, while I expect the switch from off-Fitz-plus-Rawls to hopefully-back-on-Fitz-plus-freshman-Green to be a major upgrade.
Schedule/Who We Play
A lot of people expect our record to moderately improve from 2012’s rough 8-4. But in terms of schedule, at least, 2011 is a better comparison. Last year we had the misfortune of playing the AP #1, 3 and 4 teams in the regular season and played none of them at home. This year we play a depleted version of last year’s #4 (Notre Dame) at home and then get #3 (Ohio) at home as well. Oh, and did I mention that this year we trade Alabama for UCONN? Or that we nearly beat both Notre Dame and Ohio on the road last year?
The road games that bother me are Sparty, Penn State and Northwestern. Under Hoke we’ve been lights out at home but a little iffy on the road, so each of these represents a heightened challenge compared to last year (when they were at home). It’s always hard to win in East Lansing, especially in the Dantonio era, and their defense will be good. On the other hand, I still don’t see where the offensive yards will come from. At Penn State is at Penn State, even with all the question marks. And as far as Northwestern goes, well, we’ll see. But they played us close at home last year and their fast-paced spread offense might be the most likely of our opponents to take advantage of our defensive inexperience. That said, Northwestern is essentially Sparty’s inverse—as such, I don’t see how they’ll stop our offense.
Nebraska, oddly, doesn’t really bother me. Sure they’ve got some offensive firepower, but their defense looks to be even worse than Northwestern’s. Add to that the fact that we only lost last year’s game in Lincoln because Denard went out with an injury, as well as the fact that his year we get them at home. Where we are nearly unbeatable. So yeah, not scared.
As for the rest, well, there’s a modicum of trap potential in Iowa away, but all I see are Ws beyond that.
For the algorithm, which correctly predicted 8 wins last year, I’m assigning 1.00 for “guaranteed win,” 0.75 for “likely win,” 0.67 for “likely-but win” and 0.50 for tossup. Then it reverses down to 0.00. Because of our schedule, there aren’t any games below the tossup threshold this year. I’m doing two versions, one conservative and another bullish.
The conservative estimate outputs that as: 1) 5 guaranteed wins (CMU, Akron, @UCONN, Minnesota, Indiana); 2) 2 likely ones (Nebraska, @Iowa); 3) a likely-but win against Notre Dame at home plus; 4) 3 tossups courtesy of the road (@Sparty, @PSU, @Northwestern); and 4) another tossup against Ohio, courtesy of the fact that we get them at home.
5(1.00) + 2(0.75) + 0.67 + 4(0.50) = 9.17 wins
The more bullish version suggests: 1) 6 guaranteed wins (CMU, Akron, @UCONN, Minnesota, Indiana, @Iowa); 2) 2 likely ones (Notre Dame, Nebraska); 3) 2 likely-but, the “but” coming courtesy of the road (@PSU, @Northwestern; 4) a tossup for Sparty away; and 4) a genuinely scary Ohio, but at home.
6(1.00) + 2(0.75) + 2(0.67) + 2(0.50) = 9.84 wins
The math, then, predicts a range of 9-10 wins. However, variance being variance, there are a range of scenarios that are relatively more or less plausible. So without further ado…
1. A Clash of Kings.
Scenario: Non-stop action and death dealing! Our offensive line grows up quickly, and the move from experience to talent proves fundamental to a revitalized ground game, while Devin Gardner gets enough pass protection to tear up the Big 10’s mostly mediocre defenses. Meanwhile, we hold serve on run defense and even improve against the pass, which is enough to stymie the few good offenses we face. We stare down an invasion from
Stannis Baratheon Urban Meyer and repel him with our wildfire defense and an epic flanking movement passing offense.
Record: 12-0. We run the table and get to the Big 10 Championship Game, where we probably face Ohio for the second time in a week. A BCS bowl is a lock.
Probability: P = .05. Essentially, this would be our equivalent of what Notre Dame did last year, and would require a similar amount of luck and collapsing of the once-scary opponents (in our case Ohio and Sparty, in their case Oklahoma and USC). The Clash of Kings scenario is more likely than running the table was in 2012, but still not exactly likely. Ohio is going to be good, and though we can certainly beat them, Sparty is always fired up against us and especially when playing at home. Plus there’s uncertainty tied to the rest of the road games—are we talented, experienced and lucky enough to not blow any of them and still beat all the rivals? Maybe, but probably not.
2. A Storm of Swords
Scenario: We go red wedding on the Big 10 but get caught with our pants down in the toilet at one inopportune moment. Everything else from scenario #1 still applies.
Record: 11-1. We either run the table up to The Game or beat Ohio and lose to one of the other likely candidates. We probably get a Big 10 Championship Game out of it; either way we still get our best regular season since 1997.
Probability: P = .15. Okay, now we’re talking plausible-ish! Of course, all the disclaimers for scenario #1 apply here as well, with the caveat that we’re allowed our one bad day. That automatically makes it more likely, as even Alabama has had that over the past two years. Unfortunately, I see too many question marks on the roster to really get behind this scenario: an inexperienced interior O-line, no clear sense of whether we’ll get a pass rush, questions of whether Countess, Fitz and Ryan can return to form after rehabbing from serious injuries, etc. While I do expect these things to turn out well, when the entirety of the season is considered, they may not manifest positively in each and every game.
3. A Game of Thrones
Scenario: Taut. Gripping. Tantalizing yet never delivering that crucial victory. We are generally awesome, and kick some ass in the
Whispering Wood The Game/Conquest of the Juggalos, but run into a few roadblocks on the way.
Record: 10-2. Likely losses = 1 of Sparty/Ohio and 1 more from your “tossups,” “likely-buts” and ND. Whether we win the Legends Division in its final year depends on whom we lose to and how they do over the course of the season. Just like it did in 2011.
Probability: P = .30. Though the rational part of my brain is a bit more conservative, the enthusiastic, emotional fan part feels as if this is the way things will play out. It just keeps repeating “schedule, schedule, the schedule is faaaavorable” until I believe it’s more true than “roster, roster, the roster is inexpeeerienced.”
4. A Feast for Crows
Scenario: A mostly enjoyable ride that ultimately doesn’t live up to hopes and expectations.
Record: 9-3. I’d guess this means we lose ¾ out of the “likely-but” and “tossup” games. An early loss to ND (considering we don’t have Ryan and will be working out experience issues on the O-line) is not out of the realm of possibility either.
Probability: P = .35. Unfortunately, but not too unfortunately, the math suggests this is the most likely scenario, slightly beating out the more palatable 10-2 (since both of the estimates produce predicted win totals under 10). It would still constitute a bit of progress from 2012, though. That’s good. But it will probably produce a cavalcade of obnoxious “I told you so” columns from everyone’s “favorite” Freep columnist that evince a total disregard for logic and rationality. That’s bad.
5. A Dance with Dragons
Scenario: Where are we going? Why is this
Quentyn Martell section [insert player] injury rehab taking so long to resolve? Why is this Jon Snow/Danaerys storyline offense so boring and listless?
Record: 8-4 or lower. Things just don’t go as planned. Maybe that’s due to an injury, or maybe something just doesn’t work on offense and we don’t have Denard to bail us out with his legs.
Probability: .15. Last year we went 8-4 in the regular season, having played eventual national champion Alabama (away), eventual runner-up Notre Dame (away), eventual undefeated Ohio (away) and a decent-ish Nebraska team (away) after losing Denard and not, apparently, wanting to put Devin in. The idea that we’ll do the same or worse when there’s no Alabama, a crappier Notre Dame at home, Nebraska at home and Ohio at home strikes me as unlikely. But it isn’t impossible to imagine either, especially considering our lack of depth at key positions *cough* quarterback *cough*.
Prediction is tricky business, because there's only so much you can know before you're in the moment. I've tried to factor that uncertainty into this analysis, but there's no guarantee I've done it well enough. Plus there's the fact that preseason predictions almost inevitably overvalue the previous season's results--hence why I underestimated our record in 2011 and overestimated it in 2012. But the algorithm was on the money last year, so this year I've tried to hew more closely to it and rely less on intuition. That said, it still rests on a foundation of subjective assessment, so feel free to point out where I've made mistakes in my estimations. And, of course, feel free to disagree with the aSoIaF rankings as well!