also duty-free guys falling over and grabbing their shins
[Author note: This thing is long and pretty technical. That said, I think there will be sufficient payoff and value for you the reader. Still, be ye warned.]
Have you ever wished there were a convenient way to rate rushers the same way we rate passers? Sure, passer rating has its weaknesses—all mathematical formulas do—but despite it's issues, I've come to appreciate passer rating as a very useful framework to evaluate a player/team when it comes to passing the ball. In the same way that finding a corner piece to a jigsaw puzzle helps you figure out it's entire quadrant, once you have an idea of what to expect from the passing game you can leap to other touchstones to determine what to expect from the running game. A rusher rating would be just the sort of touchstone needed to really start messing around for those of us who are so inclined. This diary lays out what I think should work for these purposes.
To recap some of my previous work: passer rating combines four important factors—completion percentage, yards per attempt, interception rate, and touchdown rate—and blends them into one number. For rushing stats, important information for coming up with an analogous metric has been hard to come by until cfbstats.com came along. Tons of fascinating and useful data, for free. God bless the internet.
To come up with the rating, I looked only at positions that would be considered normal rushers (QB, RB, TB, FB, HB, SB, WR) that have an average YPC greater than zero. If you can’t meet those criterion, then you cant represent a normal rusher, thus sayeth the me. Other positions register rushing attempts but allowing the odd rush by a punter to color your view of what normal looks like would be dumb. See the chart below for more information. Also, if a guy averages negative YPC, uh, find something else to do, kthx. Other than that, no other filter was applied but some math wonk tricks were and I’ll talk about those as we go.
Completion Percentage → Gain Percentage : Parsed play by play is necessary to generate a replacement for completion percentage. I opted to go for Gain Percentage: the percentage of attempts that resulted in more than zero yards. I figured the basic goal of a pass is to complete it (brilliant insight, I know) and the basic goal on a rush attempt is to gain positive yardage so…any gain of more than zero yards is mission accomplished. This parameter is as much about team skill as it is about player skill but the same can be said for Completion Percentage.
Interception Rate → Fumble Rate: The direct analogue would of course be fumbles lost per attempt but that’s not the right way to do it IMO. The luck factor that influences whether or not the team actually loses possession has nothing to do with the fact that bringing possession into question is a terrible idea. So, all fumbles whether lost or not are counted in the calculation.
There is also a bit of mathematical wonkiness deployed as well. Mike Hart is famous—at least around here—for his deftness at protecting the rock. It was awesome: 991 carries, 5 loose balls, 3 losses of possession. That was an aiight career, but these guys were kinda, sorta, maybe, better (!) at protecting the rock:
|Jacquizz Rodgers||Oregon State||789||1|
OK, so the wonkiness…a lot of people who register meaningful rushing attempts do so at a pretty low level of opportunity. Even stud RBs often split carries with other backs: Eddie Lacy siphoned off carries from Mark Ingram before becoming the man, and T.J Yeldon did the same to Eddie Lacie. So in order for fumbles to make sense for players that get meaningful carries in low doses, we need to consider the question: at which point does a low fumble rate cross the threshold from wait-and-see to holy-crap-check-that-dude-for-stickem?
What we have here is a chart comparing the observed percentage (red dots) and the mathematical probability (blue line) that a player will have at least 1 fumble versus the number of carries he has registered. The red dots are binned in increments of 1 so the sample sizes out past 150 are pretty thin but if bigger bins are used, you’d see a scatter of points that more closely follow the mathematical fit, because… math. The blue line was derived using logistic regression.
The weirdness at zero for the mathematical expectation might be concerning as it suggests that there’s a 20% chance you’ve fumbled despite not having a single carry to your credit. However, that is just an artifact of the data. It is possible to fumble on your one and only carry as actual observations show. What the math does, though, is it considers the sample size of the observations and then finds the best fit possible to the overall dataset. There are ways of dealing with that issue, but…I rather talk about football. Also, KISS. This is good enough for my intended purpose.
Anyway, the point of doing all that is it allows me to apply what I’ll call the Phantom Protocol. Basically, I take that curve, subtract it from 1, and add the resulting value to the player’s fumble total. As the number of carries increases, the effect of the phantom fumble recedes thus leveling the playing field and letting us evaluate players with low sample size as best we can. The result of this bit of data manipulation is that a guy with no fumbles in 16 carries is assigned an average fumble rate and by the time 100 carries are registered, the penalty is not perceivable. Below 16 carries, the assigned penalty is pretty stiff but this trick levels the playing field to let us look at guys with few carries and not just dismiss them with the low sample size red card. Sure, 16 carries is still a low sample but at least the rating self corrects for the fact that fumbles take time to manifest.
Most importantly though, the protocol adequately acknowledges players with low fumble rates even though they have a lot of carries. It’s easier to have a 1% fumble rate after 100 carries than it is to have the same rate after 789 carries. That said, after a while the fumble rates should be allowed to speak for themselves. Quizz Rodgers and Mike Hart need their proper allocation of DAP; nothing more, nothing less. I think the ghost protocol concept accomplishes exactly that.
Touchdown Rate: This one is also directly analogous but here again I’ve deployed the ghost protocol to credit guys with low sample the expectation of an eventual TD. TDs come about much more freely than fumbles do with goal line attempts and the like so this credit vanishes very quickly. But fair is fair: the protocol giveth and it taketh away.
Those are the components directly analogous to the ones used in passer rating and these would be enough to go about the business at hand. However, whereas a passer’s job is to get the ball into the hands of a play maker, players that are given the ball whether by pass of handoff are called upon to be the playmaker. Certainly the scheme, play call RPS, and execution of the supporting cast all have major influence on the results of a play but the ball carrier can do things that elevate the call from good to great. I wanted to be all formal-like and call this the Impact Run Rate but this [stuff] is s’posed to be fun, man. Hence—
Another Dimension: the Dilithium Quotient
The 20 yard threshold is usually referenced as registering a play as a big play. That would certainly qualify as a big play by any standard but that threshold seems to have been established somewhat arbitrarily in my opinion. On average, a generic runner on a generic team in a generic game gains about 4 yards per attempt with a standard deviation of about 7.5. Its called the standard deviation for a reason as a huge swath of observations (about 2/3rds) occur within 1 SD of the mean, or between –3 and +11 (remember: discrete data). The other 1/3 of observations get split evenly with 1/6 below -3 yards and 1/6 above 12. I’ve used objective criterion, you know, math, to define Impact Runs as those that register 12 yards or more. To register one of these the player’s entire team has to execute the play correctly, then the carrier he has to do something special (i.e. juke a dude, break a tackle, be fast). This is the real life manifestation of the Madden Circle Button and its informative. It’s the difference between Barry Sanders and Emmitt Smith.
Denard Robinson was great at this but it might be surprising to hear that he wasn’t the best. Percy Harvin in the spread option was ridiculous in this category. Percy had touched the ball a lot when he was a Gator and 27% of the time, he darted for an impact run. By Contrast, Denard’s DQ% was ‘only’ about 15%. Could you imagine Denard breaking loose almost twice as often? Of course, the scheme, the team’s execution of the scheme, and the player’s deployment within the scheme has a lot to do with this number. Florida circa Percy Harvin was galaxies away from Michigan circa Denard Robinson. Percy Harvin was the 3rd rushing option in Florida’s spread and shred, Denard Robinson was options 1-10. Also, being the QB in the spread-option means you are concern #1 for defenses: the cornerstone. That was triply the case when facing Michigan with Denard in the captain’s chair. Harvin was usually one-on-one with a guy 10 times slower than he was who was also probably pooping his pants.
Denard’s DQ% was pretty stable around 15% (scheme be damned) but his utility rate (723 career carries) was second to none save minor conference QBs. His closest proxy Pat White (684 career carries) broke loose at a 19% clip in RichRod’s Scheme. However, the Big EEEast sans Miami and Virginia Tech wasn’t quite the Big TEEEN. Denard went up against stout defenses way more often than Pat White did and did so without the benefit of Steve Slaton or Noel Devine and the benefit of a revolutionary offensive scheme. When Pat White lost RichRod is DQ% dropped to under 12%, Denard didn’t bat an eye. Everyone *knew* they had to stop Denard and only him on *every play* and they still had their hands full trying to actually do it. The fact that Michigan could never position itself for him to win the Heisman trophy will always be one of my sports fan laments. For ever and ever and ever. He better get a Legends Jersey or I’m qui’in’. I don't care if that’s silly. You’re silly. Where’s my bourbon?
Blending It All Together
Passer Rating was developed such that an average QB would end up with a rating of 100 according to the data set that was used to develop it, which was gathered two maybe three football eras ago when linemen couldn’t really block and scholarship limits weren’t so much. I’m not sure how they went about the process of pinning the rating to average==100 and I don’t have the data to try an replicate the results…so, I kinda, sorta, you know, pulled something outta my [hat]. That is to say: I did what I think is correct or at least valid. I normalized each parameter by it’s par value, summed them together, then forced resulting rating to equal 100. Ultimately the 100 thing is completely arbitrary, but negative numbers are weird, I guess. All said, a rating of 100 means the player was a solid runner but not special, below that you wonder if he should be running at all.
Where in the World is
Carmen San Diego Mario Mendoza
Now that we have a calibrated formula its time to get down to business, application. I calibrated the rating so that 100 was a normal guy, but to figuring out what par should be is a little more complicated. I mentioned earlier that if you cant get to a rating of 100 I don't think you should be a primary running option and I also think we should only look at primary running options to establish our benchmark. But being a primary running option means different things depending on where you’re lining up.
When trying to crack a nut like this I often find that the data itself will help you figure out where to chop it. In the chart below I have plotted Average Rating vs. Amount of Carries. Obviously, the better runner you are, the more carries you should see but runners that are REALLY good are few and far between…this chart shows that dichotomy very nicely. I like to look for population gaps and/or inflection points in a performance curve. Those usually a good places to drop an anchor as far as I’m concerned. When they are near each other it’s a dead giveaway. Based on the data itself I’m using 115 for RBs, 70 for QBs, and 120 for WR as performance benchmarks.
So, this is all well and good but the real test is whether or not things make sense. Here the values for the B1G in 2013:
|Team Name||Player Name||RB Rat||Attempt||Yds/ATT||TD%||FMB%||Gain%||Dillitium%|
This generally looks pretty reasonable to me in terms of an overall ranking as well as a relative ranking. The players/team you’d expect to be at the top and bottom of the list are where they are supposed to be. If anything I’d criticize the Mendoza line at 115 given how we all feel about Michigan’s running game last year. Maybe 115 is just the threshold of suicide and 130 or better is what we fans really want from our teams. But, even this jibes with what I think.
As with passer rating, this rating depends on player skill, surrounding support, and offensive scheme. Toussaint’s YPC and Gain%—components heavily influenced by surrounding support (i.e. the O-Line)—are way under par. So is his Dilitium % which is a skill/talent/speed thing but the dude had a bum knee and he’s not that far off of par there. Makes sense. So, he hit the Mendoza line even though he had bad support in front of him, sorta like Gardner. These numbers make sense to me.
Re: Smith Vs. Green
I mentioned in my last diary that it was interesting to hear grumblings about De'Veon Smith being ahead/competitive with Derrick Green because I think the numbers bear this out. Check this out:
|Player Name||Att||TD||Fum||Gain %||Yds/ATT||TD%||Fum%||DIL%||RB Rat|
These guys played with the same support and in the same system so the differentiators on display here are essentially Skill and Opportunity. Neither Green nor Smith actually registered a fumble but the Ghost Protocol affect Smith’s rating more because he has far fewer carries. Indeed, Smith’s rating is also bolstered by a phantom touchdown, but this effect dissipates faster because TDs occur more frequently. So the math is screwing Smith over here a bit. Meanwhile, Smith’s Gain % and YPC (hitting the right hole at the right time) and DIL% (juking, speed, whatever) were the highest on the team last season. Yep, Small samples yadda yadda. Just sayin’.
Anyway, that's a lot of words and I hope this was worth the read. Of course, I will be referring to this information in future diaries. Thanks for reading and let please provide and criticisms or comments you might have in, uh, the comments section.
I can't see where you’re comin' from / but I know just what you’re runnin' from / And what matters ain't the who's baddest / but the ones who stop you fallin' from your ladder.
For a little over four years now I’ve had a summer time hobby of trying to predict plausible performance levels from various QBs for the upcoming football season. I have tried to root these projections as deeply into the bedrock of reality as is possible for a figment of one’s imagination and at this point there is a codex of sorts in the diary archives describing my methods. It’s fun to go back and see what worked and learn from what didn’t. There’s something there, man.
For Devin Gardner 2013 I laid out two stat lines hinging on two sets of assumptions—a reasonable/prudent set, and a ‘sexy’ set. The reasonable prediction: Gardner would complete 225 of 360 passes for 2900 yards, 23 TDs, and 10 INTs. In reality he went 208 of 345 for 2960 yards, 21 TDs, and 11 INTs. There’s a HEAVY dose a good fortune involved there but, hot damn, that’s pretty good. The assumptions here were basically looking at only QB stats and nothing else Devin had shown enough in his 5 QB starts during the 2012 season to perform at the “seasoned veteran QB” level which I think of as an incumbent with 2 years of experience in tow. That's a brutal benchmark, IMO but that's what I measure guys up against. That's what we want them to be.
Anyway, the sexy set of assumptions were:
- Devin has elite talent. I believe this one held. More on that later.
- The O-line would be fine despite the possibility of being “a touch weaker than last year (2012).” Eh boy…
- The offensive scheme would be well tailored to Gardner’s skill set and that of the support around him. This was sometimes true but not consistently often enough for Borges to keep his job.
Ok, so the necessary assumptions for DG to be the second coming of Vince Young vanished into the ether. But those last two assumptions about the support and scheme are really kind of baked into the reasonable prediction too. For my money, the fact that DG put up the numbers he was able to in spite of the glaring flaws of the team is a testament to just how good he can be if the conditions are reasonable.
The fact that there are so many straight-faced questions being asked about Devin Gardner’s incumbency status is ludicrous. Sure, numbers don’t tell the whole story but they tell a good part of it. DG went from being one of the darlings of the 2013 Manning Passing Academy to needing to prove his talent simply because he couldn't compensate for all of the flaws around him last season. He did as well as could reasonably be expected without adjusting for other very real headwinds.
[After THE JUMP: Gardner under the microscope.]
I don’t practice Santeria. I ain’t got no crystal ball. I had a million dollars but I … I “spent” it all.
In an obscure part of Jim Mora's famous playoffs(?!?) presser, he gave the sports world the skinny on turnovers: "I don't care who you play--whether it be a high school team, a junior college team, a college team, much less an NFL team --when you turn the ball over 5 times...you ain't gon' beat anybody I just talked about. Anybody.” We all understand this via basic football intuition (ahem) but, stick around if you care to see if we can stick a number on that intuition.
Plenty of previous work on the subject has been done by many folks including myself. Football Study Hall recently conducted a study in similar fashion to how I’ve done it in the Blue Moon stuff and estimated the effect of per game turnover margin on season win percentage. FSH’s look lines up with the BMM, both suggesting that the gain on Season Win Percentage for per game Net TOM is about 100 basis points. The effect on overall record is useful but when watching a singular football game we’re not thinking about the whole season; we’re only thinking about the next few hours or so. How do the turnovers within a game affect the outcome of that specific game? To answer the question we’ll have to use math skills that go beyond grouping, counting, and arithmetic.
Soulja Boy Huey Lewis MC Hammer. Wha?
To answer the question at hand you need special math. In this situation you need to estimate probabilities because the outcome of a single football game is categorical (specifically binary) rather than discrete as in the case of full season wins. Herm Edwards gets it: “This is what’s great about sports …you play to win the game. [/Pitch Perfect Cumong, Man Glare]. Hellooo? You play, to win, The Game.” The point of sports is to beat Ohio State. Herm gets it. /Michigan orthodoxy
The special math is called Logistic Regression. It’s still a kind of linear regression but that regression is run through what is known as a link function to deal with the binary nature of the thing being modeled. This is done in all kinds of technical fields but for sports, um, investors this is a particularly nifty trick to have stashed next to your rabbit’s foot. The data for the model comes from NCAA.org as always. Sorry, no coefficients this time but I’ll show you a—
Here’s a useful way to think about this chart: suppose we were to play a Sunday morning game where I told you a team’s Final Turnover Margin and you had to tell me if they won the game or not—what would the payout odds need to look like for you to break even? This chart is the first step in answering that question.
Several features on this chart stand out to lend intuitive validity to the model. First there is neutral win probability at neutral TOM. Second, negative TOM hurts your odds, positive TOM helps them. Third, there are diminishing returns. By the time you get to +/- 3 in final TOM, the next turn over for/against you doesn’t affect win probability that much.
*DO NOT MISS THAT LINK. Grab a drink because its MC Hammer’s 15 minute (yezzir!) extended length 2 Legit 2 Quit video. The word epic gets tossed around a lot these days but it’s the only appropriate word to use here. BiSB, you’d dig it the most. It’s like a mockumentary / old school kung fu movie / ridiculous dance video. The hairdos, man. And the cameos: Marky Mark, EAZY E(!!!), Queen Latifah, Milli Vanilli, James M--F--in Brown in full regalia with full on wizard abilities, Hammer's Wang, Jose Canseco, Isaiah Thomas, Kirby Pucket, Jerry Rice, Ricky Henderson, Deion Sanders, Andre Rison, Roger Clemens, Roger Craig, Ronnie Lott, , and Jerry Glanville. And that’s not all of them. Epic, man. Epic.
What’s Wrong, McFly?
Here’s the rub though, actually there are two rubs. First, that curve represents a generic team facing a generic opponent and neither of these things actually exist. I’ve used this example before but its worth a reprise: the generic US household has something like 2.4 children in it, but show me a household with 2.4 kids in it and I’ll show you a crime scene. Real football games are played by real football teams and they’re not all created equal. That curve shifts and bends according to the strengths of the teams in the contest. For reference, the math says “Nick Saban’s Alabama” can survive a –3 TOM against the nameless faceless generic team before it’s a coin flip situation. Let that sink in for a minute. Personally, I think that might be an underestimate.
So what’s the second rub? It’s related to the first one, actually. Here’s where our man Marty McFly comes in. I broke a major rule of predictive analytics to create this chart, I gave the model knowledge of the future. That’s a no-no for models that are supposed to be predictive because, duh. Don't give me that look, I told you I was a sinner last time. Deal with it. In addition to Final TOM and Game Outcome, I fed the model an end of season strength rating as well.
That disclosure may spawn some skeptics and I welcome thoughtful discourse, but allow me to explain myself before you tar and feather me. I think we’re OK to do this for the specific goals at hand. Remember, the goal here isn’t to create a predictive model, it is to estimate as closely as possible the impact of Final Turnover Margin on Win probability. On the chart shown previously, you can’t make an evenly matched game a toss-up unless you know for certain that the teams are evenly matched, right? The final strength ratings serve as a discount mechanism to let the computer know “look man, we’re talking about Oregon vs. Colorado here…the Buffs are going to need a lot of help to have ANY shot.”
Here’s another and more specific example from the past but closer to home: Michigan vs. Toledo 2008. Going into the game, Michigan was a 17 point favorite. “This is Michigan vs. Toledo, fergodsakes.” Um, no, Biff, it was Michigan **2008** vs. Toldeo. If you had read the almanac you would know that Michigan 2008 couldn’t lay points on anybody. Why the hell did you risk your existence in space-time if you weren’t even going to read the damn thing?
(I can’t do Jim and Herm and not do Denny. Its the rules).
So, now that we know what that chart is and what it cannot be, what does it tell us? Well, it says that turnovers are kind of a big deal, bro. How big a deal? The first extra possession is worth 16% in Win Probability. Basically, you’d need 2:1 odds in our little game to bet against the team with +1 TOM at the end of their game . In fact, in the generic case, its a simple equation: y = 2^x.
Sans The KNOWLEDGE, we would significantly under estimate the required odds by an increasing amount with each step away from neutral. Yes, I did the math the right way too, don’t worry about it, it’s irrelevant.
News Flash: we lack The KNOWLEDGE at several junctures. First the curve needs to be adjusted according to the true strength of both teams. You wont know how good each team actually is until they are done with their schedule—and maybe not even then—so, you’ll always have an error in your estimation for one or both teams. That error is lethal over the long run.
Second, and this is a biggie, you can’t consistently predict Final TOM. Both teams are in active competition to cause and avoid turnovers. Sure, if there’s a significant mismatch between the two teams, then you might be able to get a good guess in. But then, the end effect of turnovers go down as the rating gap increases so…well, let it suffice to say that there’s an error which is convoluted within an error.
Taking Destiny by the Bit
[Author note: this bit requires further discussion, please share your thoughts.]
Before I wrap this up, I need to talk about one more thing. TOM is one of those things, man. It’s out of your control. Try as they may, the defense can not expect to to get turnovers. They can try to provide the conditions necessary for turnovers to occur but they cannot make them happen. If a QB makes good decisions, no interceptions. If the ball carriers are Mike Harty, no fumble opportunities. Even if they aren't Mike Harty, you *might* be able to force fumble opportunities but you can’t guarantee a fumble recovery. You can try as hard as you can and still come up empty.
The offense however…seems like the offense can expect to not ever turn the ball over. Don't throw a pick, don’t drop a live ball, out scrap a guy for a loose live ball if you do lose your mind and drop it. You have agency in those things even if your opponent is trying as hard as they can.
So, screw TOM. Put the onus on the offense to not turn the ball over and then see what happens…Let me show you another—
I think this chart is astounding. Basically, it says that a generic team can cough the ball up twice to a par competitor and not hurt it’s win probability in any significant way. Eliminate turnovers completely (again, generic on generic) and you can lay 3:1. Cough it up once and lay 3:2 (ish). Actually, what this really says is that the typical team gives up two turnovers in a game against an equally matched opponent.
Interceptions are the Worst
This is bogue to QBs but the data don't lie:
Again this curve shifts and flexes depending on several factors but that’s the generic shape right there.If you’re up against a par opponent, your QB is “allowed” 1 mistake before he puts the team in a bad spot. Generic-vs-generic, the team that throws no INTs, wins 75% of the time. Which team will do that? What if they both do that?
Absent from this analysis is the timing of the turnover which is of course critical to its specific effect on the outcome of a game (Anthony Thomas fumble v. Northwestern). If that’s what you’re interested in, The Mathlete is your man.
I write this often because its important to remember: football is not a math test. Your game thesis could be dead to rights down to the weather forecast and you’ll still feel the break, feel the break, feeeel the break (/Santeria) very often. Often the decision comes down to believing in things you don't understand and/or can’t necessarily prove—not guilty and innocent are different things. Failure to reject the null hypothesis is not rejection of the alternate hypothesis. The rooting interest often defies logic and reasoning but that's what makes it so damn entertaining to have.
Welcome back to football.
I am a sinner who’s probably gonna sin again. Lord, forgive me. Lord forgive me things I don’t understand.
I confess: I care about the national championship more than orthodox Michigan dogma allows by, like, a lot. I think I understand what Bo may have meant; 1973 must have been a bitter pill to swallow. The split baby in ‘97 was crap, too. Actually bringing home the hardware often requires a bunch of rhetoric just to get the opportunity. Less so recently than in Bo’s day but there still some arguing that is needed. And, If people still need to be convinced with words that you’re worthy after all the games are over, how good are you really? Relying on the scruples of seemingly unscrupulous individuals is a bad idea so a “to hell with them” mentality is totally understandable. I believe that Coach Schembechler would have won one if he cared to do so. Define your own worth and all that but there is another way, isn’t there? Make it your goal to kick everyone’s ass and you’ll accomplish all viable goals anyway. Am I wrong? /WalterSobchack.
Times they are a changin’, amen, but I think the current system actually benefitted Michigan (and others) in many “just ‘cause” ways. For too many teams winning all of your games, though difficult and unlikely, still isn't enough to get a shot at a national title. Teams in the Hegemony don’t have to worry about that and can often afford to lose a game if its to the right opponent. The playoff system improves the situation but I suspect that there will still be some jostling to get a golden ticket. What does a team need to do to maximize its control of its own destiny?
To the Stat Cave!
The preceding chart is the basis of the revised Blue Moon Model, discussed and applied in my last diary. Hollow red circles (right axis) represent how many teams fell into each bin used in the lumping process. Scan this dusty diary for discussion of the lumping maneuver. The solid blue diamonds is the average win percentage (left axis) for each bin. See how the blue diamonds start getting out of line when the binned sample size drops into the muck? That’s the “low sample size” criticism we are all so familiar with. But note that it doesn’t take that many samples to get a reasonably well behaved average—getting 10 is usually enough but more is always better. The hollow blue triangles are the focus of this diary. Those are all of the teams that have played in the BCS title game since 2000. In terms of Net Yardage Differential, there is quite a spread ranging from 45 to 275 ypg.
In pre-school most of us learned that a good way to identify differences is to ask ourselves “which of these things are not like the others?” We also learned to mock the weirdoes because why else would you go through the trouble of identifying them in the first place? The preceding charts make the MNC and B1G contending weirdoes stand out pretty well. Go forth and mock them.
Championship game losers are included because they had destiny by the bit. The mountains meet the sea at least 130. The typical team showed up with a yardage differential of about 165.
In the B1G, the Mendoza line is 100 ypg in yardage differential. What’s interesting to me is that it looks like there was a sea change in the B1G around 2005 with most of the teams underwater showing up before then. The only Michigan team that could pass muster as a national champion is the 2003 squad. Season summary:
- Lost @ Oregon by 4 with a net turnover margin of –3; ‘nuff said.
- Lost @ Iowa by 3 with a net turnover margin of 0 but: punted from the Iowa 35 in the first quarter, failed to score a TD after 1st @ Goal from the Iowa 8 in the second quarter, had a punt blocked in the third quarter leading to a 3 and out FG for the Hawkeyes.
Ah, memories / light the corners of my mind / misty watercolor memories / of the way we were … Memories / may be beautiful an yet / what’s too painful to remember / we simply choose to forget. Tell ‘em, Babs.
With the playoff system coming winning a National Championship is now harder for most. By most I mean everybody except for Nick Saban. Saban built a monster at LSU, handed off to Les Miles to terrorize for a while until he could get all three rings installed for his clown show, then came back to build an even badder monster at Alabama. Now Saban can be ranked as low as 5 and have the rhetorical juice to jump some teams. If he gets in, chances are, he will win. ‘Sall good though, dude is mortal…right? Um…maybe Kirby Smart is the real master mind??? Oh shit, do they have a succession plan in place?!? Madre de dios!!!
(Sorry about that. I actually think I’m doing well this time.)
Sure, Justice will be served more often because being undefeated and winning 1 game you have 6 weeks to prepare for aren’t enough to claim the throne anymore; now you have to beat two more teams that are pretty damn good and doing that is, like, hard. I support the cause for sure, but I recognize how much steeper that mountain is about to get. But, but, but, over signing! Look, man, you can’t blame a shark for being the baddest mofo in the ocean; you’ve got to blame the ocean. The whole over signing phenomenon that we’re seeing in the SEC may be distasteful to some but so are fried insects. I’ll walk this back: scout’s honor.
That’s a real picture of what is considered yummy street food in Cambodia.Also, I once ate Chapulines on a business trip to Mexico because I knew I wouldn’t die and it was a reeeally nice restaurant and I didn’t order it and “we’re closing this deal one way or the other, fellas” and “Yes, I’m this crazy, ese”. Also: tequila and yolo. Chapulines are fried grasshoppers and they don’t taste bad (wouldn’t say they taste good either) but its the texture and mouth feel that get you. Crunchy, gritty, all the moisture in your mouth runs for the hills…just all around nasty. I stayed cool and chewed them like a man but at a certain point I was all like “fuck this I’m swallowing” just to get that shit out of my mouth in the least ridiculous way possible and, after all, I had done this to my self. The legs, man, the legs. Those tiny little barbs aren’t meant for swallowing. Don’t judge me, man, I have lived. Thank God for Dos Equis.
Football, yes. Check it out: obviously, over signing isn’t against the rules. We don’t have to like it and we don't have to do it, but if you can choke it down -- or maybe you think its yummy with barbeque sauce -- NCAA enforcement says “bon appetit. Besides, top notch recruiting isn’t enough -- we know that first hand here at Michigan (also see Les Miles) -- you need to turn those recruits into great players and support them with great game plans and great play calling and great-never-ever-punting-on-the-opponent’s-35. Take away over signing from Alabama and Saban & Co will still kick everyone’s ass, probably.
To be a team the can get to the playoff and win the damn thing -- to be a champion’s champion -- you need to be “free to roam the plains, your majestic rippling muscles trampling over mascots that dare oppose you” as Brian once said. You must leave a barbaric path of destruction in your wake. You must leave only the lamentations of their bloggers to tell your tale. Being above 130 in NYDS since 2000 has meant you were, on average, in the top 8 nationally in that category.
There is a limit to how good your defense can be: the number 1 team in NYDS averages out with a Defensive YPG of about 270. The best team in the country in DYDS averages 230 ypg allowed. The best defense I have on record is Alabama 2011 at 183 (jeepers). Seriously, there are typically 13-ish possessions per game so you could score at TD every time and allow 107 ypg and still not get cored on. I guess you could try for onside kicks too but … what an asshole! The typical team “worthy” of a national championship has a defense that yields 315 ypg.
If you read my last entry, you should be encouraged to see that championship defense isn’t much better than what Brady Hoke and Greg Mattison have been able to field since their arrival on campus, even with all those roster issues we’ve come to know so well. Today, the roster is better and improving. It’s not easy to have great defense, but these guys know what they’re doing.
The mantra of defense wins championships isn’t [baloney], it’s just not enough. See, defensive performance is so hard to predict. With offense you have a reasonable shot at summing up the parts to get to OYDS. No such luck on defense. Proxy analysis can give us an idea of how good our defense could be but you will never know what you’ve got until you see it perform.
Football is not a math test.
I think I can see how Michigan can get to 100 NYPG this year….130 isn’t that far away.
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.
I did this in 2010 with some useful results and ideas. Let’s see if I can do it again. If you find yourself seeking more information on some of the concepts I’m taking for granted please refer to some of my previous entries (White Rainbow , QB Metamorphosis) or ask a question in the comments.
Here are some basic commonplaces:
- A passer rating of 140 is the standard for a skilled and mature college quarterback on a good team in terms of passing results. These things aren’t usually coincident.
- Quarterbacks get better with age and experience and usually max out their potential by year 3 as starter.
- A football offense is a complicated system of which QB skill is only one component. A QB’s performance as a passer will be influenced by the quality (talent and experience) of the players around him as well as the quality of the system (scheme, coaches) he plays in.
- I perform these assessments with an assumption of individual improvement (i.e. skill can only go up). If a guy’s passer rating drops, then there must be a special cause: support or system issues, injury, etc.
Bare in mind that the ratings projected below are just, like, my opinion, man. The stuff discussed above permute and combine into a mind boggling array of possible outcomes all of which depend on known-knowns, known-unknowns, and unknown-unknowns…I’m just out here tryna function.
The projected ratings proposed below are not just a function of the player’s ability and experience (skillz) but also factors around him (support /scheme). Players are listed in expected ascending order within their sub-heading; that's a challenging thing to do but that’s part of the point. I *love* hashing this stuff out so if you’re inclined to refute or challenge something, please do so.
Philip Nelson, SO, Minnesota
|2012 Rating: 104.4||Cmp %||YPA||TD %||INT %|
|Single Factor Rating||88.2||100.4||128.9||81.9|
Nelson’s first season with significant PT rated out worse than Marquis Gray in his first season. Nelson was just a true freshman so he can be expected to improve coming into this year but he has a pretty big hole to dig out of. He should be able to put up a 125 or so in passer rating but that hinges on how well Coach Kill’s system has taken root in Minneapolis.
It wouldn’t be a shock if he jumped up to the low 130 range but that would be a neat trick. For reference, Tate Forcier played at about that level in 2009. Plenty good but still some rough edges. In fact Michigan 2009 is probably a pretty good proxy for what the top end looks like for Minnesota’s 2013 offense. The Gophers return 10 of 11 on offense so the system and support should be there. Reports on the internet of spring practice state the Nelson and his main competitor, Mitch Liedner, look good. Minny has a solid shot at hitting stride this year.
This game will give Michigan’s D a good look at an offensive system that makes heavy use of a QB’s running and will serve as an early status check in preparation for Northwestern, Nebraska, and Ohio State. 125-135.
Nathan Scheelhasse, RS-SR, Illinois
|2012 Rating: 105.9||Cmp %||YPA||TD %||INT %|
|Single Factor Rating||137.4||96.3||78.6||125.1|
I don’t get it. Well, I think I might but its just a theory at this point. Nate was solid as a RSFR (2010) and again as a RSSO (2011) posting passer ratings in the 130s both years. Then, wa-wa, c'est terrible.
Here’s the theory: transition sucks way hard. Unto himself Nate did fine. Completion percentage and INT Rate are where they should be (-ish) for a guy like Nate 2012. The system / support stuff was in the toilet last year. A regime change can do that to you. What’s more is that Beckman had co-OCs last year neither of which had even been offensive coordinators before…so, yeah.
Enter 2013. Enter a third offensive coordinator in three years, four if you count the co-offensive coordinators from last year as 2. So, still in transition but maybe less so with some HC stability. At least this time the OC (Bill Cubit) has some experience calling plays. I expect Nate to return to his 130 form.
Andrew Maxwell, RS-SR, Michigan State
|2012 Rating: 107.1||Cmp %||YPA||TD %||INT %|
|Single Factor Rating||101.9||102.3||96.4||171.3|
Eh boy. I think Maxwell is way better than his stats from last season but I need to freestyle a little to make the case, so…lets skip all that. Pshhh, yeah right. DJ, gimme a beat.
Man, that's a whole lot of gabbage (rhymes with cabbage, means junk) up there, huh? As for skill categories, that INT rate is real good and though the completion percentage is fiercely competitive in its atrociousness. Even though Maxwell needs to develop some touch, I think verdict can be rendered upon a hilarious case of the dropsies. Lindy’s preview states that MSU’s receivers dropped about 66 passes last year and I believe it. Sprankle in some demigod malevolence along the O-line (injuries) and I think you can come up with a legitimate case that MSU had a support problem last year. Everyone point and laugh at Sparty: your crazy dope defense was ruined by your crappy offense. Ha-ha.
Phew, good times. So that's over, now what? Give Maxwell half of those drops and his passer rating jumps to about 120 (mayyybe 125). So that helps but the remaining problem is that they still have to replace Leveon Bell, Courtney Sims and some experience on the O-line. Also, Don Treadwell might have been a better OC than Dan Roushar, just a hunch.
Bottom Line, I think MSU’s offense improves to the basic level: meaning Maxwell (or alternate) posts a 130-ish passer rating (125-134). For MSU’s QB to hit the MROUND (Actual Rating, 10) == 140 level there’d have to be some developmental/fortune miracles so count me in as betting against that.
Kain Colter, SR, Northwestern
|2012 Rating: 129.3||Cmp %||YPA||TD %||INT %|
|Single Factor Rating||169.1||102.5||130.4||146.3|
I know Trevor Siemian gets the majority of the snaps at QB but this is another coaching decision I don’t understand. Kain has better passer accuracy than Trevor and has wheels. I look at the stat lines and I don't understand why Kain ever comes out from under center. I suppose that defenses might be cheating on NW’s heavy run tendency when Colter is under center so his throws are easier but, man, that’s nice accuracy there. Even if it might slide a little when facing more honest defense, his skill is apparent.
Kain is a true senior and already shows excellent accuracy and interception avoidance. If anything it may be difficult to repeat those feats. The only thing out of whack is the YPA but that’s a system number and its something that OC Mick McCall basically dictates. I don't think NW suddenly gets any monsters at wide out either.
I view Kain’s rating as stable and unfortunately can’t see him doing more than a 130 unless he gets the full nod as starting QB and McCall lets him throw downfield more often. I think he’s better than that but the numbers don't lie.
Cameron Coffman, JR, Indiana
|2012 Rating: 123.9||Cmp %||YPA||TD %||INT %|
|Single Factor Rating||138.0||119.2||107.1||145.7|
Heads up, if you want wins then bring the ruckus ‘cause Indiana’s offense aint nothin’ to [mess] with /wutang clan.
Coffman is a JUCO transfer who stepped in after Tre Roberson broke his leg last year and put a strong claim on this job. Also of note is Nate Sudfeld who put up some strong numbers in occasional relief of Coffman last year. It’s possible either one will be the guy this coming season but I’m going to assume Coffman’s experience gives him the nod.
Last year he put up some good Skill numbers but his system/support numbers (YPA, TD%) were not so much. I figure Coffman himself is pretty much where he’s going to be so I suspect that improvement in his performance will come completely from improvements in support/system. Support-wise, Indy also has 10 of 11 back. Word. System-wise, Wilson has had 2 years to install and refine fundamentals so his offense should be up on plane at this point. That system produced some high power Big 12 offenses at Oklahoma headlined by Sam Bradford, Jason White, and Landry Jones. Indiana is not Oklahoma but still, heads up. As for a rating projection, man its Indiana: 130 – 140.
Joel Stave, RS-SO, Wisconsin
|2012 Rating: 148.3||Cmp %||YPA||TD %||INT %|
|Single Factor Rating||129.8||168.6||125.9||152.4|
Stave is supposedly in a battle with Curt Phillips for the reigns of the offense but I don’t understand that. Well, I guess I do: coaches. That and I guess there’s more to football than numbers. But man, by the numbers it’s no contest. Phillips put up a decent 128 last year but he wouldn’t even be in the picture if Stave hadn’t broken his collar bone against MSU. Prior to his injury, Stave was killing the Spartans: 9-of-11, 127 yards, 1 TD. You might remember the MSU’s defense was the best in the B1G last year. KILLin’ ‘em. Stave looks like he has all the skill needed to be a problem and he’s just getting started.
Bielema bounced to Arkansas in the offseason so the Badgers are in transition but Gary Anderson did some nice things at Utah State. The Aggies were garbage before he got there and he brought them their first Conference Championship since John L. Smith (yep, that guy) did it in 1996 and 1997. Prior to that Anderson was DC on the 2008 Utah team that beat Alabama in the Sugar Bowl. The OC he hired (Andy Ludwig) is the guy who took over for Al Borges at San Diego State. SDSU had a meh year offensively in 2011, but they were good last year. I don't think Anderson necessarily wants to run the Pistol offense as he’s a defensive guy and hired a Pro style OC. Stave fits that bill, like whoa.
Support: Check. Wisconsin loses Montee Ball but here’s saying that James White & Co have what it takes to become the next typical Wisconsin RB, pretty dang good. In terms of targets, Abbredaris is also pretty dang good.
Transitions are tough but if Anderson and Ludwig get traction, Wisconsin’s offense could look pretty good pretty quick. I’ll wager that it takes a year for things to hum and look for Stave to slide a little to the 140 range, which, uh, that’s good. Having said that, 150+ is not out of the question. I drop him to third on this list only because of the issues that might come along with regime change.
Taylor Martinez, RS-SR, Nebraska
|2012 Rating: 141.6||Cmp %||YPA||TD %||INT %|
|Single Factor Rating||143.5||140.1||142.6||124.8|
There’s not a whole lot to say here, Taylor is a known commodity. This will be his fourth year as starter and at this point he has leveled off at the season veteran level for a passer. Nebraska has a lot of talent returning from last year’s offense and the coaching staff remains intact. If I’m a stickler, I ding him for throwing a couple interceptions too many but I don’t think his performance there is problematic for the Huskers to be honest.
There’s always a chance that he pulls a Ricky Stanzi and makes a dramatic step forward in his last season but Taylor is ahead of where Stanzi was and, regardless of that, history is not on his side there. So Pelini will have to just settle for a repeat of last year’s performance from Martinez which I’m sure is just fine by him. 140
Braxton Miller, JR, Ohio State
|2012 Rating: 140.5||Cmp %||YPA||TD %||INT %|
|Single Factor Rating||127.3||144.5||137.8||158.4|
God Damn Ohio State. That’s all I care to say about them. Word to your mothers. Ice, Ice, baby, too cold….
Okay, fine. Braxton’s PR components are interesting. The numbers that rely on support (YPA, TD%) are right where they should be for the rating he posted, but the numbers that rely on skill (CMP%, INT%) are kind of schizophrenic. The INT Rate is great; just what you hope for. On top of that he posted a similar excellent INT rate his freshmen year so you cant really chalk that up as unlikely. However, his completion percentage is lagging a bit even though he improved from his terrible freshman year of 54.1%. So, weak completion percentage, nice INT rate. I think that weirdness is reconciled by considering his athleticism: he probably errors on the side of running whenever he sees a throw that is iffy. That’s a good call in my book.
Coming into 2013 this guy is primed to be a rich man’s disappointment. Either that or he goes bonkers. He can’t realistically be expected to improve in the INT department but he should be expected to get a touch accurate. Just so you know, you wont notice the difference (1 more completion per game).
The YPA and TD% are where the magic will happen for OSU. They have been where they should be. And now they have program stability and a proven system. If those numbers improve, then Braxton will keep folks up at night. I suspect they will. 145 - 160
Rob Henry, RS-SR, Purdue
Rob isn’t really a newcomer but the last saw significant PT in 2010 as a RS-FR. That year he was pretty bad posting a 112 passer rating. He blew out his ACL the following year and played some last year but only took 38 attempts. I’m resetting the clock. Plus, there’s regime change in West Lafayette and the Boilermakers only have 5 starters returning on offense. Rob will do well to post a 125.
Sokol is a JUCO transfer and redshirted last season. So he’s had time to learn the system and has some experience under fire though at a lower level of competition. Iowa installed a new offense last year and had to replace Marvin McNutt at WR and had to fill some big holes on the line so some of their struggles last year might be attributed to those issues. I think Sokol can do 125.
Christian Hackenberg, FR, Penn State
Dude is a stud recruit with offers from Alabama and Florida. Scout and Rivals gave him 5 stars, but he was a high 4 to ESPN. Whatever, man; s-t-u-d. PSU has 8 starters returning on offense including stud receiver Allan Robinson. The Sandusky Sanctions will start taking their toll on depth soon but not yet. I’m thinking freshman Chad Henne and Braylon Edwards here. I think he can hit 130.
Other QBs of Interest
Cody Kater, JR, Central Michigan
Central Michigan’s offense has some holes to fill starting with OT Eric Fisher and QB Ryan Radcliff. Radcliff was a seasoned QB with good support around him and posted a 138 passer rating last year. Cody will be a first time starter assuming he wins the job and has support issues around him, 120 – 125.
Terry Bowden installed a spread offense that was improved from the prior year. So they have some positive momentum and are more familiar with the offensive system. Pohl played a lot in the last game of the season vs a good Toledo team and did well in that game. I wouldn’t be surprised to see him do reasonably well this coming year but Akron has been a bad football team. 120 – 130.
Chandler Whitmer, RS-SR, Connecticut
|2012 Rating: 119.0||Cmp %||YPA||TD %||INT %|
|Single Factor Rating||124.5||132.0||90.6||94.3|
Whitmer was originally a 2010 Illinois commit but transferred in search of playing time due to Nate Scheelhasse’s emergence in 2010. That’s kinda crappy I guess, but I wonder if I wouldn’t do the same thing if I were him in that situation. In hindsight he might have been able to see the field at the same time (2012) given the ouster of Zook and the Illinois’s offense struggles last year. Still, I think it would have been hard to displace Sheelhasse without straight up beating him in practice and I doubt that would be the case. He won the job outright at UConn last year and is now the incumbent in his final year of eligibility.
Unfortunately for him, the Huskies were a bad offensive outfit last year. There’s just not a whole to to say about it. This year they have a new OC, which might be a good thing, and a lot of starters returning including the whole O-line. I’m banking that an improved offensive system and a more experienced unit will lead to a better passer rating for Whitmer, but can’t see him breaking 125 - 130.
Tommy Rees, SR , Notre Dame
|2011 Rating: 133.4||Cmp %||YPA||TD %||INT %|
|Single Factor Rating||158.9||124.4||123.4||119.3|
Tommy Is an interesting cat. See, his passer rating isn’t great but its not bad. It’s the same as Chad Henne’s usual rating (Henne posted 130-ish three times and the low 140’s once in 2006). Also Everett Golson’s 2012 performance was 130 as well. In fact, Tommy’s completion percentage is significantly higher that what Henne and Golson ever did. So am I saying that Reese == Henne/Golson? Uh, no.
Passer Rating is a shifty beast, man. All 130’s aren’t created equal and it only becomes clearer when you sift through a lot of them and break them down. The key to understanding the difference between Rees, Golson, and Henne is the playmaker categories (YPA and TD%) and even then the difference is pretty subtle. Take the Names away and I really couldn’t tell you who’s who.
|Year||Name||Team||QBRat||PaPct||PaY/A||TD %||INT %|
I think Rees has checkdown-itis (High Cmp%, low YPA, low TD rate) along with bonehead syndrome (high INT rate, the empty hand pass in UTL1). Henne and Golson avoided INTs reasonably better than Rees. Henne’s TD rate were really good; and Golson brings a running threat along with his passing. They MAKE PLAYS! Tommy plays it safe.
While I’m looking at Henne I notice that his YPA’s are consistently low despite respectable Cmp% and really good TD and INT rates. I think this is a system issue: Debord.
The case of Tommy Reese illustrates the fact that football is not a math test. The box score doesn’t capture everything. These differences are subtle and virtually unperceivable but they are measurable and , I think, explainable. Those explanations are situation dependent so it can come off as BS, and maybe to a certain extent it is, but until I hear a viable alternate explanation I’m sticking to my versions.
Looking forward, maybe Tommy finally says [eff] it and let’s it rip a bit in his last go around. To me that looks like Tommy Reese 2011 with fewer Interceptions. That means 135 –140, probably though Ricky Stanzi 2012 serves as notice that big jumps are possible in similar circumstances. Otherwise, he is what he is: 130.