A dead horse? Perhaps. Sour grapes/attempted justification? Maybe so. But still an interesting read from a well-educated former college football player whose team is right in the thick of one of the biggest NCAA investigations of all time. Also somewhat of a counter argument/different perspective on Cousins' Gettysburg Address from the B1G media days.
Full disclosure: I went to Miami for undergrad, so...yea, that might explain why I felt the need to post this.
What’s that you say? The rules are the rules? I call b.s.. When the rules are propagated by the very same people they’re designed to benefit, I say the rules must be independently justifiable. What is the justification for saying that AJ Green can’t sell his jersey? That he won’t be an “amateur” anymore? Doesn’t the scholarship itself render him no longer an amateur by any objective definition? Doesn’t the fact that Georgia spent hundreds of millions of dollars advertising itself to AJ Green render him no longer an amateur? Doesn’t he stop being an amateur when UGA promises him that his career at Georgia will net him NFL millions? Doesn’t the fact that millions of dollars change hands thanks to the service he provides make him not an amateur?
Power. Strength. Toughness. Big Ten Football.
This is the new (old) Michigan football. What this actually looks like remains to be seen, but I wanted to test out some of the core tenants and clichés of the Manball philosophy to see if there power still rings true today.
Bring on the charts!
Check here for a run down of the background behind the methods.
Myth 1: Passing too much on offense makes your defense ill-prepared for the rigors of Big Ten play.
I tested this myth for both all college football and the Big Ten exclusively. If it’s going to be true anywhere, it’s going to be true in the Big Ten.
To judge how much a team passed, I looked only at first half plays where teams haven’t made half-time adjustments and should be executing their intended game plan and not reacting much to score and time considerations. I then compared the quantity of first half passes against the defensive success. First I looked at all of the FBS:
That’s a whole lot of buck shot and not a lot of trend. There is a slight trend toward more passing = better defense but the effect is not statistically significant.
But as I mentioned earlier, the Big Ten is different than the rest of FBS, it is the nativeland of Manball. So if you look at Big Ten teams in Big Ten games over the last eight years, does the picture look different?
Here at the least the slope is going in the “right” direction but the effect is still small and insignificant. Even if it was statistically significant, the difference between the low (10 passes per first half) and the high (25 passes per first half) is worth one game a season, an advantage sure, but nothing monumental.
Myth 2: Long Scoring Drives Rest a Defense
Unfortunately I don’t have any good tools to tell how rested a defense gets, but I can look at the outcomes of subsequent drives following a scoring drive of various lengths. Does a defense have better outcomes after a long or short scoring drive, does any of it matter at all?
Looks like the rest is more beneficial to the offense than the defense. Defenses give up 20% more points after a 15 play scoring drive by their offense than a 1 play scoring drive.
The usual correlation does not equal causation applies. Worse teams could be more likely to score on longer drives than good teams. Other issues could be at play but I felt comfortable that this overall myth does not hold true.
Myth 3: Running Teams Do Better in the Red Zone Than Passing Teams
I had two ways to look at this one. Is it about running the ball in general, or is it about running the ball once you are in the red zone? They are usually the same thing but I wanted to test out both to see if one rang more true than the other.
First, comparing how much teams run between the 20’s to red zone effectiveness, measured in [points on red zone trips]/[7*red zone trips]:
This looks a lot like Myth 1. Some slope but no significance. Even at a significant r sqaured, the difference between 30% rushing and 60% rushing is worth less than a touchdown in red zone production over the course of an entire season.
Here is what it looks like when you change the x-axis to reflect playing calling within the red zone:
Slope increases, as does r squared although there is still a ton of noise.
The case is not strong, and there is definitely more than one way to skin a cat in the red zone but I would leave the door open on this one:
Finding: Plausible, but evidence weak
Myth 4: Offenses With Running Quarterbacks Break Down As The Season Progresses
This one is probably not a manball myth, necessarily, but a good one to look at. Let's go straight to the you-know-what.
Did not see this one coming. Sure last year clouded my mind a little bit but I did not expect QB running offenses to be this dominant. That’s a very real gap between QB running offenses and non-QB running offenses.
The weekly data here is a bit noisy but it looks as though offenses built around running QBs peak in early November but are still pretty strong come bowl season. The overall trend roughly mirrors statue QB offenses although the statues do have a bigger uptick come bowl season than other offenses.
Myth 5: Offenses With Running QBs Have Worse Defenses
Not a lot of fancy numbers or charts on this one. Only real numbers of note are that the 100+ carry group from Myth 4 have an average defense of that is 0.2 points per game worse than then 0-99 group, that’s worth less than a game a decade.
Myth 6: Run Oriented Offenses Do Better In The Fourth Quarter
This is one of the key tenants of a run-based offense. The ability to hold the ball with a lead late. Unfortunately the NCAA doesn’t provide time stamps for plays and so I don’t have them in my database, making a good estimation of clock killing impossible to determine from my data. All I can provide analysis on is the ability of different combinations of run and pass to score points, not run out the clock.
Partially because objectives change in the fourth quarter, but the likelihood of scoring is the lowest in the fourth of any given quarter. That means all situations will tilt toward the negative in my analysis. What I can look at is how much teams run in the first three quarters and compare that with their overall performance in the fourth quarter when the game is within two touchdowns.
I hope I didn’t just give away the ending, but if you are going to be a running team you better come into the fourth quarter with a lead. One of the strongest correlations of the day points to strongly diminished returns in the fourth quarter for teams heavily invested in the run.
Finding: Busted without a lead, inconclusive running out the clock
What Does This Mean For The Future of Michigan Manball?
Right now the evidence still points to Manball being more of a philosophical theme than a practice of playcalling but that doesn’t mean it’s not going to happen either. Nothing I have seen indicates that it can’t win a lot of games but it is definitely far from a Decided Schematic Advantage. As all good Michigan fans know, Manball can be effective in most games as long as you have better talent and you aren’t playing from behind.
A quick guide to where my numbers come from and how they are calculated.
Where Does The Data Come From?
My sole source is the NCAA website, which hosts the play by play data for every year since 2003. 2004 and forward is nearly all there but 2003 is a bit hit and miss.
Thanks to MCaliber I can pull each week’s games down directly from the site into Excel where I translate the text into a variety of field and calculations that ultimately end up in an Access database. My tools are somewhat crude but they work and I can get what I need from them.
To data I have 992,624 plays in the database.
All games between two FBS teams. Any games against FCS teams don’t exist as far as I’m concerned.
Every play from these games are in the database but not all plays go into calculations. End of half drives are excluded as are any drives in the second half where one team leads by 16 points or more. Only plays under those circumstances are excluded, all other plays from those games are included.
Sacks are counted as pass plays and all fumbles are excluded due to their random nature.
What’s The Baseline?
Based on all of this historical data, each down, distance and line of scrimmage are given an expected value. For example:
1st and 10 from your own 20: 1.53 expected points
1st and goal from the 1: 6.48
Since each situation has a value, the value of any play is the change in value created. A 79-yard pass on 1st and 10 from the 20 to the other 1 is worth 4.95 points (6.48 points – 1.53 points). If the running back then punches it in from the 1, he is awarded .49 points (6.97 – 6.48). Touchdowns are worth 6.97 because they create the opportunity for the PAT which is successful 97% of the time. If the PAT is good, the values for the drive look like this:
QB/WR 3.95 points
RB: .49 points
K: .03 points
Thus the 7 points the offense generated are accounted for between the initial 1.53 from field position and the remaining 5.47 from play.
Even plays that gain yards can yield to negative expected point changes. A two-yard gain on 1st and 10 puts the offense in a worse spot than they began even though it was positive yardage. If a drive ends, all of the initial field position points are “left on the field.”
Let’s say a team hands the ball to their running back three times from the 20 and gains 3 yards each play. A punt on fourth and 1 means that the initial 1.53 expected points is now 0 so the running back now has three plays for –1.53 on the books. Third down plays are typically swing plays and can provide large deviations. Convert a lot of third downs and your value/play will be larger than your yards indicated. Fail on a lot of third downs and it quickly swings in the opposite direction.
What Adjustments Are Made?
We are finally getting to PAN, Points Against Normal. All previous calculations are done independent of opponent. Once several games are on the books in a season, we start to get a picture of who is good and who is not so we can make calibrations to performances.
The baseline as calculated above is adjusted based on the strength of opponents' rush/pass offense/defense. Last year Michigan allowed 0.19 points/rush, which [Ed-M: moment of shock coming] is really bad. So even if the opponent averaged 0.15 points per rush initially, their final tally was negative at –0.04 per play since they performed below what the average team did versus Michigan. A team would have to have an initial average of at least 0.20 to come out positive on the final scoring.
The final scoring is what I will refer to as PAN. It is a measure of actual scoreboard points above the average team you are. PAN can refer to a specific unit such as passing offense, total defense or kick returns, or for a team in total. It is also a good metric for comparing quarterbacks and running backs. It is only somewhat effective for wide receivers since they rarely yield negative plays.
What Does It All Mean?
Zero PAN means you are completely average. For a BCS conference team like Michigan this typically means bottom third of the league. A three-points swing in PAN typically equates to an additional win or loss over the course of a season.
+7 will put you around the Top 25 on the season
+14 is typically Top Ten and potential BCS game
+21 is best in class and probably playing for a national championship
The top rated team I have is Florida 2008. They finished +13 on offense, +7 on defense and +3 in special teams. The top Big Ten team is Ohio 2005 at +19 (7/9/3). The top Michigan team is 2006 at +14 (4/6/4). They come in at 50th overall in the last 8 seasons.
I will try and add relevant updates if more questions come up in the comments.
I found this article on Randy Shannon and Nevin Shapiro interesting.
It basically summarizes how Randy Shannon warned his players about Shapiro by name on multiple occasions, refused to talk to the guy, and threatened any assistant coach who dealt with Shapiro with an instant firing. There are rumors he had people around town who would let him know if they saw his players with Shapiro.
Shapiro responded with racially-charged rants to the AD about Shannon. It appears the AD and Shannon didn't exactly see eye-to-eye over Shannon's refusal to even talk to Shapiro.
And yet he still had 12 players receive illegal benefits.
Question: Is it always the coach's responsibility when this kind of thing happens? I know we all say the head coach is ultimately responsible, and maybe he is. But with a system this bad and not exactly getting full support from the AD, what is he supposed to do? Leave the job in protest?
I ask because some of the first things to come out on this board after the story broke were "thank god we didn't hire Randy Shannon as DC." Reading this article though, is Randy Shannon such a bad guy? Should he be held responsible?
In this practice video produced by Rivals yesterday, it appears that Vincent Smith and Courtney Avery are practicing either kick or punt return catches. It seems to be the impression that Smith may be in even better shape with the knee this year, and I like the idea of putting him back there. Avery played QB very efficiently in high school, so I'd also like to see if he could make some plays back there as well.
Apparently, the rock band, Pop Evil, has recorded a Michigan Football tribute song. The song will be released at the end of the month and they made a 'teaser trailer'/hype video for it. I found it here: http://banana1015.com/pop-evil-teases-2011-michigan-wolverines-football-song-in-the-big-house/ and it is pretty cool. This is my first time starting a topic. Hope the embed works.