Michigan comes tied for 65th nationally, and 5th in the B1G with 13 returning starters.
Other notables (nationally):
- Minnesota (T-65th)
- Oklahoma (T-65th)
- Clemson (T-81st)
- Iowa (T-81st)
- Alabama (T-98th)
- Notre Dame (T-113th)
- Michigan State (T-113th)
- Ohio State (128th - dead last)
Edit: Sorry, I neglected to "fact-check" Steele's numbers. Michigan should have 14 total returning starters (8 on offense, 6 on defense), which would put them tied for 41st in the nation, and 3rd in the B1G. Steele probably assumed that AJ Williams was a starter, maybe?
I was wondering if some of the more proficient members of this board had any conjectures on how high Wilton Speight, might be ranked when it's all said and done?
Offense and defense rankings based on total numbers and straight averages can be misleading at times. If a team plays opponents with strong rush offense but weak pass offense, the team's pass defense stats might look better than what they really should be. This is something Michigan was being accused of due to the fact that much of our "bad" defensive games came against strong rushing teams (Alabama and Air Force).
One way to mitigate this "effect" would be to not look at the totals and average numbers, but compare the game output against the average output the opponent has produced against all opponents. This produces numbers that show you how good your performance was compared to all other team that your opponent has played. It is more useful comparative method than using just total numbers.
So, exactly how does it work?
Here are the stats for Michigan so far this year:
|Opponents||Rush Net Total||Pass Yds Total||Total Yds||Pts||Avg Rush Total||Avg Pass Total||Avg Total Offense||Avg Scoring Offense|
|Average All Opp||145.1||145.9||291.0||17.3||196.0||194.7||390.7||27.5|
|Opponents||Avg Rush Off Diff||Avg Pass Off Diff||Avg Total Off Diff||Avg Scoring Off Diff|
|Average All Opp||-24%||-24%||-26%||-39%|
The first four columns of stats represent the actual stats from the game played against Michigan. The second set (of four) columns are the average output of that team against all opponents this year. The
last set (of four) columns second table are the differences in percentage of actual game stat versus the total year averages.
As you can see from the table, Alabama produced their average offensive output against Michigan while Purdue and Illinois barely produced about half of their normal offensive output.
By averaging all of the averages, we find that our defense is reducing our opponents' normal offensive output by about 25%, while only allowing only 61% of their normal scoring output.
Sounds pretty good, but how does that compare to rest of NCAA?
I didn't have enough time to calculate the differential averages for every team in NCAA, but I did the analysis for top 10 Pass/Rush/Total defensive teams and all of Big Ten (plus ND). I did not include stats against FCS opponents. Here it is ranked by total offense differential.
Few things that stand out:
- Alabama, LSU, and Florida St defense stand above the rest
- Michigan and Michigan St defenses stand above the rest of B1G
- Michigan is pretty good at both run and pass defense
- Ohio St pass defense is HORRIBLE!
- BYU defense is much better than I thought
- Many of the defenses highly ranked in one (pass or rush) only because they are so horrible at the other (I am looking at you Arizona St, Stanford, Nebraska and Oregon St!)
- Notre Dame is living on borrowed time - their scoring differential is MUCH higher than what rest of the defensive differentials would indicate
I do believe converting straight up numbers to percentages makes it much easier to compare between pass/rush and between different teams. I hope most of you find this useful. If I get enough upvotes, I will do the same analysis for offense as well.
This is my first time using Windows Live Writer to post something, so if something looks weird, that’s why. I’m making some changes to my database to allow me to rank past and future seasons, other sports, etc. I wanted to get these ranking out there before I make these changes and before most of the bowls get underway.
There are a few tweaks that I made to the algorithm. The major thing is that I took out FCS teams. Imagine you have Team A that plays 12 FBS teams and Team B that plays 11 FBS teams and 1 FCS team. In graph theory, playing that FCS team gives Team B shorter pathways to the 120ish teams in the FCS. I felt this was an unfair advantage for teams playing weaker schedules.
I also experimented with how to factor in Win-Loss (long story short, nothing changed with this, so you can skip to the next paragraph if you would like). Think about MSU, Wisconsin, and Iowa. MSU beat Wisconsin by 10, which gave them a path of .5 (1 / 2 scores). Iowa beat MSU by 31, which gave them a path of .25 (1 / 4 scores). When multiplied by the Win-Loss factor at the end, Wisconsin would receive a lower path score for Iowa’s win over MSU because they had the better record. I didn’t like the idea that Wisconsin could get more credit for Iowa’s win over MSU than Iowa would. I messed around with applying the winning team’s overall record to each game, then the same with the losing team’s record. I tried lots of different things, but none of them looked right. I didn’t like the idea of doing something just because it looked right, though. I decided that the additional path length that Wisconsin accrues by having to beat Iowa (they only beat Iowa by a point, so the path to Iowa is 1 and the path to MSU is 1.25) was enough of a penalty on Wisconsin.
The last main change since I last posted is that I am now factoring in homefield advantage. I have calculated homefield advantage to be worth 3.77 points in FBS games this year. In the Big Ten games it was roughly 6 or 7 points. When I did the power ranking for the Big Ten, I experimented using the two values and decided that 3.77 was the better number to use. One, it’s closer to the value that is usually associated with homefield advantage, and two, it would change from conference to conference. Mostly, though, it would change the path lengths between teams when going from the NCAA ranking to the conference power rankings, which is something that I didn’t want.
Without further ado, here are your top 25 and conference power rankings to start out the post-season. Keep in mind that because the conference power rankings only take into account the games that are played within the conference, teams might not be in the same order in the conference and in the top 25.
|FBS Top 25|
|North Carolina State|
|San Diego State|
|Troy Trojans of Troy (We’re from Troy!)|
|New Mexico State|
|San Jose State|
I have an idea for game predictions, so I’ll probably post another poll along with bowl game predictions and comparisons to actual results. Sometime in January I’ll post polls for Basketball and Hockey.
Here's this week's update to The Michigan Difference, updated with stats from this week's games.
Another bipolar game against Wisconsin. The final offensive output was pretty good, but the defense couldn't stand up to their rushing attack. We remain #5 in Total Offense (TO) and are now #112 in Total Defense (TD).
Disclaimer: The NCAA stats are not linear, of course, and a difference of 1 yd/gm can be a large or small difference in rankings depending on how closely spaced everyone is. So as I cautioned, this isn't a hard-core statistical exercise. This analysis is pretty one-dimensional because it's long and complicated enough as it is.
I think the greatest value in this is to look back at the early games and see how well we did in comparison to what other teams ended up doing against them - what seemed like a good or bad performance at the time may look different in retrospect.
Part the First: Offense
We know our offense is great, but what kind of damage has it done to the Total Defense (TD) ratings of our opponents? Here they are thus far:
|Opponent||Games||Yards Yielded||Yds/gm||NCAA Rank|
What would these guys' defensive stats look like if they hadn't played Michigan?
|Opponent||Total Offense, M||
Opp. Avg - M,
M Total Offense,
*Opponents' average Total Defense yards per game, minus the Michigan game
**Michigan's Total Offense in game as a % of the opponent's average TD minus the Michigan game
Iowa, Illinois, and Wisconsin's defenses really wish they hadn't played us. They'd be in the top 20 nationally but for one game. Michigan has gained above our opponents' average yardage yielded in every game thus far, and their Total Defense rankings have suffered as a result. What's the damage?
|Opponent||TD Rank With M||TD Rank Without M||Difference|
Average change in Total Defense ranking for all opponents: -10.1 places.
Looking at the offensive performance versus the quality of the defense:
There is little correlation between Michigan's Total Offense for a game and their opponent's average Total Defense (minus M). Whatever is limiting our offense's output in a game, it is not directly related to the number of yards the opponent usually gives up. This would suggest that the offense tends to be limited by itself, rather than the opponent.
Part the Second, Defense
So the flipside of this, then, is how much has our defensive suckitude helped out our opponents stat sheet? Where would they rank in Total Offense without having played us? We'll run the same tables again, but from the opposite tack:
|Opponent||Games||Yards Gained||Yds/gm||NCAA Rank|
Wisconsin is easily the strongest offensive team we've faced thus far. The results of the game show that. MSU was pretty good, the rest varying degrees of average to bad.
|Total Defense, M||
Opp. Avg - M,
Opp Total Offense,
% of Opp Avg - M**
* Opponents' average Total Offensive performance, minus the Michigan game
** Opponents' Total Offense as a percentage of their average offensive performance, minus the Michigan game
Here's a nifty graph of our opponents' Total Offense against Michigan, versus their average Total Offense per game without the Michigan game:
In this case, we do have a reasonably good correlation. Our defense does worse against better offenses. That would suggest that we're talent-limited somewhere (either coaches or players) and the opponents' offenses tend to have their way with us. In other words, our defense doesn't shut anybody down. The more yards our opponents average per game, the better they'll do against us.
|Opponent||TO Rank With M||TO Rank Without M||Difference|
Average boost to opponents' Total Offense NCAA ranking: +5.9 places
From this perspective, the Wisconsin game was our 4th worst defensive performance of the year. As bad as we looked, three other games were worse. We were up against a very good offense, and it showed.
Part the Third: Summary
Michigan's O Difference
on Opp TD Ranking
Michigan's D Difference
on Opp TO Ranking
|Connecticut||-12||+1||W: Good O, OK D|
|Notre Dame||-15||+11||W: Good O, Terrible D|
|Bowling Green||-20||0||W: Awesome O, OK D|
|Indiana||-8||+14||W: Good O, Terrible D|
|Michigan State||-1||+10||L: OK O, Terrible D|
|Iowa||-8||0||L: Good O, OK D|
|Penn State||-4||+6||L:Good O, Bad D|
|Illinois||-22||+9||W:, Awesome O, Terrible D|
|Purdue||-1||--1||W: OK O, OK D|
|Wisconsin||-10||+9||L: Good O, Terrible D|
In subtly maize-and-blue graphical form:
New observations for this week:
Many of our previous opponents had good weeks offensively, making our defense look a bit
betterless bad in those previous games.
- Wisconsin is easily the best team we've faced yet. Offensive and defensive performances were close to mid-pack, but we got our butts kicked.
- Our offense remains impressive and will keep getting better.
- Our defense is terrible and had better get a lot better.
- Winning is still a lot more fun than losing.