mid-week metrics

Thanks to user Feat of Clay for helping name this segment. This one comes with  lots of fun numbers to look at from an instant classic but also with lots of question marks. Will get to the good stuff first.

Win Percent Added

Let’s go straight to the chart:

Pretty nice job by BlueSeoul in his estimate on this. Michigan saw two steady descents in win probability over the first three quarters with only the first bomb to Hemingway to nudge the numbers up. It wasn’t until Michigan pulled within a field goal that the odds got above 30% for the first time since the opening Notre Dame drive.

WPA Detail

The WPA graph shows the flow through time but has a lot of approximations. Since this game was so spectacular, I decided to go back to all of the major lows then highs to see what the odds of winning were for each specific situation based on the last 9 seasons (the numbers will differ slightly from above in some cases):

Notre Dame scores first (0-7): 29%

Notre Dame extends lead to 14 (0-14): 9%

Michigan gets on the board (7-14): 28%

Half-time (7-17): 17%

Notre Dame Scores Again (7-24): 6%

Michigan scores to start the 4th (14-24): 13%

Michigan cuts it to a field goal: (21-24): 21%

Denard throws a pick in the end zone (21-24): 17%

Michigan gets the stop (21-24): 28%

Michigan completes comeback #1 (28-24): 77%

Michigan allows comeback #2 (28-31): 8%

Epic comeback #3 (35-31): 99.999% (allowing for Stanford-Cal)

Since 2003 I have 38 cases where a team was down 3 with the ball with less than a minute left and first ten at their own 20 (give or take ten yards). Of those 38 cases, 35 times the team failed to score or lost in overtime. In 3 of those cases, the team was able to kick a field goal and win overtime, including Dooley Premature Handshake Pt 2 last year against North Carolina. Michigan is the only team that was able to win in regulation under these conditions. The closest I could find were three times when teams received the ball down by 3 with 1:15-1:20 left on the clock and went on to score.

Arizona State actually came close, scoring in 31 seconds and giving the ball back to Purdue with 43 seconds left in the 2004 Sun Bowl, but they had more time if they needed it. Strangely enough, the other two times were both done by Arkansas State in 2006 and 2007.

Turnovers

Factoring in fumble and interception returns, Michigan is currently first in the nation in turnover PAN with 14.4 pts/game net between the offense and defense. This is obviously unsustainable. Last year UConn finished first overall with a season average of +4.9 points per game in turnover value.

The good news is obviously that Michigan is generating a lot of points off of turnovers through two games. The bad news is that it is unsustainable and Michigan will need to find other ways of generating points than what they have so far.

The turnover-prone Irish sit at –15.2 PPG through two games. It should be noted that for all the mileage gotten out of mocking the Irish’s turnover failures through two games, Michigan has been nearly as strong to the positive. Neither trend can continue at their current magnitude, but let’s hope they do.

Eastern Michigan Preview

Eastern Michigan is one of four teams that have yet to play a game against an FBS team this season. All numbers for them will be based on 2010. Michigan’s numbers for 2011 are still not opponent adjusted.

Michigan Rush Offense:

Michigan 2010: +6

Michigan 2011: +3

E Michigan defense 2010: -4

Michigan Pass Offense:

Michigan 2010: +3

Michigan 2011: +6

E Michigan defense 2010: -8

E Michigan Rush Offense:

Michigan defense 2010: –3

Michigan defense 2011:

E Michigan 2010: +0

E Michigan Pass Offense:

Michigan defense 2010: -3

Michigan defense 2011:

E Michigan 2010: -1

Special Teams:

Michigan 2010: -2

Michigan 2011: -2

E Michigan: –3

Prediction: Michigan by 21, 98% chance of victory (although my numbers typically underestimate blowouts)

Luckily for Michigan they have three games in a row that are at home and against teams that they should be able to win while working out some kinks. As you’ll see in my Big Ten team rankings below, the numbers are not kind to Michigan so far. The bad news is that the offense is down, the defense hasn’t show much bounce on a down by down basis and they are heavily dependent on an unsustainable turnover margin.

The good news is that they have Denard Robinson, are 2-0 and have two accomplished coordinators behind the wheels of the offense and defense. It will be critical to come away from the next month with wins in hand and a lot of holes fixed. If the coordinators live up to their reputations Michigan should be looking at a strong first year under Hoke and a stepping stone to a high expectations 2012. If they don’t it could well turn into 2009 Part 2 with a quick start thwarted by an offense that is good but incapable of hitting Ludicrous Speed and a defense that is undermanned.

Quick Hits

Denard Owns The Irish: Denard has almost a thousand yards in two games against the Irish. The number one yardage total for one player against one team in my DB is Dan LeFevour at Central Michigan against Ball St with 1,575 yards. Brady Quinn against Purdue is second. Adam Weber at Minnesota holds the top two within the Big Ten with nearly 1,200 yards against both Northwestern and Wisconsin.

Rush Defense by half (via @MeanChuckieB): Big swing from first half to second half. –4 in the first 30 and +4 in the second 30 minutes with a couple of big 3rd down stops.

Short Drives (via @drboud): Michigan’s longest drive of the day was 5 plays. 15 teams have done that in a game since 2003. Of them, only four have scored more than a single touchdown with Michigan’s 35 points far exceeding the previous high set by Boise St against Hawaii in 2003. Coincidently, the only other time the 5 plays or less on every drive has happened this year was also in Michigan last week with Michigan State’s domination of Florida Atlantic.

ND offense 2011 vs 2010 (via @doughelmreich): Eliminating the turnover portion and this year’s version of the Irish offense is far surpassing last year’s performance. Without adjusting for opponents and removing turnovers, last year the Irish were +5 and this year they are at +13. Most of the jump has been in the passing game with has gone from +4 to +10. They are identical to last year on first down, but significantly higher on 2nd and 3rd downs, especially in medium and long yardage situations.

My Top 5: 1. Oklahoma … State 2. Alabama 3. Boise St 4. Oklahoma 5. Wisconsin

Rest of the Big Ten: 9-Nebraska 13-Ohio St 14-Michigan St 23-Illinois 42-Penn St 58-Michigan 77-Northwestern 79-Iowa 88-Purdue 100-Minnesota 106-Indiana

During the season I’ll be posting a weekly review/preview/tidbit piece. For now it’s named Mid-Week Statistical Nuggetry but I am open to better ideas. Within the article I will try and look at pertinent notes from the past game, and look forward to our next opponent. Occasionally a bigger topic might come out of the previous games that will get a full treatment.

As a side note, after Week 1’s games the database now has crossed the 1,000,000 play mark.

Plays That Made the Day

A new addition for this year. I went back over the last eight years of data and have added a “live” win percentage indicator based on score, down and distance and possession. It’s still being tweaked but for the most part it is in place. Using this, I will try and pick out the most valuable and least valuable plays for each game in terms of WPA (Win Percent Added).

Bad Play #3, 5% lost. Carder hits White for 17 yards to move Western into the Red Zone on their second drive of the day.

Bad Play #2, 6% lost. Denard misses Roundtree on third down on the opening drive of the second half, forcing a three and out punt after good starting field position.

Bad Play #1, 7% lost. On 3rd and 7 on the opening drive, Carder hits White for 14 yards to set up first and goal.

Big Play #3, 8% added. Western Michigan misses a 38 yard field goal with the score tied at 7 early in the second quarter.

Big Play #2, 9% added. Kovacs sacks Carder, forcing a fumble and Herron scoops and scores to push the lead to push Michigan’s lead to 17.

Big Play #1, 35% added. No brainer here. With the score still tied at 7, Jake Ryan hits Carder and Big Play Brandon Herron is there for the pick six, taking Michigan from a 38% chance of victory to a 73% shot.

Brandon Herron’s Big Day

With two defensive touchdowns, Brandon Herron added 9.1 points of value in just the returns, not to mention 8.5 points in value from the turnovers themselves. Since 2003 only one player has ever accounted for more value in what I call miscellaneous returns (any return that’s not a punt or kickoff) than Michigan linebacker did Saturday: In 2008 Utah was playing at San Diego State and the Utes’ Deshawn Richard returned two Ryan Lindley passes for touchdowns, one for 89 yards and the second for 38. The two returns barely eked ahead of Herron with 9.2 points in added value.

Special Teams’ Bad Day

Michigan finished the day –2.8 PAN against the Broncos Saturday. All five special teams units were below zero.

Punt return was the closest to zero at –0.1. Kick return was also serviceable at –0.2. With Hagerup, the punt team was –0.5, the Gibbons was –0.9 thanks to the blocked PAT and kickoff was obviously the worst at –1.1. The kickoffs weren’t great, 48th out of 75 teams on Saturday, but the coverage was even worse, coming in 71st out of 75 on Saturday.

Field Position and the Offense’s Short Day

Last week Michigan’s offense had the fewest number of relevant drives (4) of any team facing an FBS opponent. On average, those four possessions should have yielded 7 points, they yielded 14 (2 defensive TDs and a final touchdown after they already had a 17 point lead). Michigan was +1.66 points per drive (PPD – Expected PPD), which was tenth best for the week.

The sample size is extremely minimal here so plenty of caveats apply, but considering how little opportunity the offense had, they didn’t do terrible, but they weren’t exactly Wisconsin scoring 38 in six drives with an expectation of 13 either.

On the flip side the defense faced an expected 13 points and gave up 10 and Western Michigan missed a field goal that is made about 68% of the time. This obviously doesn’t factor the defensive touchdowns which more than negated points actually allowed.

Biggest Comebacks From Back in the Day

Even if Michigan wouldn’t have been able to score with their good field position when the game was eventually called, they probably would have at least taken the game into the fourth quarter. I ran fourth quarter comebacks of 24 or more points through the database and since 2003 there has been only one.

Last year Kansas’s big comeback over Colorado from 28 down in the fourth quarter is the only game I could find in the last eight years where a team came back from 24 or more in the fourth quarter, although TCU actually came back from 24 down to take the lead against Baylor Friday before losing it in the end. Ten teams have come back from 24 down prior to the fourth quarter, most notably Auburn in last year’s Iron Bowl.

It Was Over Before the Lightning Called it a Day

One of the benefits of the WPA metric is the ability to track the progress of the day and put it in you know what form:

The big interception return from Herron made a dramatic swing and Michigan’s win percent hit 100% for the first time after Michael Shaw’s long TD run.

*This is still a bit of work in progress so some of the jaggedness in the chart isn’t real. I am having some challenges getting the win percent smooth across possession changes but overall the trends are right.

Notre Dame at Night

A quick mini-preview of Saturday’s history making showdown. Numbers from last week aren’t opponent adjusted, numbers from last year are.

Michigan Rush

Michigan last week: +4

Notre Dame defense last week: +8

Michigan last year: +6

Notre Dame defense last year: +2

Michigan Pass

Michigan last week: +2

Notre Dame defense last week: +4

Michigan last year: +3

Notre Dame defense last year: +6

Notre Dame Rush

Notre Dame last week: +5

Michigan defense last week: –2

Notre Dame last year: +0

Michigan defense last year: -3

Notre Dame Pass

Notre Dame last week: –2

Michigan defense last week: +0

Notre Dame last year: +0

Michigan defense last year: -3

Special Teams

Michigan last week: –2.8, bad in kickoff and kicking

Notre Dame last week: –5.1, really bad in kicking and punting

Prediction

My numbers are slightly more favorable than Vegas but still tilt toward the Irish.

Notre Dame by 2

Now that Brian has burned through the position previews and depth charts in beautiful excruciating detail, there is little for me to add to the personnel side, so I wanted to look a little deeper at the schedule portion to see how we got to the 8-4 I projected last week.

I also wanted to add a bit of addendum to the Denard struggles on passing downs meme, so to not clutter the site any further I have dropped that at the end of this column for those interested.

Throughout the season, I will be posting a weekly column on Wed/Thurs. I will try and pick out interesting tidbits and trends from the numbers as the week goes. If you have any questions you would like to see answered in the column or ideas on angles, don’t hesitate to hit me up on the twitters.

As always, your handy reference guide is here.

So which are the 8 wins?

Well, it doesn’t really work that way. Obviously no game is certain and no prediction is either. To get to 8-4 I assign values to each team based on the prior three seasons' performance and returning starters at QB and defense. These are factors that I have found significantly improve a season’s forecast.

Each team is then pitted against their schedule, accounting for home field which is worth about 3 points for the home team each game. Each game then gets a spread and a likelihood of winning. When you play out those probabilities, some seasons ended up with as few as 1 win and some ended up with 12. Nearly three quarters ended up with seven, eight or nine wins. My calculated odds of missing out on a bowl are about 1 in 29, about the same odds of winning 11. Going 12-0 is rated at 1 in 327. This is all assuming that Michigan plays at the projected level. If they play better or worse than I have projected, the numbers can and will change.

All that was to say, the eight wins and the four losses change each scenario. The most likely version has losses to Ohio, Nebraska, Michigan St and Notre Dame, but even that scenario is only a 1 in 60 shot. In fact the most likely specific scenario is 6-6 with losses to Illinois and Iowa added to the mix, but that’s still a 1 in 55 shot.

In summary, here is how the percentages break out:

The Individual Teams

The numbers quickly break out into four groups:

Cupcakes

Eastern Michigan and Minnesota coming into the Big House without much hope. Eastern was bad every year considered and only gets a slight uptick from returning starters. No points awarded for hiring Mike Hart.

Minnesota saw last year plummet below already-low-for-a-Big-Ten-team values and returning starters push them down slightly further.

Just Don’t Screw It Up

Western Michigan, San Diego St, Purdue, and at Northwestern all seem pretty safe on their own, but there is only a 55% chance we go 4-0 in these four games. Successfully do that and a nine-win season becomes a more attainable. Dropping one or more will make it tougher to top last season’s win total in the regular season.

Toss-Ups

Notre Dame, at Iowa and at Illinois all place Michigan a percent or two below 50/50. 5-2 between these last two groups keeps us on pace to 8 wins. Iowa overachieved last year but is brought down to earth thanks to a depleted roster. Illinois is heading in the opposite direction after [NAME REDACTED] made one last run to save his job. Notre Dame is the highest rated of the bunch as Brian Kelly begins to purge the Weis ratings from the books. The Domers get the benefit of a strong returning group but are in the mix with Iowa and Illinois thanks to an under the lights meet-up in Ann Arbor.

There’s a Clock for That

OK, so we don’t have a countdown clock for that school down south and four states over (Nebraska), but Ohio and State form the last group. To hold serve on an 8-win season, expect one win out of this group. Ohio has been the cream of the Big Ten for the last several years, but graduation and Tressel-gate have dropped the Buckeyes into the mix. Michigan State and Nebraska both saw 6+ point improvements last season and have a decent group returning. Nebraska should definitely be the better team, but they won’t have the luxury of home field.

PS: Denard and Passing Downs

In general, my data supports what Burgeoning Wolverine Star found on Denard and passing downs. I was curious about which down and distances that Denard excelled and what was their value. For the season, Denard was a non-opponent-adjusted +70 for the season. This includes rushes, passes, sacks, fumbles, picks, everything but garbage time. This is a huge number.

I broke down where the +70 came from situationally.

Denard was light years ahead on 1st and 2nd down but considerably below average on 3rd down with at least 8 yards to go. In fact, he was pretty good at 3rd and short and started quickly falling from there.

Ultimately, as long as the offense didn’t lose ground on first down they were still in good shape. Denard could turn a mediocre 1st down around quickly, but if Michigan wasn’t able to get into a third down distance that was manageable, the offense quickly become below average.

College football is 7 days away. Michigan football is 9 days away. It is time for a little Big Ten preview. Last year my numbers pegged Michigan at 7-8 wins. This year you’ll have to read on to see my predictions for Michigan and the rest of the Big Ten.

The Nuts and Bolts

If you just want to see the picks and the nice standings you skip on ahead. If this section confuses you or brings about more questions than answers, you might want to head here.

My methodology is along the same lines of user Undefeated Dream Season of 1992’s great post from last week.

Begin with the PAN from the team’s previous season. Regress that season half-way to a team-specific mean, which for me is the five preceding years, then adjust for returning starters. Every team ends up with a rating which is then plugged into the full season schedule and simulated a whole bunch to produce average results for every team in the FBS.

I weight returning starters based on what I can find validation from in past seasons. I am continually tweaking this because it is very difficult to separate out, but my best method currently accounts only for returning QBs on offense. A returning signal caller is worth 1 extra point per game vs average and a loss of QB is a 1 point reduction, leaving a 2-point spread. Once accounting for a regression to the mean and the QB effect I can’t find any other correlation across returning offensive starters. On defense the break-even point is seven returners. Each player returning above or below seven is worth 0.8 points per game. Return all 11 and it’s 3.2 points per game. Return 3 from the previous season and it’s –3.2.

For prediction purposes I exclude special teams because their success or failure isn’t typically consistent from one year to the next like offense and defense are.  Almost all teams are predicted to have 2+ losses because even though you know several teams are going to run the table or have just one loss, which teams is a challenge and my numbers are based on averages across multiple “plays” of a season.

The Power Poll

Michigan checks in right in the middle of the Big Ten at +4 predicted. I didn’t know what to do with Purdue at QB since they have just been a mess the last two years, but hedging to the negative is probably the right call. Those of you familiar with my numbers know that Northwestern has some sort of crazy luck/skill at exceeding their numbers year in and year out. They are the one team in my ratings out of 120 that just never work and it’s always to the Wildcats' favor.

It should also be noted that I had Wisconsin as a non-returner at QB even though they kind of do have a returner. If Russell Wilson is counted as a returner, Wisconsin jumps to the top of the table.

The Predictions

The power poll tells you how good I think a team is but to get a read on how they will be predicted to do you have to factor in opponents and game locations.

Woody Division (R. Wilson as returning starter)

If you drop Wisconsin down based on Wilson, Ohio State sneaks into the top spot.

SOS indicates the average PAN rating for all opponents on the season.

Bo Division

Michigan, at 8-4 (5-3 Big Ten) comes in second in the division, but Michigan, State and Iowa are all virtually indistinguishable in spots 2-4.

The Big Ten is highly bunched this season. Whether it’s Wisconsin, Ohio or Nebraska that makes it through the championship game depending on the scenario, I am projecting the Big Ten winner to have the most conference losses of any conference winner in 2011. [Ed-M: I'm predicting SEC fans will give us shit for that.]

Overall, the Big Ten is slotted third in my preseason conference ratings behind the SEC and what’s left of the Big XII. In conference strength of schedule, the Big Ten ranks fourth behind the Big XII, Pac 12 and SEC. The SEC is the only conference with a weaker non-conference lineup than the Big Ten.

Michigan’s strength of schedule is ranked 12th in the country, Notre Dame and Ohio [Ed-M: He means OSU; next thing you know Mathlete's gonna be pointing at things too.] are #1 and 2. The SEC has the seven toughest conference schedules among its ranks but its cupcake-loaded preseason leaves them lower overall.

National Notes

Predicted winners from other conferences:

Four of my top five match the AP top five (Boise, Oklahoma, Oregon and Alabama) but beyond that I have a handful of teams I think are over/underrated:

Overrated:
LSU
Florida St
Virginia Tech
Stanford
Michigan St

Underrated:
USC
West Virgnia
Air Force
Nevada
Nebraska

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.

Finding: Unlikely

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.

Finding: 

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.

Finding: Busted

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.

Finding: Busted

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.

What’s Included?

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.