This is a little off track, but I read about this at the very good Tide blog Roll Bama Roll and now have a deep need to purge this list from my head.

Traditional, plain helmets like Penn State's and old-school traditions like Michigan's wings and Alabama's numbers have their virtues, but the most important element of a good helmet design, if you ask me, is a strong, distinct logo unadorned by generics -- that is, words and/or letters, interlocking or otherwise. There's nothing wrong with, say, Tennessee or Illinois taking the easy route with the 'T' or block 'Illinois,' or North Carolina's interlocking 'NC.' If I was Carolina and my other options were a heel with tar on it or a goat, I'd definitely take that 'NC.'

But a few teams do it better:

1. Texas

2. Florida State (it always bothers me that the NCAA video games screw up this beauty by making it too small and too straight: the real thing wraps around the helmet with the subtle arc of a spear with killer intent)

3. Colorado (gets a pass on the interlocking letters because they're so well incorporated within an otherwise boring logo; also, as with FSU: love the gold)

4. Kansas State

5. Arkansas (best execution of a potentially sketchy concept: a drawing of a pig should be a disaster, but this is pretty clean and dynamic; quirky, completely original and only loses points for being monochromatic)

6. Southern Cal

7. Miami (unique, and like Texas, benefits from the clean white-on-white facemask)

8. Clemson

 9. Michigan State (only with the Spartan helmet as shown - the big 'S' does not work)

10. Arizona State

Honorable Mention: Wyoming, Iowa, Auburn, Georgia Tech (after Colorado, the best use of interlocking letters), LSU (would score higher if it was just the cool Tiger head), South Carolina (again, love the chicken, hate the big 'C'), New Mexico (right idea, but not a great logo), SMU (ditto), Western Michigan (too close to the Denver Broncos) and Washington (only because of the colors). Washington is one of several teams, along with South Carolina, Illinois, Southern Miss, Missouri, Boston College, Arizona and many others with perfectly good logos but generic, word/letter-themed helmet designs. Washington would have kick-ass helmets if the sleek Husky replaced the block 'W.'

Dishonorable mention goes to schools that try to incorporate words/acronyms and a logo in one big, unwieldy mash-up: Iowa State, Oregon State, Boise State, Memphis, San Jose State and Akron. Western Kentucky gets a pass for its sorry "hand waving a towel" logo because it's just joining the show and obviously has no idea how to represent a Hilltopper. And to Washington State and NC State, because what the hell?

That is all.

This Week on BleepingIdiots.com  we have a special treat for everyone as we sit down with Virginia Tech Hokies Redshirt Junior Running Back Jahre Cheeseman.

1. BleepingIdiots.com: First off tell us a bit about your injury, and the rehab process behind it. Do you feel you will be able to be back 100% and compete for the bulk of the carries this season?

• Jahre Cheeseman: I broke my left fibula during a scrimmage in the spring. I been doin a lot of balance and flexibility exercise along with lifting. the agility came later this summer but are also going well. I think I will be ready for the season but i expect that we will have a two-back system once again at VT. kenny lewis is now clear to go and we are workin together to compliment each other.

2. BleepingIdiots.com: On November 1st last year against Georgia Tech, you showed the coaching staff as well as the whole nation what you are capable of with the ball in your hands when you broke off that 70 yard run. Do you feel that the GT game gave you stronger pull for more carries this upcoming season?

• Jahre Cheeseman: I think it changed a couple of minds and opened some eyes. However I believe the spring determined alot of what kind of pt i will get this fall.

3. BleepingIdiots.com: Coming out High School, you were the #9 all purpose back in the nation. What was the toughest part of the transition from high school ball to the college game.

• Jahre Cheeseman: I think the hardest transition was the mental aspect, and getting more involv ed in film and my reads on the field.

4. BleepingIdiots.com: Many scouts still consider you to be a running back / cornerback… where do you prefer to play and where are you most comfortable?

• Jahre Cheeseman: I’d much rather play tailback. I feel more comfortabnle and able to produce more for the team at the running back spot, it is more natural to me.

5. BleepingIdiots.com: Which NFL running back do you compare most favorably to, and who do you feel you model your running style after?

• Jahre Cheeseman: I dont really compare my running style to anyone, I kind of just do what comes natural on the field.

6. BleepingIdiots.com: What is your opinion on the Dual QB system with Tyrod and Sean? Do you think it has a negative, positive, or no effect on team chemistry?

• Jahre Cheeseman: I really like the duel system that we have I think it brings out the best in each of them. Also I feel that it is great for the team and widely accepted

7. BleepingIdiots.com: You and Tyrod Taylor line up on the goal line, who gets to the 40 yardline first?

• Jahre Cheeseman: I couldn’t even say, In my current status I would say easily he would take me. But prior to the injury I think it would be a good race.

8. BleepingIdiots.com: Every kid’s dream is to play as themselves in a video game. This Wednesday NCAA 09 comes out, any rating predictions for yourself?

• Jahre Cheeseman: I didnt have any predictions for myself on the game I’m just curious to see what happens. I still have yet to pick it up. I’m just happy to even be in the game.

So in yesterday's WinCorr conversation, I mentioned that while WinCorr could be (and was) used to look at the overall stats that are most directly connected to success on the field.  I also mentioned that the measure could be used to create an individual 'footprint' for each team.  To illustrate this, I'm simply going to look at said footprint for a few teams and see where that takes us.

Team-Specific WinCorr's

We'll start with Colorado since, well, I've already done them.  Here are Colorado's top WinCorr's (and my conclusions) from Tuesday's Colorado piece:

Colorado's Top WinCorr's (any categories that were > 0.800)

1. Offensive Close-game Passing S&P+ (Correlation: 0.875)
2. Offensive Close-game Passing S&P (0.864)
3. Offensive Close-game Passing PPP (0.860)
4. Defensive Passing Success Rate, Pressure Situations (0.854)
5. Defensive Success Rate, Pressure Situations (0.843)
6. Offensive Line Yards on Non-Passing Downs (0.837)
7. Offensive EqPts gained in Non-Passing Downs (0.829)
8. Total Offensive EqPts gained (0.821)
   
9. Offensive EqPts+ (0.819)
10. Defensive Rushing Success Rate, Pressure Situations (0.817)
11. Offensive Rushing S&P, Pressure Situations (0.806)

There's some repetition in there obviously, and it builds a very distinct narrative.

• When Colorado was able to move the ball through the air, they probably won (especially when they were able to rip off a couple big plays).
• The Buffs found themselves in quite a few 'pressure' (i.e. 4th quarter, score within two possessions) situations, and when the defense stepped up, they won.
• Offensive 'Pressure' numbers weren't as important, but to the extent that they were, it was all about moving the ball on the ground and staying out of passing situations.
  
• Actually, that went for the whole game.  Staying out of uncomfortable situations was key for the Buffs--if they were able to move the ball well in Non-Passing Downs and stay out of Passing Down situations, they stood a chance.  But with a shaky freshman QB behind center, Passing Downs were murder.

In some cases, you can see how this would be useful in making your 2008 predictions/rankings.  Obviously the basic criteria--returning starters, etc.--are still useful, but in discussing Colorado we can see that how they deal with things like finding a more consistent passing game is a little more important than other things.

Anyway...let's take a look at some other teams to determine a) how different one team's 'footprint' is to another's, and b) what all this Team WinCorr concept can tell us.

We'll start with Phil Steele's preseason #1 team, Florida.

Florida's Top WinCorr

1. Defensive Passing Success Rate, pressure situations (0.960)
2. Defensive Success Rate, pressure situations (0.928)
3. Offensive Rushing Success Rate, redzone (0.899)
4. Offensive S&P (0.888)
5. Defensive Rushing Success Rate, pressure situations (0.869)
6. Defensive Rushing S&P, pressure situations (0.861)
7. Offensive S&P+, close games (0.856)
8. Offensive S&P, close games (0.849)
9. Offensive Success Rates (0.847)
10. Offensive Passing Success Rates, close games (0.845)
11. Offensive PPP, close games (0.843)
12. Offensive Passing S&P, close games (0.837)
13. Defensive Rushing S&P+, Non-Passing Downs (0.832)
14. Defensive S&P, pressure situations (0.827)
15. Offensive Passing S&P (0.827)

Tie this with what we already (think we) know about Florida, and you can reach the following conclusions.

• Florida found themselves in a lot of pressure situations (kind of like Colorado), and their defensive performance was key (as with Colorado).  We kind of already knew this--the offense had no problem generating points against Georgia and Michigan in particular, but they couldn't keep points off the board.
  
• Even though we discovered yesterday that Points Per Play was more important than S&P and success rates overall, for Florida it was all about Success Rates.  The explosiveness was always there, but the efficiency broke down at inopportune times.
• '+' numbers weren't as strong in correlation than the more raw statistics.  This most likely tells you that their performance didn't have much to do with the opponent they were playing.  Offense was pretty much good no matter what and defense was average(ish) no matter what.
• The importance of redzone success running the ball was very much unique to Florida.  Either it tells you that the predictability of "Tebow right, Tebow left, and Tebow up the middle" held them back at times, or that their rushing game down there was bad until they instituted "Tebow right, Tebow left, and Tebow up the middle."  I'm leaning toward the latter.

So...being that Florida was breaking in a lot of young players on defense, and they've now gotten a year's worth of pressure situations under their belt, you figure the defense will respond better to pressure than they did last year.  That's obviously a good thing for UF's title chances in '08.  Meanwhile, we can't say anything definitive about the Gators' consistency/efficiency, but we do know that UF's non-Tebow running game should be in better shape in '08.  Not only will Kestahn Moore have another year of experience, but if the spring is any indication, USC transfer Emmanuel Moody will be more than ready to make some noise, and true freshman Chris Rainey could be dangerous as well.  Having more realistic threats in the redzone should alleviate that issue.

Okay, so the WinCorr concept says encouraging things about Florida.  How about my own team, Mizzou?

Continue reading this post »

Gary Pinkel has always mentions that playing defense is all about leverage.  If two guys are pursuing a runner who is running toward the sideline, the job of the first guy isn't necessarily to tackle him, but to make him cut back toward the middle of the field to be tackled.  (I'm horrifically paraphrasing here, I realize.)  Whereas if you miss a tackle on the outside, the guy might run for 70 yards, if you turn him inside and there's a missed tackle, there are about 4 others guys in pursuit to make the tackle.  It's not necessarily about making the big play yourself--it's about making it harder for the runner to make the big play.  Or something like that.

Why am I mentioning this?  Because my latest BTBS idea--Win Correlations (WinCorr)--further suggests that it's not necessarily how many big defensive plays you make that determines how well you do...it's more about leveraging the offense into uncomfortable situations (a.k.a. Passing Downs).  I've covered this a bit in the past, but these numbers demonstrate that principle even further.

As I mentioned the other day in my Colorado preview, WinCorr is, in short, the correlation between a given statistical category and wins/losses.  As you'll see, they can serve a couple different purposes: 1) we can use them to identify the most important of all the BTBS categories we've looked at to date, and 2) we can look at team-specific WinCorr's to develop a unique footprint for each team.  We'll look at the former today and the latter tomorrow.

To illustrate the various uses of WinCorr, let's jump right in.

National WinCorr

There are two ways to look at WinCorr on a national level--determining which statistical categories are most tied to winning a specific game and determining which categories are most tied to winning seasons...i.e. being a good team.  We'll look at both.

As I said above, we compare each statistical category with overall wins and losses, but...how do we come up with a number for wins and losses when we're talking about a single game?  We have two options: either we 1) give wins a 1 and losses a 0 and run correlations off of that (the black and white way), or we 2) compare the stats from each game with the % of points a team scored in that game (the gray area way). (So if a team wins 20-10, instead of giving the winning team a 1, we'd give them a 0.667, as they scored 66.7% of the game's points.) 

The former is cleaner (and leads to lower correlations, obviously), but the latter is probably a bit more telling.  It determines a difference between winning 24-23 and winning 41-3. Plus, the correlations are simply stronger using % of pts, so that's what I'm going with here.

Two other things to note: 1) I ran Spearman correlations for these numbers--if you're a nerd, you probably know what Spearmans are, and if you don't, you probably don't care; and 2) below is a list of the strongest correlations...meaning there is the possibility of a negative correlation on the list with positive correlations.  I did this because we're looking at what most directly impacts a game, not what impacts it in a positive or negative way...if that makes any sense.

Also...for each of these, I'll get rid of the obvious ones--you don't need lots of stats to figure out that things like '% of pts' and 'total points' are going to be highly correlated to wins.

Okay, one more thing: I've tried to highlight the most important information in boldface, so if numbers make your eyes glaze over, skip right to the bolded parts.

WinCorr (Correlations using % of pts)

1. PPP, close game (0.682)
2. S&P, close game (0.678)
3. PPP, overall (0.642)
4. S&P, overall (0.634)
5. Total EqPts (0.617)
6. Total EqPts, Non-Passing Downs (0.597)
7. Total Rushing EqPts (0.587)
8. Passing S&P, close game (0.583)
9. Total Rushing EqPts, Non-Passing Downs (0.582)
10. Total Rushes, Q4 (0.579)
11. Success Rate, close game (0.578)
12. PPP, Non-Passing Downs (0.575)
13. Passing S&P, overall (0.573)
14. Passing PPP, close game (0.565)
15. S&P, Non-Passing Downs (0.565)
16. Passing PPP, overall (0.563)
17. Success Rate, overall (0.540)
18. Total Rushes on 1st Down (0.534) ???
19. Total Rushing EqPts on 1st Down (0.529)
20. Rushing PPP, close game (0.529)
21. Rushing S&P, close game (0.523)
22. Total EqPts on 1st Down (0.521)
23. Total Line Yards, Non-Passing Downs (0.517)
24. Total Passes, Q4 (-0.516)
25. Total Rushes (0.510)

I listed 25 because the correlations for all were over 0.500.  Pretty strong correlations abound.  And I realize your eyes probably glazed over looking at that list, but this tells a few really interesting stories.

• Success on Non-Passing Downs is crucial.  Here's where the 'leverage' idea comes into play.  Stopping a 1st-and-10 rush for 3 yards instead of 5 creates a much less comfortable situation for the offense.  Little things like that could be seen as just as important as big hits and singular big plays over time.
• What strikes me as most interesting here is that PPP is worth a smidge more than S&P.  The idea of S&P (Success Rates + Points Per Play) is to combine the efficiency of Success Rates and the explosiveness of PPP.  However, PPP's correlation is 0.642 (in close games, 0.682), while the Success Rate correlation is only 0.540 (in close games, 0.578).  Granted, that's only a 0.1 difference, but that's still a difference.  And therefore it drags down the overall applicability of S&P.  May have to think about retooling the idea of S&P.
• It's also interesting that PPP (the ability to make big plays) and S&P are more important to the passing game, while pure EqPts (the Pts you've racked up over the course of the game, not the average) is more important to the rushing game.  Not totally sure what that means yet, but it's interesting.  It's like the threat of a good passing game is as important as actually performing well in the passing game.
• One of my initial suspicions rings true--in the end, the 'close game' numbers are a bit more important than the 'overall' numbers.  This makes sense--and it's why I initially created a 'close game' measure in the first place--but it's nice to get some affirmation on it.
• Obviously the presence of "Total Q4 Rushing Attempts" (as a decent positive correlation) and "Total Q4 Passing Attempts" (as a negative one) is a bit of 'correlation vs causation' here.  It's not that you win more because you're rushing in Q4--it's that you rush more in Q4 because you're winning.  This does, however, verify that bit of conventional wisdom.

Continue reading this post »

via www.landonhowell.com

[Additional screen shots from a local TV station can be seen here.]

Decals on the helmet? How about the facemask? Southern Miss is one of two teams in the NCAA this year making original and unique adjustments to the facemask.

Southern Miss will use the 'Eagles' seen on the facemask above during the 2008 football season. As is the case with other decals, these can be customized to team specifications and are expected to be adopted by other collegiate and professional football teams in 2009.

Last year, Missouri shocked the nation with its climb up the BCS and Associated Press polls (in week 13 they were ranked number one in both polls).  On Saturday night ESPN Game Night was debating which teams had a shot at the '08 BCS title.  The crew at Game Night came up with seven possible championship contenders.  I don't know if I necessarily agree with that assessment, but I do think it raises an interesting question.  Which team, in your mind, has the best shot to be this year's Missouri?  In other words, what sleeper team will you have your eye on in '08?

So after yesterday's look at Turnovers, it's now time to establish point values for special teams.  Leaving PATs out of it for now (it will obviously be easy to add them later), there are three major special teams category (and a fourth minor one): Field Goals, Punts, and Kickoffs (and then Free Kicks).  Let's attack them one at a time.

Field Goals

Figuring out what to do about Field Goals was by far the easiest of these categories.  I broke FGs into 5-yard increments (18-22 yards, 23-27, 28-32, etc.), looked at the % made in each group, and determined the expected number of points from each kick.  Here's what I found:

• 18-22: kickers made 91.4% of these kicks.  3 points * 0.914 = 2.74 expected points
• 23-27: 88.1%, 2.64 expected points
• 28-32: 80.3%, 2.41 expected points
• 33-37: 69.4%, 2.08 expected points
• 38-42: 67.1%, 2.01 expected points
• 43-47: 58.1%, 1.74 expected points
• 48-52: 45.6%, 1.37 expected points
• 53-57: 35.0%, 1.05 expected points
• 58+: 20.0% (1-for-5), 0.60 expected points

So with that, we can treat every FG like an addition or loss of points.  For instance, if you miss a 25-yard FG, it's a loss of 2.64 points. If you make it, it's worth 0.36 points.  That may not seem like a lot, but you have to remember that the team has been adding (and possibly subtracting) points all the way up the field.  To get to the opponent's 8-yard line, they've probably earned at least somewhere in the neighborhood of 2-3 EqPts, so the 0.36 points seems a lot more reasonable in that regard.

So for the season, who benefited the most from their field goal kickers?

Top 10, FG pts/game

1. Arizona State (1.27/g)
2. Indiana (1.12)
3. North Carolina (1.07)
4. Utah (1.07)
5. UCLA (1.07)
6. New Mexico (1.03)
7. UTEP (1.01)
8. Illinois (1.01)
9. Florida State (0.89)
10. Georgia Tech (0.82)

24. Texas (0.48)
31. Nebraska (0.45)
33. Oklahoma (0.42)
37. Missouri (0.34)

2007 All-American kickers included Arizona State's Thomas Weber, Indiana's Austin Starr, Utah's Louie Sakoda, and New Mexico's John Sullivan, so this seems right to me.  It also seems to me that UNC's Connor Barth and UCLAs Kai Forbath didn't get enough recognition.

Bottom 5

126. San Jose State (-1.56)
125. FCS Tier 3 (-1.34)
124. Duke (-1.26)
123. Miami-OH (-1.00)
122. Oklahoma State (-0.90)

(As a method of verifying this, I looked at OSU's 2007 stats.  Their two kickers--Jason Ricks and Dan Bailey--made 10 FGs, 9 of which were from under 30 yards.  Meanwhile, not only did they miss an under-30 kick, but they were also 1-for-8 from beyond 30.  Sounds like a Bottom 5 performance to me.

On to the kicking/return game...

Continue reading this post »

In most of my previous BTBS posts, I've acknowledged that the whole thought behind my EqPts measure (and therefore the PPP and S&P measures as well) is only one part of scoring points.  It's the most important part, but there are other factors involved--namely Turnovers and Special Teams (and luck, but we're not measuring that).

The main questions are a) how much of an impact do TO's and Special Teams actually make, and b) how are we going to measure that?  Well, it's time to look into that.  Today, we'll look at turnovers.

In a previous BTBS glossary, I talked about measuring Turnover Costliness this way:

Each turnover is assigned two values: 1) the point value (see below) of the offense's field position at the time of the turnover, and 2) the point value of the resulting starting field position for the opposition.

Turnover Costliness = (0.75*the higher of the two values)+(0.25*the lower of the two).

I previously had a factor in here regarding closeness of the game, and I'm sure I will again, but for now this is what I'm working with.

Let's throw that idea out.  What happens if we count both values (the value of the offense's field position at the time of the turnover and the value of the resulting starting field position for the opposition) fully, combining the two to gauge the 'points' involved in a given turnover. Where does that take us?

For one thing, it means we're looking at quite a few different numbers here.  For every time your offense turns the ball over, you've got a "Points Lost" number (your own field position at the time of the t/o) and a "Points Given" number (opponent's resulting field position).  For every time your defense benefits from a takeaway, you have a "Points Prevented" number (your opponents' field position at the time of the t/o) and a "points Taken" number (your resulting field position). Obviously Points Lost and Points Prevented are the same number (depending on whether you're on offense or defense), as are Points Given and Points Taken.

Let's quickly look at the best and worst from each category, then try to figure out what this all means.

(And through all of these numbers, realize this--I also count botched punts/field goals as turnovers, so my Turnover Margin figures will likely be different than the official NCAA stats.)

Continue reading this post »

New slogan, SMQ?    "Every team, every day, all year long" ??  Unless, of course, this means you're going to write 119.5 posts a day (even on Christmas and stuff), I liked the old one better.  But now it's SamKellering over there by the journalism thing. 

Was it a corporate decision?  That sort of sounds like something a consultant would come up with.  And have I stretched this to the requisite 75 words? Yes.

When I started entering all this play-by-play data, one of my main goals was simply to apply some of the basic sabermetric ideas to football.  I mean, if they make sense in one sport, they should make sense in another, no?  The idea behind my 'EqPts' (and therefore PPP) measure came from two baseball measures: 'EqR', the Equivalent Runs concept that take a series of offensive stats and determines how many runs those stats should have produced on average, and Expected Runs, the matrix that shows you, on average, how many runs you can expect out of specific "__ runners on, __ outs" situations.  And of course the S&P (Success Rate + PPP) measure was an obvious rip-off of OPS.

Well, the next one I'm going to rip off (actually, I prefer co-opt) is the '+' concept.  The idea of an Adjusted ERA or Adjusted OPS figure (also known as ERA+ or OPS+) starts with saying, basically, that not every 3.68 ERA (or 0.890 OPS) is created equal.  Was it during the deadball era?  Was it in a hitter's park or the Polo Grounds?  You try to put everybody on as even a playing field as possible to evaluate their stats.  That idea should work for football too, right?

Last year Colt Brennan threw for 416 yards and 6 TDs against Northern Colorado on September 1, while Tim Tebow threw for 304 yards and 2 TDs (and 15.8 EqPts) against a decent South Carolina defense on November 10. By all basic statistical accounts, Brennan's stats were insanely good and easily better than Tebow's performance against SC. However...could Brennan have put up Tebow's numbers against SC? What would Tebow have done against Northern Colorado?  In a nutshell, the goal of the '+' concept is, for me, to adjust for what's expected against different opponents.

For every major measure I use, both the ones I created and the ones I, uhh, co-opted--Success Rate, PPP, S&P, Line Yards/Sack Rates, etc.--you could create '+' measures that compare an offense's or defense's performance to what their opponents typically averaged.  And here's how we're going to do it, using a blurb from my Buffalo BTBS piece as an illustration:

Let's take [Buffalo's] October 4 matchup against Ohio, a game they won 31-10. For that game (without taking turnovers into account) they put up a 0.780 S&P and scored 30.3 EqPts, while Ohio garnered a .576 S&P and 12.0 EqPts. How did that compare to what an average opponent did against Ohio? Ohio gave up 22.0 EqPts per game and a 0.704 S&P while gaining 18.3 EqPts and a 0.665 S&P. So Buffalo gained 1.66 times more than the average Ohio opponent gained, 166% of normal. Ever heard of the OPS+ measure? Basically it compares people to averages, with a score of 100 more-or-less meaning that the person gained exactly 100% of what was expected. So if we use this concept, we can say that Buffalo's offense put up an EqPts+ measure of 166 against Ohio, meaning they gained 166% of what Ohio normally gave up. Get it? They also put up a 110.9 S&P+.

Meanwhile, if you flip the equation, you can come up with a defensive score as well. (You have to flip the equation so a good defensive performance also results in a score above 100.) Buffalo's defensive scores against Ohio were a 109.6 EqPts+ and a 115.6 S&P+.

So to summarize that in pretty bullet points...

• For every game they play, a team's output (offensive and defensive) is compared to the expected output considering the team they're playing.
• 100 = dead average.  Over 100 = good, under 100 = bad.
• The purpose of this is to give (or take away) credit for teams' statistics based on the quality of their opponents.  Technically you could do this same thing with rushing yards, points (the real kind), or anything else, but since I've been doing all this measuring of EqPts, success rates, etc., and since I'm very much sold on the quality of these measurements, by god we're going to use them.
• The trick here is that, for each game played, there are two sets of offensive ratings and two sets of defensive ratings.  Why two?  Well, taking into consideration the Buffalo-Ohio game above, Buffalo got an offensive score for their performance against Ohio's averages and a defensive score for their performance against Ohio's averages.  However, Ohio also got offensive/defensive scores compared to Buffalo's averages (a 47.3 EqPts+ on offense and a 51.8 EqPts+ score on defense, if you're scoring at home...or if you're alone).  The key is that Ohio's score isn't simply an inverse of Buffalo's score.  If that's still not clear, I'll illustrate with more examples, but for now we'll move on.
  

So what's the point of doing all of this?  Quite simply, we can more accurately measure how good teams really were.  The best way to illustrate that is to show you some rankings.  I have lots of '+' measures to choose from, and I haven't yet figured out the most accurate one to use, but let's just run through some for now.

EqPts+ (Offense)

1. Florida (172.45 avg)
2. Oregon (159.16)
3. Louisville (156.05)
4. West Virginia (155.88)
5. Tulsa (154.83)
6. Kentucky (151.31)
7. Missouri (151.02)
8. Texas Tech (150.68)
9. LSU (149.22)
10. Navy (148.74)

Now, none of the names on that list are particularly surprising, but how do these rankings compare to pure scoring and yardage rankings?

Florida: #3 scoring offense, #14 total offense
Oregon: #12 scoring offense, #10 total offense
Louisville: #18 scoring offense, #6 total offense
West Virginia: #9 scoring offense, #15 total offense
Tulsa: #6 scoring offense, #1 total offense
Kentucky: #15 scoring offense, #24 total offense
Missouri: #8 scoring offense, #5 total offense
Texas Tech: #7 scoring offense, #2 total offense
LSU: #11 scoring offense, #26 total offense
Navy: #10 scoring offense, #22 total offense

And what about some of the teams who ranked high in the 'regular' rankings but didn't appear in the top 10 above?

Hawaii: #1 scoring offense, #3 total offense...#12 in EqPts+
Kansas: #2 scoring offense, #19 in EqPts+
Boise State: #4 scoring offense, #18 in EqPts+
Houston: #4 total offense, #37 in EqPts+
Oklahoma: #5 scoring offense, #14 in EqPts+

As you would expect, teams with tougher slates--i.e. a lot of SEC teams--were held in higher regard using the '+' concept.

So what about the S&P+ measure?  That takes efficiency and explosiveness into account instead of simply explosiveness.

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