Sunday Morning Quarterback: FanPosts

Best CFB Helmets

SMQ — Tue, 22 Jul 2008 18:19:56 -0000

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.

Jahre Cheeseman: BleepingIdiots.com Interview

BleepingIdiots — Tue, 22 Jul 2008 02:36:30 -0000

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.

Beyond the Box Score: Win Correlations (Part Two)

The Boy — Sun, 20 Jul 2008 19:27:32 -0000

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)

Offensive Close-game Passing S&P+ (Correlation: 0.875)

Offensive Close-game Passing S&P (0.864)

Offensive Close-game Passing PPP (0.860)

Defensive Passing Success Rate, Pressure Situations (0.854)

Defensive Success Rate, Pressure Situations (0.843)

Offensive Line Yards on Non-Passing Downs (0.837)

Offensive EqPts gained in Non-Passing Downs (0.829)

Total Offensive EqPts gained (0.821)

Offensive EqPts+ (0.819)

Defensive Rushing Success Rate, Pressure Situations (0.817)

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

Defensive Passing Success Rate, pressure situations (0.960)
Defensive Success Rate, pressure situations (0.928)
Offensive Rushing Success Rate, redzone (0.899)
Offensive S&P (0.888)
Defensive Rushing Success Rate, pressure situations (0.869)
Defensive Rushing S&P, pressure situations (0.861)
Offensive S&P+, close games (0.856)
Offensive S&P, close games (0.849)
Offensive Success Rates (0.847)
Offensive Passing Success Rates, close games (0.845)
Offensive PPP, close games (0.843)
Offensive Passing S&P, close games (0.837)
Defensive Rushing S&P+, Non-Passing Downs (0.832)
Defensive S&P, pressure situations (0.827)
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?

Top Missouri WinCorr's

Many of Mizzou's 'Pressure Situation' numbers came into play here with very high correlations (including numerous with 1.000 correlations), but I'm not including them here because, well, they didn't find themselves in many pressure situations. And beyond that, their correlations weren't as strong as Florida because, I assume, they only lost twice maybe? Not sure.

(And I'm still enjoying being able to casually mention that Mizzou was better than Florida, Michigan, etc., last year. Gotta milk it while it lasts.)

Opponents' score (0.950)
Defensive PPP (0.870)
Defensive Passing PPP, redzone (0.859)
Defensive S&P (0.851)
Defensive EqPts (0.840)
Defensive Passing S&P (0.828)
Defensive Passing S&P, redzone (0.824)
Defensive Rushing S&P (0.807)
Offensive Rushing S&P+, Q4 (0.797)
Defensive Rushing EqPts, Non-Passing Downs (0.791)
Offensive Rushing S&P, Q4 (0.787)
Offensive Line Yards+, Q4 (0.783)
Offensive Line Yards (0.782)
Defensive Passing S&P+ (0.781)
Offensive Rushing PPP, Q4 (0.779)

Uhh...see any trends there? There are blatant trends, and there are blatant trends.

The first 8 measures (and 10 of the 15) were defensive--which is, I guess, what happens when your offense is scoring 30+ points in 13 of 14 games. The Mizzou defense came up big, but when Mizzou lost (to OU), or when a game was a bit closer than it should have been (against Iowa State, for instance), defensive breakdowns (however minor) were the likely cause (aside from the second half of the Big 12 title game, when it was alllll on the offense).
Redzone defense was a bigtime key for Mizzou. If they held you to FGs, you were toast.
Where offense did come into play, it was late-game rushing, late-game rushing, and late-game rushing. They were able to do it against everybody but Oklahoma.

So defense and rushing are key for Mizzou. Gotcha. The Mizzou defense returns 10 Coton Bowl starters--everybody but DT Lorenzo Williams--so that has to strike optimism in Mizzou fans. As for the rushing game...I guess that's still a question mark with the loss of Tony Temple, though anybody who saw sophomore Derrick Washington carrying the ball this spring had their concerns alleviated a bit. Washington and Jimmy Jackson (along with Earl Goldsmith, I guess) got some experience in the past simply because Temple was always rather injury-prone, and Jackson proved himself to be as efficient a runner as you could ask for. Washington's the homerun threat, but if these two RBs with presidential names can get some yards when asked, the Mizzou offense will run just as smoothly as last year.

Okay, one more. How bout the defending national champion?

Top LSU WinCorr's

Defensive Passing EqPts (0.866)
Opponents' score (0.863)
Defensive Passing S&P (0.861)
Defensive Passing PPP (0.858)
Defensive EqPts+ (0.839)
Defensive EqPts (0.834)
Defensive S&P+ (0.831)
Defensive Passing Success Rates (0.831)
Defensive Success Rate, Passing Downs (0.827)
Defensive Passing S&P+ (0.826)
Defensive S&P+, Passing Downs (0.823)
Defensive PPP, close games (0.820)
Defensive PPP (0.815)
Defensive S&P (0.813)
Defensive EqPts, Passing Downs (0.807)

Wow. Okay. So...LSU's offensive performance more or less made no difference whatsoever in the results of their games (relatively speaking). They were solid offensively, but their margin of victory (and their two defeats) were decided by defensive breakdowns.
Why do I say defensive breakdowns? Because a) while LSU had a great overall passing defense, 7 of these 15 categories had to do with passing defense, and b) 3 of these categories were related to passing downs...meaning LSU had the occasional breakdown on passing downs (against, predictably, Arkansas and UK in particular), and when they did, their odds of losing went way up.
For the record, here were the top 3 offensive categories: 1) PPP, 1st downs (0.677), 2) EqPts, 1st downs (0.655), 3) S&P, 1st downs (0.639). The #5 and #6 offensive categories were also 1st-down related. I'd call that a trend, wouldn't you? Basically that says that the offense's job was, at its most simplified, to spell the defense long enough to keep it dominant. If they got some yards on 1st downs (and therefore made themselves more likely to move the chains and eat a bit of clock), they were likely going to win.

This actually bodes well for LSU in 2008. Their biggest question mark is at QB, but...all they have to do is find a QB who doesn't hand the ball to the other team and a RB who can get 4-5 yards on 1st down, and they'll be in good shape. I know it's not that simple, but still...this bodes well for LSU.

Predictive Potential of WinCorr

When you boil it all down, statistics are fun for three reasons: 1) you can use them to understand the game better, 2) you can use them to evaluate and compare players/teams, and 3) you can derive quite a bit of predictive ability from them over time. At this point, I can say that #1 has been a rousing success so far, and #2 is slowly rounding into shape, but #3 is completely and conspicuously absent so far for one simple reason: I only have one full year of data.

So while it's easy to say that these WinCorr 'footprints' bode well for LSU, Florida, and Missouri (and not so much for Colorado), at some point we run into a bit of a brick wall with the WinCorr concept for now. We don't know for sure how much of the WinCorr idea is arbitrary from year to year, but it's certainly something to watch. And the fact that the numbers are so dramatically different for every team certainly make you think there's something going on there. But as we backtrack into the 2006 numbers and, obviously, prepare for an onslaught of 2008 numbers, we'll have to see what becomes of this.

Beyond the Box Score: Win Correlations (Part One)

The Boy — Sat, 19 Jul 2008 16:54:44 -0000

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)

PPP, close game (0.682)
S&P, close game (0.678)
PPP, overall (0.642)
S&P, overall (0.634)
Total EqPts (0.617)
Total EqPts, Non-Passing Downs (0.597)
Total Rushing EqPts (0.587)
Passing S&P, close game (0.583)
Total Rushing EqPts, Non-Passing Downs (0.582)
Total Rushes, Q4 (0.579)
Success Rate, close game (0.578)
PPP, Non-Passing Downs (0.575)
Passing S&P, overall (0.573)
Passing PPP, close game (0.565)
S&P, Non-Passing Downs (0.565)
Passing PPP, overall (0.563)
Success Rate, overall (0.540)
Total Rushes on 1st Down (0.534) ???
Total Rushing EqPts on 1st Down (0.529)
Rushing PPP, close game (0.529)
Rushing S&P, close game (0.523)
Total EqPts on 1st Down (0.521)
Total Line Yards, Non-Passing Downs (0.517)
Total Passes, Q4 (-0.516)
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.

Here's another long list--it's WinCorr over the course of a season. See if you can pick out trends.

WinCorr - season numbers (Correlation to '% of pts')

EqPts Per Game (0.752)
PPP (0.749)
PPP, Non-Passing Games (0.748)
S&P, Non-Passing Downs (0.745)
S&P, close games (0.733)
PPP, close games (0.725)
Rushing PPP, Non-Passing Downs (0.722)
S&P, 1st downs (0.711)
Success Rate (0.708)
PPP, 1st downs (0.706)
Rushing PPP (0.702)
Rushing PPP, close games (0.693)
Passing S&P (0.692)
Success Rate, close games (0.690)
Rushing S&P (0.687)
Rushing PPP, 1st downs (0.687)
Rushing S&P, Non-Passing Downs (0.686)
Rushing S&P, close games (0.682)
S&P, 3rd downs (0.681)
S&P, Q1 (0.675)
Success Rate, Non-Passing Downs (0.675)
Success Rate, 3rd Downs (0.674)
Passing PPP (0.663)
PPP, Q1 (0.654)
Rushing S&P, 1st Downs (0.653)

Of the 25 stats on the list, 8 were either related to Non-Passing Downs or 1st Downs. Leverage, leverage, leverage.
EqPts and PPP continue to exceed S&P and Success Rates in importance. Ten of the 25 most important stats were variations of PPP...11 if you include EqPts Per Game.
Interesting, though, is the fact that over the course of a season, overall PPP becomes more important than Close-Game PPP. Figure that one out.
Nine rushing statistics on the list...2 passing statistics. Interesting. And these categories have nothing to do with pure quantity of rushes (which means the 'correlation vs causation' argument really doesn't come into play).

'+' Number Correlations

Since I spent all that time developing the '+' Number concept, you knew I was going to look at that too. What's funny, though, is that for this one, the correlations with Win % were significantly stronger than the correlations with % of Pts. I mean seriously, correlations in the 0.9 range? That's significant.

So that's what we're going to use.

Win Corr - + numbers (Correlation to Win %)

Offense	Defense
S&P+, close games (0.910) EqPts+ (0.899) S&P+ (0.896) Passing S&P+ (0.801) Rushing S&P+ (0.765) Rushing EqPts+ (0.763) Rushing S&P+ (0.745) S&P+, 2nd Downs (0.680) Rushing S&P+, close games (0.674) Passing S&P+, close games (0.669)	EqPts+ (0.919) S&P+, close games (0.898) S&P+ (0.889) Passing S&P+ (0.787) Rushing S&P+ (0.775) Rushing S&P+, close games (0.734) S&P+, Non-Passing Downs (0.733) S&P+, 1st Downs (0.730) Rushing S&P+, Non-Passing Downs (0.699) Rushing S&P+, 1st Downs (0.696)

I guess if you're a "Defense wins championships" kind of person, then here you go: Defensive EqPts+ is more tied to Win % than Offensive EqPts+. Granted, we're talking 0.919 vs 0.899, but...again, it's a difference!
While EqPts+ are more tied to winning on the defensive side, S&P+ numbers are more tied to winning on the offensive side. So...pure numbers are more important defensively, while averages are more important offensively? Something like that? I dunno. Can't make much of that.
Four more 'leverage stats' show up on the defensive side (S&P+, Non-Passing Downs; S&P+, 1st Downs; Rushing S&P+, Non-Passing Downs; Rushing S&P+, 1st Downs), however they're conspicuously absent from the offensive list. Granted, they're still important (garnering correlations of 0.663, 0.573, 0.557, and 0.482 respectively), but they're rather significantly less important. Can anyone explain that one? Creating leverage against the offense is better than...creating leverage against the defense? Eh?

So we can reach some pretty interesting conclusions from this data, and most of it comes back to the idea of leverage. I have data broken out for all quarters, all downs, the redzone, etc., and by far the most significant category is how teams perform in Non-Passing Downs.

Passing Downs = Turnaround?

This brings me to an interesting question: if Passing Downs = death, on average, then would the teams with the best numbers on Passing Downs be privy to a possible turnaround in luck the next year? In other words, are Passing Downs a lot like Turnovers? Is success in the category somewhat arbitrary...and does it even out over time? Being that we only have one year of play-by-play data so far, all we can do is take a look at the best (and worst) teams in the category, speculate, and see what happens at the end of the year. When I have multi-year data, it's going to be fun to tie all these season stats to success the next season, so we can see which stats are the best predictors of future success, but alas that is not an option just yet.

S&P+, Passing Downs (Offense)

1. Nebraska
2. Florida
3. Oregon
4. Texas Tech
5. Tulsa
6. Washington State
7. Kentucky
8. Hawaii
9. Louisville
10. West Virginia

Now...Florida, Texas Tech, Tulsa, Kentucky, Hawaii, and Louisville were six of the best passing teams in the country, so their presence on the list should surprise no one. Plus, Oregon and West Virginia had great all-around offenses as well. But...Nebraska? Washington State? Last year it seemed like NU was about 110% likely to run a screen to Marlon Lucky on third-and-long, and while it got yards (decent PPP), it certainly didn't move the chains very often (low success rate). Maybe they ate up enough yards each time to get them to #1, but...that just seems weird. I'm officially putting NU and Wazzu on my "watch" list. If they underachieve (not that either could do much worse than last year), this may be one reason why.

What about the bottom of the list?

120. Utah State
119. Northern Illinois
118. Idaho
117. Iowa
116. Temple
115. UCLA
114. Navy
113. Tulane
112. Akron
111. Notre Dame

Okay, none of these teams were known for their offense (or for having a good team at all), but there are a few major-league teams on the list that, if this measure means anything, could be in for a nice turnaround season--Iowa, UCLA, Notre Dame.

Summary

One thing I'm going to accomplish in the future is the entry of all the regular (i.e. non-BTBS) statistics as well. I would obviously like to see what the WinCorr's are in regard to things like 'yards per carry' (as opposed to my version, PPP). I have some tools at my disposal now that should make that pretty easy to compile, but we'll see when it actually happens. In all, though, I'm extremely satisfied with the WinCorr idea. Like I said, tomorrow we'll take a look at what a specific team's WinCorr says about them. In the meantime, let me know what you think!

Facemask Decals: New Form of Collegiate Marketing

bamassippi — Wed, 16 Jul 2008 23:24:24 -0000

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.

College Football Question: Sleeper Teams

SteveMeredith — Sun, 13 Jul 2008 23:15:44 -0000

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?

Beyond the Box Score: Special Teams

The Boy — Sun, 06 Jul 2008 15:33:23 -0000

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...

Punts

The FG idea above was something of a no-brainer for me, but for Punts, Kickoffs, and Free Kicks, I had to toss around a few different ideas. Here's what I did (and this applies roughly to all three):

Take the receiving team's point value for the line of scrimmage of the punt. for example, you're punting from your 20 yard line. If your opponent had the ball on your 20 yard line, it would be worth 3.898 points.
Take the point value of where the ball ended up. In the above example, let's say the ball was punted 40 yards (to your opponent's 40) and returned 10 (to the 50). The point value of the 50 is 2.095 points.
3.898 - 2.095 = 1.803. That punt was worth 1.803 points.
Got it? So the higher the point total, the better it is for the kicking team. The lower, the better for the receiving team.

Top 10 Teams, Punting

1. Ohio (2.03 pts/punt) (#53 in net punting-yards)
2. Texas A&M (1.89) (#4)
3. Toledo (1.86) (#2)
4. West Virginia (1.83) (#15)
5. Georgia Tech (1.81) (#7)
6. Eastern Michigan (1.81) (#6)
7. Cincinnati (1.81) (#1)
8. Penn State (1.80) (#3)
9. Oklahoma (1.80) (#24)
10. New Mexico (1.79) (#12)

12. Kansas State (1.75)
34. Texas Tech (1.64)
44. Iowa State (1.61)

So other than the crazy Ohio outlier, there's nothing too strange about these numbers. When you think about it, the biggest difference between my numbers and the typical net punting-yards numbers are that...well, a touchback isn't too big a penalty with my numbers. The difference between downing a punt at the 3 and having a touchback may be 17 yards (which is huge), but it's only 0.903 EqPts, a miniscule point margin compared to other areas of the field.

Bottom 5 Teams, Punting

126. Missouri (1.14)
125. San Jose State (1.19)
124. Kent State (1.23)
123. Nevada (1.27)
122. Oregon State (1.31)

Wow. Strangely, I'm now more excited for 2008. The single biggest question mark Mizzou has is at punter, where Adam Crossett is gone and Jake Harry and Random Walk-Ons #1-3 are fighting it out, but...well, we were already the worst in the nation in punting last year, and it didn't slow us down much! What's it going to be in '08...worse than last place?

So what about the punt return side of things?

Top 10 Teams, Punt Returns (remember, the lower the better)

1. Kansas State (0.92 points per punt) (#1 overall in punt return avg-yds)
2. Utah State (1.23) (#2)
3. San Jose State (1.23) (#10)
4. Western Kentucky (1.26) (#9)
5. Army (1.27) (#5)
6. Ole Miss (1.27) (#95?)
7. Florida (1.27) (#7)
8. Alabama (1.29) (#11)
9. Idaho (1.33) (#3)
10. Navy (1.34) (#13)

And the worst?

126. Wyoming (2.04) (#101)
125. Middle Tennessee (1.91) (#86)
124. Virginia (1.88) (#56)
123. Southern Miss (1.79) (#93)
122. South Florida (1.78) (#65)

Kickoffs

For simplicity's sake, I set this up exactly the same. You kick off from the 30, so that's the first point value in consideration. The second is, naturally, where the ball ends up. Simple = better in my eyes here. I mean, I played around with the idea of figuring out the average point value of each kick (for kickoffs that was 1.46) and comparing teams' averages to that (so that about half the teams would be positive, half negative). However, that basically leads you to the same order of teams, so in the end it just became an extra, meaningless step.

So here we go. The only difference between kickoffs and punts is that the lower number is better here (this is because the team listed as 'offense' in punts is the punting team, while the 'offense' here is the returning team--simply a technicality).

Top 10, Kickoffs

1. Virginia Tech (0.98)
2. UTEP (1.07)
3. Virginia (1.13)
4. Western Kentucky (1.19)
5. San Diego State (1.19)
6. Rice (1.19)
7. Iowa (1.21)
8. USC (1.23)
9. Ohio State (1.23)
10. Georgia Tech (1.24)

Beamer Ball strikes again.

Bottom 5, Kickoffs

(FCS Tier 5 and FCS Tier 6 were the bottom two, so we'll skip those)

126. New Mexico State (1.74)
125. Iowa State (1.73)
124. Temple (1.69)
123. Wyoming (1.68)
122. Washington State (1.66)

On the other end of things...

Top 10, Kickoff Returns

1. Akron (1.74)
2. UCLA (1.71)
3. Fresno State (1.69)
4. UConn (1.69)
5. Kansas (1.68)
6. Wake Forest (1.67)
7. Hawaii (1.67)
8. East Carolina (1.65)
9. Oklahoma (1.64)
10. Purdue (1.64)

Bottom 5, Kickoff Returns

126. Oregon State (1.08)
125. Duke (1.11)
124. New Mexico State (1.13)
123. Northern Illinois (1.14)
122. Virginia (1.14)

Free Kicks

I mentioned that this was a minor category--that's simply because, of 141,000+ plays in 2007, there were 54 free kicks. They make a difference...but not really. Very few teams were involved in more than one free kick in 2007, so we won't waste the time listing bests and worsts here. Just know that they're set up exactly the same as kickoffs, only they're from the 20 instead of the 30.

Special Teams Average

I've got a bit of Phil Steele in me, obviously. Part of the reason I've done all these 'points' measures is for predictive purposes, by all means, but...like Steele, I just love ranking stuff. (How many "power rankings" does he discuss at given times? Like, 1,403?) So with that in mind, you knew I'd attempt to come up with an "overall" Special Teams ranking as well. I did this by adding together the 'higher is better' numbers, subtracting the 'lower is better' numbers. So we get something like this:

Kickoff Return Avg + Punt Avg + (FG Avg * 2) - Kickoff Avg - Punt Return Avg = Special Teams Avg

(I multiplied FG by 2 so that FGs would carry the same weight as kickoffs and punts.)

For the hell of it, here are all 120 teams ranked by Special Teams Avg (I removed the 6 FCS 'tier 1-6' teams from this list):

San Diego State (1.69)
Arizona State (1.55)
Georgia (1.50)
UTEP (1.43)
Indiana (1.38)
UCLA (1.36)
Nebraska (1.35)
Illinois (1.27)
Toledo (1.26)
Utah (1.21)
Oklahoma (1.18)
Virginia Tech (1.12)
NC State (1.09)
Wake Forest (1.07)
Kansas State (1.02)
North Carolina (1.01)
Ohio (0.98)
Georgia Tech (0.95)
UConn (0.93)
USC (0.91)
Troy (0.87)
Idaho (0.86)
South Carolina (0.84)
UAB (0.83)
New Mexico (0.81)
Purdue (0.77)
Boise State (0.76)
Akron (0.72)
Central Florida (0.67)
Texas (0.66)
LSU (0.63)
Florida State (0.63)
Maryland (0.61)
UNLV (0.57)
Arizona (0.57)
Wisconsin (0.55)
Hawaii (0.52)
Ohio State (0.44)
West Virginia (0.38)
Bowling Green (0.35)
Syracuse (0.34)
Auburn (0.33)
Buffalo (0.30)
Penn State (0.30)
Alabama (0.30)
Tennessee (0.30)
Missouri (0.24)
Air Force (0.21)
TCU (0.21)
Mississippi State (0.20)
Western Kentucky (0.17)
Oregon (0.16)
Colorado State (0.16)
Fresno State (0.13)
Texas Tech (0.11)
Navy (0.11)
Louisville (0.05)
Marshall (0.05)
Nevada (0.05)
Pittsburgh (0.03)
Virginia (0.02)
Arkansas (-0.01)
Kansas (-0.02)
Washington (-0.02)
Tulane (-0.10)
Florida (-0.11)
Rutgers (-0.14)
Southern Miss (-0.15)
SMU (-0.16)
Utah State (-0.16)
Kent State (-0.18)
South Florida (-0.18)
Clemson (-0.19)
Western Michigan (-0.24)
Florida Atlantic (-0.26)
BYU (-0.26)
Michigan (-0.26)
Michigan State (-0.29)
UL-Lafayette (-0.36)
Minnesota (-0.39)
Cincinnati (-0.41)
Texas A&M (-0.43)
Stanford (-0.44)
California (-0.45)
Wyoming (-0.45)
Colorado (-0.47)
Arkansas State (-0.51)
Kentucky (-0.51)
Florida International (-0.52)
Washington State (-0.54)
Northwestern (-0.58)
Eastern Michigan (-0.58)
Oregon State (-0.59)
Boston College (-0.62)
Iowa (-0.69)
Miami-FL (-0.74)
Houston (-0.74)
Louisiana Tech (-0.75)
Northern Illinois (-0.75)
Miami-OH (-0.76)
Central Michigan (-0.78)
Ball State (-0.78)
Ole Miss (-0.79)
Rice (-0.79)
Army (-0.85)
UL-Monroe (-0.96)
Memphis (-1.07)
Tulsa (-1.11)
East Carolina (-1.11)
Iowa State (-1.12)
Vanderbilt (-1.14)
Oklahoma State (-1.35)
New Mexico State (-1.41)
Temple (-1.46)
North Texas (-1.52)
San Jose State (-1.66)
Notre Dame (-1.67)
Baylor (-1.73)
Middle Tenenssee (-1.99)
Duke (-2.95)

I've bolded the teams who finished in the AP Top 10, just to see how much of an impact Special Teams made on their success. As you see, some finished high on the list, some in the middle, but 9 of 10 were on the plus side of the ledger. (Of course, Boston College finished tied for #10 with Texas, and they were pretty bad on this list.) So I guess this shows that you don't necessarily have to be great in the special teams department, but you can't be too bad unless you're playing a weak schedule (Kansas) or in a weak conference (Boston College).

My original intent here was to rank teams based on a "special teams points per game" type of measure. However, there's a problem with that--the teams that score a lot are penalized a lot because, well, they also kickoff a lot. So while the per-game numbers are important as far as adding up the components of a final score goes, it's not very good for ranking teams on their special teams units. The above way works better, I'd say.

SUMMARY

So what have we accomplished today, class? Well, we've ranked teams. That's always fun. And beyond that, we've now come up with ways to measure the remaining component of the typical football game that impacts the number of points put on the scoreboard. You can see where this is going, I'm assuming--next up, we'll rank the teams! I'm a-twitter just thinking about it, and I know you are too!!

Beyond the Box Score: Turnovers

The Boy — Sat, 05 Jul 2008 19:16:46 -0000

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.)

Points Lost - Best (who didn't turn the ball over in scoring opportunities)

1. Clemson (1.69/game)
2. Ball State (1.84)
3. Texas A&M (1.93)
4. East Carolina (1.99)
5. LSU (2.06)

Points Lost - Worst (who did)

120. Western Kentucky (8.52)
119. Troy (7.64)
118. Western Michigan (7.32)
117. Idaho (7.07)
116. Baylor (7.06)

Points Given - Best (whose t/o's didn't set their opponents up with easy points)

1. Boise State (2.34)
2. Iowa (2.38)
3. Kansas (2.52)
4. Clemson (2.72)
5. LSU (2.92)

Points Given - Worst (whose did)

120. Florida International (10.49)
119. Idaho (9.80)
118. Western Kentucky (9.48)
117. NC State (8.66)
116. Baylor (8.38)

Points Prevented - Best (whose defenses' Bend-but-Don't-Break styles included forcing timely turnovers)

1. Western Kentucky (11.32)
2. South Florida (7.96)
3. Cincinnati (6.83)
4. West Virginia (6.65)
5. Central Michigan (6.40)

Points Prevented - Worst (whose didn't)

120. Marshall (1.44)
119. Fresno State (1.97)
118. Georgia Tech (2.14)
117. NC State (2.37)
116. Nebraska (2.38)

Points Taken - Best (who set their offenses up with easy scores via turnovers)

1. Western Kentucky (12.03)
2. Oregon State (10.11)
3. UCLA (8.85)
4. South Florida (8.84)
5. Wake Forest (8.37)

Points Taken - Worst (who didn't)

120. Marshall (1.49)
119. Fresno State (2.31)
118. Tulane (2.52)
117. Northwestern (2.62)
116. Nebraska (2.95)

Thoughts...

I'll say this for Western Kentucky: their games must have been super-exciting. There were huge, timely turnovers on both sides of the ball, at all parts of the field.
On average, the Points Taken/Given per-game numbers (5.50) were higher than the Points Lost/Prevented numbers (4.43), which suggests that more turnovers took place in the offense's territory.
It quickly becames clear that the teams on the 'best' lists were much more likely to be successful than the teams on the 'worst' lists.
Where does Missouri fall, you ask? They were #26 in Turnover Points Margin and #19 in Turnover Margin. They benefited from turnovers overall, but not so much that they can likely expect a host of bad luck in 2008.

So we can break up a simple "Turnover Margin" figure into four parts--is that really useful? Think about it this way: thanks in part to Phil Steele's "Turnovers = Turnaround" concept, gauging teams' potential turnarounds from one season to another relies in large part on their past season's Turnover Margin. That's great--turnovers are partially arbitrary (the nature of playing a game with an oblong ball that bounces unpredictably) and there's data to back up the usefulness of that concept. But right now, the following two turnovers count for exactly the same:

Turnover #1: Illinois is about to score against Missouri in the 2007 season opener. Backup QB Eddie McGee carries the ball inside the 1 before he's stripped and fumbles...the ball bounces into the arms of Pig Brown, who carries it 100 yards for a TD.
Turnover #2: Team A fumbles at their 45 yard line, and Team B recovers. Team B will immediately go 3-and-out and punt the ball back to Team A.

Using my numbers, Turnover #1 was worth 12.62 points (Illinois having the ball at the Mizzou 1 was worth 5.62 points, while Mizzou returning the fumble for a TD was worth, obviously, 7.00). Turnover #2 was worth 4.26 points (1.92 points lost/prevented + 2.34 points given/taken). Is that not a much more accurate read of which turnover truly impacted the result of the game and which did not? So looking at these point readings can give us a much more accurate feel for teams' "Turnovers = Turnaround" potential in 2008.

So let's look at the teams with the best and worst Turnover Points Margins.

Best Turnover Points Margins

1. Kansas (+8.38 points per game) (tied for #1 in regular turnover margin)
2. Middle Tennessee (+7.48) (#3)
3. East Carolina (+7.43) (tied for #1)
4. LSU (+6.96) (#4)
5. West Virginia (+6.42) (#8)
6. Oklahoma (+6.36) (#19)
7. Ball State (+6.07) (#6)
8. UConn (+5.53) (#5)
9. Western Kentucky (+5.35) (#12)
10. Virginia Tech (+5.32) (#10)

Not a lot of variation from the typical Turnover Margin numbers there, though my numbers do show that Oklahoma benefitted a lot more from their takeaways than they were hurt by their turnovers. What about the teams with the worst margins?

Worst Turnover Points Margins

120. NC State (-8.87 points per game) (tied for #116)
119. Florida International (-8.67) (#119)
118. Idaho (-8.43) (#113)
117. Baylor (-8.16) (tied for #116)
116. Nebraska (-6.58) (tied for #116)

Okay, so list at the bottom isn't all that different from the 'real' Turnover Margin list. So let's look at the team whose Turnover Margin and Turnover Points Margin were vastly different (in the rankings)...see what that tells us.

Hawaii: #40 Turnover Points Margin, #85 Turnover Margin (difference: 45)
Akron: #101 Turnover Points Margin, #62 Turnover Margin (difference: 39)
Mississippi State: #89 Turnover Points Margin, #62 Turnover Margin (difference: 27)
Louisville: #74 Turnover Points Margin, #48 Turnover Margin (difference: 26)
Houston: #80 Turnover Points Margin, #105 Turnover Margin (difference: 25)
UL-Monroe: #95 Turnover Points Margin, #70 Turnover Margin (difference: 25)

Other than showing that Hawaii really was pretty lucky to go undefeated (their turnovers were less impactful than their takeaways), this doesn't tell us a lot. In all, though, the average difference between a team's Turnover Points Margin ranking and its Turnover Margin ranking was 7.68, which isn't a tremendously hefty difference, but it does tell us some things about some teams, like Hawaii (likely due a turnaround in luck), Kansas (almost certainly due a turnaround in lucky), et cetera.

(Is 'impactful' a word? I don't think it is.)

And beyond all that, we've covered one of the two remaining concepts that impact the scoreboard. We can now assign an actual point value to different turnovers of different types, which I find pretty damn cool.

Next up: Special Teams.

!!!!

Passive Voice — Wed, 25 Jun 2008 04:12:54 -0000

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.

Beyond the Box Score: The '+' Concept

The Boy — Sat, 21 Jun 2008 21:53:49 -0000

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.

S&P+ (Offense)

1. Florida (again!) (156.69)
2. West Virginia (134.71)
3. Navy (131.61)
4. Texas Tech (130.81)
5. Louisville (129.48)
6. Hawaii (129.07)
7. Oregon (128.11)
8. Missouri (127.30)
9. LSU (126.82)
10. California (125.07)

So first of all...kudos to Florida and to Heisman voters. I admit it--they nailed it with Tim Tebow. I'm a Mizzou (and therefore Chase Daniel) fan, so naturally I'm supposed to think that Tebow is overrated and Daniel should have won the Heisman. I was rather unimpressed with the '20 TDs passing, 20 rushing' thing (if Mizzou had only called QB keepers inside the 5, Daniel would have that many rushing TDs too), but...no matter what I thought of that particular distinction, Tebow QB'd what was simply the best offense in the country according to these numbers, and since he basically was the rushing game...yeah, Tebow gets some dap.

And just for fun...

Rushing EqPts+ (Offense)

1. Navy
2. West Virginia
3. Oregon
4. LSU
5. Florida

Some dap for Navy there as well...of course they put up big-time rushing numbers running Paul Johnson's option system, but they apparently did it against a series of respectable rushing defenses. This should make Georgia Tech fans happy.

Passing EqPts+ (Offense)

1. Louisville
2. Texas Tech
3. Hawaii
4. Tulsa
5. Florida

Again, no surprising names here...though it's doubly impressive that Florida was in the Top 5 in both.

Quick sidebar: a few interesting conclusions were reached about the 'national averages' post I wrote last week, including this one from SMQ:

I welcome this, personally, as an empirical base that bolsters my usual emphasis on keeping the entire playbook open: outside of talent, predictability is the number one killer of offenses, and defenses that stop the run and make offenses one-dimensional are, well, see above.

The 'above' being a youtube video of Auburn sacking Alabama roughly 176 times. Well, an EDSBS commenter made an interesting point--Florida was one of the most predictable teams in the country, and yet they were by far the most successful. I figure there are two main explanations for that. 1) Execution matters. Call it the Remember the Titans Postulate. Even if they know what's coming, they still have to stop you from doing it. 2) Down yardage matters. Florida was able to succeed on third downs because they kept things manageable on third downs. Not even Superman Tebow would succeed very often if most of his third downs were 3rd-and-9's instead of 3rd-and-3's. It does go to show how interesting football is, though. There are a million things that have to happen for you to succeed or fail. Though I guess you don't need a bunch of new statistics to reach that conclusion...so forget I said anything.

Anyway...on to defense. I should mention that, while I have no problem sharing the rankings, there's a caveat: I still have some tinkering to do with these numbers. For one thing, it's very much possible for an offense to put up like 0.32 EqPts in a given game. Since you're flipping the equation now, the opposing team's offensive average is in the numerator, and the 0.32 would be in the denominator. If you take that team's average (say, 15.0) and divide it by their 0.32 output for that game, you're going to get an insanely high defensive EqPts+ score (4687.5, to be exact), and obviously that can skew averages.

The first thing I did was put a cap on scores. For all '+' numbers so far, no particular unit's game score can be higher than 300. I have to do some further tinkering, as that still leads to a lot of 300's (and therefore higher averages than on the offensive side of the equation), but here's what we've got so far.

EqPts+ (Defense)

1. Ohio State (216.02)
2. USC (200.94)
3. Kansas (193.43)
4. LSU (187.53)
5. Hawaii (180.24)
6. Boise State (179.52)
7. Oklahoma (171.53)
8. Texas (171.08)
9. Texas Tech (170.65)
10. Arizona State (168.49)

Beyond the first 4 teams and OU, that's not exactly what you would have considered a murderer's row of defenses there.

S&P+ (Defense)

1. Ohio State (182.64)
2. USC (161.45)
3. LSU (161.18)
4. Virginia Tech (156.96)
5. Rutgers (156.62)
6. Oregon State (156.51)
7. Oklahoma (150.14)
8. Penn State (149.37)
9. Boise State (148.44)
10. Arizona State (146.95)

These numbers as a whole run a little lower, and that makes me a bit more comfortable about them, but...those are still a lot of the same teams there. With either measure, tOSU is by far #1, which suggests that they at least somewhat earned their good fortune last year despite a horrendously weak schedule. They will probably remain #1 no matter what kind of tweaking I do, so...good for them, I guess.

Rushing EqPts+ (Defense)

1. West Virginia (#18 in plain old rushing yards allowed per game)
2. Texas (#6)
3. Florida (#10)
4. Air Force (#45)
5. Navy (#81)

Passing EqPts+ (Defense)

1. Texas Tech (#12 in passing yards allowed per game)
2. Hawaii (#37)
3. Ohio State (#1)
4. Rutgers (#5)
5. Kansas (#49)

Now...this is pretty damn interesting, really. Teams with good rushing games (WV, AFA, Navy) came out of nowhere to place at the top of the rushing defense list, while pass happy teams (Tech, Hawaii) were at the top of the passing defense list. Of course, this is measuring EqPts Allowed in those areas...teams playing Tech and Hawaii were likely to run the ball a lot (and therefore avoid passing) to keep the ball out of Tech/Hawaii's hands. So let's check out S&P+...since it looks a per-play average instead of a per-game average.

Rushing S&P+ (Defense)

1. Ohio State (#3 in rushing yards allowed)
2. Oregon State (#1)
3. UCLA (#14)
4. Penn State (#7)
5. Wyoming??? (#27)

Passing S&P+ (Defense)

1. Ohio State (#1 in passing yards allowed)
2. Rutgers (#5)
3. Utah (#11)
4. Arkansas (#23)
5. Virginia Tech (#31)

Okay, this is better. I obviously don't need these numbers to precisely resemble the yards per game stats--why the hell would I be doing all this if that were the case?--but it is certainly strange that these numbers could be so drastically different. At least the offensive numbers were in the same ballpark. Any ideas as to what I should maybe do different are welcome.

CONCLUSION

So what I've just done with some basic S&P and EqPts numbers, I could do this with every stat in the catalog...S&P+ by down, quarter, field position, etc. Line Yards/Sack Rates, etc. And to some degree, I'm going to do just that (though I don't yet know what I'll find that will be interesting enough to share!). There are four purposes to all of this.

1) This could obviously be interesting form an evaluative perspective. It's always fun trying to come up with more and more precise ways of evaluating and ranking teams.

2) It could be even more interesting from a predictive perspective. The only thing more fun than ranking teams is making accurate predictions, am I right? Of course, the main problem is, with only one year of data it's somewhat impossible to actually know what's predictive and what just seems like it should be predictive. We'll all be discovering together which tools are and are not good forecasting tools.

3) It will make for a more informed experience while watching football...which is just awesome. It was fun watching Mizzou games last year, knowing that an opponent's best quarter is Q2, or little things like that. It will be even more fun this year, knowing even more.

4) It's becoming clear to me that there are little pockets of stat nerds out there--stat nerds who enjoy the practicality of stats, which is even better--and this is an excellent opportunity to build something of a communicative community based around college football stats. I'm just going to keep on writing these posts and asking for opinions and new ideas and seeing where this goes.

Cal's successes and failures in the Tedford era

royrules22 — Fri, 20 Jun 2008 20:24:15 -0000

Not sure if anyone here care's too much about it but why not.

I made a little script that would basically calculate Cal's success and and failures in the Tedford era.
Thus I have two tables below that show Cal's W-L record against all the teams it has played since 2002 and another table showing Cal's W-L record in eachy city Cal has played in since 2002.
The tables are color coded from blue to red with blue representing good (100% WR %age) and red representing bad (0% of games were won). Last item is an image of the map of US with circles centered
around the cities Cal has played in colored from blue to red. It's the same thing as the 2nd table except shown graphically.

It would be interesting now to perhaps overlay the location information with recruiting information to see how much of a correlation success in an area has to do with recruits.
Or we could overlay with another team's record to see something.

Interesting information that can be gleamed is that we (Cal) have a fair bit of success in Northern areas but yucky luck in southern areas (see: SoCal)

And yes I already posted this on the Cal blog.

Team	W	L
Sacramento State	1	0
Virginia Tech	1	0
Illinois	2	0
BYU	1	0
Minnesota	1	0
Louisiana State	1	0
Texas A&M	1	0
Portland State	1	0
Baylor	1	0
Southern Mississippi	2	0
New Mexico State	3	0
Michigan State	1	0
Stanford	5	1
Washington	5	1
Arizona State	4	1
Washington State	3	1
Arizona	4	2
Air Force	2	1
Oregon	3	2
Tennessee	1	1
UCLA	3	3
Colorado State	1	1
Oregon State	2	4
USC	1	5
Kansas State	0	1
Utah	0	1
Texas Tech	0	1

Location:	W	L
Las Cruces NM	1	0
Champaign IL	1	0
Hattiesburg MS	1	0
Colorado Springs CO	1	0
Las Vegas CA	1	0
Pullman WA	1	0
East Lansing MI	1	0
Phoenix AZ	1	0
Fort Worth TX	1	0
Fort Collins CO	1	0
Berkeley CA	28	9
Seattle WA	3	1
Palo Alto CA	2	1
Corvallis OR	2	1
Tempe AZ	2	1
Tucson AZ	1	1
San Diego CA	1	1
Eugene OR	1	2
Los Angeles CA	0	3
Salt Lake City UT	0	1
Pasadena CA	0	3
Kansas City MO	0	1
Knoxville TN	0	1

Question for Firefox Users

SMQ — Fri, 20 Jun 2008 18:29:53 -0000

On the 'Premature' and 'Anticipatory' assessments, is the 'The least you should know about...' box floating in the middle of the screen by itself, rather than as a sidebar to the right of the main text? I'm accessing the site on a Firefox browser for the first time since the switch to the new format and that's what I'm seeing. If that's the case for everyone, I have to make some changes.