Seattle Seahawks


Quick note:

There is a nice video on Youtube about Brandon Staley’s 2020 Rams defense and some of the moving parts involved. Hat tip to @AlexRollinsNFL and @falcfans for this nugget. Interestingly, a move of a similar kind, to combat RPOs, has been reported in coverage of Pete Carroll’s defensive fronts.

One of the things I noticed when rereading Chris Brown’s article on Grantland, “Ode to the War Daddies” are how much the same those hybrid fronts are and the Seattle fronts are. In short, they are the same, so Bill Belichick was playing those “Seattle” fronts back in 2012.

There are a couple writers for the Dallas Morning News Dallas fans need to take note of. Michael Gehiken, Twitter handle @GehlkenNFL, is a good Dallas news guy. The other, John Owning, Twitter handle @JohnOwning is a good draft analysis guy and has some of the best articles on Dan Quinn targeted at “intelligent fans”. This link is a discussion of DQ’s pass defense philosophy, and this older draft video has it online.

If you have not seen any video of the new Atlanta Falcons DC, Dean Pees, try to give yourself 20 minutes and listen to him. I enjoyed him a lot.

Dallas DC Dan Quinn spoke recently, and said that the base defense of the Cowboys was going to be more like a 3-4. The actual quote is this:

As far as in the base packages go, it will look more like a 3-4 look, and that would have been consistent whether it was the team last year or my times with Atlanta as well. But more often than not, with most teams, the nickel packages, which teams play, I’d say, close to 60% or 70% of the time are more out of a four-down.

https://twitter.com/therealmarklane?lang=en

Some people have taken this to mean he’s going to a 3-4, and that could not be further from the truth. An examination of the coaching tree of Monte Kiffin should make that pretty clear, and we’ll provide some evidence that in fact the base defense will remain the same as the Marinelli years, even if it’s going to be tweaked a bit.

In the late 1970s Pete Carroll was a defensive assistant at Arkansas, where the head coach was Lou Holtz and his DC was Monte Kiffin. Kiffin was and is a proponent of a 4-3 under defense that he felt could stop the run and also rush the passer. In a coaching clinic recorded on Jerry Campbell Football, the effect Kiffin had on Carroll is marked.

After all the years I’ve been in football I’ve never coached anything but the 4-3 under defense. So I know this defense inside and out. I know the good side of the defense and I know the problems and weaknesses of this defense. I run it with one gap principles but can also make it work with some two gap principles.

https://jcfb.forums.net/thread/14894/the-4-3-under

So Dan Quinn is on the Kiffin tree as so. Kiffin -> Carroll -> Quinn. Marinelli is on the same tree, serving as a line coach with the Buccaneers when Kiffin was their defensive coordinator. More recently, Marinelli has been a line coach and defensive coordinator with various assignments with the Cowboys, and Marinelli’s 4-3 is what they are most familiar with.

So, some simple conclusions. The base of the C owboys base is the 4-3 under that the Cowboys have played for years, and the base is likely to be a version of the 4-3 under tweaked to have some two gapping added to the front.

But is it? Dan Quinn is known for tweaking his fronts to put his best players on the field, and he will find his best 7 over time. There are two fieldgulls.com articles (here and here) which show that he tries things in order to get best fits. The latter article talks about the successful adaptation of Red Bryant to becoming a two gap defensive end, but also that DQ that year was going to let Red Bryant 1 gap some.

Bryant said he’ll return to being more of a penetrating, one-gap defensive end and playing mostly over the right tackle.

https://www.fieldgulls.com/football-breakdowns/2013/5/31/4382318/the-seahawks-and-the-4-3-under-front-winds-of-change

Dan Quinn doesn’t let fronts get etched in stone. He crafts them.

There is a great article in Sports Illustrated that gives us some language to use for the component parts of a Seattle Hybrid defense. If the front from the defenses POV and from left to right is 4i-1-3-9, then the position names are BIG END, NOSE, 3T and LEO. In the hybrid, the BIG END is a two gap player, but because it shares so many component parts with the 4-3 under one gap, it does not have to be. Letting a BIG END 1 gap is pretty simple. The Mike linebacker just has to cover the strong side B gap. Let’s let Pete describe the left over gap assigments in the 4-3 under one gap.

The front five players I mentioned are playing aggressive defense with their outside arms free. The only thing we can’t allow to happen is for them to get hooked or reached by the defender. This alignment leaves open the strong side B Gap and the weak side A gap which are played by the Mike and Will linebackers.

https://jcfb.forums.net/thread/14894/the-4-3-under

A lot of this exercise is to eliminate any nonsense that suggests Dallas is going to a base 3-4 and DeMarcus Lawrence will have to be an OLB. He doesn’t have to be anything but DeMarcus, and he has a proven ability to defend the run as a one gapping defensive end. For that matter he might be able to two gap as well, though the addition in free agency of guys like Urban and drafting men like Osa Odighizuwa suggest perhaps a rotation at BIG END.

More importantly to me, most of the new defense should seem familiar to the veteran players. Going from a 4-3 under one gap to a hybrid isn’t a huge shift. DQ will have an off season to install, which the unfortunate Mike Nolan did not. The biggest shift is in the players the new regime likes, what physical traits they emphasize.

The first round of the playoffs were full of upsets. The Titans upset the Patriots and the Vikings upset the Saints. Both upsets were driven by ground games that both scored and consumed the clock.

The methodology of how we pick is given here.

Second Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
San Francisco 49ers Minnesota Vikings 0.372 0.59 2.8
Green Bay Packers Seattle Seahawks -0.194 0.45 -1.4
Baltimore Ravens Tennessee Titans 0.985 0.73 7.3
Kansas City Chiefs Houston Texans 0.429 0.61 3.2

The methodology of this work is described here.

This year, the formulas favor the Baltimore Ravens and the Seattle Seahawks. Baltimore has the advantage in any possible encounter in the AFC. Seattle has the advantage over any team not named the New Orleans Saints. As the Saints lose their HFA against Green Bay, they are not favored against Green Bay. The odds of a Seattle-New Orleans matchup are small.

2019 NFL Playoff Teams, C&F Worksheet.
NFC
Rank Name Home Field Adv Playoff Experience SOS Total Score
1 San Francisco 49ers 0.660 0 0.125 0.785
2 Green Bay Packers 0.660 0.747 -0.225 1.182
3 New Orleans Saints 0.660 0.747 0.015 1.422
4 Philadelphia Eagles 0.660 0.747 -0.511 0.896
5 Seattle Seahawks 0.0 0.747 0.690 1.376
6 Minnesota Vikings 0.0 0.747 -0.334 0.413
AFC
1 Baltimore Ravens 0.660 0.747 0.015 1.422
2 Kansas City Chiefs 0.660 0.747 0.061 1.468
3 New England Patriots 0.660 0.747 -0.535 0.872
4 Houston Texans 0.660 0.747 0.292 1.699
5 Buffalo Bills 0.0 0.747 -0.380 0.367
6 Tennessee Titans 0.0 0.747 -0.310 0.437

 

The total score of a particular team is used as a base. Subtract the score of the opponent and the result is the logit of the win probability for that game. You can use the inverse logit (see Wolfram Alpha to do this easily) to get the probability, and you can multiply the logit of the win probability by 7.4 to get the estimated point spread.

Because the worksheet above can be hard to decipher, for the first week of the 2019 playoffs, I’ve done all this for you, in the table below. Odds are presented from the home team’s point of view:

First Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
New Orleans Saints Minnesota Vikings 1.009 0.733 7.5
Philadelphia Eagles Seattle Seahawks -0.541 0.368 -4.0
New England Patriots Tennessee Titans 0.435 0.607 3.2
Houston Texans Buffalo Bills 1.332 0.791 9.9

It’s a new playoff season, and another time to try our new playoff formulas. Methodology of this work is described in depth here.

The playoff formulas like New Orleans and Kansas City. They like Baltimore, but Baltimore, which will lose home field after the first round, is unlikely to be favored after that point. The formulas place a substantial penalty on the lack of playoff experience, and so does not favor Chicago, the Chargers, or the Colts. Update: Baltimore has not been in the playoff since 2014, and so the results have been amended.

2017 NFL Playoff Teams, C&F Worksheet.
NFC
Rank Name Home Field Adv Playoff Experience SOS Total Score
1 New Orleans Saints 0.660 0.747 0.192 1.599
2 LA Rams 0.660 0.747 -0.134 1.273
3 Chicago Bears 0.660 0.0 -0.711 -0.051
4 Dallas Cowboys 0.660 0.747 0.046 1.453
5 Seattle Seahawks 0.0 0.747 -0.170 0.577
6 Philadelphia Eagles 0.0 0.747 0.167 0.914
AFC
1 Kansas City Chiefs 0.660 0.747 -0.033 1.374
2 New England Patriots 0.660 0.747 -0.535 0.872
3 Houston Texans 0.660 0.747 -0.465 0.942
4 Baltimore Ravens 0.660 0 0.195 0.855
5 LA Chargers 0.0 0.0 -0.070 -0.070
6 Indianapolis Colts 0.0 0.0 -0.693 -0.693

 
The total score of a particular team is used as a base. Subtract the score of the opponent and the result is the logit of the win probability for that game. You can use the inverse logit (see Wolfram Alpha to do this easily) to get the probability, and you can multiply the logit of the win probability by 7.4 to get the estimated point spread.

Because the worksheet above can be hard to decipher, for the first week of the 2018 playoffs, I’ve done all this for you, in the table below. Odds are presented from the home team’s point of view:

First Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Chicago Bears Philadelphia Eagles -0.965 0.276 -7.1
Dallas Cowboys Seattle Seahawks 0.876 0.706 6.5
Houston Texans Indianapolis Colts 1.635 0.836 12.1
Baltimore Ravens LA Chargers 0.925 0.716 6.8

 

But to summarize, the formulas used here were found by logistic regressions and each element in the formula has a playoff significance of 95%. I promise if the more common offense metrics could say that, they would. I’ll also note that in vogue stats like FPI don’t really give answers markedly different from other common offensive metrics, such as Pythagorean expectation.

That said, offensive metrics like Pythagorean Expectation favor Seattle over Dallas by about half a point, or 52% win probability for Seattle. Offensive stats still favor Baltimore, but not as much. Simple Ranking stats favor Chicago by around 8 points, circa 75% WP. Houston-Indianapolis have approximately even offensive stats, so the difference between the teams is about 3 points. HFA is worth a bit more in the playoffs, circa 63%.

The competitors are Denver and Seattle, and as stated previously, my model favors Seattle substantially.

Super Bowl
NFC Champion AFC Champion Score Diff Win Prob Est. Point Spread
Seattle Seahawks Denver Broncos 1.041 0.739 7.7

 

Of course by this point my model has been reduced to a single factor, as there is no home field advantage in the Super Bowl and both teams are playoff experienced. Since every season 8 of the 11 games are before the Conference chanpionships and Super Bowl, the model works best for those first eight games. Still, it’s always interesting to see what the model calculates.

At least as interesting is the Peyton Manning factor, a player having the second best season of his career (as measured by adjusted yards per attempt). I thought it would be interesting to try and figure out how much of the value above average of the potent Denver Broncos attack that Peyton Manning was responsible for. We’ll start by looking at the simple ranking of the team, divided into the offensive and defensive components. Simple rankings help adapt for the quality of opposition, which for Denver was below league average.

Denver Broncos Simple Ranking Stats
Margin of Victory Strength of Schedule Simple Ranking Defensive Simple Ranking Offensive Simple Ranking
12.47 -1.12 11.35 -3.31 14.65

 

Narrowed down to the essentials, how much of the 14.65 points of Denver offense (above average) was Peyton Manning’s doing? With some pretty simple stats, we can come up with some decent estimates of the Manning contribution to Denver’s value above average.

We’ll start by calculating Peyton’s adjusted yards per attempt, and do so for the league as a whole. We’ll use the Pro Football Reference formula. Later, we’ll use the known conversion factors for AYA to turn that contribution to points, and the subtract the league average from that contribution.

Passing Stats, 2013
Player(s) Completions Attempts Yards Touchdowns Interceptions AYA
Peyton Manning 450 659 5477 55 10 9.3
All NFL passing 11102 18136 120626 804 502 6.3

 

The difference between Peyton Manning’s AYA and the league average is 3 points. Peyton Manning threw 659 times, averaging about 41.2 passes per game. This compares to the average team passing about 35.4 times a game. To convert an AYA into points per 40 passes, the conversion factor is 3.0. This is math people can do in their head. 3 times 3 equals 9 points. In a game situation, in 2013, where Peyton Manning throws 40 passes, he’ll generate 9 points more offense than the average NFL quarterback. So, of the 14.65 points above average that the Denver Broncos generated, Peyton Manning is at least responsible for 61% of that.

Notes:

There is a 0.5 point difference between the AYA reported by Pro Football Reference and the one I calculated for all NFL teams. I suspect PFR came to theirs by taking an average of the AYA of all 32 teams as opposed to calculating the number for all teams. To be sure, we’ll grind the number out step by step.

The yards term: 120626
The TD term: 20 x 804 = 16080
The Int term: 45 x 502 = 22590

120626 + 16080 – 22590 = 114116

Numerator over denominator is:

114116 / 18136 = 6.29223… to two significant digits is 6.3.

I suspect  to a first approximation almost no one other than Baltimore fans, such as Brian Burke, and this blog really believed that Baltimore had much of a chance(+). Well, I should mention Aaron Freeman of Falc Fans, who was rooting for Baltimore but still felt Denver would win. Looking, his article is no longer on the Falcfans site. Pity..

WP graph of Baltimore versus Denver. I tweeted that this graph was going to resemble a seismic chart of an earthquake. Not my work, just a screen shot off the excellent site Advanced NFL Stats.

WP graph of Baltimore versus Denver. I tweeted that this graph was going to resemble a seismic chart of an earthquake. Not my work, just a screen shot off the excellent site Advanced NFL Stats.

After a double overtime victory by 3 points, it’s awfully tempting to say, “I predicted this”, and if you look at the teams I’ve  favored, to this point* the streak of picks is 6-0. Let me point out though, that you can make a limiting assumption and from that assumption figure out how accurate I should have been. The limiting assumption is to assume the playoff model is 100% accurate** and see how well it predicted play. If the model is 100% accurate, the real results and the predicted results should merge.

I can tell you without adding up anything that only one of my favored picks had more than a 70% chance, and at least two were around 52-53%. So 6 times 70 percent is 4.2, and my model, in a perfect world, should have picked no more than 4 winners and 2 losers. A perfect model in a probabilistic world, where teams rarely have 65% chances to win, much less 100%, should be wrong sometimes. Instead, so far it’s on a 6-0 run. That means that luck is driving my success so far.

Is it possible, as I have argued, that strength of schedule is an under appreciated playoff stat, a playoff “Moneyball” stat, that teams that go through tough times are better than their offense and defensive stats suggest? It’s possible at this point. It’s also without question that I’ve been lucky in both the 2012 playoffs and the 2013 playoffs so far.

Potential Championship Scenarios:

 

Conference Championship Possibilities
Home Team Visiting Team Home Win Pct Est. Point Spread
NE BAL 0.523 0.7
HOU BAL 0.383 -3.5
ATL SF 0.306 -6.1
SF SEA 0.745 7.9

 

My model likes Seattle, which has the second best strength of schedule metric of all the playoff teams, but it absolutely loves San Francisco. It also likes Baltimore,  but not enough to say it has a free run throughout the playoffs. Like many modelers, I’m predicting that Atlanta and Seattle will be a close game.

~~~

+ I should also mention  that Bryan  Broaddus tweeted about a colleague of his who predicted a BAL victory.

* Sunday, January 13, 2013, about 10:00am.

** Such a limiting assumption is similar to assuming the NFL draft is rational; that the customers (NFL teams) have all the information they should, that they understand everything about the product they consume  (draft picks), and that their estimates of draft value thus form a normal distribution around the real value of draft picks, and that irrational exuberance, or trends, or GMs falling in love with players play no role in picking players. This, it turns out, makes model simulations much easier.

There were eight trades in the first day involving the first round of the 2012 NFL draft. Most of them involved small shifts in the primary pick, with third day picks added as additional compensation. The one outlying trade was that of the St Louis Rams and the Dallas Cowboys, which involved a substantial shift in  the #1 pick (from 6 to 14) and the secondary compensation was substantial. This high secondary compensation has led to criticism of the trade, most notably by Dan Graziano, whose argument, boiled to its essence, is that Dallas paid a 2 pick price for Morris Claiborne.

Counting  picks is a lousy method to judge trades. After all, Dallas paid a 4 pick price for Tony Dorsett. Was that trade twice as bad a trade as the Morris Claiborne trade?  The Fletcher Cox trade saw Philadelphia give up 3 picks for Fletcher Cox. Was that trade 50% worse than the Morris Claiborne trade?

In order to deal with the issues raised above, I will introduce a new analytic metric for analyzing trade risk, the risk ratio, which is the sum of the AV values of  the picks given, divided by the sum of the AV values of the picks received. For trades with a ratio of 1.0 or less, there is no risk at all. For trades with ratios approaching 2 or so, there is substantial risk. We are now aided in this kind of analysis by Pro Football Reference’s new average AV per draft pick chart. This is a superior tool to their old logarithmic fit, because while the data may be noisy, they avoid systematically overestimating the value of first round picks.

The eight first round trades of 2012, interpreted in terms of AV risk ratios.

The first thing to note about the 8  trades is that the risk ratio of 6 of them is approximately the same. There really is no difference, practically speaking, in the relative risk of the Trent Richardson  trade, or the Morris Claiborne trade,  or the Fletcher Cox trade. Of the two remaining trades, the Justin Blackmon trade was relatively risk free. Jacksonville assumed an extra value burden of 10% for moving up to draft the wide receiver. The other outlier, Harrison Smith, can be explained largely by the noisy data set and an unexpectedly high value of AV for draft pick 98. If you compensate by using 13 instead of 23 for pick #98, you get a risk ratio of approximately 1.48, more in line with the rest of the data sets.

Armed with this information, and picking on Morris Claiborne, how good does he  have to be for this trade to be break even? Well, if his career nets 54 AV, then the trade breaks even. If he has a HOF career (AV > 100), then Dallas wins big. The same applies to Trent Richardson. For the trade to break even, Trent has to net at least 64 AV throughout his career. Figuring out how much AV Doug Martin has to average is a little more complicated, since there were multiple picks on both sides, but Doug would carry his own weight if he gets 21*1.34 ≈ 28 AV.

Four historic trades and their associated risk ratios.

By historic measures, none of the 2012 first round trades were particularly risky. Looking at some trades that have played out in  the past, and one  that is still playing out, the diagram above shows the picks traded for Julio Jones, for Michael Vick, for Tony Dorsett, and also for Earl Campbell.

The Julio Jones trade has yet to play out, but Atlanta, more or less, assumed as much risk (93 AV) as they did for Michael Vick (94 AV), except for a #4 pick and a wide receiver. And although Michael is over 90 AV now, counting AV earned in Atlanta and Philadelphia, he didn’t earn the 90+ AV necessary to balance out the trade while in Atlanta.

Tony Dorsett, with his HOF career, paid off the 96 AV burden created by trading a 1st and three 2nd round choices for the #2 pick. Once again, the risk was high, the burden was considerable, but it gave value to Dallas in the end.

Perhaps the most interesting comparison is the assessment of the Earl Campbell trade. Just by the numbers, it was a bust. Jimmie Giles, the tight end that was part of the trade,  had a long and respectable career with Tampa Bay. That, along with the draft picks, set a bar so high that only the Ray Lewis’s of the world could possibly reach. And while Campbell was a top performer, his period of peak performance was short, perhaps 4   years. That said, I still wonder if Houston would still make the trade, if somehow someone could go back in to the past, with the understanding of what would happen into the relative future. Campbell’s peak was pretty phenomenal, and not entirely encompassed by a mere AV score.