Atlanta Falcons


A much easier week to take than before, though Atlanta fans are going through a serious crisis at the moment. The head coach, a defensive expert, took over the defense this year and it is horrible. So local talk is about who is going to be let go at the end of the year and why. I’m not as sure the HC is the cause here. Thomas Dimitroff, as far as I recall, has never drafted a successful defensive end, and his track record on the OL is not good as well. So, who will be running the Falcons in 2020? It’s anyone’s guess.

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
106        46     43.4      27.95        16.55     11.41

Calculated Pythagorean Exponent:  3.12


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     NE      26.0     7   7   0   0 100.0  99.2  16.14  25.00 -8.86
2     SF      13.5     6   6   0   0 100.0  94.2  13.45  15.33 -1.89
3     GB       7.0     7   6   1   0  85.7  70.6   6.97   6.43  0.54
4     NO       6.0     7   6   1   0  85.7  58.5   5.52   2.43  3.09
5     BUF      5.5     6   5   1   0  83.3  70.9  -0.62   5.00 -5.62
6     MIN     16.0     7   5   2   0  71.4  80.0   7.76   9.86 -2.10
7     BAL      6.0     7   5   2   0  71.4  72.8   4.06   8.29 -4.22
8     KC       5.0     7   5   2   0  71.4  71.7   8.27   7.43  0.84
9     SEA      1.0     7   5   2   0  71.4  52.2   0.80   0.71  0.08
10    CAR      6.5     6   4   2   0  66.7  66.6   7.77   5.50  2.27
11    IND      2.5     6   4   2   0  66.7  52.8   0.17   0.83 -0.66
12    DAL     10.0     7   4   3   0  57.1  79.1   3.03   9.43 -6.40
13    LA       3.0     7   4   3   0  57.1  61.3   6.22   3.71  2.50
14    HOU      1.0     7   4   3   0  57.1  59.3   4.47   3.00  1.47
15    ARI      0.0     7   3   3   1  50.0  36.6  -6.14  -4.43 -1.71
16    CHI     -0.5     6   3   3   0  50.0  55.0   1.81   1.17  0.65
17    OAK     -7.5     6   3   3   0  50.0  30.6  -2.45  -6.33  3.88
18    JAX     -1.0     7   3   4   0  42.9  47.9   1.59  -0.57  2.16
19    TEN     -2.0     7   3   4   0  42.9  56.0  -1.18   1.29 -2.46
20    PHI     -3.0     7   3   4   0  42.9  43.5  -4.42  -2.14 -2.28
21    DET     -0.5     6   2   3   1  41.7  44.5  -0.24  -1.83  1.59
22    PIT     -2.5     6   2   4   0  33.3  45.1   2.58  -1.33  3.92
23    TB      -4.0     6   2   4   0  33.3  44.8   3.76  -2.00  5.76
24    CLE     -5.5     6   2   4   0  33.3  31.5  -3.62  -5.67  2.05
25    DEN     -2.0     7   2   5   0  28.6  35.3  -1.70  -3.43  1.73
26    LAC     -3.0     7   2   5   0  28.6  49.4  -2.90  -0.14 -2.76
27    NYG    -14.0     7   2   5   0  28.6  25.2  -6.19  -7.86  1.67
28    NYJ    -18.0     6   1   5   0  16.7   5.6 -11.06 -15.50  4.44
29    WAS    -10.0     7   1   6   0  14.3  11.0 -12.23 -12.29  0.06
30    ATL    -14.0     7   1   6   0  14.3  20.7 -10.16 -11.14  0.98
31    CIN     -6.0     7   0   7   0   0.0  17.8  -8.04 -10.29  2.24
32    MIA    -22.5     6   0   6   0   0.0   2.3 -23.42 -24.67  1.25

Summary: Replacing Wentz with Foles removes about 6.5 points of offense from the Philadelphia Eagles, turning a high flying offense into something very average.

Last night the Atlanta Falcons defeated the LA Rams. Now we’re faced with the prospect of the Falcons playing the Eagles. I have an idiosyncratic playoff model, one I treat as a hobby. It is based on static factors, the three being home field advantage, strength of schedule, and previous playoff experience. And since it values the Eagles as 0.444 and the Falcons as 1.322, the difference is -0.878 (win probability in logits). The inverse logit of -0.878 is 0.294, which is the probability of the Eagles winning, and an estimated point spread would be a 6.5 point advantage for the Falcons.

Another question that a Falcons or Eagles fan might have is how much is Carson Wentz worth as a QB, in points scored? We can use the adjusted yards per attempt stat of Pro Football Reference to estimate this, and also to estimate how much Carson Wentz is better than Foles. We have made these kinds of analyses before for Matt Ryan and Peyton Manning.

Pro Football Reference says that Carson Wentz has a AYA of 8.3 yards per attempt. Nick Foles has a AYA of 5.4. Now lets calculate the overall AYA for every pass thrown in the NFL. Stats are from Pro Football Reference.

(114870 yards + 20*741 TDs – 45*430 Ints) / 17488 Attempts
(114870 yards + 14820 TD “yards” – 19350 Int “yards”) / 117488 Attempts
110340 net yards / 17488 yards
6.31 yards per attempt to three significant digits

So about 6.3 yards per attempt. Carson Wentz is 2 yards per attempt better than the average. Nick Foles is 0.9 yards less than the average. The magic number is 2.25 which converts yards per attempt to points scored per thirty passes. So Carson, compared to Foles, is worth 2.9 * 2.25 = 6.5 points per game more than Foles, and 4.5 points more than the average NFL quarterback.

This doesn’t completely encompass Carson Wentz’s value, as according to ESPN
‘s QBR stat
, he account for 10 expected points on the ground in 13 games, so he nets about 0.8 points a game on the ground as well.

Now, back to some traditional stats. The offensive SRS assigned to Philadelphia by PFR is 7.0 with a defensive SRS of 2.5. If Carson Wentz is worth between 6.5 and 7.3 points per game, then it reduces Philadelphia’s offense to something very average, about 0.5 to -0.3. That high flying offense is almost completely transformed by the loss of their quarterback into an average offense.

Note: logits are to probabilities as logarithms are to multiplication. Rather than multiplying probabilities and using transitive rules, you just add the logits and convert back. Logarithms allow you to add logarithms of numbers rather than multiplying them.

It seems as if it has been a long off season. The two teams I follow the most, Dallas and Atlanta, had good drafts and appear to be in position to have winning seasons once more. But there are spanners in the works for both, though for now it appears that Dallas has the more serious short term issues.

Dallas has had more than the usual suspensions, including a potential six game suspension of Ezekiel Elliott. There have been a host of minor injuries, bleeding the rookie secondary of repetitions. The secondary, in the preseason, appears to lack coordination, as the loss of man-years in the backfield shows. All that said, the offense is deep and talented, and Dak Prescott shows no sign of a sophomore slump.

The issue for Atlanta are twofold. One, the loss of the offensive coordinator, Kyle Shanahan, and two, the odds that Matt Ryan can come close to the third best QB season in NFL history. Seasons like that are not a product of pure talent. If Atlanta is lucky, he’ll come fairly close, but it’s possible you’ll see something more like the 2014-2015 Matt Ryan. Not as many yards. A few more interceptions.

Summary: with some calculations based on adjusted yards per attempt, Matt Ryan’s value as a passer in the 2016 season can be shown to be almost 9 points a game more than the average QB.

Mark Zinno is a host on a sports talk show, 92.9 the Game, in the 7pm ET time slot. Often booted out of the slot by Atlanta Hawks games, he nonetheless has been a dogged supporter of Matt Ryan. This isn’t new, btw. Even in years where Matt Ryan wasn’t at his best, he would doggedly argue that Matt Ryan was an elite quarterback, and said repeatedly that compared to an average NFL team, that Atlanta was blessed.

So, we’re dedicating this blog post to Mark Zinno.

It’s hard to understand the scope of what Matt Ryan has done until you look at his adjusted yards per attempt in 2016. Pro Football Reference lists it as 10.1, which is one of the highest I’ve seen, and comparable to Peyton Manning’s 2004 season, where PM’s AYA was 10.2. Looking a little further, you can see that PFR ranks this the 4th best performance in history. Aaron Rogers is in the top 4, and for some reason, so is Nick Foles.

The value in using AYA is that you can build an expected points curve that satisfies all the requirements of the AYA function, and then use the slope of that curve to relate yards to points. Don’t worry, I did that long ago, and the result is documented here. The simple take home is the magic conversion 2.25, which converts AYA from yards to “expected points generated per 30 passes”.

Then, using the 2016 annual data from Pro Football Reference, you can calculate  what the average QB did, by calculating an AYA using the overall season’s statistics.  So the formula is:

(123639 yards + 20*786 TD – 45*415 Ints)/  18295 attempts 

(123639 yards + 15720 “TD” yards – 18675 “Int” yards) / 18295 attempts

120684 yards / 18295 attempts

6.60 AYA to 3 significant digits.

Now things become simpler. Matt Ryan generated 10.1*2.25 = 22.7 points per 30 attempts, while Joe QB generated 14.8 points per 30 attempts. The difference, rounded to a whole number, suggests that Matt Ryan was worth about 8 more points in 30 attempts than the average NFL QB this season.

That doesn’t entirely encompass his per game value. Matt threw 534 attempts  this season for an average of 33.4 passes per game. So his per game value, to the nearest tenth of a point, was more like 8.8 points a game more than the average quarterback.

But if the numbers baffle you, then the simple take home is that Matt’s statistical efficiency in 2016 is comparable to the best single season Peyton Manning ever had.

I’m trying to get a handle on the new season of play, particularly teams that are playing better than expected and those that are playing worse. In Atlanta, quite a few fans have zoned in on the offense, in particular the right guard and right tackle play, and are subsequently using the inability of Thomas Dimitroff to refurbish the lines as a critique (including the “should we have drafted Julio Jones” trope).

None of my further analysis will answer any of those questions, but they’ll set a baseline we can use.

Atlanta Falcons SRS OSRS DSRS
Year Team W L T SRS OSRS DSRS MOV SOS
2009 ATL 9 7 0 5.03 2.78 2.25 2.38 2.66
2010 ATL 13 3 0 6.09 2.05 4.04 7.88 -1.79
2011 ATL 10 6 0 3.53 3.22 0.30 3.25 0.28
2012 ATL 13 3 0 6.44 3.53 2.91 7.50 -1.06
4 Year Avg Performance
AVG ATL 11 5 0 5.27 2.90 2.37 5.25 0.02
2013 Season through 10/15
2013 ATL 1 4 0 -1.80 1.35 -3.15 -2.40 0.60

 

Though some fall off on offensive is observed, the greater fall off is on defense. Two themes are notable at this time. First, the relative lack of pass rush on Atlanta’s part (though this has been an persistent issue with this club for anyone not named John Abraham). The second issue has to be injuries in the back 7 and the resultant use of rookies at linebacker and in the secondary. In conclusion, the Falcons’ problems have been relatively mild on the offensive side.* The Falcons have to solve their defensive woes first.

* As Julio Jones’s injury was really in the last of the five games above, the effect of his absence is really not a part of the stats so far.

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.

Over some five years, the whole of the Matt Ryan – Mike Smith era, Atlanta has had a habit of outperforming its Pythagoreans:

Atlanta outperforming its Pythagoreans
Year WL% Pythag Delta
2008 69 62 7
2009 56 56 0
2010 81 72 9
2011 63 59 4
2012 (to date) 90 71 19

 

But they’ve never outperformed their Pythagoreans as substantially as they have this year. It can’t be blamed on early season New Orleans collapse, as their only loss was inflicted by New Orleans. New Orleans has only hindered this process. Is it turnover that are causing all this? While the 2010 team had a +14 turnover ratio and the 2011 team had a +8 turnover ratio, the 2012 team has only a +5 turnover ratio at this point and the 2008 team had a -3 turnover ratio. No, it’s something else. For now, perhaps noting that this team tends to outperform its Pythagoreans is enough.

Week 11 scoring stats:

Chicago’s biggest weakness was on display this Monday night, as Aldon Smith had a career day. Aaron Schatz (@FO_Schatz) has sent digging into his archives for the biggest DVOA blowouts of all time. The 32-7 demolition of the Bears by the 49ers wasn’t the worst, but it clearly evoked the worst.

The game plan was heavy on traps and wham blocks, and would have warmed the hearts of anyone who ever played NFL Strategy against a blitz heavy opponent.

It does lead to the question of whether Chicago is in the same downward spiral they experienced last year. At this point, however, you would expect Jay Cutler to return and thus slow down the bleeding.

Next Page »