This question came up when I was looking up the last year in the playoffs for seven probable NFC playoff teams. Both New Orleans and Philadelphia last played in the playoffs four years ago, in 2013. And then the thought came up in my head, “But Drew Brees is a veteran QB.” This seems intuitive, but wanting to actually create such a definition and then later to test this using a logistic regression, there is the rub.

There are any number of QBs a fan can point to and see that the QB mattered. Roger Staubach seemed a veteran in this context back in the 1970s, Joe Montana in the 1980s, Ben Roethlisberger in the 21st century, Eli Manning in 2011, and Aaron Rogers last year. But plenty of questions abound. If a veteran QB is an independent variable whose presence or absence changes the odds of winning a playoff game, what tools do we use to define such a person? What tools would we use to eliminate entanglement, in this case between the team’s overall offensive strength and the QB himself?

The difference between a good metric and a bad metric can be seen when looking at the effect of the running game on winning. The correlation between rushing yards per carry and winning is pretty small. The correlation between run success rate and winning are larger. In short, being able to reliably make it on 3rd and 1 contributes more to success than running 5 yards a carry as opposed to 4.

At this point I’m just discussing the idea. With a definition in mind, we can do one independent variable logistic regression tests. Then with a big enough data set – 15 years of playoff data should be enough, we can start testing three independent variable logistic models (QB + SOS + PPX).

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There is so much going on this week that my best suggestion is to prowl Youtube for the poison of your choice. I caught the end of the Vikings – Bears game, just enough to see the interception that Trubisky threw. Y A Tittle passed away. Brandon Marshall is out for the year. Odell Beckham is injured. The best team with just one loss is probably the Philadelphia Eagles.

I’m not sure how much that means, as the best team with two losses, Jacksonville, is via scoring stats, superior. But we’ve seen different Jacksonville teams show up. It’s as if the Jaguars identity hasn’t been established yet. A few more games will tell.

I know it’s still early in the season, but over the short term, home field advantage has just about vanished.

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
77         39     50.6      27.61        16.16     11.45

Calculated Pythagorean Exponent:  2.92


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     KC       9.0     5   5   0   0 100.0  75.8  17.52  10.60  6.92
2     GB       4.0     5   4   1   0  80.0  64.3   3.46   5.00 -1.54
3     PHI      3.0     5   4   1   0  80.0  72.1   8.65   7.60  1.05
4     CAR      3.0     5   4   1   0  80.0  58.0   4.18   2.20  1.98
5     ATL      5.0     4   3   1   0  75.0  61.2   4.99   3.75  1.24
6     DEN      4.5     4   3   1   0  75.0  69.4   6.93   6.00  0.93
7     JAX     21.0     5   3   2   0  60.0  81.9  11.16  11.20 -0.04
8     BAL     13.0     5   3   2   0  60.0  44.5  -1.91  -1.40 -0.51
9     DET      7.0     5   3   2   0  60.0  66.7   3.26   5.20 -1.94
10    BUF      6.0     5   3   2   0  60.0  63.2   5.62   3.00  2.62
11    NE       3.0     5   3   2   0  60.0  53.0   8.47   1.20  7.27
12    SEA      3.0     5   3   2   0  60.0  66.5  -2.44   4.60 -7.04
13    PIT      3.0     5   3   2   0  60.0  57.7  -0.30   2.00 -2.30
14    MIN      3.0     5   3   2   0  60.0  54.6   1.33   1.20  0.13
15    NYJ      3.0     5   3   2   0  60.0  39.8  -3.60  -2.80 -0.80
16    LA       2.0     5   3   2   0  60.0  66.1  -0.41   6.20 -6.61
17    NO       5.0     4   2   2   0  50.0  62.6   5.54   3.75  1.79
18    WAS     -1.0     4   2   2   0  50.0  51.6   7.12   0.50  6.62
19    TB      -1.5     4   2   2   0  50.0  51.7  -0.46   0.50 -0.96
20    MIA     -6.0     4   2   2   0  50.0  19.2  -6.80  -6.50 -0.30
21    HOU     -3.0     5   2   3   0  40.0  57.4   9.56   2.80  6.76
22    IND     -3.0     5   2   3   0  40.0  19.1 -21.30 -12.40 -8.90
23    CIN     -3.0     5   2   3   0  40.0  50.9   0.61   0.20  0.41
24    DAL     -4.0     5   2   3   0  40.0  46.0  -3.44  -1.40 -2.04
25    TEN     -6.0     5   2   3   0  40.0  32.2  -3.96  -6.40  2.44
26    OAK     -6.0     5   2   3   0  40.0  49.3   0.71  -0.20  0.91
27    ARI    -11.0     5   2   3   0  40.0  22.0 -13.96  -8.80 -5.16
28    LAC     -2.0     5   1   4   0  20.0  39.2   0.81  -3.20  4.01
29    CHI     -6.0     5   1   4   0  20.0  20.5  -7.40  -9.20  1.80
30    SF      -3.0     5   0   5   0   0.0  29.4 -12.99  -6.20 -6.79
31    CLE     -3.0     5   0   5   0   0.0  19.9 -14.70  -9.40 -5.30
32    NYG     -5.0     5   0   5   0   0.0  23.8  -6.24  -8.00  1.76

Just as the pundits decided that the NFL was boring, some truly epic games were played. The Thursday night game was good, Rams-49ers 41-38, the Atlanta – Detroit game was the kind people will remember for many years to come. On the back of a ferocious defensive rush Dallas got out of its Denver malaise and began playing football again. Even at 37, Larry Fitzgerald is a monster player.

I should warn people that simple rankings are not much to be believed at this point. It takes a few games to have enough results to be tame. You can add in games from the previous year, but then people might not recognize just how pathetic the New York Giants have been.

The nature of the recent sports scene has made me realize how much I miss Bill Simmon’s Grantland and Brian Burke’s Advanced NFL Stats. Both of these were edgy and ambitious, understanding of the new analytics and doing their best to apply it to games. Quite the opposite of the Sports Illustrated football preview, which was parochial and stodgy and the entire opposite of fearless. No, they were a dull recitation of how orthodox and Northeastern US focused they have become, statistically/sabermetrically illiterate in ways that all-22 will not fix. It read like a Frank Caliendo parody of Sports Illustrated, a bit senile and stuck in their ways.  So I cancelled my SI subscription.

I suspect I bought into Sports Illustrated because of how much I hated the form factor of ESPN the Magazine, which was foisted onto me without my wanting it for being a ESPN Insider. The classic ESPN cover was always an angry black man with his arms crossed who looked like he wanted to mug readers in a dark alley. They never seemed to get that Magic Johnson had hit on an incredible formula for being popular, which was to smile. I much prefer the look of people who seem as if I could share a meal with them.

But I guess people want numbers at this point. I can ponder the desert of cutting edge sports analysis later.

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
47         27     57.4      27.87        15.70     12.17

Calculated Pythagorean Exponent:  3.41


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     KC      14.0     3   3   0   0 100.0  84.1  17.10  12.00  5.10
2     ATL      6.0     3   3   0   0 100.0  71.9   3.66   7.00 -3.34
3     JAX     22.0     3   2   1   0  66.7  87.0  13.38  12.67  0.72
4     BAL     14.0     3   2   1   0  66.7  45.1  -6.79  -1.00 -5.79
5     DET     12.0     3   2   1   0  66.7  73.5   0.36   7.33 -6.97
6     DAL     11.0     3   2   1   0  66.7  52.7  -5.56   0.67 -6.22
7     OAK     10.0     3   2   1   0  66.7  70.2  14.77   6.00  8.77
8     MIN     10.0     3   2   1   0  66.7  62.5   4.42   3.33  1.09
9     BUF      9.0     3   2   1   0  66.7  73.6   6.50   4.33  2.17
10    WAS      7.0     3   2   1   0  66.7  64.0  14.20   3.67 10.53
11    TEN      6.0     3   2   1   0  66.7  67.9  14.25   5.67  8.58
12    CAR      6.0     3   2   1   0  66.7  59.9   2.74   1.67  1.08
13    NE       3.0     3   2   1   0  66.7  53.5   6.19   1.33  4.86
14    DEN      3.0     3   2   1   0  66.7  70.0   5.91   6.00 -0.09
15    PHI      3.0     3   2   1   0  66.7  60.4  10.34   3.00  7.34
16    GB       3.0     3   2   1   0  66.7  50.0  -4.42   0.00 -4.42
17    PIT      3.0     3   2   1   0  66.7  69.9  -1.26   4.67 -5.93
18    LA       2.0     3   2   1   0  66.7  77.1   6.48  10.67 -4.18
19    TB       2.5     2   1   1   0  50.0  59.7   1.73   2.50 -0.77
20    MIA     -6.0     2   1   1   0  50.0  20.8  -7.68  -6.00 -1.68
21    HOU     -3.0     3   1   2   0  33.3  24.3  -5.31  -7.00  1.69
22    IND     -3.0     3   1   2   0  33.3  14.1 -20.69 -12.33 -8.35
23    SEA     -6.0     3   1   2   0  33.3  33.1  -2.41  -3.67  1.26
24    CHI     -6.0     3   1   2   0  33.3  21.3  -5.95  -7.33  1.38
25    NYJ     -9.0     3   1   2   0  33.3  24.8  -2.14  -6.67  4.53
26    NO     -10.0     3   1   2   0  33.3  44.4   2.78  -1.67  4.45
27    ARI    -11.0     3   1   2   0  33.3  26.1 -15.29  -6.67 -8.63
28    CLE     -3.0     3   0   3   0   0.0  26.1 -16.24  -6.67 -9.58
29    SF      -3.0     3   0   3   0   0.0  20.4  -6.06  -8.33  2.27
30    LAC     -3.0     3   0   3   0   0.0  24.3  -1.22  -6.33  5.11
31    CIN     -4.0     3   0   3   0   0.0  11.5 -14.51  -9.00 -5.51
32    NYG    -14.0     3   0   3   0   0.0  10.2  -9.29 -11.00  1.71

When doing fits to my playoff formula while excluding Super Bowl data, I noted the following:

./logistic_game.pl --nosb --start=2001 --end=2015
b[0]   = prev playoff experience
b[1]   = strength of schedule
b[2]   = constant
b_p[i] = probability the term could be a product of chance.


Start year    = 2001
Ending year   = 2015
Data points   = 150
Home team won = 94

2 = Years checked for playoff appearance.
complete results
D0	198.211779799779
Dm	181.040960409088
Dm_chisq	17.1708193906915
Dm_df	2
Dm_p	[0.00018681164]
b	[0.93247135 0.25819533 0.74094435]
b_chisq	[ 9.5962352   7.944765  15.099918]
b_p	[0.0019497672 0.0048226681 0.0001019677]
iter	6

There are 5 playoff games every year in the NFC, and 5 in the AFC, and over the last 10 years, the home team has won 94 of them. Nominally HFA is usually assigned to be a 3 point betting advantage, and nominally that advantage is approximately 60%. 94*100/150 is 62.66666 repeating, which rounds to 62.7 percent. That’s more than the nominal 60% and more than the average of the last five NFL seasons. To calculate, 2012-2016 home field wins are 146, 153, 145, 138, and 147 respectively. That totals to 729 wins over 5*256 games. The percentage calculates to be 56.95%. Expressed in logits, and then in points, these advantages then become:

Home Field Advantage
Type of HFA Win Percent Logit Prob Calculated Point Spread
Playoff HFA 62.7 0.518 3.8
Traditional HFA 60.0 0.405 3.0
Seasonal HFA 56.95 0.280 2.1

I wasn’t around for week 15 because of the holidays, so I will be adding a couple weeks of data today.

So week 15 is below:

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
224       126     56.2      27.38        17.45      9.93

Calculated Pythagorean Exponent:  3.47


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     NE      12.0    14  12   2   0  85.7  82.6   7.28   9.43 -2.15
2     DAL      6.0    14  12   2   0  85.7  77.1   6.91   7.71 -0.80
3     OAK      3.0    14  11   3   0  78.6  59.9   4.43   2.93  1.50
4     KC       4.0    14  10   4   0  71.4  62.9   4.15   3.21  0.93
5     NYG      3.0    14  10   4   0  71.4  57.3   1.68   1.57  0.11
6     SEA      4.0    14   9   4   1  67.9  69.5   3.09   4.50 -1.41
7     PIT      7.5    14   9   5   0  64.3  67.6   4.97   4.64  0.33
8     ATL      7.0    14   9   5   0  64.3  71.9   8.08   7.93  0.15
9     MIA      3.5    14   9   5   0  64.3  50.3  -2.16   0.07 -2.23
10    DET      3.0    14   9   5   0  64.3  54.7  -0.09   1.14 -1.24
11    GB       3.5    14   8   6   0  57.1  55.9   2.14   1.71  0.43
12    TB       2.5    14   8   6   0  57.1  47.5   0.40  -0.64  1.04
13    HOU      2.0    14   8   6   0  57.1  36.3  -2.47  -3.14  0.68
14    TEN      1.5    14   8   6   0  57.1  54.4   0.58   1.21 -0.64
15    BAL      1.5    14   8   6   0  57.1  62.9   2.24   3.07 -0.83
16    DEN      1.5    14   8   6   0  57.1  62.5   4.45   2.93  1.52
17    WAS      1.0    14   7   6   1  53.6  50.5   1.32   0.14  1.18
18    IND      0.5    14   7   7   0  50.0  55.7   1.14   1.64 -0.50
19    BUF      0.5    14   7   7   0  50.0  61.2   1.55   3.14 -1.60
20    MIN      0.5    14   7   7   0  50.0  51.7   0.42   0.36  0.06
21    NO      -1.5    14   6   8   0  42.9  53.0   1.37   1.00  0.37
22    CAR     -2.0    14   6   8   0  42.9  46.2  -0.44  -1.07  0.64
23    ARI     -2.5    14   5   8   1  39.3  53.9  -0.48   1.07 -1.55
24    CIN     -2.5    14   5   8   1  39.3  48.5  -0.21  -0.36  0.15
25    SD      -3.0    14   5   9   0  35.7  50.0   1.59   0.00  1.59
26    PHI     -3.0    14   5   9   0  35.7  54.8   2.25   1.21  1.03
27    NYJ     -4.5    14   4  10   0  28.6  20.4  -9.28  -8.29 -0.99
28    LA      -5.5    14   4  10   0  28.6  14.5  -9.21  -9.36  0.14
29    CHI     -6.0    14   3  11   0  21.4  29.2  -5.37  -5.14 -0.23
30    JAX     -6.0    14   2  12   0  14.3  24.6  -6.19  -7.07  0.88
31    SF     -15.0    14   1  13   0   7.1  15.1 -12.05 -12.14  0.09
32    CLE    -14.0    14   0  14   0   0.0  10.5 -12.06 -13.43  1.37

and week 16 is here:

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
240       137     57.1      27.67        17.61     10.06

Calculated Pythagorean Exponent:  3.30


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     NE      13.0    15  13   2   0  86.7  85.7   8.65  11.33 -2.69
2     DAL      6.0    15  13   2   0  86.7  77.8   8.01   8.60 -0.59
3     OAK      3.0    15  12   3   0  80.0  60.3   4.52   3.27  1.26
4     KC       5.0    15  11   4   0  73.3  67.0   5.26   4.53  0.72
5     ATL      7.0    15  10   5   0  66.7  72.5   8.47   8.53 -0.06
6     PIT      7.0    15  10   5   0  66.7  66.3   5.46   4.60  0.86
7     MIA      3.0    15  10   5   0  66.7  51.0  -1.82   0.27 -2.09
8     NYG      3.0    15  10   5   0  66.7  54.9   1.67   1.13  0.54
9     SEA      2.0    15   9   5   1  63.3  66.0   2.76   4.00 -1.24
10    GB       4.0    15   9   6   0  60.0  57.9   2.69   2.47  0.23
11    DET      3.0    15   9   6   0  60.0  48.7  -1.14  -0.33 -0.80
12    HOU      2.0    15   9   6   0  60.0  38.0  -2.40  -2.80  0.40
13    WAS      2.0    15   8   6   1  56.7  54.8   2.42   1.47  0.95
14    TB       2.0    15   8   7   0  53.3  46.2  -0.07  -1.07  1.00
15    TEN      1.0    15   8   7   0  53.3  49.1  -1.35  -0.27 -1.08
16    BAL      1.0    15   8   7   0  53.3  60.1   2.71   2.60  0.11
17    DEN      1.0    15   8   7   0  53.3  54.9   2.85   1.20  1.65
18    NO      -1.0    15   7   8   0  46.7  54.1   1.48   1.40  0.08
19    MIN     -2.0    15   7   8   0  46.7  47.8  -0.38  -0.53  0.16
20    BUF     -3.0    15   7   8   0  46.7  59.1   1.51   2.73 -1.22
21    IND     -3.0    15   7   8   0  46.7  53.3   0.38   1.00 -0.62
22    ARI     -2.0    15   6   8   1  43.3  54.1  -0.04   1.20 -1.24
23    PHI     -1.0    15   6   9   0  40.0  55.5   2.77   1.47  1.30
24    CAR     -3.0    15   6   9   0  40.0  42.9  -1.05  -2.13  1.08
25    CIN     -2.0    15   5   9   1  36.7  48.1  -0.11  -0.47  0.36
26    SD      -3.0    15   5  10   0  33.3  49.4   0.33  -0.20  0.53
27    LA      -4.0    15   4  11   0  26.7  17.3  -9.51  -8.80 -0.71
28    NYJ     -5.0    15   4  11   0  26.7  16.7 -10.30 -10.27 -0.04
29    JAX     -5.0    15   3  12   0  20.0  31.7  -4.87  -5.20  0.33
30    CHI     -6.0    15   3  12   0  20.0  27.5  -6.30  -6.13 -0.17
31    SF     -13.0    15   2  13   0  13.3  17.8 -11.78 -11.27 -0.52
32    CLE    -14.0    15   1  14   0   6.7  13.2 -10.83 -12.33  1.50

Playoff slots by now are almost all determined. AFC playoff contenders are set. NFC playoff contenders are set but for one wild card slot and one division champion (NFC North). Pittsburgh beat Baltimore in a thrilling game. Kansas City played the heaviest QB in my memory. Two good QBs broke legs and the loss of Carr could have a serious impact on Oakland’s playoff success.

At this point races are getting tighter, and games become tougher. QB play suffered as elements of winter weather affect play. Atlanta’s SRS took a big leap as the 42-14 game with LA factored in.

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
208       119     57.2      27.38        17.54      9.83

Calculated Pythagorean Exponent:  3.39


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     NE      11.0    13  11   2   0  84.6  80.4   6.47   9.15 -2.69
2     DAL      6.0    13  11   2   0  84.6  77.0   7.49   7.85 -0.35
3     KC       5.0    13  10   3   0  76.9  63.9   4.22   3.62  0.60
4     OAK      3.0    13  10   3   0  76.9  59.4   4.12   2.92  1.20
5     DET      3.0    13   9   4   0  69.2  58.1   0.94   2.08 -1.13
6     NYG      3.0    13   9   4   0  69.2  53.7   1.48   0.85  0.63
7     SEA      2.0    13   8   4   1  65.4  63.7   2.26   3.23 -0.97
8     PIT      8.0    13   8   5   0  61.5  67.4   5.02   4.69  0.32
9     ATL      7.0    13   8   5   0  61.5  67.5   7.36   6.38  0.98
10    TB       3.0    13   8   5   0  61.5  49.1   0.13  -0.23  0.36
11    MIA      3.0    13   8   5   0  61.5  44.2  -3.27  -1.54 -1.73
12    DEN      2.0    13   8   5   0  61.5  66.4   4.75   4.15  0.59
13    WAS      2.0    13   7   5   1  57.7  53.4   2.85   1.00  1.85
14    GB       4.0    13   7   6   0  53.8  55.5   2.61   1.62  0.99
15    MIN      3.0    13   7   6   0  53.8  61.4   2.80   2.54  0.27
16    HOU      3.0    13   7   6   0  53.8  35.3  -2.42  -3.46  1.04
17    BAL      2.0    13   7   6   0  53.8  63.5   2.24   3.23 -0.99
18    TEN      1.0    13   7   6   0  53.8  54.0   0.04   1.15 -1.11
19    IND     -3.0    13   6   7   0  46.2  48.7  -0.88  -0.38 -0.49
20    BUF     -3.0    13   6   7   0  46.2  56.5   0.91   1.85 -0.94
21    CIN     -1.0    13   5   7   1  42.3  49.7  -0.13  -0.08 -0.06
22    ARI     -2.0    13   5   7   1  42.3  56.4  -0.04   1.69 -1.73
23    NO      -2.0    13   5   8   0  38.5  51.7   0.78   0.54  0.24
24    SD      -3.0    13   5   8   0  38.5  50.7   1.14   0.23  0.91
25    CAR     -3.0    13   5   8   0  38.5  43.2  -1.40  -2.00  0.60
26    PHI     -5.0    13   5   8   0  38.5  55.4   2.81   1.38  1.43
27    NYJ     -4.0    13   4   9   0  30.8  23.6  -8.47  -7.31 -1.17
28    LA      -4.0    13   4   9   0  30.8  17.9  -8.81  -8.46 -0.35
29    CHI     -6.0    13   3  10   0  23.1  28.5  -5.47  -5.31 -0.16
30    JAX     -7.0    13   2  11   0  15.4  23.9  -6.38  -7.54  1.15
31    SF     -13.0    13   1  12   0   7.7  18.0 -11.65 -10.92 -0.73
32    CLE    -14.0    13   0  13   0   0.0  11.8 -11.49 -12.92  1.43

We’re getting to a time and place where playoff positions will begin to be decided. Both Seattle and Dallas can make their own destinies. In the AFC, New England and Oakland have identical records and so unraveling the order there will be trickier. Those that guess that Dallas has about a 90% chance of getting the #1 seed seem accurate by my rough calculations. Using the SRS values available about 8 pm last Sunday, Dallas appeared to have a 90.6% chance of getting first seed, while Pythagorean expectations yielded a 92% chance. If Dallas beats New York, those odds will exceed 98%.

Just to be sure the (relatively high) Pythagorean exponent was real, I did tests where I changed the upper and lower bounds of the search to 2.0 and 3.0 respectively. When I did, the solution was 3.0 to 3 significant digits. There is no hidden minimum in between 2.0 and 3.0 that the normal algorithm does not catch.

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
192       110     57.3      27.62        17.72      9.90

Calculated Pythagorean Exponent:  3.38


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     DAL      6.5    12  11   1   0  91.7  78.2   7.75   8.75 -1.00
2     NE      12.0    12  10   2   0  83.3  81.2   6.73   9.33 -2.61
3     OAK      4.5    12  10   2   0  83.3  61.9   4.73   3.83  0.89
4     KC       4.0    12   9   3   0  75.0  62.4   3.60   3.25  0.35
5     SEA      4.0    12   8   3   1  70.8  73.9   4.84   5.83 -0.99
6     DEN      5.0    12   8   4   0  66.7  67.9   5.42   4.75  0.67
7     DET      3.0    12   8   4   0  66.7  57.6   0.82   2.00 -1.18
8     NYG      2.0    12   8   4   0  66.7  52.8   0.51   0.67 -0.16
9     PIT      9.0    12   7   5   0  58.3  66.7   4.66   4.50  0.16
10    ATL      4.0    12   7   5   0  58.3  62.7   6.57   4.58  1.99
11    BAL      3.5    12   7   5   0  58.3  67.2   2.42   4.08 -1.66
12    MIA      3.5    12   7   5   0  58.3  42.8  -3.31  -1.92 -1.39
13    TB       2.5    12   7   5   0  58.3  47.6   0.01  -0.67  0.68
14    WAS      1.0    12   6   5   1  54.2  52.3   2.16   0.67  1.50
15    GB       1.5    12   6   6   0  50.0  48.0  -0.14  -0.58  0.44
16    IND      0.5    12   6   6   0  50.0  50.0  -0.79   0.00 -0.79
17    BUF      0.5    12   6   6   0  50.0  59.0   1.70   2.58 -0.88
18    MIN      0.5    12   6   6   0  50.0  59.1   2.27   2.00  0.27
19    HOU     -2.0    12   6   6   0  50.0  32.5  -3.23  -4.17  0.94
20    TEN     -3.0    12   6   6   0  50.0  53.4  -0.87   1.00 -1.87
21    ARI     -1.0    12   5   6   1  45.8  57.9   0.80   2.08 -1.28
22    NO      -1.5    12   5   7   0  41.7  53.0   1.52   1.00  0.52
23    SD      -2.0    12   5   7   0  41.7  53.9   2.29   1.25  1.04
24    PHI     -3.0    12   5   7   0  41.7  57.5   2.87   1.92  0.96
25    CIN     -2.5    12   4   7   1  37.5  45.3  -0.24  -1.17  0.93
26    CAR     -3.0    12   4   8   0  33.3  39.5  -2.09  -3.17  1.07
27    LA      -3.5    12   4   8   0  33.3  22.0  -7.47  -6.83 -0.64
28    NYJ     -4.5    12   3   9   0  25.0  20.6  -8.30  -8.42  0.12
29    CHI     -6.0    12   3   9   0  25.0  28.0  -6.19  -5.50 -0.69
30    JAX     -6.0    12   2  10   0  16.7  24.4  -6.60  -7.42  0.81
31    SF     -15.0    12   1  11   0   8.3  17.5 -10.91 -11.33  0.42
32    CLE    -14.0    12   0  12   0   0.0  12.3 -11.52 -12.92  1.40