It’s an interesting part of the season, as the trade deadline approaches and teams get talent to reload for the latter half of the season. Amari Cooper is in Dallas, Demaryius Thomas is in Houston, and Golden Tate has been traded to the Eagles. “Snacks” Harrison is in Detroit.

Despite the fire sale in Oakland, they are not on the bottom of the charts, just yet, saved by a half game. Comparing records to Pythagoreans, Washington, Cinncinnati and Miami are the teams most overperforming presently. By Simple Ranking, Buffalo, despite their two wins, is the worst team in football.

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
121        73     60.3      29.07        18.32     10.74

Calculated Pythagorean Exponent:  2.91


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     LA       9.5     8   8   0   0 100.0  82.5  11.20  13.62 -2.42
2     KC       8.5     8   7   1   0  87.5  73.3  11.63  10.62  1.00
3     NO       6.0     7   6   1   0  85.7  67.1   6.39   7.29 -0.89
4     NE       7.0     8   6   2   0  75.0  67.8   5.90   6.75 -0.85
5     WAS      6.0     7   5   2   0  71.4  56.2   1.38   1.71 -0.33
6     CAR      4.0     7   5   2   0  71.4  61.3   4.47   3.71  0.76
7     LAC      2.0     7   5   2   0  71.4  62.7   2.32   4.57 -2.25
8     PIT      3.0     7   4   2   1  64.3  62.1   5.70   4.57  1.13
9     HOU      3.0     8   5   3   0  62.5  61.8   0.76   3.75 -2.99
10    CIN      2.0     8   5   3   0  62.5  44.9   0.33  -2.00  2.33
11    SEA      3.0     7   4   3   0  57.1  68.4   5.66   5.71 -0.05
12    CHI      2.0     7   4   3   0  57.1  70.4   5.68   7.14 -1.46
13    MIN      1.0     8   4   3   1  56.2  50.7  -1.62   0.25 -1.87
14    BAL      5.5     8   4   4   0  50.0  74.2   7.29   7.50 -0.21
15    PHI      1.0     8   4   4   0  50.0  59.5   0.93   2.75 -1.82
16    GB       0.0     7   3   3   1  50.0  50.8  -0.67   0.29 -0.95
17    MIA     -3.5     8   4   4   0  50.0  33.9  -6.41  -5.62 -0.79
18    ATL     -1.0     7   3   4   0  42.9  42.1  -1.74  -3.14  1.40
19    TEN     -1.0     7   3   4   0  42.9  37.2  -4.62  -3.00 -1.62
20    DET     -2.0     7   3   4   0  42.9  43.9  -2.56  -2.14 -0.41
21    TB      -3.0     7   3   4   0  42.9  39.4  -2.60  -4.57  1.97
22    DAL     -3.0     7   3   4   0  42.9  59.3   2.57   2.43  0.14
23    IND     -3.5     8   3   5   0  37.5  55.9   0.32   2.25 -1.93
24    DEN     -3.5     8   3   5   0  37.5  47.7   2.45  -0.75  3.20
25    JAX     -4.5     8   3   5   0  37.5  33.4  -3.30  -4.50  1.20
26    NYJ     -6.0     8   3   5   0  37.5  47.0  -2.12  -1.00 -1.12
27    CLE     -3.0     8   2   5   1  31.2  34.7  -3.50  -5.12  1.62
28    ARI     -6.5     8   2   6   0  25.0  15.1  -9.99 -11.12  1.14
29    BUF    -15.0     8   2   6   0  25.0   8.2 -12.91 -14.12  1.21
30    OAK    -14.0     7   1   6   0  14.3  20.9  -9.71 -11.43  1.72
31    SF      -5.5     8   1   7   0  12.5  28.9  -7.83  -7.88  0.04
32    NYG     -6.0     8   1   7   0  12.5  28.7  -5.44  -6.88  1.43
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Sorry if this item has come out a little later than usual. There was a mini-emergency at my work and most of my time (and efforts) were concentrated on that.

Upcoming is the contest in London between the Eagles and the Jaguars. I’m actually looking forward to that.

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
107        65     60.7      28.96        18.20     10.77

Calculated Pythagorean Exponent:  2.60


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     LA      12.0     7   7   0   0 100.0  82.9  13.26  15.29 -2.03
2     KC      10.0     7   6   1   0  85.7  71.7  12.33  11.14  1.19
3     NO       4.5     6   5   1   0  83.3  64.2   6.10   6.83 -0.73
4     NE       7.0     7   5   2   0  71.4  61.4   5.87   5.00  0.87
5     LAC      2.0     7   5   2   0  71.4  61.4   3.49   4.57 -1.08
6     WAS      4.5     6   4   2   0  66.7  52.6  -0.10   0.83 -0.93
7     CAR      3.0     6   4   2   0  66.7  55.2   0.40   1.83 -1.43
8     MIN      2.0     7   4   2   1  64.3  54.6  -1.09   1.71 -2.81
9     PIT      1.5     6   3   2   1  58.3  56.8   5.35   2.83  2.52
10    GB       0.5     6   3   2   1  58.3  51.8  -2.22   0.67 -2.88
11    BAL     12.0     7   4   3   0  57.1  80.9  10.17  10.71 -0.55
12    HOU      3.0     7   4   3   0  57.1  54.8  -1.39   1.57 -2.96
13    MIA      3.0     7   4   3   0  57.1  39.8  -4.15  -3.71 -0.43
14    CIN      1.0     7   4   3   0  57.1  43.6   0.22  -2.71  2.93
15    DET      3.0     6   3   3   0  50.0  49.6  -1.19  -0.17 -1.02
16    CHI      0.5     6   3   3   0  50.0  65.0   4.50   6.00 -1.50
17    SEA      0.5     6   3   3   0  50.0  62.8   5.01   4.33  0.68
18    TB       0.0     6   3   3   0  50.0  39.7  -3.00  -4.83  1.83
19    ATL     -1.0     7   3   4   0  42.9  42.9  -2.88  -3.14  0.27
20    TEN     -1.0     7   3   4   0  42.9  38.5  -4.20  -3.00 -1.20
21    PHI     -2.0     7   3   4   0  42.9  57.1  -0.34   2.29 -2.62
22    DEN     -3.0     7   3   4   0  42.9  50.4   3.29   0.14  3.15
23    DAL     -3.0     7   3   4   0  42.9  58.3   1.36   2.43 -1.07
24    JAX     -3.0     7   3   4   0  42.9  35.5  -3.33  -4.29  0.96
25    NYJ     -4.0     7   3   4   0  42.9  52.2  -0.42   0.86 -1.27
26    CLE     -3.0     7   2   4   1  35.7  39.8  -1.73  -3.71  1.98
27    IND     -4.0     7   2   5   0  28.6  51.4  -0.72   0.57 -1.29
28    BUF    -11.0     7   2   5   0  28.6  11.9 -12.85 -13.43  0.58
29    OAK    -12.0     6   1   5   0  16.7  22.8  -7.80 -11.00  3.20
30    NYG     -5.0     7   1   6   0  14.3  31.4  -6.87  -6.86 -0.01
31    SF      -8.0     7   1   6   0  14.3  30.2  -6.56  -8.57  2.01
32    ARI    -10.0     7   1   6   0  14.3  14.2 -10.53 -13.14  2.61

I’ll continue posting my odds, though this has not been the best season for them. Jacksonville continued to be best modeled by their median point spread, as opposed to their playoff formula. Philadelphia outperformed any reasonable prediction of their play once Wentz went down.

My system gives an edge to New England. Philadelphia played a tougher schedule but lacks playoff experience by my system. There is no home field in the Superbowl.

Super Bowl Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
New England Patriots Philadelphia Eagles 0.586 0.642 4.3

Outside of the New England game, all the games were good and exciting, from the final goal line stand by the Eagles, to the win with ten seconds left by the Vikings. The Jacksonville Jaguars are just not well managed by this system. It was easy to see that through the year that they were a boom or bust team. They could win big or lose big, and in the game with the Steelers, they were enough in “win big” mode that the Steelers could not keep up.

Philadelphia won because of their stout defense, a Nick Foles that gave them a AYA of 8.2 for the game, much akin to Carson Wentz’s average.

To remind people, the 2017 worksheet is here, and the methodology is here. The odds for the next round are below.

Conference (NFC/AFC) Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Philadelphia Eagles Minnesota Vikings -0.604 0.353 -4.5
New England Patriots Jacksonville Jaguars 1.872 0.867 13.9

The first round is over and in terms of predicting winners, not my best (by my count, 1-2-1, as we had Jax and Bills in a de facto tie). I was pleased that the model got Rams and Atlanta correct, and the Sunday games all came down to the wire. One or two plays and my formal results would have been impressive. Still, back to the predictions for this week.

To add some spice, we will predict results for New Orleans normally, and also as if Drew Brees is elite. Values in parentheses are the elite numbers. With elite status or no, Minnesota is still favored in this data set.

The only home team not favored is Philadelphia. We discussed this in part in this article.

Second Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Philadelphia Eagles Atlanta Falcons -0.878 0.294 -6.5
Minnesota Vikings New Orleans Saints 1.231 (0.484) 0.774 (0.619) 9.1 (3.6)
New England Patriots Tennessee Titans 1.674 0.842 12.4
Pittsburgh Steelers Jacksonville Jaguars 1.915 0.872 14

As I’m really away from home and short of time, I’ll mention my methodology is described in depth here.

The first table is the worksheet. Values will not change throughout the playoffs.

2017 NFL Playoff Teams, C&F Worksheet.
NFC
Rank Name Home Field Adv Playoff Experience SOS Total Score
1 Philadelphia Eagles 0.660 0 -0.216 0.444
2 Minnesota Vikings 0.660 0.747 0.301 1.708
3 Los Angeles Rams 0.660 0.0 -0.046 0.614
4 New Orleans Saints 0.660 0 0.471 1.131
5 Carolina Panthers 0.0 0.747 0.629 1.376
6 Atlanta Falcons 0.0 0.747 0.575 1.322
AFC
1 New England Patriots 0.660 0.747 -0.377 1.030
2 Pittsburgh Steelers 0.660 0.747 -0.334 1.073
3 Jacksonville Jaguars 0.660 0 -0.842 -0.182
4 Kansas City Chiefs 0.660 0.747 -0.404 1.003
5 Tennessee Titans 0.0 0.0 -0.644 -0.644
6 Buffalo Bills 0.0 0.0 -0.146 -0.146

 

LA is not favored by this model and neither are AFC teams. The NFC South’s toughness shows through in the SOS marks for this data set. Minnesota and/or NFC South Teams largely have advantages over almost any matchup the AFC can offer. Finally, an open Q is, is Drew Brees elite enough that his team should be granted the PPX bonus? For now I’d consider this a question to be answered later.

This second table shows odds for the first round, calculated for you. The only home team it favors is Kansas City.

First Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Los Angeles Rams Atlanta Falcons -0.708 0.330 -5.2
New Orleans Saints Carolina Panthers -0.245 0.439 -1.8
Jacksonville Jaguars Buffalo Bills -0.036 0.491 -0.3
Kansas City Chiefs Tennessee Titans 1.647 0.838 12.1

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).