2017 NFL Season. Superbowl Odds

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

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2017 Playoff Odds. Conference Championship Round.

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

 

2017 Playoff Odds. Second Round.

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

 

2017 Playoff Odds. First Round.

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

 

Statistically, does a veteran QB matter in the playoffs?

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).

 

NFL Week 5, 2017 season

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

 

Week 3, 2017 season

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