NFL Power Rankings: Week 2
By Keith Myers
In the top spot this week is the San Diego Chargers. They’ve played decently, but lets not pretend that their placement of the top doesn’t have more to do with their opponents (Oakland and Tennessee) than it does with their talent level. This is a team that will definitely come down to Earth once they start playing some decent teams.
Out of all the surprises, seeing Arizona down at #22 has to be among the biggest. They’ve played well and beat 2 good teams (Seattle and New England), but the math just doesn’t support the idea that they are a good team. They’ve clearly been the benefit of a few missed FGs. (which is something that the model doesn’t currently take into account because I want it to be predictive, and not just descriptive).
Another big surprise is that the Seahawks are currently at #5 on the list. I expected them to be much lower, but there they are. The Seahawks are positive in each major index. I included the 3rd down % in the table this week because it was the only column in the data set where the Seahawks ended up below average. When you can be at least above average in every phase of the game, you’re going to end up pretty high on these rankings.
There is some huge movements by teams from last week, but that is to be expected. Performances from 1 week to another, and the differences in the quality of the opponent can lead to massive swings early in the season when the sample sizes are still very small. Things will become less volatile as we get further into the season. I also used a simplified formula to determine the Yards Index (a composite of offensive and defensive stats) last week due to the small sample size issue, while this week I used the full formula. This has also led to there being more movement than usual.
| Rank | Last | Team | Yds | PTDif | TO | ST | 3rd% | Power |
1 |
9
San Diego
2.15
18
1
-8.9
50
95.9
2
6
Houston
0.6
20
5
-23.92
44.1
95.3
3
1
Baltimore
1.4
15
4
18.22
30.4
94.5
4
4
New England
3.4
9.5
3
14.88
40.7
91.4
5
18
Seattle
1.6
8
2
27.28
33.3
85.7
6
11
San Francisco
4.45
8
1
-26.8
30
84.3
7
5
Atlanta
-1.65
11
7
-6.76
43.5
81.6
8
28
Miami
3.15
1
-3
26.82
40.7
77.1
9
10
Washington
1.65
2.5
5
-14
28.6
74.7
10
7
Denver
3.65
3
-4
-2.08
42.9
76.5
11
19
Carolina
3.45
1
-1
8.7
36.4
76.4
12
12
Minnesota
2.75
0
-1
15.34
36
74.3
13
3
Chicago
0
3.5
2
14.96
33.3
74.3
14
31
Buffalo
1.95
-1
0
9.8
52.4
72.1
15
25
Pittsburgh
-1.9
2.5
1
8.98
55.9
68.6
16
22
Green Bay
-1.55
2.5
1
15.98
37
69.2
17
2
NY Jets
-1.8
1.5
2
7.96
53.8
68.1
18
20
St. Louis
-0.6
-0.5
1
3.64
45.8
66.7
19
16
Philadelphia
1.8
1
-3
-27.98
44.1
67.4
20
14
Tampa Bay
-1.05
-0.5
3
-13.78
29.2
63.9
21
24
NY Giant
-1.8
0
-1
10.7
36
63.9
22
15
Arizona
-1.2
3
-1
-21.7
29.2
64.7
23
13
Detroit
-0.9
-2
-3
10.2
41.7
62.6
24
8
Dallas
1.4
-6.5
-2
-9.7
47.8
60.5
25
17
Cleveland
0.15
-4
1
-23.12
32.1
60.3
26
30
Indianapolis
-1.6
-8.5
-3
-18.42
34.6
48.5
27
26
New Orleans
-4.55
-8
-4
5.08
37.5
45.1
28
32
Cincinnati
-3.9
-12
-2
26.76
29.6
45.1
29
21
Jacksonville
-2.95
-11.5
-1
-13.9
33.3
43.6
30
23
Oakland
-3.75
-15
-2
-5.54
22.2
36.9
31
27
Kansas City
-1.25
-17
-6
-48.94
53.3
33.7
32
29
Tennessee
-5.2
-24.5
-2
1.3
26.1
22.3
PtDif = point differential
YDS = yards – this is a collection of offensive and defensive yards per play stats. Due to massive small sample size problems, I had to use a similar form of my algorithm this week for this rating.
TO = turn over differential
ST = special teams rating, calculated from kicking, punting and returning stats
The model also uses 3rd down efficiency, time of possession and a host of other stats that aren’t included in the chart above.