NFL Power Rankings – Week 14
By Keith Myers
San Francisco remains in the top spot for another week, but Green Bay is on the rise and has closed the gap by quite a bit. Houston followed up it’s first week of not being in the top spot by dropping down another spot to #3. I seriously doubt they’ll fall any further though, there’s a huge gap between the top 3 teams and the rest of the league. Interestingly, SF and the Packers are up there because of turnovers. Houston is there because they’re a solid team in all aspects of the game.
The Seahawks moved up past Miami into the 15th spot. They continue to rise up the rankings as they continue to play better and overcome their slow start to the season. They are mostly being held back by special teams and point differential. A couple more wins will fix the points problem, but I don’t think anything will fix the special teams problems the Seahawks have. They just don’t cover kicks as well as they should.
| Rank | Last | Team | Yds |
ST
Pt Dif
TO
Power
1
1
San Francisco
1.3
3.3
9.6
21
84.13
2
3
Green Bay
0.9
-0.9
14.4
20
83.90
3
2
Houston
2.2
0.8
9.4
10
79.39
4
4
New England
0.2
1.1
9.4
9
67.79
5
6
Pittsburgh
2.7
1.7
6.5
-7
66.95
6
7
Baltimore
1.1
-1.0
9.1
3
66.89
7
5
Chicago
0.4
6.5
3.6
8
65.04
8
11
Detroit
0.3
-4.0
4.7
11
62.63
9
8
New Orleans
0.6
1.2
9.9
-2
62.31
10
9
Dallas
1.1
-1.2
2.8
5
62.00
11
10
Tennessee
0.0
3.4
1.2
5
56.66
12
14
NY Jets
0.0
1.4
4.4
0
55.11
13
13
Atlanta
0.0
2.2
2.6
1
54.46
14
12
Cincinnati
0.2
1.8
1.1
1
53.85
15
16
Seattle
0.1
-0.8
-1.0
4
52.15
16
15
Miami
0.6
1.0
0.8
-5
50.84
17
27
San Diego
0.3
-3.3
1.9
-7
46.66
18
18
NY Giants
-0.9
-1.4
-1.9
4
45.48
19
19
Denver
-0.4
3.3
-2.5
-4
43.94
20
22
Philadelphia
0.4
0.2
0.3
-12
43.59
21
21
Arizona
0.5
1.6
-2.7
-11
42.60
22
17
Oakland
0.2
-2.9
-4.9
-4
41.74
23
23
Carolina
0.1
-4.5
-3.2
-5
41.34
24
20
Buffalo
-0.8
-1.0
-4.0
0
41.13
25
25
Jacksonville
-1.5
-2.6
-4.6
5
39.61
26
24
Minnesota
0.0
-1.9
-6.9
-6
37.68
27
26
Cleveland
-2.0
-1.6
-5.8
0
32.40
28
28
Washington
-0.7
1.9
-4.7
-14
31.91
29
29
Kansas City
-1.7
0.0
-10.2
-3
28.18
30
30
Tampa Bay
-1.7
0.3
-10.7
-10
22.61
31
31
St. Louis
-2.3
-0.3
-13.3
-5
20.13
32
32
Indianapolis
-2.0
-8.4
-15.2
-12
10.63
All stats except turnovers are per-game stats.
Mathematical explanation:
Here’s the formula I decided to go with this week: Power = A*Yds+B*ST+C*PD+D*TO+50
A,B,C and D are coefficients that I can use to properly weight each of the 4 variables. For this week, A=5.5, B = .75, D=.8, and the others are all 1.
Yds = yards index = (YpC-YpCA)+(YpP-YpPA)
ST = special teams index = ((KR-KRA)+2(PR-PRA))/4
PD = point differential
TO = turnover differential
YpC = yards per carry, YpCA = yards per carry against
YpA = yards per pass attempt, YpPA = yards per pass attempt against
KR = kick return average, KRA = kick return against average
PR = punt return average, PRA = punt return against average
Why this particular formula? I wanted to move away from total yards because there’s too many things that go into those totals. Yards per carry and yards per pass attempt tell a much clearer story of the proficiency of an offense or defense. These stats also have a much higher correlation to wins and losses than do total yards.
The special teams index takes into account punt and kick returns. Punt returns are given twice the weight in the formula for a few reasons. First, the values are naturally smaller and secondly, initial results from my statistical work indicate that punt return yards have a larger impact in the results of the game.
I’ve put turnovers back into the formula, and they will remain there. I had taken them out in light some initial results that suggested quite strongly that turnovers had no correlation to wins and losses. I’ve been continuing to work with the turnover data and have uncovered some additional relationships between the variables that indicate that turnovers do indeed belong in this model. I’ll be posting some of those results in next day or so.
Point differential seems self explanatory, especially in light of the fact that point differential correlates very strongly to wins and losses even across an entire season.
The coefficients A through D are there because I will be tinkering with how much weight to give each of the 4 variables in this model. Weighing the natural magnitude of each variable (Yds has a maximum of 2.7 this week while TO has a maximum of 13) along with the statistical importance of each variable (Yds having the strongest correlation to wins except for PD) will constitute the bulk of the tweaking that is left to do to the formula.
The +50 at the end the formula just shifted every result so that the numbers were between 0 and 100. I will be switching to a more precise and accurate way to normalize the raw results in the next few weeks. For now, this works and does not change the order, nor the relative distance between each team.
At some point, I would like to implement a penalty component as well, but that will likely have to wait until next season.