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NFL Power Rankings

No changes to the formula this week. I haven’t had time to work on any of my analysis, so I don’t have any more info than last time and thus there’s no reason to make changes.

There’s no changes in the top 5 this week. Interestingly, Houston remains on top despite no Matt Shaub and losing Matt Leinart midway through the game. I expect them to begin dropping now that they have problems at QB. I’m a little surprised that Chicago didn’t drop after their sad showing on Sunday.

Our Seahawks remain at #20 despite the loss to Washington. I think this is because the teams above them played pretty well, and the teams below them didn’t. It would appear that the formula is starting to become predictive of results, which is what I’d hoped would happen.

 

Rank Last Team Yds ST Pt Dif TO Power

1

1

Houston

2.2

2.6

10.3

11

83.78

2

2

San Francisco

1.1

2.9

9.2

16

80.95

3

3

Green Bay

0.9

-0.9

14.1

15

80.15

4

4

Chicago

0.2

7.3

5.1

9

70.73

5

5

New England

0.5

1.4

9.8

6

68.70

6

10

New Orleans

0.9

1.7

10.0

-3

64.20

7

8

Pittsburgh

2.5

1.9

4.1

-8

63.35

8

6

Dallas

1.1

-2.0

4.0

5

62.03

9

7

Detroit

0.6

-4.6

6.3

6

59.80

10

9

Baltimore

0.2

-2.8

8.2

4

59.68

11

11

Cincinnati

0.4

2.2

4.0

1

59.18

12

12

Atlanta

0.3

1.7

2.9

0

56.20

13

13

Tennessee

-0.3

2.9

1.2

3

54.85

14

17

Oakland

0.6

-2.3

-1.3

1

50.55

15

14

NY Jets

-0.2

1.8

1.4

-2

50.48

16

15

Philadelphia

1.0

0.4

0.6

-9

49.33

17

18

Miami

0.4

0.0

0.6

-5

48.78

18

19

Buffalo

-0.5

-0.2

-1.8

4

48.48

19

16

NY Giants

-0.9

-1.5

-2.3

5

45.28

20

20

Seattle

-0.1

-0.9

-4.3

0

44.28

21

21

Denver

-0.6

4.0

-3.5

-4

43.98

22

22

Arizona

0.1

3.1

-3.9

-8

43.30

23

23

Minnesota

0.0

-1.6

-7.3

1

41.90

24

28

Carolina

-0.2

-4.1

-4.8

-4

36.78

25

24

Cleveland

-1.4

-0.5

-4.6

-1

36.43

26

26

Washington

-0.8

1.5

-3.6

-11

34.73

27

25

Jacksonville

-1.4

-3.7

-5.7

1

33.68

28

27

San Diego

-0.5

-4.3

-2.4

-10

32.60

29

30

Tampa Bay

-1.9

0.6

-8.4

-4

28.53

30

29

Kansas City

-1.8

1.2

-10.2

-5

27.05

31

31

St. Louis

-2.1

0.1

-11.8

-3

24.33

32

32

Indianapolis

-2.2

-9.7

-16.1

-11

3.30

 

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, 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.

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