|
Rank
|
Team
|
Record
|
Score
|
Last
Score
|
Last
Month Score
|
Last
Year Score
|
Week
Difference
|
Month
Difference
|
Year
Difference
|
Last
Year Rank
|
|
1
|
|
12-4
|
87.29
|
78.88
|
62.69
|
79.20
|
8.41
|
24.60
|
8.09
|
1
|
|
2
|
|
12-4
|
61.67
|
52.26
|
45.18
|
29.17
|
9.41
|
16.49
|
32.50
|
2
|
|
3
|
|
12-4
|
47.90
|
47.52
|
39.47
|
-7.11
|
0.38
|
8.43
|
55.01
|
19
|
|
4
|
|
10-6
|
35.08
|
29.68
|
33.26
|
-21.40
|
5.40
|
1.82
|
56.48
|
25
|
|
5
|
|
11-5
|
31.59
|
32.82
|
24.37
|
-5.73
|
-1.23
|
7.22
|
37.32
|
17
|
|
6
|
|
12-4
|
31.02
|
27.79
|
8.28
|
-28.75
|
3.23
|
22.74
|
59.77
|
29
|
|
7
|
|
8-8
|
31.00
|
20.65
|
18.81
|
50.64
|
10.35
|
12.19
|
-19.64
|
3
|
|
8
|
|
9-7
|
28.53
|
28.73
|
15.82
|
-29.98
|
-0.20
|
12.71
|
58.51
|
30
|
|
9
|
|
7-8-1
|
10.42
|
-0.98
|
-13.42
|
33.53
|
11.40
|
23.84
|
-23.11
|
4
|
|
10
|
|
10-5-1
|
9.25
|
7.90
|
4.13
|
23.05
|
1.35
|
5.12
|
-13.80
|
6
|
|
11
|
|
12-4
|
8.95
|
7.85
|
10.92
|
2.61
|
1.10
|
-1.97
|
6.34
|
12
|
|
12
|
|
4-12
|
6.44
|
3.97
|
-6.00
|
-1.99
|
2.47
|
12.44
|
8.43
|
15
|
|
13
|
|
8-8
|
4.33
|
6.35
|
15.22
|
-17.49
|
-2.02
|
-10.89
|
21.82
|
23
|
|
14
|
|
11-5
|
3.37
|
4.87
|
-2.19
|
-2.55
|
-1.50
|
5.56
|
5.92
|
16
|
|
15
|
|
10-6
|
-1.82
|
-4.07
|
5.32
|
64.89
|
2.25
|
-7.14
|
-66.71
|
2
|
|
16
|
|
9-7
|
-1.84
|
-0.28
|
7.81
|
8.64
|
-1.56
|
-9.65
|
-10.48
|
9
|
|
17
|
|
4-12
|
-4.74
|
-1.55
|
6.03
|
-7.86
|
-3.19
|
-10.77
|
3.12
|
20
|
|
18
|
|
6-10
|
-6.23
|
2.82
|
-0.64
|
7.38
|
-9.05
|
-5.59
|
-13.61
|
11
|
|
19
|
|
7-9
|
-7.17
|
-1.51
|
9.70
|
-10.96
|
-5.66
|
-16.87
|
3.79
|
21
|
|
20
|
|
11-5
|
-8.74
|
-8.72
|
5.04
|
2.60
|
-0.02
|
-13.78
|
-11.34
|
13
|
|
21
|
|
11-5
|
-9.08
|
1.16
|
3.78
|
7.97
|
-10.24
|
-12.86
|
-17.05
|
10
|
|
22
|
|
7-9
|
-15.40
|
-16.68
|
-14.78
|
-22.22
|
1.28
|
-0.62
|
6.82
|
26
|
|
23
|
|
6-10
|
-20.47
|
-9.21
|
-11.76
|
-45.66
|
-11.26
|
-8.71
|
25.19
|
31
|
|
24
|
|
9-7
|
-22.55
|
-23.78
|
-22.95
|
19.22
|
1.23
|
0.40
|
-41.77
|
7
|
|
25
|
|
9-7
|
-23.52
|
-22.08
|
-7.97
|
-6.34
|
-1.44
|
-15.55
|
-17.18
|
18
|
|
26
|
|
5-11
|
-23.67
|
-22.86
|
-10.61
|
-23.21
|
-0.81
|
-13.06
|
-0.46
|
27
|
|
27
|
|
7-9
|
-30.99
|
-31.08
|
-20.64
|
18.75
|
0.09
|
-10.35
|
-49.74
|
8
|
|
28
|
|
3-13
|
-34.10
|
-34.67
|
-32.71
|
-60.39
|
0.57
|
-1.39
|
26.29
|
32
|
|
29
|
|
2-14
|
-35.18
|
-35.59
|
-24.37
|
-13.21
|
0.41
|
-10.81
|
-21.97
|
22
|
|
30
|
|
2-14
|
-37.41
|
-38.01
|
-33.09
|
1.66
|
0.60
|
-4.32
|
-39.07
|
14
|
|
31
|
|
6-10
|
-45.14
|
-42.81
|
-47.84
|
-27.67
|
-2.33
|
2.70
|
-17.47
|
28
|
|
32
|
|
3-13
|
-55.01
|
-45.84
|
-53.21
|
-17.76
|
-9.17
|
-1.80
|
-37.25
|
24
|
Playoff Teams:
|
Rank
|
Team
|
Record
|
Score
|
Last
Score
|
Last
Month Score
|
Last
Year Score
|
Week
Difference
|
Month
Difference
|
Year
Difference
|
Last
Year Rank
|
|
1
|
|
12-4
|
87.29
|
78.88
|
62.69
|
79.20
|
8.41
|
24.60
|
8.09
|
1
|
|
2
|
|
12-4
|
61.67
|
52.26
|
45.18
|
29.17
|
9.41
|
16.49
|
32.50
|
2
|
|
3
|
|
12-4
|
47.90
|
47.52
|
39.47
|
-7.11
|
0.38
|
8.43
|
55.01
|
19
|
|
4
|
|
10-6
|
35.08
|
29.68
|
33.26
|
-21.40
|
5.40
|
1.82
|
56.48
|
25
|
|
5
|
|
11-5
|
31.59
|
32.82
|
24.37
|
-5.73
|
-1.23
|
7.22
|
37.32
|
17
|
|
6
|
|
12-4
|
31.02
|
27.79
|
8.28
|
-28.75
|
3.23
|
22.74
|
59.77
|
29
|
|
9
|
|
7-8-1
|
10.42
|
-0.98
|
-13.42
|
33.53
|
11.40
|
23.84
|
-23.11
|
4
|
|
10
|
|
10-5-1
|
9.25
|
7.90
|
4.13
|
23.05
|
1.35
|
5.12
|
-13.80
|
6
|
|
11
|
|
12-4
|
8.95
|
7.85
|
10.92
|
2.61
|
1.10
|
-1.97
|
6.34
|
12
|
|
14
|
|
11-5
|
3.37
|
4.87
|
-2.19
|
-2.55
|
-1.50
|
5.56
|
5.92
|
16
|
|
20
|
|
11-5
|
-8.74
|
-8.72
|
5.04
|
2.60
|
-0.02
|
-13.78
|
-11.34
|
13
|
|
21
|
|
11-5
|
-9.08
|
1.16
|
3.78
|
7.97
|
-10.24
|
-12.86
|
-17.05
|
10
|
Biggest 1 week risers:
1. Carolina (+11.40)
2. San Francisco (+10.35)
3. Denver (+9.41)
Biggest 1 week fallers:
1. Atlanta (-11.26)
2. Arizona (-10.24)
3. Oakland (-9.17)
Biggest 4 week risers:
1. Seattle (+24.60)
2. Carolina (+23.84)
3. Dallas (+22.74)
Biggest 4 week fallers:
1. Cleveland (-16.87)
2. San Diego (-15.55)
3. Indianapolis (-13.78)
Biggest 1 year risers:
1. Dallas (+59.77, 4-win improvement)
2. Houston (+58.51, 7-win improvement)
3. Baltimore (+56.48, 2-win improvement)
Biggest 1 year fallers:
1. Philadelphia (-66.71, same record)
2. New Orleans (-49.74, 4-win decline)
3. Kansas City (-41.77, 2-win decline)
The Forumla:
I broke down my formula into three parts:
Part 1: Yards per play.
Here I take each teams yards per carry (rushing) and yards per attempt (passing) numbers and subtract from them the YPC and YPA their defense allows. The theory being that, if Team A's offense is better per play than what their opponent's offense can muster against Team A's defense, Team A should be consistently better than their opponents over a full game's worth of plays (60 to 70 per game approximately).
Part 2: Toxic Differential
A better yards per play differential is helpful to a team's chances of winning, but just how often is an NFL team able to consistently drive down the field taking 5-8 yards at a time? You're essentiall asking an NFL offense to put together 10-12 plays without more than 1-2 negative plays, be they incompletions, sacks, no-gainers, or worse: turnovers. It's doable, but it's really hard to do with any sort of consistency in a single game.
This is why coaches harp on turnovers so much. A turnover a) takes away an opponent's possession which decreases their chances of scoring more points, and b) can give your team a shorter field so you don't have to put together an 80+ yard drive to get points of your own. The problem with turnovers is you can't count on them. So much of what goes into a turnover is dependent on a) the other team and b) luck that relying on turnovers is a dangerous proposition.
So yes, turnovers are important. But there's something else that can make getting points in a drive much easier: big plays. If my offense can get 20 or 30 yards in a single play, that cuts out 4-6 plays of grinding, or 4-6 plays where something could go wrong. Now my offense only has to put 5-6 plays together on a drive where they also get a chunk play.
Brian Billick is credited with coming up with the toxic differential statistic. This adds your takeaways and big plays generated by your offense and subtracts your giveaways and the big plays given up by your defense. Again, the theory goes that teams with a better toxic differential will be better at turning drives into points and games into wins. Pete Carroll also bases his offensive and defensive identity around turnovers and big plays being the most important indicators for both sides of the ball.
Note: For this formula, a big play is considered a rushing play of 10+ yards or a passing play of 25+ yards.
Part 3: Points Per Drive
What's the most important job of an NFL team? Score more points than your opponent. Rather than look simple points per game differential, I wanted to dig a little deeper and normalize the data a little further. Game-to-game the number of possessions can vary based on team tempo, weather coniditons, etc. So instead I looked at points per drive data for each team's offense and defense, and multiplied the difference by 10. Why 10? A typical NFL game has 12 possessions, but 1-2 of those come at a point where a team isn't really interested in scoring (maybe they get the ball with 12 seconds to go before halftime, or they get it with 3 minutes to go in the game up 14+ points already. 10 seemed like a good number of possessions per game where the end goal is to score points.
A better yards per play differential is helpful to a team's chances of winning, but just how often is an NFL team able to consistently drive down the field taking 5-8 yards at a time? You're essentiall asking an NFL offense to put together 10-12 plays without more than 1-2 negative plays, be they incompletions, sacks, no-gainers, or worse: turnovers. It's doable, but it's really hard to do with any sort of consistency in a single game.
This is why coaches harp on turnovers so much. A turnover a) takes away an opponent's possession which decreases their chances of scoring more points, and b) can give your team a shorter field so you don't have to put together an 80+ yard drive to get points of your own. The problem with turnovers is you can't count on them. So much of what goes into a turnover is dependent on a) the other team and b) luck that relying on turnovers is a dangerous proposition.
So yes, turnovers are important. But there's something else that can make getting points in a drive much easier: big plays. If my offense can get 20 or 30 yards in a single play, that cuts out 4-6 plays of grinding, or 4-6 plays where something could go wrong. Now my offense only has to put 5-6 plays together on a drive where they also get a chunk play.
Brian Billick is credited with coming up with the toxic differential statistic. This adds your takeaways and big plays generated by your offense and subtracts your giveaways and the big plays given up by your defense. Again, the theory goes that teams with a better toxic differential will be better at turning drives into points and games into wins. Pete Carroll also bases his offensive and defensive identity around turnovers and big plays being the most important indicators for both sides of the ball.
Note: For this formula, a big play is considered a rushing play of 10+ yards or a passing play of 25+ yards.
Part 3: Points Per Drive
What's the most important job of an NFL team? Score more points than your opponent. Rather than look simple points per game differential, I wanted to dig a little deeper and normalize the data a little further. Game-to-game the number of possessions can vary based on team tempo, weather coniditons, etc. So instead I looked at points per drive data for each team's offense and defense, and multiplied the difference by 10. Why 10? A typical NFL game has 12 possessions, but 1-2 of those come at a point where a team isn't really interested in scoring (maybe they get the ball with 12 seconds to go before halftime, or they get it with 3 minutes to go in the game up 14+ points already. 10 seemed like a good number of possessions per game where the end goal is to score points.
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