Week 13 Efficiency Rankings

NFL team efficiency rankings are listed below in terms of generic winning probability. The GWP is the probability a team would beat the league average team at a neutral site. Each team's opponent's average GWP is also listed, which can be considered to-date strength of schedule. GWP modifies the generic win probability to reflect the strength of past opponents. Offensive ranking (O Rank) is based on each team's offensive GWP, i.e. it's the team's GWP assuming it had a league-average defense. D Rank is vice-versa. Rankings are based on a logistic regression model applied to data through week 12. A full explanation of the methodology can be found here.





































RankTeamLast WkGWPOpp GWPO RankD Rank
1NE10.890.5415
2DAL30.860.5124
3IND20.840.5451
4TB40.770.4747
5PIT50.690.43152
6JAX70.680.55320
7GB60.670.50613
8SEA80.660.411211
9SD100.640.52136
10PHI90.590.54819
11MIN130.570.481014
12NYG110.520.49219
13WAS120.510.57228
14TEN150.510.51253
15DEN140.470.501131
16CLE170.470.50730
17BUF190.460.591717
18HOU160.460.481628
19NYJ240.450.551924
20CIN200.450.54929
21BAL260.430.482412
22ARI230.400.431816
23KC210.380.523110
24NO250.370.511432
25ATL220.360.492022
26MIA180.350.552323
27CAR280.330.462715
28STL300.290.472821
29OAK290.280.432925
30DET270.270.492618
31CHI310.250.513027
32SF320.160.453226

How the Ravens Can Beat the Patriots

Simple--Never punt.

I might go a step further and say, "Never kick a FG" (unless it provides a lead in the final 3 minutes of the game).

What do they have to lose?

QB Rating Week 12

The QB Wins Added Per 16 Games stat (+WP16) estimates how many wins a quarterback adds to his team's record over the course of a 16-game season. +WP16 is explained here and here. Last year's ratings can be found in the second link. Here is the list of 2007 QBs and their vital stats through week 12. Air Yards is the yards passed forward of the line of scrimmage not including WR yards after catch (YAC). TO% is total turnovers (fumbles and interceptions) per pass attempt, sack, or rush.













































RankNameQBRAttPctYdsY/ARushYdsSk YdsAir YdsTO %+WP16
1Brady127.939272.434398.822648019781.43.30
2Garrard103.120965.116267.836145779870.42.92
3Romo107.138266.033528.8228611820024.02.11
4Anderson89.536558.127587.624518417143.31.76
5Schaub87.328466.522077.8175211914234.01.53
6Warner90.825462.220097.911-48913044.31.45
7Garcia96.028264.521357.6341168410941.41.45
8Manning P89.437963.328217.414-910117833.51.42
9Roethlisberger106.028867.023808.32114125414873.81.15
10Palmer90.841867.530767.4151510018603.81.10
11Cutler89.630064.723847.9281219313033.90.89
12Hasselbeck89.640262.428877.2275513315752.40.76
13Delhomme111.88664.06247.3626463111.50.72
14Favre97.943967.434127.822-68916852.90.56
15McNabb87.332659.823297.13013515111792.30.35
16Campbell79.136460.223846.5311699613413.80.28
17Brees85.045667.329976.615526416343.70.26
18Kitna87.438165.428977.6215526417513.90.24
19Pennington87.519067.413176.917231028193.60.21
20Boller74.620859.612426.09411137643.0-0.06
21Jackson62.814454.29006.321122304744.0-0.18
22Testaverde69.811854.26995.9528283893.5-0.23
23Young65.326461.416636.368297969264.6-0.44
24Harrington79.731463.120316.514331699802.0-0.48
25Manning E75.037158.523766.4204614413384.3-0.61
26Losman76.917563.412046.9201101035963.8-0.61
27Bulger73.327960.917896.471421110873.7-0.61
28Culpepper78.018658.113317.220401307504.0-0.64
29Rivers81.133061.223017.0233110712164.6-0.66
30Lemon69.416657.89235.61870864822.6-0.73
31Green72.614160.39877.0732535155.2-0.81
32Smith57.219348.79144.713891215953.1-0.83
33Huard74.726262.217666.79-11669514.2-1.18
34Clemens56.915949.18925.61371804693.8-1.22
35McNair73.920564.911135.41032855683.5-1.41
36Griese77.121761.815086.99161017305.4-1.71
37Grossman63.317354.911016.414271466334.9-1.74
38Carr58.313653.76354.71759743153.3-2.04
39Dilfer62.817153.89505.65251385175.1-2.54
40McCown J59.512258.27606.21999783488.5-3.71

The Vikings' and Lions' Paths to the Playoffs

I recently noted that according to NFL Forecast's computation of playoff odds for each team, MIN at 5-6 appears to have a far better shot at a wildcard spot than 6-5 Detroit. In fact, there are five more teams at 5-6, but none with as a good a shot as MIN. But since DET plays at MIN this Sunday I'll focus on those two teams.

First, MIN is the better team according to their efficiency stats. Although their passing game is below average, they don't throw as many interceptions as DET. Their running game is spectacular, on both sides of the ball. Usually, the running game is not as important as passing, except when it is exceptionally good as it is with MIN.








TeamO Pass
O Run
O Int Rate
O Fum Rate
D Pass
D Run
D Int Rate
Pen Rate
MIN5.65.60.0290.0316.43.00.0280.38
DET6.14.00.0350.0476.43.70.0340.50
NFL Avg6.14.00.0320.0266.14.00.0320.37


But the real difference between the two teams lies in some stats that usually go overlooked. One thing that really hurts DET is their fumble rate. They lead the NFL in fumbles per play. Almost 5% of all offensive snaps result in a fumble. Their penalty rate is also the worst in the league at half a yard per snap. Accordingly, DET's generic win probability (GWP-the chance they'll beat a league-average team at a neutral site) ranks 27th at 0.33. In contrast, MIN's GWP ranks 13th at 0.56.

This Sunday MIN hosts DET in a crucial game. The prediction model gives MIN a solid advantage with a 0.78 probability of winning at home. That would split the season series and even up their records at 6-6. Now let's look at their remaining schedule.

DET's remaining schedule is listed in the table below. Their opponent's efficiency rank and the probability DET will win the game are also listed. DET has a tough schedule ahead. Their opponents' average GWP is 0.64. Plus, only two of their five remaining games are at home. The GB odds are relatively uncertain because by week 17, the Packers might be resting their starters if they've locked up their playoff seed.











OpponentOpp RankWin Prob.
at Min130.22
DAL30.11
at SD100.81
KC210.51
at GB60.13


Here is MIN's remaining schedule. They have a much easier schedule with an average opponent GWP of only 0.37, with three home games.









OpponentOpp RankWin Prob.
DET270.78
at SF320.80
CHI310.86
WAS120.57
at DEN140.57


Anything can happen, but it looks like MIN has a much better shot at a wildcard than DET despite being a game behind in the standings. That's one reason why this weekend's game is so important. If DET can upset MIN, they'd own the tiebreaker and a two game lead, possibly eliminating MIN from contention.

But let's not forget about the other teams in contention. Here are the other 5-6 teams scrapping for a wildcard berth, their efficiency ranking, and their average future opponent GWP.









TeamRankOpp GWP
PHI90.57
WAS120.52
ARI230.42
NO250.48
CHI310.55


I'm not saying MIN will definitely or even probably make the playoffs, but they do have the best shot of all the teams behind the Giants (7-4). Of the other teams, PHI appears strongest, but they also have the toughest schedule. WAS is about even with MIN in terms of team efficiency but it has a much tougher schedule. The loss of Sean Taylor may also affect the team in unpredictable ways. ARI has a relatively weak schedule, but has not been playing well themselves and their secondary is banged-up. NO has been playing well lately but their defense is practically non-existent. They're probably the hardest team to predict this year because of their slow start. CHI is simply bad, even on defense. It's surprising they have 5 wins at this point.

It's possible and perhaps likely we'll see an 8-8 team make the playoffs out of the NFC again this year. I think MIN has the best shot to be that team.

Winning on the Road

Teams with good running offenses do not win any more road games than other teams, all things being equal. That's right--being good at running the ball does not help teams win on the road. Contrary to what we've been told for years, having strong passing game is far more important to visiting teams than having a good running game.

Let me make a clarification up front. I'm not suggesting that having a great running performance in an individual road game doesn't help win that game. I'm saying that being a "running team" doesn't help win on the road. Teams that are "built" to win on the road are those that pass well and don't fumble.

I stumbled on this somewhat by accident when I was studying the effect of climate on home field advantage. My usual game model is a logistic regression that estimates the probability of winning based on team efficiency stats and home field. A simplified version looks like this:

Team A season efficiency stats
Team B season efficiency stats
Team A at home [1 if true, 0 if false]

This method resulted in balanced weights for the coefficients for Team A and Team B, and the importance of home field was captured in the coefficient for 'Team A at home.' But during my research into climate effects, I decided to try an alternate model without the home field variable. It looked like this:

Visiting team season efficiency stats
Home team season efficiency stats

With this method we would expect unbalanced coefficients. Because home teams win more often, the coefficients for the home team were generally stronger than for the visiting team. This means that if two theoretically equal teams played, the home team would have a higher probability of winning, which is exactly what we observe.

By examining the imbalance of each stat we can see what kind of teams tend to win on the road. Here are the regression coefficients for each efficiency stat for both home and visiting teams. (Logisitic regression is more difficult to interpret than linear regression. The coefficients indicate the change in the log of the odds ratio of the outcome. But we are comparing the relative strength of each coefficient, so don't worry about the "log odds ratio" for now. Just pay attention to the relative size of the coefficient between home and away team stats.)












Team StatHome CoeffVisitor Coeff
O Pass0.40-0.49
O Run0.48-0.04*
D Pass-0.600.48
D Run-0.270.16
O Int Rate-16.4015.61
D Int Rate19.30-17.63
O Fum Rate-11.5730.77
Pen Rate-1.651.30


The model has very solid goodness-of-fit stats, and it is 69.9% accurate (retrodictively) in predicting game winners. The regression is based on all regular season game outcomes in the past five years (n=1280).

First, compare the coefficients for offensive passing efficiency. The coefficient for home teams is 0.40, and for visiting teams is 0.49. We can interpret these numbers by saying "having a good passing game is slightly more important in winning for the visiting team than for the home team." But the difference is slight, and may not be significant.

Next, compare the coefficients for offensive running efficiency. The coefficient for home teams is 0.48, but for visiting teams it is only 0.04--practically zero! (The difference of 0.44 is strongly significant.) The near-zero coefficient for visiting teams' offensive running efficiency is what tells us that running well simply doesn't matter on the road.

Also, for some reason, teams that tend to fumble more often than others are at a greater disadvantage as visiting teams. And conversely, teams that don't fumble tend to have a greater advantage as road teams.

There are imbalances between nearly all of the team efficiency stats, but none as stark as that for offensive running. This was such an unexpected result and I had no prior theoretical basis for the observation, so I confirmed the results with a simpler analysis using correlation coefficients.

For each team over the past 5 seasons (n=160), I added up their road and home wins. The correlations of each efficiency stat with home and road wins were calculated. If different stats affect a team's ability to win at home and on the road differently, as we saw in the regression, then we should see different correlation coefficients. For example, the correlation between offensive running efficiency and home wins should be much stronger than running and road wins.















O PassO RunO Int RateO Fum RateD PassD RunD Int RatePen Rate
Away Wins0.550.10-0.40-0.42-0.340.000.31-0.16
Home Wins0.480.22-0.36-0.36-0.45-0.070.36-0.17


The correlations confirm the results from the regression. Being a good running team is more important to winning when at home, and not nearly as important to the visiting team. (Also note that stopping the run is just not important whether on the road or at home, as we've seen in previous research.)

I also did an even simpler analysis by comparing the average season running efficiency stats of all road winners and for all home winners. Again, the data was from every regular season game over the past five years (n=1280). The average offensive running efficiency for road winners was 4.13 yds/rush and for road losers was 4.10 yds/rush--a difference of only 0.03 yds/rush.

We see the opposite with passing efficiency. Road winners average 6.18 yds/att and road losers average 5.90 yds/att--a difference of 0.28 yds/att which is about 9 times larger than the difference we found for running. Again we see indications that passing efficiency tends to be the more important stat for the road team.

The obvious question is "why?" Why doesn't being a capable running team help win road games? My only theory is that passing well helps come from behind far more than running well. If road teams tend to find themselves behind more often than home teams, then unless they can pass, they wouldn't be able to score quickly and come back from a deficit. But home teams need to come back from deficits too, so the reason why running teams don't win on the road remains puzzling.

Playoff Race Predictions Wk 12

Beginning this week, NFL Stats and NFL Forecast are teaming up to predict the playoff races for the 2007 NFL season. NFL Forecast offers a free a web/java-based program that calculates playoff probabilities for each team and for each possible playoff seeding based on individual game probabilities. Check out the java program here. For the rest of the season, NFL Forecast will be using the game outcome probabilities published here at NFL Stats as its official baseline. The resulting playoff chances will be published weekly at the NFL Forecast blog. (By the way, some of the java code is being updated, so be patient if some of the functions are temporarily disabled. But the blog site has all the final output based on the NFL Stats game probabilities.)

NFL Forecast's playoff prediction engine goes beyond wins and losses, and accounts for the complex NFL tiebreaker and seeding procedures that baffle many fans.

My personal observations of the current playoff predictions focus on the second NFL wildcard spot. (Scroll down past the division ranking probabilities to the AFC/NFC Playoff Seeding Probability tables). It appears totally up for grabs. DET is currently holding the spot at 6-5, but there are 7 teams right behind at 5-6. I was very surprised to see 5-6 MIN with a 46% chance to grab a playoff spot, while DET has only a 4% chance. More on the reasons for this later.

Season Win Projections Week 12

Season win totals and division standing projections are listed below. As before, projections are based on each team's opponent-adjusted generic win probability (GWP). The projections account for future opponent strength (Fut Opp), and projected wins (proj W) is a total of current and estimated future wins. The methodology is described more fully here.













































TeamRankProj GWPFut OppProj W
AFC E
NE10.910.4815.6
BUF190.390.536.9
NYJ240.340.543.7
MIA180.430.492.1
AFC N
PIT50.700.5111.5
CLE170.610.3510.1
CIN200.500.406.5
BAL260.180.704.9
AFC S
IND20.880.4513.4
JAX70.650.5211.3
TEN150.450.548.2
HOU160.310.676.6
AFC W
SD100.690.409.5
DEN140.580.467.9
KC210.430.476.2
OAK290.140.673.7
TeamRankProj GWPFut OppProj W
NFC E
DAL30.850.5014.2
NYG110.530.549.7
WAS120.550.527.7
PHI90.540.577.7
NFC N
GB60.790.3813.9
MIN130.680.378.4
DET270.230.627.2
CHI310.190.556.0
NFC S
TB40.870.3411.3
NO250.390.486.9
ATL220.410.485.1
CAR280.210.625.0
NFC W
SEA80.710.4110.5
ARI230.470.427.4
SF320.180.503.9
STL300.230.523.2

Week 12 Efficiency Rankings

NFL team efficiency rankings are listed below in terms of generic winning probability. The GWP is the probability a team would beat the league average team at a neutral site. Each team's opponent's average GWP is also listed, which can be considered to-date strength of schedule. GWP modifies the generic win probability to reflect the strength of past opponents. Offensive ranking (O Rank) is based on each team's offensive GWP, i.e. it's the team's GWP assuming it had a league-average defense. D Rank is vice-versa. Rankings are based on a logistic regression model applied to data through week 12. A full explanation of the methodology can be found here.





































RankTEAMLast WkGWPOpp GWPO RankD Rank
1NE10.910.5413
2IND30.860.5461
3DAL20.850.5025
4TB40.770.4847
5PIT50.710.42152
6GB70.690.48511
7JAX80.670.53319
8SEA60.630.381112
9PHI120.600.55720
10SD100.600.54139
11NYG90.570.53178
12WAS110.570.59206
13MIN150.560.511213
14DEN130.540.51827
15TEN140.490.52274
16HOU160.480.471624
17CLE170.460.49929
18MIA230.420.561921
19BUF190.410.591818
20CIN200.400.501031
21KC210.400.502610
22ATL220.390.502217
23ARI180.390.422115
24NYJ270.370.562330
25NO280.370.481432
26BAL240.350.422814
27DET260.330.492416
28CAR250.300.502523
29OAK310.250.452925
30STL290.250.463122
31CHI300.230.523028
32SF320.180.453226