Team Efficiency Rankings - Week 16

Not much movement at this point in the season. The most significant exception is NE, which vaults from from #5 to #2.

The team rankings below are in terms of generic win 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, and all ratings include adjustments for opponent strength.

Offensive rank (ORANK) is offensive generic win probability, which is based on each team's offensive efficiency stats only. In other words, it's the team's GWP assuming it had a league-average defense. DRANK is is a team's generic win probability rank assuming it had a league-average offense.


GWP is based on a logistic regression model applied to current team stats. The model includes offensive and defensive passing and running efficiency, offensive turnover rates, defensive interception rates, and team penalty rates. If you're scratching your head wondering why a team is ranked where it is, just scroll down to the second table to see the stats of all 32 teams.

Click on the table headers to sort.


RANKTEAMLAST WKGWPOpp GWPO RANKD RANK
1 SD10.850.4421
2 NE50.730.57119
3 PIT30.710.5272
4 NYG20.710.4983
5 GB40.710.5169
6 PHI60.700.52310
7 BAL80.630.51128
8 MIA70.620.56227
9 IND90.580.541413
10 TEN120.580.55204
11 NYJ110.580.56255
12 KC130.560.451511
13 CHI150.560.51296
14 HOU100.550.58428
15 DAL160.530.55527
16 MIN140.500.552414
17 BUF190.490.562321
18 NO170.480.40922
19 CLE180.470.501918
20 ATL200.420.451723
21 WAS220.420.551829
22 OAK230.410.491317
23 TB210.410.401030
24 CIN240.390.542624
25 SF260.380.442115
26 DET250.370.562720
27 JAC270.370.571631
28 DEN280.330.481132
29 SEA290.300.422825
30 STL300.250.393016
31 CAR310.230.433212
32 ARI320.190.403126

Core team efficiency stats:


TEAMOPASSORUNOINT%OFUM%DPASSDRUNDINT%PENRATE
ARI4.84.43.51.16.54.43.20.44
ATL6.03.91.80.36.34.64.00.32
BAL6.43.71.80.75.74.02.70.35
BUF5.84.33.11.26.24.62.30.32
CAR4.44.24.42.35.94.03.70.43
CHI6.03.84.50.25.63.83.70.41
CIN5.83.73.41.16.54.63.10.35
CLE6.04.13.01.86.44.13.90.35
DAL7.04.03.30.57.14.33.50.44
DEN6.43.81.71.57.14.61.90.48
DET5.74.02.60.86.44.62.50.51
GB6.93.92.50.45.44.64.00.32
HOU6.54.72.10.67.43.92.20.34
IND6.73.62.50.56.14.72.20.35
JAC6.14.74.41.17.64.62.70.35
KC6.04.81.20.55.84.22.30.36
MIA6.03.73.81.45.83.62.50.28
MIN5.84.45.60.96.13.92.90.39
NE7.14.31.10.16.54.23.90.37
NO6.74.03.30.75.84.42.10.42
NYG6.84.74.21.35.34.33.30.40
NYJ5.74.32.51.25.63.61.50.48
OAK6.24.93.81.16.24.52.00.62
PHI6.85.51.90.76.14.04.80.56
PIT6.64.22.21.05.63.03.20.49
SD8.03.92.41.25.03.73.20.37
SF6.04.13.20.86.63.52.80.48
SEA5.93.74.10.56.64.02.10.45
STL5.23.82.70.35.84.62.60.43
TB6.24.51.40.96.14.93.90.44
TEN6.24.43.30.85.93.92.90.51
WAS6.14.33.30.96.94.82.20.32
Avg6.24.23.00.96.24.22.90.41

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11 Responses to “Team Efficiency Rankings - Week 16”

  1. Jim Glass says:

    You have SD as the #1 team in the league and GB tied for #1 in the NFC, and it looks like neither team will make the playoffs.

    This is not just a quirk of your system. PFR.com's "simple rating system" has GB as #1 in the NFC, #2 in the league, and SD as #4 in the league, while numbers I just put at "the community" have GB #1 in the NFC and SD #2 in the league. Three completely different methodologies reaching pretty much the same result -- the best team in the NFC and arguably an even better team in the AFC probably aren't going to make the playoffs.

    You've made past posts saying random chance determines 40% or 50% of NFL game outcomes. That seems like an awful lot -- but when a 16-game season isn't long enough to identify by W-L what are at least very plausibly the best team in one conference and maybe even a better one in the other as deserving to make the playoffs, that 40%-50% figure looks true. On to a 30-game season!

  2. slushhead says:

    I think there's a lot more than chance involved here. Simply put, there are too many variables involved in football for a statistical model to be perfect. That's not to say that these models are worthless - to the contrary, early in the season Advanced NFL Stats correctly identified San Diego as a team which was better than their record indicated. At the same time, we need to take these models with a grain of salt - the claim that Green Bay and SD are the best teams in their respective conferences is far-fetched at best.

  3. Jim Glass says:

    the claim that Green Bay and SD are the best teams in their respective conferences is far-fetched at best

    I don't see how the idea of GB being the best team in the NFC is "far-fetched at best". Apart from the three different methods mentioned above that suggest GB is at the top, GB also has clearly the best Pythagorean expectation of any team in the NFC. And as Pythagorean predicts future W-L significantly better than past W-L does, saying "But they are only 8-6" is no answer. So even if they aren't the best, what exactly is it that makes the idea that they are "far fetched"?

  4. Chris says:

    On the question of how often the best team misses the playoffs, the series of classic blogs on the simulation of ten-thousand seasons is required reading:

    http://www.pro-football-reference.com/blog/?p=56

    Chris

  5. slushhead says:

    Alright, I will admit that considering GB the best team in the NFC is not far-fetched (although I place several teams above them). The San Diego claim is the one which bothers me more. San Diego has a roster good enough to win most of their games, but they consistently lose to teams that they should beat. I don't buy the argument that their turnovers and special teams breakdowns are just random chance. Maybe it's the fault of the coaches - they really should be employing more conservative strategies which minimize variance in outcome rather than racking up yards and turning the ball over.

    As to the Pythagorean expectation statistic - great for baseball where offense has no strategy to prevent the opposition from scoring and defense can't help a team score. Baseball teams are (almost always) only trying to maximize (minimize) scoring when on offense (defense). This is too simplistic of a view for football.

  6. Bruce D. says:

    slushhead,

    The following Pythagorean expectation formula:

    Win% = (Points Scored^2.37)/((Points Scored^2.37) + (Points Allowed^2.37))

    Includes both the the ability of a team to score points((Points Scored), and the ability of a team to prevent points(Points Allowed), so margin of victory seems to be the key.

    An NFL team's offense and defense both play equal parts in determining margin of victory.

    Maybe not the be all, end all, but a valid barometer of team ability, and I'd argue, a lot more accurate than wins and losses.

  7. Bigmouth says:

    Slushhead, your point about the limits of statistical models is well taken. But do you have any support besides your subjective perception for the claim that neither SD nor GB is the best team in its division?

  8. David says:

    Win-loss record? You can talk about the limitations of win-loss record all you want, but I think it certainly counts as "any support besides subjective perception" as to the quality of teams.

  9. Anonymous says:

    what was new englands GWP at the end of the regular season in 07?

  10. Jonathan says:

    IIRC, it was close to 0.90--their expected win total was 14.2.

  11. Jonathan says:

    I think San Diego is the type I error in the statistical study showing that special teams are random chance. That's all just speculation, though.

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