Week 7 Efficiency Rankings

The ratings are listed below 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.

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, and team penalty rates. A full explanation of the methodology can be found here. This year, however, I've made one important change based on research that strongly indicates that defensive interception rates are highly random and not consistent throughout the year. Accordingly, I've removed them from the model and updated the weights of the remaining stats.

Offensive rank (ORANK) is the ranking of 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.

1 WAS10.760.5513
2 PHI30.720.5744
3 SD20.680.52513
4 CAR40.680.5492
5 BUF140.650.49148
6 PIT120.650.45181
7 CHI60.630.53107
8 NYG50.630.43218
9 NO80.620.54616
10 DAL70.590.521115
11 ATL110.590.46323
12 MIA90.590.50727
13 ARI100.580.57820
14 TB130.550.54175
15 TEN170.540.392010
16 NYJ150.500.50289
17 MIN180.490.512111
18 GB220.480.461612
19 JAX200.460.521222
20 IND190.460.491319
21 DEN160.450.481529
22 OAK230.440.522421
23 BAL240.420.48276
24 HOU210.410.482231
25 SF250.400.503014
26 STL300.400.611926
27 NE260.360.472528
28 CLE280.350.562324
29 SEA290.340.512625
30 CIN270.310.523117
31 KC310.230.523230
32 DET320.230.502932

The to-date season efficiency stats are listed below.


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5 Responses to “Week 7 Efficiency Rankings”

  1. Brian Burke says:

    Some observations:

    -WAS is still #1 primarily based on having zero ints, but they also have very solid all-around stats, and the 5th toughest schedule so far.

    -Undefeated TEN is only ranked #15 mostly because they have played a very soft schedule. Except for the blown call in BAL, they'd have a loss too.

    -Don't be too concerned with teams moving 2 or 3 spots in either direction. Most teams are tightly bunched, and it takes very little to leapfrog a spot or two.

    -How can PHI have a bye and move up from #3 to #2. There are 2 possible reasons. First, the teams they've played may have improved. Second, the teams around them may have had a poor week.

    -How can last place 3-3 PHI be the second ranked team in the league? They're playing really well but have been unlucky. Their stats look really good--great off passing, def passing, def rushing, turnover rates, and even penalty rates are all solidly better than average. They've also faced the 3rd toughest schedule so far. If they stay healthy, look for them to be very competitive.

    -BUF jumped really far this week. A solid victory over the #3 team can do that. Plus, many teams previously ahead of them had poor outings this past week.

    Lastly, a couple of technical notes. ORANK and DRANK don't have as advanced an adjustment for opponent strength I use for the overall ranking, so sometimes they might not 'add up.' For example a team with the #5 ORANK and #5 DRANK might be the #7 team.

    Also, I've listened to the comments here and I am no longer satisfied that my adjustments for overconfidence due to early-season sample size are big enough. It's probably better to be underconfident than overconfident, so I've increased the compensation slightly. It doesn't affect the rankings, but it reduces the spread of the GWPs.

  2. Anonymous says:

    Do special teams factor into your ranking? Field position wins and loses a lot of games.

  3. Brian Burke says:

    I agree whole-heartedlly about field position. But special teams are far less consistent and self-predicting than any other part of the game. In other words, because a team has had a great ST record to-date, it does not mean that team will continue to have good ST performance. It's very random.

    ST explains a lot about past wins, but does not predict future wins very well.

  4. Brian says:

    So does that mean you are going to use MORE "phantom games?"

    For example, take turnover rates which you said tend to stabilize around week 9. The way I understood it, you would calculate a team's TO rate in Week 3 using the data from their first 3 games as well as 6 "phantom games" at league average. Each week you would use one less phantom game until there weren't anymore. Are you now saying that you would use more than 6 phantom games (in this example), and if so how many more?

  5. Brian Burke says:

    Right. By this week there would be only 0, 1 or 2 phantom "average" games included, depending on which stat we're talking about. (Some converge quicker to their season-stable rate than others). I set back the clock 2 weeks so there are now 2,3, or 4 phantom weeks. For those wondering, the purpose of adding in dummy weeks of average data is to account for regression to the mean. For example, we shouldn't expect WAS to continue all season with a 0.0 interception rate, nor would we expect NO to continue with over an 8.0 YPA average. Both are possible, but it's far, far more likely that extreme stats like that will regress toward (and not to) the league mean.

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