Team Rankings: Week Eight

I know you must be curious about who gets to be the top dog this week, so here are your week eight rankings.

The New Number One
After demolishing a team that was ranked inside the top-ten last week, the Houston Texans leapfrogged into the top spot. Schaub was able to pick apart the Titans' defense despite not having Andre Johnson to throw to, and Arian Foster just went plain nuts on Tennessee's butts. After the loss, the Titans fell all the way from 8th to 18th. The Texans get the Jaguars next week while the Titans get a cushy bounce-back game against the Colts.

The Old Number One
Did the Ravens really lose to the Jaguars on Monday, or are my eyes deceiving me? The Ravens shied away from the running game despite the game being close, and Flacco's shoulders can only hold so many perched birds. Anquan Boldin was targeted a whopping 12 times, but he only managed to haul in four balls for forty yards. Maurice Jones-Drew kept the ball out of the Ravens' hands, leading to a ugly game that a rare few enjoyed watching. The Ravens have a lucky draw in week eight, as they get to face the lowly Arizona Cardinals in Baltimore.

Other Notes and Fun Facts

  • While Green Bay's defense didn't show me much against a rookie quarterback, Aaron Rogers looked absolutely dominant. He looked like he could have hit a penny from twenty yards the way he was throwing, and I don't think there's any question that he's the best quarterback in football.
  • The Saints are going to give the Packers a run for their money, and they showed everyone that losing Peyton Manning hasn't caused all of the Colts' problems this year. 
  • I absolutely love that Cleveland moved down five spots despite a win. As a Pacific Northwest resident, I was forced to watch the 6-to-3 monstrosity, and Cleveland may be the worst 3-3 team of all time.
  • As far as the rankings go, make sure to keep an eye on the Patriots and Steelers matchup this week. The teams are ranked back-to-back in the top-10, and it's entirely possible a victory could mean the top ranking.
Now, without further to do, here is your precious data.

2 NO70.640.50411
3 GB40.640.48226
5 PIT30.610.48710
6 NE60.590.52131
9 PHI110.550.50918
11 SD130.540.461214
16 SF170.510.49189
28 KC310.410.482719
31 TB300.370.492029


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30 Responses to “Team Rankings: Week Eight”

  1. Anonymous says:

    Brian, we haven't seen a team go above .7 in GWP this season. Is this a result of changes in the model?

  2. Eric says:

    I ran the numbers through my ranking system today too and came out with Houston ranked #1 as well. I thought something must be wrong with my system, but I guess not. You have them #1 too.

    Last week I also had Baltimore #1 as well.

    ESPN's rankings are comical. They have Houston #12, Philadelphia at #22, and Dallas at #18.

  3. Anonymous says:

    Yep. ESPN's rankings is fun for kicks, but aren't very accurate..

  4. Whispers says:

    ESPN doesn't believe in diverging from W-L record. Their rankings are also very inertial - once a team is rated high, they'll stay there even if they barely win a game against a relatively weak team.

  5. ShroffishBoil says:

    Does subtracting opp gwp from GWP (GWP - Opp GWP) tell us anything?

    Does that spread indicate a better relative dominance or anything?

  6. nottom says:

    Opp GWP is basically just strength of schedule from what I understand.

  7. nottom says:

    I have to wonder about Atlanta's ranking here. I wonder if they are just an example of a team that doesn't fit the mold for your model and is therefore punished as they certainly seem to routinely over-perform according to this model.

    I understand that their OPASS rating is low and that is a major component of the model, but they do have a decent PassSR. Granted, I think they are generally overrated by the media but I don't see them as the 7th worst team in the league either.

  8. probablepicks says:

    Houston is #1 because Tennessee is still ranked too high here, in my humble opinion. I predict that you will see both drop in the rankings in the coming weeks.

  9. Jonathan says:

    I'm surprised the Packers are still #26. Maybe it's just me, but it seems like their defense plays way too conservatively with a big lead.

    Surprised the 49ers are #16, but I shouldn't be.

    If Indy gets Andrew Luck, just as Manning is headed out, then I'm going to be very angry :|

  10. Anonymous says:

    Indy, Denver, Miami, Oakland

  11. Tarr says:

    I would like to see a team with New England's offense and Jacksonville's defense play a team with Jacksonville's offense and New England's defense.

  12. James says:

    Tarr, we basically saw that during the Colts-Saints game on Sunday. #4 O vs #32 D, #19 O vs #11 D. Your next closest bet might be Cowboys vs Dolphins (#6 O vs #28 D, #24 O vs #8 D), or the Texans when they play the Bucs or Colts.

    probablepicks, Tennessee's ranking only marginally effects Houston's ranking. Even if Ten was the worst team in the league, Houston's Opp GWP would drop only 0.02 points. Houston's played really well, the only question is can they keep it up without Mario Williams.

  13. Brian Burke says:

    Yes. Opp GWP is opponent average GWP. The plain GWP is really "adjusted GWP"--it already incorporates to-date opponent strength.

    Also, yes. The input stats (the second table) are regressed slightly more than they have been in previous years at this point in the season. That's partly why the top teams aren't getting about 0.70 GWP.

  14. Eric G says:

    Seven of the Rams first eight opponents this season are currently ranked in the top nine (and the eighth is ranked #14).

    Brian, how does this kind of schedule brutality rate historically during the years you've been running your numbers?

    Regardless, the degenerate gamblers amongst us should be seeing a lot of opportunities to bet on St. Louis during the second half of the season.

  15. Eric G says:

    Sorry, I should have written:

    Six of the Rams first seven opponents are currently ranked in the top nine (and the seventh is ranked #14).

    (My mistake, as they don't play Pittsburgh until December.)

    Still a pretty ugly way to have to open a season.

  16. weinsteinium says:

    Brian - I'm confused by how you are regressing the offensive and defensive breakdown. I've focused on OPASS because I can get that from pro-football-reference's net yards per attempts.

    I can see that top teams have been regressed towards the mean:

    GB OPASS=8.8 NY/A=9.3
    NE OPASS=8.5 NY/A=8.7
    NO OPASS=7.7 NY/A=7.9

    But for teams with bad OPASS it seems that they have been regressed away from the mean:

    JAC OPASS=4.3 NY/A=5.0
    CLE OPASS=4.9 NY/A=5.2
    STL OPASS=4.9 NY/A=5.4

    Am I misunderstanding what you mean by "regresssing"?

  17. Brian Burke says:

    Hmmm. The table above is supposed to be each teams true net YPA directly from game stats. It's before it gets regressed. There might be a bug somewhere in my code. I'll have to check. I might be leaving out sacks in the denominator or something like that.

  18. Brian Burke says:

    No. No bug. Everything looks correct. Let's look at NO as an example.

    I show NO with 2477 gross passing yds, 90 sack yds, 299 attempts, and 13 sacks. PFR,, all agree. That comes to 2387 net passing yards on 312 dropbacks.

    2387/312 = 7.7

    PFR gets 7.9 because they are not including the number of sacks in the denominator:

    2387/299 = 7.9

    If you look at the PFR NO team page, the NY/A is listed correctly at 7.7. It's just their 2011 year page that's got the error.

  19. Michael Beuoy says:


  20. weinsteinium says:

    Thanks Brian!

  21. Anonymous says:


    yeah you are correct. They (PFR) made (still make?)the same error when calculating the yearly league wide passer rating. I told them, but only got an arrogant answer. Otherwise a great site, they just should be open to critics and infos from readers.

    Karl, Germany.

  22. says:

    James, thanks for the clarification. I still think both teams will drop, based on my own modeling. But my prediction above wasn't all that strong, since Houston has nowhere to go but down. So I'll make it a little stronger: I predict Houston will drop out of the top 4 and Tenessee will drop even further below #18 before Week 17. Then again, the rest of the Texans schedule isn't exactly a murders' row, so maybe they'll continue to post good stats for this system.

  23. Mike M says:

    Interesting rankings, according to my power rating method which includes ave gain per pass att of both off and def and carries the most weight, rushing yards of both off and def and turnovers, which I know you say are random, but I do count them.

    1. GB, 2. Houston, 3. 49ers, 4. Dallas, 5. Ravens

    3 of our top 5 are the same and the Ravens you have 7th, just out of the top 5.

    Very interesting since I count TO and you don't.

    The biggest difference is I have 49ers 3rd to your 16th mostly because the 49ers are 5-1 at winning the TO battle and you have NO 2cd where I have them closer to 16th mostly because they are 1-4-2 at winning the TO battle.

    It will be very interesting to see where these 2 teams go from here, I suspect the 49ers get further and deeper into the postseason than the Saints, but I could always be wrong.

  24. Sam's Hideout says:

    Brian, if I'm understanding your model correctly, you compute game probability like so:

    P(Home Team A defeats Visitor Team B) = 1/(1+exp(-gl))

    where gl = hf + logit(GWP_A) - logit(GWP_B)
    logit(x) = log(x/(1-x)) (i.e. usual definition)
    hf = home field adjustment (near 0.5)
    GWP_A = Team A (adjusted) Generic Win Probability (from above table)
    GWP_B = Team B (adjusted) Generic Win Probability (also from above table)

    Following the above, I get roughly but not exactly the same game probabilities that you publish in the NYT (with hf=0.48).

    Does that look right? If so, there are some minor errors on the pages where you describe your model (I originally tried to implement exactly what was on your pages and failed, then went back to the definition of logistic regression...)

    I'd like to get this correct to go into my exact division championship calculator. By focusing on a single division at a time, I can run through all the remaining season permutations (roughly about 2^30 for each division at this point) instead of doing a monte carlo simulation. Currently for the AFC South this enumeration takes about 20 seconds.

    However, my computation for the AFC South division champion differs substantially from the playoff projection posts for week 7 and 8, so I'm trying to decide if this is due to a bug in my game probability calculation. (My code currently has a 0.67 probability that Houston wins the division which differs substantially from the 0.88 that the week 8 playoff post. Tennessee picks up about half the difference and almost all of the rest goes into tie-breakers my code doesn't yet resolve.)

  25. Sam's Hideout says:

    Well that's embarrassing, I discovered two bugs in my data tables that I feed my program, with the result that I now get pretty good agreement on the AFC South division winner probabilities.

    HOU: .8916
    IND: .0001
    JAC: .0213
    TEN: .0765

    Unresolved ties: .0105
    (currently only implement the head-to-head tie-breaker)

  26. Brian Burke says:

    SH-There are a couple additional things I do with GWP. First, and most importantly, the input stats are regressed according to how steady and reliable each stat tends to be league-wide. Second, all the teams's final adjusted GWP is adjusted to be exactly 0.50. This is because there are year-to-year fluctuations in the average team stats, but we know the average team must be average.

  27. Ian B says:

    Hey Brian/Zach, could we please link these articles in the Stats > Team Efficiency section? I reference this series a lot and don't like scrolling through all the posts to find them.

  28. Sam's Hideout says:

    Brian, to check my understanding, the first ("input stats are regressed...") affect the input stats used to calculate the GWP for each team, and the second (all the team's final adjusted GWP...") affects the regression over past seasons used to compute the coefficients used to weight the (regressed) input stats in your model to compute GWP.

    Since I'm currently just using the computed GWPs from the table, I don't really have to worry about that (for now), or is my formula for computing game win probabilities incorrect?

    [Originally also included a question about what your current coefficient were but now I found them here.]

    OK, now I'm computing GWP from the efficiency stats and the values I'm coming up differ quite a bit from the GWPs from the above table. I'm guessing bugs in my code and/or I'm not adjusting (regressing) the efficiency stats (though.. I'm doing this in Octave and the code is so minimal it's hard to see where bugs could be hiding...)

  29. Sam's Hideout says:

    Hmm.. one more thing, the model coefficients you posted here have adrunsr > 0, is that right, or did you drop a negative sign in your post?

  30. Ian B says:

    Why is Washington's defense ranked 6th above the 49ers (9th) when all of the 49ers' inputs are superior to Washington's? Is it a strength of schedule thing?

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