2008 Final 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.

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, 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.






































RANKTEAMLAST WKGWPOpp GWPO RANKD RANK
1 CAR10.790.5546
2 PHI20.770.52112
3 PIT30.750.53201
4 ATL40.730.54317
5 SD50.720.53110
6 NYG60.690.53213
7 NO80.680.55519
8 IND120.680.49715
9 TEN70.670.46154
10 BAL110.670.52173
11 MIA90.640.44620
12 WAS100.630.511211
13 DAL140.590.53168
14 TB130.580.55199
15 CHI150.570.51227
16 NE160.570.471324
17 GB170.540.511418
18 MIN190.530.52235
19 ARI180.520.50923
20 DEN200.480.50827
21 HOU220.450.501028
22 NYJ210.450.452412
23 BUF240.340.442722
24 SEA280.340.473014
25 OAK270.330.562916
26 JAX230.320.501830
27 KC260.310.532126
28 CIN290.300.553221
29 SF250.290.492525
30 CLE300.200.572829
31 STL310.180.522631
32 DET320.120.573132




To-date efficiency stats below. As always, click on the headers to sort.






































TEAMOPASSORUNOINTRATEOFUMRATEDPASSDRUNDINTRATEPENRATE
ARI7.13.50.0240.0286.54.00.0250.39
ATL7.44.40.0250.0156.04.90.0180.29
BAL6.04.00.0280.0255.13.60.0490.40
BUF5.94.20.0310.0346.34.30.0200.28
CAR7.34.80.0290.0145.74.40.0220.32
CHI5.53.90.0270.0165.93.40.0350.29
CIN4.33.60.0290.0276.33.90.0240.30
CLE4.63.90.0410.0247.14.50.0520.35
DAL6.64.30.0370.0325.34.20.0160.49
DEN7.14.80.0290.0197.05.00.0120.37
DET5.33.80.0370.0367.95.10.0090.38
GB6.64.10.0240.0236.04.60.0420.49
HOU7.34.30.0360.0286.94.50.0250.34
IND6.83.40.0210.0105.94.20.0310.32
JAX5.84.20.0240.0217.34.00.0280.42
KC5.44.80.0300.0227.05.00.0250.32
MIA7.04.20.0140.0176.24.20.0330.34
MIN6.04.50.0380.0286.03.30.0230.35
NE6.14.40.0210.0166.44.10.0300.25
NO7.74.00.0280.0186.44.20.0290.39
NYG6.15.00.0200.0175.84.00.0340.42
NYJ5.94.70.0430.0246.13.70.0240.28
OAK5.24.30.0260.0336.44.70.0340.42
PHI6.24.00.0260.0155.13.50.0290.31
PIT5.93.70.0300.0264.33.30.0380.41
SD7.74.10.0230.0176.34.00.0250.38
SF6.04.00.0370.0436.13.80.0220.37
SEA5.14.20.0320.0216.94.20.0160.30
STL5.24.00.0370.0237.34.90.0270.37
TB6.14.10.0230.0205.94.30.0460.42
TEN6.14.30.0200.0195.23.70.0350.43
WAS5.54.40.0120.0205.83.80.0250.33
Avg6.14.20.0280.0236.24.20.0280.36

  • Spread The Love
  • Digg This Post
  • Tweet This Post
  • Stumble This Post
  • Submit This Post To Delicious
  • Submit This Post To Reddit
  • Submit This Post To Mixx

5 Responses to “2008 Final Efficiency Rankings”

  1. Anonymous says:

    I have a question for you. I'm trying to make a logistic regression formula like the one you mention but for the NBA. I have the data (stats) I think are most relevant but I don't know how to fit it for two reasons I was hoping you could help me with. First, how do I determine what the intecept should be? Also, I have stats that are different (%, totals per game, etc)I know a logistic regression formula will allow to "weight" each one differently but how do I get them all in the same "format" to start with. I'm not a genius or a statitician but I would love to make this work-can you help?

  2. Adam says:

    Hello Anonymous,

    Your comment strikes me because I have been spending months doing the exact same thing (logistic regression analysis for NBA games). I've actually come up with a pretty darn accurate formula myself so, if you want to cut straight to the chase, I could just give it to you directly. Or, if you want to learn more about the process, I can explain to you how I came up with each coefficient. Either way, I don't want to muddy up Brian's awesome site with a bunch of NBA analysis stuffed into the comments section because I don't think his readers would care for it. I don't want to post my email address on a public site (spam, spam, spam, spam), but I think Brian can see the email addresses of those who have posted (?) and, if so, I wouldn't mind if he gave it to you. If that's not possible, is there another way for me to contact "Anonymous" without me publicly posting my email address, Brian??

  3. Sports Picks System says:

    Why not writing an article for the community blog? Even if it does not deal with the NFL, the methodology is important. Thanks to this site I discovered the logistic regression. What do you think Brian?

  4. Adam says:

    If Brian is cool with me putting an NBA-analysis article in the community site, I will do so. Brian?

  5. Brian Burke says:

    That's fine with me.

Leave a Reply