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.RANK TEAM LAST WK GWP Opp GWP O RANK D RANK 1 CAR
1 0.79 0.55 4 6 2 PHI
2 0.77 0.52 11 2 3 PIT
3 0.75 0.53 20 1 4 ATL
4 0.73 0.54 3 17 5 SD
5 0.72 0.53 1 10 6 NYG
6 0.69 0.53 2 13 7 NO
8 0.68 0.55 5 19 8 IND
12 0.68 0.49 7 15 9 TEN
7 0.67 0.46 15 4 10 BAL
11 0.67 0.52 17 3 11 MIA
9 0.64 0.44 6 20 12 WAS
10 0.63 0.51 12 11 13 DAL
14 0.59 0.53 16 8 14 TB
13 0.58 0.55 19 9 15 CHI
15 0.57 0.51 22 7 16 NE
16 0.57 0.47 13 24 17 GB
17 0.54 0.51 14 18 18 MIN
19 0.53 0.52 23 5 19 ARI
18 0.52 0.50 9 23 20 DEN
20 0.48 0.50 8 27 21 HOU
22 0.45 0.50 10 28 22 NYJ
21 0.45 0.45 24 12 23 BUF
24 0.34 0.44 27 22 24 SEA
28 0.34 0.47 30 14 25 OAK
27 0.33 0.56 29 16 26 JAX
23 0.32 0.50 18 30 27 KC
26 0.31 0.53 21 26 28 CIN
29 0.30 0.55 32 21 29 SF
25 0.29 0.49 25 25 30 CLE
30 0.20 0.57 28 29 31 STL
31 0.18 0.52 26 31 32 DET
32 0.12 0.57 31 32
To-date efficiency stats below. As always, click on the headers to sort.TEAM OPASS ORUN OINTRATE OFUMRATE DPASS DRUN DINTRATE PENRATE ARI 7.1 3.5 0.024 0.028 6.5 4.0 0.025 0.39 ATL 7.4 4.4 0.025 0.015 6.0 4.9 0.018 0.29 BAL 6.0 4.0 0.028 0.025 5.1 3.6 0.049 0.40 BUF 5.9 4.2 0.031 0.034 6.3 4.3 0.020 0.28 CAR 7.3 4.8 0.029 0.014 5.7 4.4 0.022 0.32 CHI 5.5 3.9 0.027 0.016 5.9 3.4 0.035 0.29 CIN 4.3 3.6 0.029 0.027 6.3 3.9 0.024 0.30 CLE 4.6 3.9 0.041 0.024 7.1 4.5 0.052 0.35 DAL 6.6 4.3 0.037 0.032 5.3 4.2 0.016 0.49 DEN 7.1 4.8 0.029 0.019 7.0 5.0 0.012 0.37 DET 5.3 3.8 0.037 0.036 7.9 5.1 0.009 0.38 GB 6.6 4.1 0.024 0.023 6.0 4.6 0.042 0.49 HOU 7.3 4.3 0.036 0.028 6.9 4.5 0.025 0.34 IND 6.8 3.4 0.021 0.010 5.9 4.2 0.031 0.32 JAX 5.8 4.2 0.024 0.021 7.3 4.0 0.028 0.42 KC 5.4 4.8 0.030 0.022 7.0 5.0 0.025 0.32 MIA 7.0 4.2 0.014 0.017 6.2 4.2 0.033 0.34 MIN 6.0 4.5 0.038 0.028 6.0 3.3 0.023 0.35 NE 6.1 4.4 0.021 0.016 6.4 4.1 0.030 0.25 NO 7.7 4.0 0.028 0.018 6.4 4.2 0.029 0.39 NYG 6.1 5.0 0.020 0.017 5.8 4.0 0.034 0.42 NYJ 5.9 4.7 0.043 0.024 6.1 3.7 0.024 0.28 OAK 5.2 4.3 0.026 0.033 6.4 4.7 0.034 0.42 PHI 6.2 4.0 0.026 0.015 5.1 3.5 0.029 0.31 PIT 5.9 3.7 0.030 0.026 4.3 3.3 0.038 0.41 SD 7.7 4.1 0.023 0.017 6.3 4.0 0.025 0.38 SF 6.0 4.0 0.037 0.043 6.1 3.8 0.022 0.37 SEA 5.1 4.2 0.032 0.021 6.9 4.2 0.016 0.30 STL 5.2 4.0 0.037 0.023 7.3 4.9 0.027 0.37 TB 6.1 4.1 0.023 0.020 5.9 4.3 0.046 0.42 TEN 6.1 4.3 0.020 0.019 5.2 3.7 0.035 0.43 WAS 5.5 4.4 0.012 0.020 5.8 3.8 0.025 0.33 Avg 6.1 4.2 0.028 0.023 6.2 4.2 0.028 0.36
- Home
- team rankings
- 2008 Final Efficiency Rankings
2008 Final Efficiency Rankings
By
Brian Burke
published on 12/31/2008
in
team efficiency,
team rankings
Subscribe to:
Post Comments (Atom)
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?
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??
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?
If Brian is cool with me putting an NBA-analysis article in the community site, I will do so. Brian?
That's fine with me.