Week 15 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 PIT30.760.53201
2 CAR50.760.5475
3 ATL10.760.56117
4 PHI20.750.52124
5 NYG40.690.52610
6 NO90.680.56313
7 TEN70.680.43162
8 SD60.670.51512
9 MIA110.660.43219
10 WAS80.650.511111
11 DAL150.640.52147
12 TB120.630.56186
13 BAL100.630.52193
14 IND130.620.48918
15 CHI160.610.51218
16 ARI140.560.51422
17 GB170.520.551320
18 MIN200.510.50249
19 DEN180.490.49827
20 NE190.480.461524
21 NYJ210.470.432314
22 HOU220.440.511028
23 JAX240.380.481725
24 BUF230.340.432821
25 SEA250.330.482716
26 SF260.320.492526
27 KC290.290.522230
28 CIN300.280.603123
29 CLE280.270.572629
30 OAK270.270.553215
31 STL310.150.532931
32 DET320.140.573032




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








































TEAMOPASSORUNOINTRATEOFUMRATEDPASSDRUNDINTRATEPENRATE
ARI7.23.30.0230.0256.44.00.0250.38
ATL7.64.20.0230.0156.14.90.0210.29
BAL5.83.80.0310.0255.13.40.0480.41
BUF5.94.20.0350.0366.24.20.0210.29
CAR6.94.80.0320.0155.64.10.0230.35
CHI5.53.90.0260.0165.73.50.0380.31
CIN4.23.40.0320.0276.63.90.0180.30
CLE5.03.90.0310.0257.14.50.0540.32
DAL6.94.30.0370.0305.34.00.0170.49
DEN7.14.50.0290.0206.94.90.0130.34
DET5.33.70.0340.0407.84.90.0110.38
GB6.54.00.0250.0236.14.80.0400.50
HOU7.24.40.0390.0296.94.50.0290.34
IND6.63.40.0230.0106.04.20.0330.32
JAX5.74.20.0210.0186.84.10.0320.43
KC5.34.70.0280.0207.34.90.0260.30
MIA7.14.10.0140.0166.13.90.0260.37
MIN5.94.50.0410.0236.03.20.0250.38
NE5.94.50.0230.0186.74.20.0310.24
NO7.63.80.0310.0196.34.00.0270.41
NYG6.14.80.0230.0205.53.90.0350.45
NYJ6.04.80.0390.0246.13.70.0270.28
OAK4.94.20.0270.0376.44.70.0350.45
PHI6.34.00.0300.0145.23.40.0310.34
PIT5.83.60.0270.0234.33.20.0370.42
SD7.53.80.0260.0206.23.90.0210.38
SF6.03.90.0340.0426.33.80.0230.40
SEA5.04.40.0320.0166.94.10.0120.28
STL5.23.90.0400.0277.64.70.0180.39
TB6.04.00.0200.0205.74.30.0500.41
TEN6.24.40.0220.0204.93.70.0360.44
WAS5.74.50.0130.0215.83.80.0280.36
Avg6.14.10.0290.0236.24.10.0280.37

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4 Responses to “Week 15 Efficiency Rankings”

  1. Alex says:

    Nice - the Eagles recently seem to have justified models having them highly all year.

    One (unrelated) thing I was kind of curious about is, with your win probability graphs and such, if you could calculate some sort of crude WPA? It seems like that's what stats are essentially trying to get at anyways - incorporating the differing values of first downs/turnovers/varying events, just in a less direct way. Obviously, this wouldn't necessarily be a good stat for comparing individual players, but even something like, say, total running WPA vs total passing WPA on first down would imo be very interesting.

  2. mileslibbey4 says:

    Have you looked at subdivisions of the season (last 3|5|x games; last 80% of games; etc) to see if that increases the game projections? Thought is that teams seem to go through ups and downs during the season. Romo goes out, and Dallas becomes horrible for several games. The Redskin's tackles get hurt, and they seem to be a different team. Do the stats back that up, or is it just my mind playing tricks?

  3. Brian Burke says:

    Miles-So far, nothing really improves accuracy, but it doesn't hurt it either. You can look at week 5 or 6 and see that the model's accuracy isn't much different than in week 15.

    One thing to mention is that there is an upper limit to any prediction system accuracy. Sometimes there are upsets, and there are plenty of 55/45 toss-up type games. I have done extensive research on that, and I think 80% is the theoretical maximum achievable.

    Alex-Yes, eventually we could get to a player-level WPA. I agree that any player-level stat in football is relatively meaningless, but that hasn't stopped me in the past!

  4. Anonymous says:

    Out of curiosity, have you checked to see how the GWPs correlate with point differential as well? I always thought this may be a better measure of team strength than wins - if a team is 1-1 with a thirty point win and a three point loss, that says they're likely a better than average team. Looking at the data in terms of that seems to show a strong correlation, though there are still some surprises (i.e., Atlanta having a poorer PD than the Jets and being substantially higher)

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