Week 5 Efficiency Rankings

Team efficiency rankings are listed below in terms of generic winning 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 strength of schedule. The adjusted GWP (Adj GWP) modifies the generic win probability to reflect the strength of to-date opponents. Rankings are based on data through week 5. A full explanation of the methodology can be found here.

Team	Rank	Last Wk	Adj GWP	Opp GWP
NE	1	4	0.92	0.36
IND	2	1	0.90	0.47
NYG	3	8	0.82	0.72
DAL	4	5	0.82	0.34
WAS	5	12	0.81	0.56
JAX	6	7	0.76	0.54
SEA	7	3	0.73	0.53
TB	8	6	0.73	0.33
PHI	9	9	0.70	0.58
PIT	10	11	0.70	0.34
DEN	11	2	0.70	0.58
TEN	12	10	0.69	0.57
ARI	13	13	0.62	0.55
HOU	14	15	0.61	0.55
GB	15	16	0.60	0.54
CIN	16	14	0.60	0.60
MIA	17	18	0.47	0.59
SD	18	29	0.47	0.55
KC	19	17	0.43	0.39
BUF	20	22	0.43	0.67
MIN	21	19	0.40	0.45
ATL	22	25	0.40	0.53
BAL	23	24	0.37	0.41
CAR	24	27	0.36	0.49
OAK	25	21	0.34	0.41
CLE	26	23	0.34	0.55
NYJ	27	28	0.28	0.52
SF	28	30	0.26	0.57
DET	29	20	0.25	0.41
STL	30	31	0.24	0.58
NO	31	26	0.23	0.79
CHI	32	32	0.16	0.55

Opponent Strength

To get a feel for which teams are really doing well, and which have just been beating up on weaker teams, we should look at strength of schedule. One of the interesting applications of accounting for opponent strength using a regression model is that offensive and defensive opponent strength can be analyzed separately. We can see which squads have faced the toughest and easiest opposing units so far this year.

The table below lists each team and their to-date opponents' strength ranked from toughest (1) to weakest (32). The table includes overall opponent strength, opponent offensive strength, and defensive oppoenent strength.

Team	Opp Str	Opp O Str	Opp D Str
NO	1	1	1
BUF	2	2	5
NYG	3	4	2
CIN	4	3	12
JAX	5	5	10
STL	6	8	6
CHI	7	20	4
MIA	8	10	11
SF	9	14	18
PHI	10	21	8
SD	11	24	3
DEN	12	9	21
SEA	13	18	14
HOU	14	6	20
ARI	15	22	9
TEN	16	7	27
OAK	17	16	16
WAS	18	12	19
CLE	19	19	15
IND	20	25	23
MIN	21	23	13
ATL	22	17	24
NYJ	23	11	25
CAR	24	13	26
GB	25	26	17
BAL	26	15	30
DET	27	31	7
PIT	28	29	29
KC	29	32	22
TB	30	28	32
NE	31	27	31
DAL	32	30	28

The defensive units that have faced the toughest offenses so far are NO, BUF, and CIN. Look for those defenses to improve if only because they're not likely to face as difficult a schedule as they have so far. Likewise, the teams that have faced the weakest defenses so far are KC, DET, and DAL. They might appear better than they really are.

The offensive units that have faced the toughest defenses so far are NO, NYG, SD, and CHI. The Giants haven't been too bad on offense so far this year, so perhaps they'll put up some bigger numbers when their schedule eases up. NO has started with the most brutal schedule of all 32 teams. The teams that have faced the weakest offenses so far are TB, NE, BAL, PIT, and TEN. Those defenses might be overrated at this point.

Game Predictions Week 5

Game probabilities for week 5 are listed below. The probabilities are based on an efficiency win model explained here and here. The model considers offensive and defensive efficiency stats including running, passing, sacks, turnover rates, and penalty rates. Team stats are adjusted for previous opponent strength.

V Prob	VISITOR	HOME	H Prob
0.85	ARI	STL	0.15
0.13	ATL	TEN	0.87
0.62	CAR	NO	0.38
0.04	CLE	NE	0.96
0.22	DET	WAS	0.78
0.73	JAX	KC	0.27
0.33	MIA	HOU	0.67
0.10	NYJ	NYG	0.90
0.69	SS	PIT	0.31
0.19	TB	IND	0.81
0.49	BAL	SF	0.51
0.03	SD	DEN	0.97
0.09	CHI	GB	0.91
0.90	DAL	BUF	0.10

Season Win Projections Week 4

Season win totals and division standing projections are listed below. Projections are based on each team's opponent-adjusted generic win probability (Adj GWP). Total wins account for current and projected wins. Methodology can be found here.

Team	Adj GWP	Current	Projected
AFC E
NE	0.79	4	13.5
MIA	0.43	0	5.2
BUF	0.32	1	4.8
NYJ	0.26	1	4.1
AFC N
PIT	0.61	3	10.3
CIN	0.55	1	7.6
CLE	0.32	2	5.8
BAL	0.30	2	5.6
AFC S
IND	0.92	4	15.0
JAX	0.70	2	11.1
TEN	0.64	2	10.3
HOU	0.53	2	8.4
AFC W
DEN	0.86	2	12.3
KC	0.44	2	7.3
OAK	0.41	2	6.9
SD	0.23	1	3.8
NFC E
DAL	0.78	4	13.3
WAS	0.61	2	9.9
NYG	0.65	2	9.8
PHI	0.65	1	8.7
NFC N
GB	0.50	4	10.0
DET	0.42	3	8.0
MIN	0.42	1	6.0
CHI	0.14	1	2.6
NFC S
TB	0.75	3	12.0
CAR	0.26	2	5.1
ATL	0.29	1	4.5
NO	0.26	0	3.4
NFC W
SS	0.82	3	12.8
ARI	0.59	2	9.0
SF	0.23	2	4.7
STL	0.18	0	2.1

For now, the projections don't account for future strength of schedule. At this point only 4 games into the season, you should pay more attention to the relative ranking of each team than the projected win total. Further into the season, the projections will account for future opponent strength.

2006 Season Win Projections

Here are the projections from the same timeframe in 2006. Each column indicates the season win projections of a very similar model following weeks 2, 3, and 4. The actual total numbers of wins aren't terribly great, but the relative position of teams within each division is pretty good. I can't emphasize that point enough.

Seven of eight division winners were correctly projected. The only exception was Atlanta in the NFC South, which suffered an epic collapse at the end of the '06 season. The model actually had NO winning the division in week 3, but gave it to ATL by week 4.

Team	Week 2	Week 3	Week 4	Actual
AFC E
06New Engl	9.2	6.6	8.7	12
06Buffalo	5.7	7.0	8.1	7
06NY Jets	7.5	6.9	7.0	10
06Miami	5.6	6.1	5.6	6
AFC N
06Baltimor	9.8	10.6	10.9	13
06Cincinna	10.5	10.9	9.5	8
06Pittsbur	7.4	6.9	6.3	8
06Clevelan	5.4	5.5	4.6	4
AFC S
06Indianap	7.1	9.4	10.4	12
06Jacksonv	10.9	9.5	8.2	8
06Houston	3.8	3.6	4.3	3
06Tennesse	3.9	3.2	2.1	8
AFC W
06San Dieg	12.6	14.2	13.0	14
06Denver	6.0	10.2	10.1	9
06Kansas C	7.2	7.4	9.4	9
06Oakland	5.7	4.4	4.3	2
NFC E
06Philadel	10.4	12.1	13.1	10
06Dallas	10.5	9.7	12.1	9
06NY Giant	11.4	11.5	11.5	8
06Washingt	7.5	9.3	10.9	5
NFC N
06Chicago	11.3	11.3	12.2	13
06Minnesot	8.8	10.2	9.3	6
06Green Ba	6.7	6.0	4.9	8
06Detroit	6.1	5.2	4.4	3
NFC S
06Atlanta	10.3	9.4	11.1	7
06New Orle	9.2	11.1	10.1	10
06Carolina	4.5	4.1	5.8	8
06Tampa Ba	4.4	1.6	1.6	4
NFC W
06Seattle	9.5	10.6	8.6	9
06St. Loui	6.3	6.5	7.8	8
06San Fran	10.7	8.3	6.5	7
06Arizona	7.2	5.0	3.1	5

The biggest misses were the Jets and Redskins. The Jets have returned to mediocrity and were most likely very luckly last year. The model was very wrong about the Redskins, and I have no idea why they took the downturn they did last year.

One more note: Although these predictions were based on a similar model to the current one, the coefficients were derived solely from 2005 season data. 2005 turned out to be uniquely favorable to running offenses and defenses, which skewed the projections. I redid the model using data from 2002-2006 and the results were much better.

Why do the Chargers Stink?

The San Diego Chargers are 1-3 so far in 2007 and are on pace to win between only 3 to 4 games this year according to their efficiency stats. Their sole win has come against perhaps the only team considered more disappointing this year, the Chicago Bears. Media attention focuses on star running back LaDanian Tomlinson and his woes, but what are the real reasons for San Diego's slow start?

By comparing San Diego's stats in 2006 and 2007 through an efficiency model, we can see how the difference in each stat impacts the number of expected wins. The table below lists each efficiency stat in the model for both years. The column "+/-Wins" lists the estimated number of wins lost or gained due to the difference in each stat. Also listed is the Generic Win Probability (GWP) for the Chargers for 2006 and 2007. The GWP is the probability a team would beat the league-average team at a neutral site. The Adjusted GWP accounts for opponent strength.

STAT	SD '06	SD '07	+/-Wins
OPass	6.60	5.54	-1.6
ORun	4.90	3.10	-1.6
OIntRate	0.019	0.043	-1.2
OFumRate	0.016	0.029	-1.0
DPass	5.36	7.39	-3.4
DRun	4.20	3.96	+0.1
DIntRate	0.030	0.031	0.0
PenRate	0.40	0.38	+0.1
GWP	0.77	0.20
Adj GWP	0.77	0.23
Exp. Wins	12.3	3.7	-8.6

First, note that the Chargers were expected to win 12.3 wins last year, 1.7 wins short of their actual total. This indicates they were a solidly talented team, but there was some luck involved in getting to a 14-2 record. This year they are on pace to win 3.7 wins, a difference of 8.6 wins.

LT has seen his yards per rush attempt fall from 5.2 in 2006 to 3.4 in 2007, certainly a significant difference. But San Diego's running game only accounts for 1.6 of the 8.6 win difference between '06 and '07. Their passing game shares and equal amount of blame with a 1.6 win difference.

San Diego's opponents so far have an average GWP of 0.54, slightly above average. But this would only account for 0.03 in their own GWP so far. This equates to only 0.1 wins.

The Chargers' pass defense is the prime culprit, accounting for 3.4 wins of the 8.6 difference in expected wins. Last year their defense gave up only 5.36 yards per pass attempt, but this year they are allowing 7.39 yards per attempt (including sack yards). Although they've faced a ferocious New England passing attack and a resurgent Brett Favre, they've also played against below average QBs Rex Grossman and Damon Huard.

But in total, it's the Charger offense that's responsible for the bulk of their difficulties. Added together, their passing game and running game including turnovers are costing them 5.4 expected wins over the course of a season.

Are the Chargers a 4-win team? Maybe not. But for them to turn their season around, their pass defense needs to improve and not necessarily just their running game.

Week 4 Efficiency Rankings

Team efficiency rankings are listed below in terms of generic winning 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 strength of schedule. The adjusted GWP (Adj GWP) modifies the generic win probability to reflect the strength of to-date opponents. Rankings are based on data through week 4. A full explanation of the methodology can be found here.

Team	Rank	Last Wk	GWP	Opp GWP	Adj GWP
IND	1	1	0.92	0.50	0.92
DEN	2	2	0.84	0.53	0.86
SEA	3	9	0.79	0.53	0.82
NE	4	3	0.96	0.28	0.79
DAL	5	5	0.94	0.28	0.78
TB	6	7	0.89	0.31	0.75
JAX	7	8	0.64	0.59	0.70
NYG	8	16	0.52	0.68	0.65
PHI	9	4	0.61	0.54	0.65
TEN	10	10	0.61	0.53	0.64
PIT	11	6	0.76	0.31	0.61
WAS	12	11	0.60	0.51	0.61
ARI	13	14	0.57	0.52	0.59
CIN	14	15	0.47	0.61	0.55
HOU	15	12	0.51	0.53	0.53
GB	16	13	0.54	0.44	0.50
KC	17	20	0.59	0.31	0.44
MIA	18	18	0.39	0.56	0.43
MIN	19	19	0.43	0.49	0.42
DET	20	22	0.50	0.38	0.42
OAK	21	29	0.40	0.51	0.41
BUF	22	24	0.16	0.71	0.32
CLE	23	30	0.32	0.50	0.32
BAL	24	23	0.37	0.41	0.30
ATL	25	25	0.31	0.47	0.29
NO	26	31	0.03	0.81	0.26
CAR	27	21	0.29	0.46	0.26
NYJ	28	26	0.28	0.47	0.26
SD	29	28	0.20	0.54	0.23
SF	30	17	0.18	0.56	0.23
STL	31	27	0.12	0.58	0.18
CHI	32	32	0.09	0.56	0.14

Game Model Coefficients

In the interest of full disclosure, or for those fellow uber-geeks, here is the actual model I'll be using for estimating outcome probabilities for each NFL game.

It is a logit regression model based on the outcomes of all regular season games in 2002-2006. I looked at each game twice, once from the point of view of the home team and from the point of view of the visiting team. I called each team, Team A and Team B. To identify the home team, I used a dummy variable AHome, which was 1 or 0 depending on whether Team A was home or away. The dependent variable is AWon, which is 1 if Team A won or 0 if Team B won. There were 2560 cases (games) considered.

VARIABLE	COEFFICIENT	STD ERROR	T-STAT	SLOPE at mean
const	-0.26	1.36	-0.19
AHOME	0.74	0.09	8.29	0.19
AOPASS	0.45	0.07	6.56	0.11
AORUN	0.27	0.10	2.65	0.07
ADPASS	-0.54	0.09	-5.90	-0.13
ADRUN	-0.21	0.11	-1.87	-0.05
AOINTRATE	-15.90	6.26	-2.54	-3.98
ADINTRATE	17.68	5.16	3.43	4.42
AOFUMRATE	-20.50	7.79	-2.63	-5.12
APENRATE	-1.49	0.72	-2.07	-0.37
BOPASS	-0.45	0.07	-6.54	-0.11
BORUN	-0.27	0.10	-2.64	-0.07
BDPASS	0.53	0.09	5.83	0.13
BDRUN	0.20	0.11	1.79	0.05
BOINTRATE	15.71	6.26	2.51	3.93
BDINTRATE	-18.95	5.16	-3.67	-4.74
BOFUMRATE	21.01	7.79	2.70	5.25
BPENRATE	1.47	0.72	2.04	0.37

Retrodictively, the model predicts 69.5% of the games correctly. But keep in mind there are many evenly matched games and upredictable upsets, so it may be impossible for even the most perfect model to get past 75% or so.

I realize the numbers in the table above are meaningless to most people, but I want to ensure everything I do is out in the open.

Key:
OPASS = (offensive pass yds - sack yds) / pass plays
ORUN = offensive run yds / run plays
DPASS = (defensive pass yds - sack yds) / pass plays
DRUN = defensive run yds / run plays
OINTRATE = offensive interceptions / pass attempts
DINTRATE = defensive interceptions / pass attempts
OFUMRATE = fumbles / offensive plays
PENRATE = team penalty yds / total plays

The t-stat indicates the significance of each variable. For this sample size, a t-stat of approximately 1.8 or greater (or -1.8 or less) indicates a signifcance level of p=0.05 or better.

Below is a graph of the spectrum of game probabilities divided ramdomly into two sets, training cases for the regression, and test validation cases. The graph is of the actual outcome rates vs. the model's predicted probabilities.