Run Defense Dominates in the Playoffs

In the last post I indirectly analyzed playoff games by looking at regular season games that only featured opponents who both went on to win at least 10 games. We saw that teams with the better running games actually won less than 50% of the time.

In this post, I'll look at actual playoff games directly. Compared with the 114 "good vs. good" regular season games I looked at yesterday, there were 50 playoff games, plus 5 Super Bowls, in the 2002 through 2006 seasons.

The table below lists the winning percentage of the team with the superior season-long performance in each stat. In other words, the team with the better [stat] won [x] percent of the time. The regular season good vs. good match-ups are also listed for comparison. (The home win percentage excludes the five Super Bowls during the period.)

Stat	Good vs Good	Playoffs
Home	59.6	64.0
O Run	45.6	45.5
D Run**	48.2	67.3
O Pass*	52.6	63.6
D Pass	51.8	56.3
O Int Rate	50.9	58.1
D Int Rate	55.3	58.1
O Fum Rate**	55.3	40.0
D FFum Rate	54.4	54.5
Pen Rate	47.3	52.7

** = good vs. good / playoff difference is significant at the p=0.05 level
* = good vs. good / playoff difference is significant at the p=0.10 level

The home team won more often in the playoffs than in good vs good regular season match-ups, which is expected because higher seeded teams host the playoff games.

The team with the better run efficiency won only 45.5% of the 55 playoff games during the '02-'06 period. Keep in mind the small sample size could make these results misleading. 45.5% is only 2.5 games below 50%. But the result echoes the same result for the regular season good vs. good match-ups.

Defensive run efficiency is a different story. Although the team with superior run stopping ability won only 48.7% of the regular season good vs. good match-ups, it won 67.3% of playoff games. This is a striking difference to say the least, especially considering how unimportant run defense is based on regression models of regular season games.

The passing game stands out as well. Both offensive and defensive passing stats appear to be more important in the playoffs. Interception rates also appear very important.

Another striking result is that of offensive fumble rates. The team with the lower fumble rate wins only 40% of playoff games. As one of the more random stats, it's not too surprising to see a spurious result for fumble rate, but 40% is fairly low, even for such a small sample size.

I suspect that coaches may become too conservative in the playoffs, relying on the run. This might explain why having a good running game doesn't help teams win and why the ability to stop the run suddenly becomes very important in the playoffs.

My own gut feeling is that coaches don't coach to win. They coach to avoid a loss. It sounds inane, but there is a difference. Maybe the play-calling in the playoffs becomes even more timid. But then again, perhaps the January weather has something to do with it. Previous research has established the importance of climate, especially when dome teams play outdoors. Weather may explain the 67% win percentage of teams with the better run defense. Defense may win championships after all, particularly run defense.

Maybe Defense Does Win Championships...

...but the running game probably doesn't help at all.

I previously thought that the "defense wins championships" theory was conventional wisdom bunk. But after doing a new analysis, I think it might be true. It's the importance of the running game, however, that really surprised me.

In a recent post, I illustrated the distribution of offenses and defenses in terms of total efficiency (yards per play). The distribution for offensive efficiency was wider than for defensive efficiency. This indicated that "good" offenses were better than the equivalent "good" defense. In other words, the best offenses in the league tend to get more yards per play above average than the best defenses in the league give up below average.

"Having a good running game is not only unimportant, it actually seems counter- productive."

However, that analysis was for regular season games. Post season games comprise a smaller sample size, usually too small for very meaningful analysis. They're also biased in certain ways. For example, the home team is usually the better team, and it would therefore be hard to separate the advantage in team strength from home field advantage. But the biggest difference between the regular and post seasons is the level of competition.

As an indirect way to infer tendencies about post-season competition, I analyzed regular season games that featured only opponents that would go on to win at least 10 games. I think this criteria best reflects the level of competition usually found in the playoffs. Although 9-win or even 8-win teams occasionally make the playoffs, many do not. Plus, 9 wins is only 1 win above a .500 win percentage, and a 9-win team has never won a championship.

First, I looked at how important various team stats were in determining the winner of match ups between 10+ win teams. I looked at offensive running and passing efficiencies, turnover efficiencies, penalty rates, and home field advantage. The data is from the 2002-2006 seasons, and there were 114 such games between "good" teams. (The stats used here are year-long stats, not stats only within that particular game.)

Instead of an advanced regression analysis, I started by looking at how often a team with an advantage in each particular stat won. The table below lists various team efficiency stats along with the win percentage of the team superior in that stat. For example, the team with home field advantage won 59.6% of the match-ups between 10+ win teams. And the team with the better offensive pass efficiency won 52.6% of the match-ups. The winning percentage for all regular season games is included for comparison. Significant differences in winning percentages between good vs. good games and all games are noted.

Stat	Good vs. Good	All Reg Season
Home	59.6	57.4
O Run**	45.6	55.0
D Run	48.2	50.0
O Pass**	52.6	63.8
D Pass**	51.8	59.8
O Int Rate**	50.9	59.5
D Int Rate	55.3	59.4
O Fum Rate	55.3	60.8
D FFum Rate	54.4	58.0
Pen Rate*	47.3	54.1

** = difference is significant at the p=0.05 level
* = difference is significant at the p=0.10 level

What immediately strikes me is that being good in the running game, both on offense and defense, appears to be no help in beating other good teams. Teams with the better offensive running efficiency won only 45.6% of the games, and teams with the better defensive running efficiency won only 48.2% of the games.

Teams with superior passing, fumbles, defensive interception rate, and penalties win slightly more than 50%-55% of the games. I'm surprised passing efficiencies don't appear to be more important. The stats that tend to be more random, such as fumbles and interceptions, appear to make the biggest difference. This result may be due to the fact that when good teams play each other stats like passing efficiency and offensive interception rates are very good for both teams, and the difference is in the more random stats.

"When teams very close in ability meet, the more important other factors such as randomness and home field advantage become."

Home field advantage also appears more important than is typical in the NFL. Home teams usually won 57.4% of all regular season games in the period studied. In the good team vs. good team match-ups, home field advantage appears slightly stronger. Again, the closer the teams are in ability, the more important other factors become.

But having a good running game is not only unimportant, it actually seems counter-productive. How can this be? (First, I should note this is not a regression tested for significance, but with 114 observations, and the fact that both offensive and defensive running abilities appear unhelpful, the results are likely somewhat meaningful.) If true, my theory is that winning teams that count on the running game to win might overuse the run against better opponents. Leaning on the running game wouldn't help, and may actually hurt.

Running too frequently would do harm because the pass does have a higher expected return per attempt (link requires registration), even accounting for the possibility of an interception. Every run attempt precludes a pass attempt, reducing ultimate effectiveness.

To get a better context of the results in the table above, I also calculated the winning percentage of teams with superior stats for other types of match-ups. I analyzed "bad vs. bad" match-ups which featured both opponents that ultimately earned 9 or less regular season wins. Also analyzed were "good vs. bad" match-ups which featured a 10+ win team against a 9- win team. (I realize 9 wins is not "bad," but it's a lot shorter than "other than good.")

The winning percentage of teams with the better stat are listed for each type of match-up in the table below.

Stat	Good vs Good	Bad vs Bad	Good vs Bad
Home	59.6	57.0	56.0
O Run	45.6	53.7	55.8
D Run	48.2	51.4	67.9
O Pass	52.6	63.6	65.4
D Pass	51.8	59.8	63.7
O Int Rate	50.9	59.5	53.8
D Int Rate	55.3	59.4	57.3
O Fum Rate	55.3	60.5	63.7
D FFum Rate	54.4	58.0	62.3
Pen Rate	47.3	53.8	71.9

The results for the other types of match-ups seem to make sense. Being superior in any of the stats does not appear to be unhelpful (as with running in the good vs. good match-up). The bigger the difference in team record, the larger we would expect the difference in each team stat. Accordingly, the winning percentages are higher for the bad vs. bad and good vs bad match-up types than the good vs. good match-up type.

I could go on and on with observations. Penalty rates appear critical in good vs bad match-ups, offensive passing efficiency appears most important in the bad vs. bad match-ups, etc. I'll leave it to others to draw their own inferences.

Ultimately, when teams very close in ability meet, the more important other factors such as randomness and home field advantage become. Playoff teams are by definition relatively similar in ability, so home field and randomness become critically important. Turnovers are the most random of the stats, especially defensive turnover efficiency. Perhaps then it is randomness that wins championships. And because defensive performance trends are more random than offensive trends, perhaps that's why we see defense as more important come January.

Playoff Predictions - Divisional Round

Game probabilities for the NFL divisional playoff round 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. Click here for a sortable comparison of playoff team stats.

V Prob	Game	H Prob
0.26	JAX at NE	0.74
0.20	SD at IND	0.80
0.45	SEA at GB	0.55
0.19	NYG at DAL	0.81

Is 3rd Down Conversion Percentage a Good Stat?

I've previously commented that using 3rd down percentage in an analysis of team strength or a game prediction model is not a good practice. I realize this is counter intuitive. 3rd down percentage is highly correlated with winning, and unlike total rushing yards, the direction of causation is clear. Converting the always-critical 3rd down leads to winning. So why wouldn't it be a good stat?

"When we rate how good a team is, we're better off knowing how likely it is to win future games than dissecting past games into molecular detail."

3rd down percentage is a function of a team's passing ability, running ability, an opponent's ability to stop them, and often random luck (you guess pass I call a draw). 3rd down percentage is an intermediate result between running/passing and the final result of interest--team wins. Injecting an intermediate result into a regression model may be useful in analyzing why teams won or lost past games, but it does not help evaluate how good a team is, or will be.

Bill Parcells once barked, "You are what your record says you are." I prefer the saying that "you're only as good as your next game." A team's record may be what matters when deciding who goes to the playoffs, but a team can't really change its record--except by winning or losing its next game. So when we rate how good a team is, we're better off knowing how likely it is to win future games than dissecting past games into molecular detail. Many of those details are unique to the circumstances of the past. Models like this are known as "over-fit."

Stats such as 3rd down percentage tell us more about what has happened to a team in the past than how well it will do in the future. In a recent article, I tested how well various stats endure through the season. If a team stat from the first half of the season does well predicting itself in the second half of the season, we have a good idea that it is an enduring and repeatable skill, and not primarily the result of randomness and non-repeating circumstances. The table below lists how well each team stat correlates with itself between the first and second half of a season.

Variable	Correlation
O 3D Rate	0.43
D Int Rate	0.08
D Pass	0.29
D Run	0.44
D Sack Rate	0.24
O Fumble Rate	0.48
O Int Rate	0.27
O Pass	0.58
O Run	0.56
O Sack Rate	0.26
Penalty Rate	0.58

Offensive 3rd down rate endures fairly well within a season, with a correlation coefficient of 0.43. But what if I could predict a team's 3rd down percentage with a completely different stat better than past 3rd down percentage itself? What does that tell us about 3rd down percentage as a stat?

The table below lists other offensive efficiency stats as predictors of 3rd down percentage. In other words, these are the correlations between a team's other stats from the first half of a season and the team's 3rd down percentage from the second half of the same season.

Predictor	Correlation
O 3D Pct	0.43
O Pass	0.56
O Run	0.08
O Sack Rate	-0.53
O Int Rate	-0.42

We can actually predict a team's 3rd down percentage better with offensive pass efficiency, or with sack rate, better than with a team's to-date 3rd down percentage. And with the correlation with run efficiency at a very small 0.08, we see that the passing game has almost everything to do with 3rd down conversions. (Teams tend to pass on anything longer than 3rd and 1 these days.)

So why include 3rd down percentage in a rating of team strength or a win prediction model when passing stats are already included? It would only serve to add random noise. Instead of telling us how good a team is or will be, it would tell us more about the unique circumstances and random luck the team experienced in the past.

If we still want to use 3rd down percentage as a stat to predict how good a team will be, we can. After all, 3rd down success is critical in sustaining drives and scoring points. It correlates with team wins at about 0.49 and with points scored at 0.65, both relatively very high. Instead of actually using to-date 3rd down percentage, we should estimate what the 3rd down percentage will be based on the stats we know to be predictive.

The table below is a regression model using passing stats to estimate future 3rd down percentage.

Variable	Coefficient	p-value
constant	39.6	0.00
O Sack Rate	-11.6	0.00
O Pass Efficiency	1.29	0.01
O Int Rate	-1.53	0.00

The actual model coefficients aren't as important as the fact that the r-squared is 0.94. That means that we can predict a teams's future 3rd down percentage with almost crystal ball-like accuracy using passing efficiency stats. And ironically, if we add previous 3rd down percentage itself to the model, it is the only non-significant variable (p=0.13) and r-squared is (strangely) reduced.

An r-squared of 0.94 is the equivalent of a correlation coefficient (r) of 0.97. Remember, this compares to the self-correlation of previous 3rd down percentage of only 0.46.

So if we want to know a team's ability to covert 3rd downs, we're far better off looking at passing stats than previous 3rd down conversion rates. And a prediction model is far better off using those passing stats (pass efficiency, interception rate, sack rate) and excluding to-date 3rd down percentage.

Explanation vs. Prediction

I'm always interested in improving my model for predicting game outcomes. My logistic regression model is based on straightforward variables: offensive and defensive passing and running efficiencies, turnover rates, and penalty rates. In this post, I'll question some of my own assumptions and begin to look at which variables really belong in a prediction model.

Explanatory vs. Predictive Models

As I updated the data for the predictions each week over the recent regular season, I noticed that some of the variables were more consistent than others. Turnover rates were particularly erratic. A team with a very good interception rate in the first half of the season would very often have a below average interception rate in the second half of the season.

Any basic regression model that attempts prediction is based on an assumption that the variables used as predictors, the 'to-date' variables, are indicative of what the same variables will be in the future. In football terms, when I include each team's interception rates from weeks 1-8 in the model to predict outcomes for week 9, I'm assuming that previous team interception rates are representative of future team interception rates.

But what if interceptions were completely random? Weeks 1-8 would not be predictive of week 9. Even though interceptions would still explain a large part of previous outcomes, past interceptions would not predict future outcomes at all. Just like mutual funds, past turnover performance does not guarantee future returns.

What if interception rates were just 'mostly' random? Should they still be included in a prediction model? Perhaps variables with lots of random noise such interceptions or fumbles should not be included in predictive models even though they explain a large part of past outcomes. The question becomes 'what is the critical signal-to-noise ratio that makes a variable appropriate for inclusion as a predictor?' Building on my previous efforts to devise a better passer rating, and on my analysis of Air Yards, I've created a more complete passer rating formula.

"Interceptions are very random, and they are 'thrown' by an offense much more than they are 'taken' by a defense."

This question underscores an important part of model construction. There are two kinds of models. One kind explains past outcomes, and the other predicts future outcomes. The 'explanatory' kind can contain all kinds of random variables, but the 'predictive' kind should limit the amount of random noise as much as possible. Ideally, it should be all signal and no noise.

There is so much statistical data available for football teams that it is tempting to dump them all into regression software. Doing that would produce a very high r-squared, but would include so much noise, so many non-repeating circumstantial conditions, that it would not be an effective prediction model. I believe this is why so many other models out there do so poorly. A system like DVOA may be very good at quantifying how well teams have done to date--something we already know, but not as good at telling us which teams are likely to do well in the future.

Team Stat Self-Correlations

To test which variables should remain in a prediction model, I tested how well each variable predicted itself from the first half of a season to the second half. This is known as longitudinal auto-correlation. This method tests how enduring and repeatable each variable is.

I tested how well team efficiency stats from weeks 1-8 predicted themselves from weeks 9-17. For example, I tested how well offensive passing efficiency from the first half of the season predicted pass efficiency in the second half of the season. Both offensive and defensive stats were tested. I used data from the 2006 and 2007 regular seasons for all 32 teams (n=64, with two exceptions: mid-season 3rd down conversion rate and penalty rates were not available for 2006.)

The correlation coefficients between team stats from weeks 1-8 with stats from weeks 9-17 are listed in the table below.

Variable	Correlation
D Int Rate	0.08
D Pass	0.29
D Run	0.44
D Sack Rate	0.24
O 3D Rate	0.43
O Fumble Rate	0.48
O Int Rate	0.27
O Pass	0.58
O Run	0.56
O Sack Rate	0.26
Penalty Rate	0.58

The longitudinal correlations range from as high as 0.60 for defensive pass efficiency and 0.58 for offensive pass efficiency, to as low as 0.08 for defensive interception rate.

The defensive interception rate stands out as the least enduring, least consistent team stat. In contrast, offensive interception rates correlate significantly better, with a coefficient of 0.27.

This indicates there is a lot of randomness in interceptions, which is no surprise. But producing defensive interception does not appear to be an enduring, repeatable ability of a team. Instead, it appears that defensive interceptions are more of a function of 1) randomness, and 2) their opponents' tendency to throw interceptions. In other words, interceptions are very random, and they are 'thrown' by an offense much more than they are 'taken' by a defense.

In following posts, I'll demonstrate that some of the more random team stats can be more accurately predicted by using other, less noisy stats instead of the to-date stats themselves. This may have large implications for an improved game prediction model.

Response to Luck and Belichick Article Comments

The traffic to this site has increased quite a bit lately. A lot of it is likely due to the interest in the playoffs, but much of it is from direct links from other sites. Most direct links are to my articles about Rating 'Gameday' Coaches and about Belichick Cheating Evidence. They have appeared on many message boards across the football world, and many of the comments and criticisms are outstanding. Allow me to address some of the comments here. The foundation of both articles deals with luck. Because there are so many new readers here, I'd like to clarify what I mean by luck, and how I use this definition when I apply statistics to make observations about coaching.

Luck

To me, a good example of luck is the "bunching" of successful events. In football, first downs are nice, but consecutive first downs are what allow touchdown drives. The number of first downs and the yardage gained in each should be ascribed to skill. Those are things the teams on the field control. But whether those first downs come in bunches or are interspersed is something different.

Perhaps a baseball analogy illustrates my point best. One single per inning gets a team zero runs after nine innings. But nine singles in one inning, followed by zero hits in eight innings would usually yield about six runs. In football, think of a drive as an inning--a team usually needs consecutive successes to score. If players could control when successful plays occurred, sports would be very different. Batters would save their hits for when runners are on base, or when the game is on the line. Receivers would save their dropped passes for the 4th quarter of blowouts.

Here is a football example I've used before: Let's say both PIT and CLE each get 12 1st downs in a game against each other. PIT's 1st downs come as 6 separate bunches of 2 consecutive 1st downs followed by a punt. CLE's 1st downs come as 2 bunches of 6 consecutive 1st downs resulting in 2 TDs. CLE's remaining drives are all 3-and-outs followed by a solid punt. Each team performed equally well-same yards, first downs, turnovers, kicking etc. But the random "bunching" of successful events gave CLE a 14-0 shutout.

That's what I mean by luck. There are several other factors that could be considered random, but my theory is that the bunching effect could explain the bulk of the observed differences in in-game performance and ultimate outcomes.

Luck and the Model of Team Wins

So when I rank teams by luck, I am using my prediction model to estimate how many games a team would be expected to win given their on-field performance. If they win more than expected, I say they're lucky. If they win less, they're unlucky.

Admittedly, the model can't possibly account for every possible consideration on the field of play. There are too many moving parts and inter-dependencies in football to just say, "whatever I don't account for must be luck." There are other factors, such as weather or coaching tactics, some of which are unmeasurable.

But we do know how accurate the model is. We know it accounts for 80% of the variance in team win totals. And there are sound techniques showing that luck, or randomness, accounts for a very large part of the 20% that's left over. That's why I call the model's residual (the difference between estimated and actual wins) luck. Or least the bulk of it is.

Coaching Tactics

Coaching tactics on gameday, such as clock management and whether to kick or go for a first down, are one of the things the model does not account for. It is a small part of the residual. When I ranked coaches on their gameday tactics I used the residual of the model. But a critic would rightfully point out the obvious contradiction--How can the residual be considered luck in one case and 'coaching tactics' in the other?

The answer is that luck is random by definition. It does not correlate with anything. So if you average out enough years of performance, the luck part of the residual tends to cancel itself out, and what's left over is non-random considerations, including coaching tactics. The more years you have in the data, the more likely it is that the luck cancels out. When there is only one year of data, the residual will still contain the luck. This isn't my own personal theory, but one of the central tenets of inferential statistics.

Further, when you have enough years of data and divide up and analyze the data by coaches, and not by something else, you get a good estimate of that coach's gameday contribution to his teams' win totals. Essentially, I'm saying other coaches, given the same on-field capability of their players, would win X many games. Coach so-and-so won on average X+1.2 games per year, so he is credited with a +1.2 "wins added wins per year" score.

A coach that takes a fantastically talented football team to a 10-6 record would not score high. But a coach that can consistently take an average team to a 10-6 record would be ranked at the top.

By the way, I call it 'gameday,' because the preparation and practice part of the coaching job would be reflected in the on-field performance stats and not in the residual. It's the 4th down decisions and such that aren't captured in the efficiency data I use.

Belichick and Cheating

After ranking all the coaches, I had expected to see Belichick at or near the top of the list. He was actually near the middle. So I split his ratings for his tenures at Cleveland and New England. His 'wins added' score was literally off the charts. It was 3 standard deviations beyond any other coach, and he never had any single year that wasn't off-the chart itself. That would make him not only a once-in-a-lifetime type of tactician, but a once-in-a-millennium super-genius.

At first I thought, wow, he really is something special. But then the cheating revelations hit, and I thought this could be due to more than just genius. In fact, it makes a lot of sense given that we already know he is willing to break rules for a competitive edge. There were many other reports of cheating by the Patriots, beyond taping signals, such as exploiting QB helmet radio communications in various ways.

I'm not saying the Patriots aren't a great team or even that Belichick isn't a great coach. They obviously are. But both things can be true. They can be both great and cheating.

A solid criticism of my approach would be that I can't just chalk up the one team that breaks my model to cheating. I'm not. I was scratching my head wondering why this one team defies the statistical tendencies of the 31 other teams. Then several weeks later, it was revealed that that same team had been cheating.

By no means do I claim that my analysis is iron-tight. I think it's useful and interesting. Feel free to disagree.

The Patriots and the Conjunction Fallacy

The conjunction fallacy is when people judge the probability of a series of events to be larger than one probability of its component events. In simple terms, many people mistakenly assign a higher probability to a specific outcome than a more general one. In football terms, this means that many fans underestimate how difficult it is for a team, even an extremely good one, to win the Super Bowl.

In my very simple poll asking if the Patriots had a better than 50/50 chance to win the Super Bowl, 64% of the (few) respondents said yes. I'd vote no, but let's look at what it would take for NE to win the championship.

The Patriots need to win three consecutive games, two of which are at home and one at a neutral site, against the NFL's top teams. What kind of win probability would they need for each game to arrive at a 50/50 chance to win it all? x * y * z = 0.50. For a rough estimate, let's assume their chance to win each game is roughly equal. Their probability would need to be 0.79 in each game. (0.79^3 = 0.50.)

This seems reasonable, but since they wouldn't have home field advantage in the Super Bowl, they would need slightly higher probabilities for the division and conference rounds of the playoffs.

Let's look at what Vegas thinks. According to a major online gambling site, NE is given 9 to 4 odds (0.69 probability) of winning the AFC championship, and 3 to 2 odds (0.60 probability) of winning the Super Bowl. They are also 13 point favorites to beat JAX this Saturday. With a 49 point over/under, 13 points roughly equates to a 0.78 probability (using this method).

There is something out of whack. A 0.60 probability of winning the Super Bowl and a 0.69 probability of winning the AFC, means the individual Super Bowl game probability must be 0.60/0.69 = 0.87. That's amazingly high. And I suspect that's where the conjunction fallacy may be having an effect. The individual game odds are incongruent with the conjunctive odds of NE winning all three games.

I'd guess there is some kind of arbitrage opportunity there for gamblers. Personally, I just think it's interesting how some people intuitively estimate the odds of future events.

My own model estimates NE has a 0.74 probability of winning this weekend. Against IND they get a 0.65 probability. But against SD they would have a 0.84 probability of winning. In total, that gives NE a 0.68 chance at appearing in the Super Bowl. To have a 50/50 chance of winning the Super Bowl, NE would need a 0.74 chance of beating the NFC representative. That would be the same chance they have against the AFC's #5 seed, a (pretty good) Florida team playing in Foxboro in January.

My own sense is that the Patriots have about a 40-45% chance of winning the Super Bowl. I would say 40, but as a dome team, the Colts would have a tougher time in Foxboro due to the January weather.

Luckiest Teams 2007

Based on opponent-adjusted generic win probability (GWP), the number of expected wins can be estimated for each team. Teams that have won more games than expected can be considered lucky, while teams with fewer wins than expected can be considered unlucky.

The list of NFL teams sorted from luckiest (positive numbers) to unluckiest is posted below. We would expect most teams to be within +/- 1.0 wins. So teams outside that margin can be deemed significantly lucky or unlucky.

Click on the table headers to sort.

Rank	Team	GWP	Actual	Expected	Luck
1	GB	0.64	13	10.2	2.8
2	SF	0.15	5	2.5	2.5
3	DET	0.28	7	4.6	2.4
4	ARI	0.35	8	5.6	2.4
5	CHI	0.29	7	4.6	2.4
6	CLE	0.48	10	7.7	2.3
7	NE	0.87	16	13.9	2.1
8	NYG	0.52	10	8.2	1.8
9	CAR	0.37	7	6.0	1.0
10	DAL	0.75	13	12.0	1.0
11	NO	0.40	7	6.4	0.6
12	SD	0.66	11	10.6	0.4
13	SEA	0.60	10	9.6	0.4
14	OAK	0.24	4	3.8	0.2
15	MIN	0.49	8	7.8	0.2
16	TEN	0.62	10	9.8	0.2
17	HOU	0.49	8	7.9	0.1
18	PIT	0.65	10	10.3	-0.3
19	BUF	0.46	7	7.3	-0.3
20	CIN	0.46	7	7.4	-0.4
21	WAS	0.60	9	9.6	-0.6
22	IND	0.85	13	13.6	-0.6
23	STL	0.25	3	4.0	-1.0
24	DEN	0.52	7	8.3	-1.3
25	JAX	0.77	11	12.3	-1.3
26	BAL	0.40	5	6.4	-1.4
27	KC	0.34	4	5.4	-1.4
28	PHI	0.62	8	9.9	-1.9
29	ATL	0.38	4	6.0	-2.0
30	TB	0.73	9	11.7	-2.7
31	NYJ	0.47	4	7.5	-3.5
32	MIA	0.34	1	5.4	-4.4