Team Efficiency Rankings - Week 4

The team rankings below are 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.

RANK	TEAM	GWP	Opp GWP	O RANK	D RANK
1	IND	0.80	0.49	1	11
2	NO	0.71	0.47	2	7
3	DEN	0.68	0.34	7	15
4	PHI	0.65	0.50	10	2
5	NYG	0.63	0.47	8	3
6	BAL	0.60	0.41	6	18
7	NYJ	0.59	0.49	23	1
8	DAL	0.59	0.44	3	30
9	SD	0.59	0.45	4	23
10	PIT	0.59	0.50	9	16
11	TEN	0.56	0.55	18	5
12	JAC	0.53	0.59	17	13
13	GB	0.52	0.41	16	20
14	CHI	0.51	0.51	14	8
15	ARI	0.50	0.60	20	9
16	ATL	0.49	0.42	12	31
17	WAS	0.49	0.40	15	24
18	SF	0.47	0.46	22	12
19	BUF	0.47	0.50	24	17
20	HOU	0.46	0.56	5	32
21	NE	0.46	0.52	11	19
22	MIN	0.44	0.34	29	21
23	CIN	0.43	0.59	13	14
24	SEA	0.43	0.42	21	28
25	MIA	0.42	0.63	26	4
26	KC	0.42	0.53	19	26
27	CAR	0.37	0.58	31	6
28	OAK	0.34	0.56	32	10
29	TB	0.33	0.56	28	25
30	DET	0.30	0.55	25	27
31	STL	0.29	0.48	27	29
32	CLE	0.24	0.57	30	22

Raw team efficiency stats are listed below.

TEAM	OPASS	ORUN	OINT%	OFUM%	DPASS	DRUN	DINT%	PENRATE
ARI	6.0	3.2	3.1	2.7	7.2	3.1	1.8	0.45
ATL	6.8	3.4	1.1	1.4	6.2	4.7	1.8	0.34
BAL	7.5	4.7	1.9	1.1	6.7	2.5	6.3	0.48
BUF	5.5	5.3	2.2	0.7	6.0	4.4	2.3	0.61
CAR	4.9	4.3	7.4	2.1	5.8	5.4	2.2	0.33
CHI	6.8	2.8	5.0	0.7	5.1	3.9	1.9	0.44
CIN	5.6	4.2	4.3	1.4	6.7	4.0	1.0	0.44
CLE	4.2	3.3	6.5	0.8	6.8	5.4	0.0	0.42
DAL	7.7	6.8	3.4	0.0	6.9	4.7	1.8	0.47
DEN	7.1	4.7	0.0	0.6	4.3	3.3	5.9	0.33
DET	5.2	3.5	4.9	1.3	7.7	4.5	1.9	0.48
GB	6.1	4.1	0.0	0.0	6.3	3.9	7.4	0.46
HOU	7.1	3.3	1.9	2.2	7.2	6.3	2.1	0.37
IND	9.9	3.5	2.1	1.4	4.7	4.3	2.6	0.28
JAC	5.7	5.1	1.0	0.7	7.8	3.7	1.9	0.31
KC	5.5	3.6	2.4	1.4	6.9	3.8	1.0	0.47
MIA	4.6	4.7	3.2	0.6	8.2	3.0	0.0	0.23
MIN	5.0	4.8	1.0	1.3	5.1	3.5	4.4	0.36
NE	6.0	4.0	1.4	0.0	6.4	4.0	0.0	0.41
NO	7.9	5.0	2.1	1.2	5.6	3.2	5.6	0.37
NYG	7.9	4.0	1.1	0.6	4.4	6.1	6.2	0.38
NYJ	6.3	3.8	2.4	1.9	4.3	3.9	3.4	0.43
OAK	4.3	3.9	5.2	2.5	6.1	4.4	3.1	0.37
PHI	6.6	4.4	3.4	1.3	4.8	3.6	6.9	0.37
PIT	6.9	3.3	3.7	0.6	5.5	3.8	0.9	0.45
SD	7.8	2.8	2.6	0.0	5.8	4.5	4.4	0.43
SF	5.4	4.2	1.2	1.4	5.5	3.0	3.1	0.39
SEA	5.9	4.0	3.3	1.2	5.3	5.6	1.1	0.30
STL	4.6	4.9	1.0	1.6	7.8	4.3	2.1	0.45
TB	5.3	4.3	2.5	1.6	8.4	5.2	1.2	0.42
TEN	5.5	5.7	3.8	0.0	6.9	2.2	2.7	0.27
WAS	6.9	4.0	1.9	0.7	6.1	4.4	1.1	0.45
Avg	6.2	4.2	2.7	1.1	6.2	4.1	2.8	0.40

A New Academic Study on Game Theory and Run-Pass Balance

There’s a new study on run-pass balance based on game theory minimax equilibrium. The study is called Professionals Do Not Play Minimax: Evidence from Major League Baseball and the National Football League and it’s from Kenneth Kovash and Steven Levitt (of Freakonomics fame).

The authors created their own version of  Expected Points as their measure of play success. Using a giant regression model that accounts for all sorts of confounding variables, they find passes lead to more success than runs. Game theory would say that, ideally, both strategies should yield the same amount of success.

Worst NFL Commentary of All Time

Whatever the former worst commentary was, Chris Berman just shattered the record. Sunday night, basking in Brett Favre's miraculous game winning pass, Berman reflected on Favre's year in New York. He said that the Jets' current success should be attributed to Favre's presence there a year ago. He said, "Favre taught them...He taught them about winning."

He actually said that.

If I were a member of the Jets organization, I'd be extremely offended. Is there no shame from the Favre-ophiles? Is there no limit to their inanity? Are we really expected to believe that this single human, well known for his insistence his job was not to teach his younger teammates, is responsible for three wins by a former team? This is a team with a new coach, mostly new staff, new rookie quarterback, and a significantly upgraded defense. It's a team of professional athletes and coaches accustomed to winning their entire lives.

Zorn Is My Hero 2

Jim Zorn may have his hands full managing the Redskins, but I'll give him credit for his courage. Last week, I made the case that his two daring 4th down decisions on the final drive were the right calls. This week, before the sun had even set on the day his team couldn't beat the lowly Lions, he was excoriated for two more controversial decisions.  In this post, I'll examine if he made the right calls in Detroit.

Roundup 9/26

The roundup is back. Here are some interesting links relevant to NFL analysis. I've been negligent in keeping up with this feature, so although there are some items that are a few months old, they're still good reads.

Time of Possession

Last night’s Colts-Dolphins game was a statistical anomaly. Miami had the ball for over three quarters of the game and yet lost. Time of Possession (TOP) is viewed by some as one of the keys to victory in football, but I’ve got a different take.

Coincidentally, just yesterday I received an email from a reader asking me if I had done much research on TOP stats. I haven’t done much, and I’ll explain why.

I believe TOP is an “intermediate outcome” in a football game. In other words, it is a natural byproduct of being good at something else. You can’t be good at “time of possession.”

Jim Zorn on 4th Down

Zorn is my hero today. On the Redskins’ final drive of their game against the Rams yesterday, head coach Jim Zorn went for it on 4th down not once, but twice. The network commentators were shocked, and the local media coverage has been decidedly critical. Were they good decisions?

The first decision “felt” right to me. Up by 2 points with 3:47 left in the 4th quarter, Washington faced a 4th and 1 at St. Louis’ 20 yd line. A FG attempt from there is an 84% proposition. A kickoff with a 5-point lead gives the Redskins a 0.76 Win Probability (WP). A missed FG attempt gives the ball to the Rams at the 27 and leaves the Redskins with a 0.56 WP. The net WP for the FG attempt is:

Exciting Sunday So Far

I'm a Comcast subscriber so I'm loving my Red Zone Channel, and I highly recommend it. The most exciting games of the day (according to WP) were the Baltimore-San Diego game and the Oakland-Kansas City game. Both came down to the last drive. The biggest comebacks were in the Houston-Tennessee and Pittsburgh-Chicago games. Chicago's comeback wasn't the classic come-from-behind type. But they were looking at a 10% WP until the missed field goal attempt.

Live WP for the New York-Dallas game.

The 4th Down Study - Part 4

The is the fourth and final part of my article on 4th down decisions. In the first part, I reviewed the concept of Expected Points and the concept of expected utility. The second part detailed the kicking game and its expected values. The third part explored the value of 4th down conversion attempts. This, the final part of the article puts all the concepts together. I also discuss some of the explanations for why coaches are so reluctant to go for it when they should.

The 4th Down Study - Part 3

The is the third part of a four-part article on 4th down decisions. In the first part, I reviewed the concept of Expected Points and the concept of expected utility. The second part detailed the kicking game and its expected values. This, the third part explores the value of 4th down conversion attempts. The final part of the article will put all the concepts together. Ultimately, I'll conclude with a chart of recommended decisions for 4th downs at every combination of field position and distance to go.

The 4th Down Study - Part 2

The is the second part of a four-part article on 4th down decisions. In the first part, I reviewed the concept of Expected Points and the concept of expected utility. This part of the article, details the kicking game. The third part will explore the value of 4th down conversion attempts. The final part of the article will put all the concepts together to ultimately produce a chart of recommended decisions for 4th downs at every combination of field position and distance to go.

The 4th Down Study - Part 1

If there's one topic where quantitative analysis can change the way football is played, it's 4th down decision-making. Many articles here have chronicled the conservative nature play-calling on 4th down in the modern NFL. In this post I'll explain, as clearly and simply as possible, why the evidence points to a more aggressive attack on 4th down.

Previous studies on 4th down decision-making include Carroll, Palmer, and Thorn's book Hidden Game of Football (1988, 1998) and Professor David Romer's Do Firms Maximize? (2005). The first serious study of the concepts used in these studies was by former NFL quarterback Virgil Carter, who co-authored an operations research paper examining the value of field position using data from the first 56 games of the 1969 season.

My own analysis published in this post largely repeats the methods used in previous studies. But I think I can add a good deal to the topic. First, this analysis is based on a much larger data set compared to previous research. Second, this analysis offers possible confirmation of previous results. Third, I think I can explain a complex, abstract subject such as this in a straightforward manner, which is essential if the 4th down revolution is going to make any headway. Frankly, it doesn't matter how strong the analysis is if it can't be communicated clearly and convincingly.

This is how the study goes: At each yard line, I'll calculate and compare the expected point value, based on recent historical averages, of each of the three 4th down options--punt, field goal, or go for it. The option with the highest value is the recommended choice.

Hawks, Doves, and Home Field Advantage

Sports researchers have been studying home field advantage for decades. It’s a universal phenomenon found in virtually every sport, and professional football is no exception. Home teams win 57% of all regular season games in the NFL. Measuring it is easy. The question is, what causes it?

Several studies have tested theories about crowd noise, referee bias, time zone effects, climate, and peculiarties of ballparks. But these effects have not been shown to account for much if any of HFA.

Some recent research looked at when HFA manifests itself in games. In the NBA, HFA (or HCA rather)  is strongest in the beginning of the game and then diminishes as it goes on. I found the same phenomenon in the NFL. The first quarter shows the strongest HFA by far. Now, baseball research reveals the same phenomenon. It’s also been shown that HFA in the NFL is strongest between inter-conference games and weaker between intra-divisional games. With the advent of inter-league play, baseball also appears to have the same tendency.

I think these findings all point toward the same theory, namely that a significant portion of HFA comes from environmental familiarity. I’m not talking about the quirks of an outfield or the type of turf in a stadium. I’m talking about the whole picture—the way we all feel comfortable when we’re in familiar surroundings and often feel anxious in strange places.

I think that game theory can help explain why this is the case. I’m not referring to the usual run-pass or fastball-curve game theory we talk about in sports. Instead, I’m talking about natural selection and behavioral evolution. I realize this sounds a little out-there, but bear with me.

Live In-Game Win Probability 2.0

Live Win Probability graphs are back with tonight's season opener. This year, the graphs have more features, are easier to read, and update much quicker compared to the 2008 version. They feature the new format debuted earlier in the year for NBA and NHL.

For those of you new to the site, here is a primer on what you'll see. The graph charts the probability each team will win the game play by play. The closer the line gets to the top of the graph, the more likely the visiting team will win. The closer it gets to the bottom of the graph, the more likely the home team will win. Think of it as an NFL Richter scale or maybe a cardiogram of a 'football heart attack.'

More on the Cost of Interceptions

In a recent post I looked at the cost of interceptions in terms of equivalent yards and expected points. In this post, I'll look at them in terms of win probability added (WPA).

In a comment on my Fifth Down post regarding the context of Jay Cutler's 2008 interceptions, Will wrote:

"I know you can calculate a change in WP for a given play; this is how you came up with the best plays of the year for last season. Can you also calculate an average change in WP for a type of play? For example, can you find the average change in WP for a Cutler interception vs. a Favre interception vs. the league average, to see who throws more bad picks? I've long felt that many of Favre's interceptions equate to punts, as he throws it up deep on a late-down, long-yardage situation. By the same token, it might be good to know which passers have the highest delta-WP per attempt, or which rushers most change their team's fortunes per rush."

You can read my response in the original post, but I'll expand on it here. Will was getting a little ahead of me because I'm planning on publishing some neat stuff on individual player WPA (win probability added) later this season.

To make things a little easier on myself, I'll cite Bronco and Jet passing game numbers from 2008, not necessarily Cutler and Favre, but I think they're identical for practical purposes. I'll also calculate the league average.

Denver's 18 INTs cost a total of -1.56 WPA (or, in a sense, lost 1.56 games ). That averages to -.087 WPA/INT.

New York's 23 INTs cost a total of -2.49 WPA. That averages to -.108 WPA/INT. You could say that the Jets' interceptions were about 20% more costly than the Broncos' last year.

For reference, there were 465 INTs in the league in 2008, costing a total of -46.98 WPA. That averages to -.101 WPA/INT. So on average, an interception costs a team a 10% chance of winning. An interception equates to 3.8 points, 60 yards, or 10% WP lost.

Interceptions are not typically similar to punts. I've read elsewhere (I think at footballcommentary.com) that interceptions are on average returned to about the line of scrimmage. My data shows something slightly different. Interceptions are on average returned to within 8.1 yds of the original line of scrimmage. Removing the 2nd and 4th quarters from the data to account for Hail Mary interceptions, it's 7.2 yds. So, I suppose you could consider a typical interception like an incomplete pass and then a really, really short punt.

But WPA for any particular interception is dependent on a number of factors. Field position and score are obviously critical, so here is a graph of interception WPA by field position, broken out by selected score differences. Despite the noise in the graph, there are some points to be made.


It's interesting how the WPA drops the steepest for when a team is down by 3 points (the red line). Throwing a pick deep in one's own territory when up by 3 points is nearly equally as costly. The bigger the difference in score, whether ahead by a lot or down by lot, the smaller the impact of the interception. The graph makes sense (at least to me)--it's what I'd intuitively expect.

Time is also critical. So here is the same graph, except limited to only 4th quarter interceptions. (WPA is a function of many things--score, time, fld position, down, to go distance--that I can't show everything on a single graph.) It's a little noisier, so I grouped the field position by 20-yard chunks instead of 10.


Again, we see what we'd expect. The tighter the score, the more costly the interception. Tied or up by 3 in opponent territory is where they're the costliest.

Adjusting Adjusted Yards Per Attempt

Reader Jeff Clarke sent me an email a few weeks ago asking about the interception yardage value used for the Adjusted Yards Per Attempt (AdjYPA) passing statistic. AdjYPA is total passing yards minus 45 yds for every interception thrown, divided by total attempts. It's a really handy stat because it encapsulates passing performance as a simple, single number, and better still, it's a rate stat.

The 45 yard adjustment number comes from the 1988 book Hidden Game of Football. The authors don't fully explain how they arrived at that figure, but I gather it was based on an analysis based on expected points. They do however, make a good intuitive case for it. An interception can always be thought of as costing any chance at a first down and precluding a punt. Punts net between 35 and 40 yds, and forfeiting a chance of the first down costs and extra few yards, which together comes to about 45 yds. Perfectly reasonable...for 1988.

Fast forward 21 years and the passing game, and offense in general, has become more potent. With offenses being more efficient, the value of having the ball is therefore greater, and turnovers would accordingly be more costly.

Jeff Clarke made a great observation. He wrote,"your own 15 yard line is the point of indifference. Holding everything else neutral, you are indifferent between having the ball at your own 15 and your opponent having it at his 15. Doesn’t this mean that the penalty for throwing an interception on first down should be 70 yards – the distance between the 15s?" (Expected Point curve below).


Jeff went on to point out that the cost of an interception would be less on 3rd and 15 than say, 2nd and 1 because the expectation of a first down is different for each situation. Jeff's analysis predicts that the true yardage equivalent would be something shy of 70 yds--the distance between the 15 yd lines. I was convinced to dig a little deeper.

The average difference between interception plays and non-interception passes is 3.81 expected points. This is the weighted average for all plays on 1st, 2nd, and 3rd downs for all yard line. It accounts for return yards, and down & distance situation. I excluded 4th down passes, as those are often thrown in desperation situations, where high levels of risk are acceptable and the cost of the interception is not much different than a simple incomplete pass.

3.81 points equates to approximately 60 yards of field position. The graph below plots it nicely. The green line is the EP for non-interception plays, and the blue line is fit to the EP following interception plays.


EP is roughly linear away from the end zones. So if we look at the expected points graph for non-interception plays (the green line), +3.8 EP is at the opponent's 20 yd line. And the 0 EP point intersects at a team's own 20 (80 yds from the end zone on my graph). That's a difference of 60 yds.

60 passing yards is the modern interception equivalent of an interception, not 45.

The Value of a Touchback

This season will be the first that the Baltimore Ravens will start the year without place kicker Matt Stover. Stover has been a reliable fixture for the franchise for its entire existence. He's known for his reliable medium-range accuracy, but his field goal range and, possibly more importantly, his kickoff distance dwindled in recent years. Last year's kickoff specialist Steven Hauschka will now take over as the full-time field goal kicker.

Keeping a kickoff specialist on the roster has become somewhat fashionable in the NFL, but I'm not sure when the trend started or exactly how many teams do it. It's an expensive thing to do, not just in terms of salary, but in terms of a roster spot too. If you've read John Feinstein's Next Man Up, you know how precious every spot is for the coaches, and how difficult the weekly decisions are about who to dress for each game. A kick-off specialist is a costly luxury.

But maybe we're thinking about this backwards. Maybe we should ask whether it's worth it to have a field goal specialist.

Assessing FG Accuracy

It's been shown here and elsewhere that FG kickers are very hard to tell apart from one another. I have no doubt that it takes great skill and countless hours of dedication be as good as NFL kickers are. However, almost all kickers at the professional level can be considered statistically as accurate as any other.

By "statistically accurate" I mean that accounting for small sample size, environmental variables, and attempt distance, it is virtually impossible to tell one kicker apart from another. A kicker who is highly accurate one year is not likely to be as accurate the next. A big part of this variability in accuracy can be attributed to what's known as 'sample error.'

Typically, NFL FG kickers have between 30 and 40 attempts in a season. Think of baseball batters' averages after only 40 at bats, which would be about 8-10 games into the season. By this point some replacement-level guys are batting .500, and some future Hall of Fame sluggers are batting .100. But absolutely no one thinks the batters are truly .500 or .100 hitters. It's just a matter of a small statistical sample, which makes it impossible to really assess individual batting skill. And if batting had a wrinkle similar to FG attempt distance, it would be even harder to assess skill.

To me, it's absolutely laughable that some teams' kicker jobs are decided by pre-season contests based on maybe 4 or 5 attempts per kicker. I can only hope that coaches are really making these decisions based on many more attempts in practice.

The point is that we need dozens and dozens of attempts, from various distances and in various conditions, just to begin to be able to tell one FG kicker apart from another. And I bet that if we actually could tell good kickers from lesser ones, a very large part of the difference would be due to range.


So if range is important to both kinds of kicks, wouldn't a team prefer the guy with the deeper kickoffs? Plus, range is something we can actually measure. I can't definitively prove my point of view in a single post, but I can begin to look at some aspects of the value of deep kickoffs. In this post, I'll look at the value of something I think is often overlooked: the touchback.

The Value of a Touchback

About 10% of all NFL kickoffs (not including onside kicks) are touchbacks. Forcing the opponent to start at their own 20 doesn't exactly seem like a death blow, but it is modestly valuable.

The average starting position following all kickoffs (including penalties on the play) is the 30 yd line. But the average starting position for all non-touchback kickoffs is the 32. The difference between a touchback an non-touchback is 12 yds. If the 32 seems a little far down the field to you (like it does to me), it's because the median starting field position for non-touchbacks is the 27 yd line.

Here is the distribution of starting field position for non-touchback kicks.


[A couple of interesting notes. First, the spike at the 60 (a team's own 40 yd line) is from kicks out of bounds. Second, I think it's interesting that of long returns, there are many more that make it to the opponent's 30 or 20 or so than make it only to just past midfield. Then, if a returner makes it past the 20, he's probably going to make it all the way to the end zone.]

Back to touchbacks. Using the concept of Expected Points (EP), the average point value of a first down at each field position(see graph below), we can estimate the nominal value of a touchback. The 20 yd line represents 0.1 EP, and the weighted average of the distribution of non-touchback field position is 0.9 EP. That's a value of 0.8 EP per touchback. (This includes turnovers and penalties.)

Sacks are worth 1.7 EP, so a touchback could be considered the equivalent of about half a sack.


An alternative way of thinking of those 12 yards is to think of them as one additional first down required for a team to score. It's one more first down the offense will need to either score a TD or get into FG range. The average first down conversion rate in the NFL is 67%, so a touchback turns a TD drive into a FG drive or a FG drive into a punt 33% of the time.

We can also use the concept of win probability to asses the value of a touchback. Over the past 9 seasons, non-touchback kicks average a change of 0.002 in WP. Touchback kicks average an increase of 0.01 WP. The net value of a touchback is therefore an increase in 0.008 WP, or about 1%. One percent isn't much at all, but with about 5 kickoffs per game for each team, the effect can add up.

The WP added (WPA) of any given kickoff depends on the leverage of its particular game situation. With the game close and time dwindling, a touchback or deep kick can make a 2-minute offense that much harder for the offense.

The biggest two touchbacks in my database (going back to 2000) were each for 0.13 WPA. Kicker Steve Lindsay, who played only two seasons in the NFL, was picked up by Denver from Jacksonville halfway through the 2000 season. With the Broncos Trailing 37-31 to the Chargers and 4:05 left in the 4th quarter, Lindsay boomed the touchback heard 'round the world (not exactly--but it should have been.) Field position in this situation was critical. A FG by San Diego would have clinched the game, and Denver needed the ball back in as good field position as possible. As fate would have it, the Broncos went on to win the game 38-37.

Arizona kicker Neil Rackers owns the other touchback of the decade. In a 2007 game against Seattle, tied 20-20 with 4:53 remaining in the 4th, Rackers' touchback made it that much more difficult for the Seahawks to put together a game-winning drive, and would have made Arizona's own drive that much easier. What actually happened was that Seattle fumbled on a 1st and 5 from the Arizona 36, allowing the Cards to put together a FG drive to win the game. Sure, the game turned on a turnover and not field position, but had Seattle found itself on the Arizona 24 and not the 36, maybe the play call would have been a little safer. We'll never know.

Anyway, those are my two nominations for the touchback hall of fame. It's not the most glamorous play in football, but it's certainly overlooked and worthy of examination.

Worst 4th Down Decision of 2008

Last November, the Eagles and Bengals were both desperately trying not to win. And they both succeeded, as their game was the first to end in a tie in several years. It's hard to forget that game thanks to Donovan McNabb's comment that he was preparing for a second overtime period.

McNabb's comment aside, the game was remarkable in that it featured not one, but two of the most timid 4th down decisions in the 2008 season. In both cases, had the offense gone for the first down, it would have significantly improved its chances of winning. Note I'm not saying simply that a successful conversion would have helped the team win. I am saying that on balance, considering the chance of a failed conversion, the far wiser decision would have been to go for it.

With the game tied 13-13 and 1:56 left in the 4th quarter, the Eagles offense faced a 4th and 1 from its own 49-yard line. A punt would have made their WP 0.31. That's lower than you might think at first because handing the ball to the Bengals with two minutes on the clock guaranteed they would not have enough time to respond to a successful Cincinnati scoring drive.

A successful conversion would have given Philadelphia a tremendous advantage. With a 1st down and the ball at midfield, they would only need a few more yards to get into field goal range for the win. Conversion attempts on 4th and 1s are converted about 74% of the time. All things considered, had the Eagles lined up to go for it, their 'expected' WP would have been 0.60. That's a difference of 0.29 compared to the punt--essentially doubling their chance of winning. In terms of costing a team in its likelihood of winning, this was the single worst 4th down decision of the 2008 season.

The Eagles may not have possessed the NFL's best power running game last year, and that 4th and 1 may have been a "long" 1 yard. But to make a decisive difference, the particular details of the situation must have been so overwhelmingly disadvantageous that it's hard to believe.

It's not as though the Bengals defensive line was an impenetrable brick wall, and the Eagles did successfully convert 3rd and 1s 60% of the time in 2008, a task not much different than 4th and 1. Further, we can solve for the break-even conversion success rate. In this case, the Eagles would have needed to convert just 5% of the time for the attempt to be worthwhile.

I know what you might be thinking. Wouldn't a failed attempt at the 50 give the Bengals the identical situation that a successful attempt would give the Eagles? True, but don't forget the alternative: punting gives the Bengals the upper hand anyway.

Fortunately for the Eagles, the reason they even had the opportunity to consider a 4th down and 1 was thanks to the 13th worst 4th down decision of 2008. Cincinnati punted on the previous drive when a successful 4th down conversion would have given them a firm upper hand.

Coaches talk a lot about "momentum" when it comes to 4th down decisions. A failed 4th down attempt deflates a team and encourages the opponent. Although teams might feel that way, however, it's not clear at all this makes much difference in terms of who wins. We're talking about professional athletes with plenty of experience at many levels of play.

Besides, think of it this way: Imagine you're a Bengals defender, elated you made a stop on 3rd down while trotting triumphantly off the field. Then you realize the Eagles are lining up for an easy 4th and 1. Chances are they'll convert, and now you're lining up for a whole new 1st and 10. How's the momentum now?

Live In-Game Win Probability 2.0

The newest version of the in-game win probability site is going live tonight for the last remaining games of the preseason. You can also see the graphs from Thursday's games. I realize no one cares who actually wins any of these games, but I'd like to test it out before the real games begin. Please leave any comments or bugs on this post. A few notes below:


-For now, play descriptions aren't available until after the week's games are over. But if you hover over the graph, you'll see the down,distance, yard line, and score at the time of the play. Full play descriptions, like I have in the archive, will be my next addition.
-Some of the extra advanced stats, including 1st down probability, current expected points, and scoring probabilities are off line for now. I'll bring them back very soon.
-The WP model underneath hasn't changed, but I am working on a major upgrade to it. I like the model now, but it's very noisy and there are some situations on which it relies on sparse data. The new improvements will drastically reduce the noise, which will make the WPA estimates for smaller plays much more accurate.
-The new graphs are flash based, which are not mobile-friendly. I intend to continue the ajax/javascript version which is suited well for iPhones and Blackberries.

As always, it's wp.advancednflstats.com.