Projecting Pass Ratios: Part 2

A detailed look at what each NFL team wanted their offensive identity to be and how can use that to project this year's pass ratios

In part one of this two part series I looked at the pass ratios in a variety of game situations including score, down and distance, field position, and weather. I used this information to come up with a ranking that I called Situation Neutral Pass Ratio (SNPR) and ranked both last year’s offensive and defensive identities. In this article I will show how I use this information to project this year’s pass ratios that I use for my rankings and some insight into how I will be projecting pass ratios for this season’s daily fantasy games.

But first let’s start by discussing some interesting aspects of these rankings. As a reminder here’s the full list and how to read it. The Dallas Cowboys passed on 64.9% of their plays last year. Based on the game situations mentioned above we would have expected the Cowboys to pass only 57.2% of the time. This led to an offensive identity that was 114% of the league average. Since the league average pass ratio last year was 58.3% if the Cowboys faced league neutral situational plays they would have passed at a situational neutral pass ratio (SNPR) of 66.2% which was 2% lower than what they passed at.

Team Actual Pass Ratio Expected Pass Ratio Offensive Identity League Avg Ratio Situation Neutral Pass ratio Difference
DAL 64.9% 57.2% 114% 58.3% 66.1% -2%
CLE 67.9% 59.9% 113% 58.3% 66.0% 3%
DEN 60.6% 53.5% 113% 58.3% 66.0% -8%
ATL 68.7% 60.6% 113% 58.3% 66.0% 4%
MIA 65.3% 58.4% 112% 58.3% 65.2% 0%
NO 62.9% 56.4% 111% 58.3% 65.0% -3%
PIT 61.5% 57.5% 107% 58.3% 62.3% -1%
KC 57.3% 53.9% 106% 58.3% 62.0% -7%
IND 61.2% 58.1% 105% 58.3% 61.4% 0%
BAL 61.2% 58.9% 104% 58.3% 60.6% 1%
DET 59.7% 58.1% 103% 58.3% 59.8% 0%
ARI 59.3% 58.4% 102% 58.3% 59.2% 0%
CIN 57.0% 56.3% 101% 58.3% 59.0% -3%
NE 57.9% 56.6% 102% 58.3% 59.6% -3%
NYG 61.5% 61.1% 101% 58.3% 58.6% 5%
CHI 60.3% 60.1% 100% 58.3% 58.4% 3%
GB 56.8% 57.6% 99% 58.3% 57.5% -1%
HOU 62.0% 62.6% 99% 58.3% 57.7% 7%
Jax 62.9% 64.6% 97% 58.3% 56.7% 11%
SD 53.4% 56.1% 95% 58.3% 55.5% -4%
CAR 51.9% 54.7% 95% 58.3% 55.3% -6%
PHI 53.0% 56.6% 94% 58.3% 54.6% -3%
MIN 58.2% 62.6% 93% 58.3% 54.2% 7%
TB 57.2% 60.5% 95% 58.3% 55.1% 4%
STL 56.0% 59.6% 94% 58.3% 54.8% 2%
WAS 59.0% 63.9% 92% 58.3% 53.8% 10%
TEN 55.2% 60.2% 92% 58.3% 53.5% 3%
OAK 56.5% 62.8% 90% 58.3% 52.4% 8%
SF 47.8% 53.3% 90% 58.3% 52.2% -9%
SEA 47.3% 52.7% 90% 58.3% 52.3% -10%
BUF 51.0% 57.6% 89% 58.3% 51.6% -1%
NYJ 51.6% 61.6% 84% 58.3% 48.8% 6%

Here is the same table but for the defensive side of the ball.

Team Actual Pass Ratio Expected Pass Ratio Defensive Identity League Avg Ratio Situation Neutral Pass ratio Difference
MIN 61.0% 56.3% 109% 58.3% 63.2% -3%
NYJ 59.8% 55.2% 108% 58.3% 63.2% -5%
ARI 64.5% 59.6% 108% 58.3% 63.0% 2%
WAS 55.4% 52.2% 106% 58.3% 61.8% -10%
SD 60.7% 57.8% 105% 58.3% 61.1% -1%
NYG 58.4% 56.3% 104% 58.3% 60.4% -3%
CIN 61.3% 59.8% 103% 58.3% 59.7% 3%
DET 61.6% 60.5% 102% 58.3% 59.3% 4%
CAR 62.8% 61.8% 102% 58.3% 59.2% 6%
CLE 58.3% 57.4% 102% 58.3% 59.1% -1%
PIT 58.3% 57.5% 101% 58.3% 59.1% -1%
PHI 60.8% 60.1% 101% 58.3% 59.0% 3%
OAK 57.1% 56.9% 100% 58.3% 58.5% -2%
DAL 60.1% 60.0% 100% 58.3% 58.3% 3%
TEN 56.6% 56.5% 100% 58.3% 58.3% -3%
BAL 57.5% 57.5% 100% 58.3% 58.2% -1%
GB 57.1% 57.3% 100% 58.3% 58.1% -2%
jax 53.5% 54.1% 99% 58.3% 57.7% -7%
DEN 60.9% 61.7% 99% 58.3% 57.5% 6%
TB 57.0% 58.0% 98% 58.3% 57.3% -1%
MIA 56.2% 57.5% 98% 58.3% 57.0% -1%
BUF 56.6% 58.0% 98% 58.3% 56.9% -1%
NE 57.8% 59.4% 97% 58.3% 56.8% 2%
KC 60.6% 62.8% 96% 58.3% 56.2% 8%
STL 56.7% 58.9% 96% 58.3% 56.1% 1%
ATL 54.6% 57.0% 96% 58.3% 55.9% -2%
IND 55.4% 57.8% 96% 58.3% 55.8% -1%
HOU 53.2% 55.6% 96% 58.3% 55.8% -5%
CHI 52.8% 55.2% 96% 58.3% 55.7% -5%
SF 59.8% 62.6% 96% 58.3% 55.7% 7%
NO 58.0% 60.7% 96% 58.3% 55.7% 4%
SEA 58.6% 63.8% 92% 58.3% 53.5%

9%

The ordering of the offensive identity makes a lot more sense to me than simply looking at the actual pass ratios. For example, the two biggest extremes we see with these offensive identities is that Denver ranks third in SNPR where as with standard pass ratios they only rank 12th. On the other hand, the Jaguars ranked 5th in actual pass ratio but only rank 19th in SNPR. In both cases these adjusted rankings make a lot more sense to the public’s perception of their offensive identities.

One ranking that surprised many is Cleveland as the number two ranked team by SNPR. Unfortunately for Johnny Manziel I don’t see this continuing with Mike Pettine and Kyle Shanahan taking over the coaching duties and Josh Gordon potentially missing the entire season. We also see that Jacksonville was put in situations where they should have led the league in pass ratio but they didn’t want to pass nearly enough to keep up with the situations they were in. That’s what happens when you are relying on Blaine Gabbert and Chad Henne to anchor your team’s passing game.

 On the defensive side we also see the ranking coincide more with our general ideas of how afraid teams are to pass on each other. For example, we see the Seattle Seahawks are the team that everyone was really afraid of passing on. Their SNPR was at a league low 53.5%. If we were to look only at the actual pass ratios you would think no one was afraid of them since their standard pass ratio was 11th. The San Francisco 49ers were in very nearly the same situation ranking 28th in SNPR but 10th in actual pass ratios. On the other hand, the Washington Redskins rank 2nd in SNPR but only 25th in pass ratio.

How can we use this information to help us predict next year’s pass ratios?

In order to see how well these numbers predict next year’s actual pass ratios I ran a regression analysis using two inputs - prior year’s actual pass ratio and prior year’s SNPR. Next year’s actual pass ratio was my output. Here was the best fit line:

Next year’s pass ratio = .30 - .20 * actual pass ratio + .25 * SNPR

Here we can see that the prior year’s SNPR is highly significant in predicting next year’s actual pass ratio. We also see that the actual pass ratio is also significant. The significance of actual pass ratio tells us that at least some of the game situations that teams are in are consistent from year to year. This makes sense because teams that are good one year tend to be the same teams that are good the next year. This is partially why actual pass ratios are more consistent than expected when new coaches take over. No matter how much they want to change the offensive identity of the team the game situations may not let them.

However, we can actually do much better at predicting pass ratios because we already know what some of these key game situations are. Most notably,

  1. We know how close every game should be this year since Las Vegas has released point spreads for every game through week 16.
  2. We can easily get historical weather data for every stadium for every month of the year.
  3. We know the defensive SNPR’s and the expected pass ratios against each of these defenses.
  4. We know if teams have new coaches who want to change their identity

I took all of these data points and first sorted them by teams that had the same coaches as last year and those that had new coaches. With this data I ran a regression to predict the pass ratio for every game through week 16.

As expected the regression results are a lot more accurate for coaches that stayed the same from year to year. The biggest difference between the two regressions is that new coaches let the opponent’s defense dictate their strategy three times more often than existing coaches did. This may be an effort of over planning for opponents the first year on the job or not trusting your own team as much a coach that has been around the team longer. But for new coaches you may want to pay more attention to match ups than normal

However, the new coach sample size is much smaller and not nearly as accurate so I didn’t use that regression. Instead I manually adjust the SNPR numbers from last year to what I would project this year and plugging the adjusted SNPR’s into the formula. We will talk about this adjustment process at the end of the article. For now I have left the original values in the table below.

In my rankings I use the specific weekly pass ratios as a starting point but for illustrative purposes I took a simple average of each week’s game and arrived at the following pass ratios for each team. Here’s how you can read the table which is sorted by projected pass ratio. The Atlanta Falcons are projected to pass the ball 63.0% of the time over the course of the entire season and 63.5% of the time during the fantasy playoffs of weeks 14-16. In both cases this is the league’s highest ratio and they also ranked number 1 in pass ratio last year.

Team Projected Season Pass Ratio Projected Playoff Ratio Season Rank Playoff Rank Prior Year Rank
ATL 63.0% 63.5% 1 1 1
DAL 61.2% 60.7% 2 2 4
MIA 61.2% 60.5% 3 4 3
NO 60.7% 60.0% 4 6 6
Jac 60.4% 58.9% 5 8 5
CLE 60.3% 60.6% 6 3 2
NYG 59.7% 58.4% 7 11 8
DEN 59.2% 58.3% 8 13 12
ARI 59.0% 60.2% 9 5 15
IND 59.0% 58.7% 10 9 11
PIT 58.5% 59.8% 11 7 9
BAL 58.5% 56.4% 12 21 10
WAS 58.4% 57.7% 13 15 16
DET 58.2% 56.6% 14 19 14
OAK 58.2% 58.4% 15 12 23
HOU 58.1% 57.6% 16 16 7
CHI 58.1% 56.6% 17 18 13
STL 57.6% 55.3% 18 26 24
MIN 57.5% 57.3% 19 17 17
TB 57.4% 58.7% 20 10 20
NE 57.1% 56.5% 21 20 18
KC 57.0% 56.1% 22 22 19
GB 56.9% 55.6% 23 23 22
CIN 56.4% 55.3% 24 24 21
SD 56.0% 58.0% 25 14 26
TEN 56.0% 54.0% 26 28 25
PHI 55.2% 55.3% 27 24 27
CAR 54.7% 53.7% 28 29 28
NYJ 54.0% 53.5% 29 31 29
BUF 53.4% 54.3% 30 27 30
SF 52.9% 52.9% 31 32 31
SEA 52.4% 53.6% 32 30 32

The first thing that stands out to me is the Atlanta Falcons are far and away projected to be passing on the highest percentage of plays in both the regular season and the playoffs. This is due to several reasons. First, of all based on the Vegas odds nearly every one of the Falcons games is projected to be a tossup game which drives a lot of passing. Additionally, we see that the Falcons are going to be playing the majority of their games in favorable weather, including 11 games in domes. Their week 14 game in Green Bay is the only game that causes much weather concern. Finally, they were the only team that ranked in the top 10 in both NSPR and expected pass ratios ranking 4th, and 8th respectively.

If these pass ratios hold up this could lead to big things for Matt Ryan especially if Steven Jackson can’t carry a full load of carries at age 29. But even more importantly, this could make Julio Jones and Roddy White very intriguing targets since they no longer have to share their receptions with Tony Gonzalez. I could also see bigger than expected things out of Harry Douglas especially if Julio Jones isn’t able to make it back to full strength.

We also see a pretty big increase in passing ratios by the Arizona Cardinals who move from 15th last year to 9th for the season. But even more importantly they move up to 5th for the fantasy playoffs. This could be great news for QB streamers who want to get great value out of a late round pick of Carson Palmer as part of their QB by committee. Maybe this will also be the year that Larry Fitzgerald comes back to being Larry Fitzgerald now that he has another year of Palmer and the emergence of Michael Floyd taking some of the pressure off of him. But, don’t right off Andre Ellington just yet. Remember one of Ellington’s strengths is being involved in the passing game so he could be a very nice value in PPR settings.

But the best way you can use these numbers is as a starting point for your projections which you can adjust by what you think teams NSPR’s should be. The formula I used says that prior year NSPR should be multiplied by .4 in order to project this year’s pass ratios. So we can use this same process to see how much affect changes in their offensive identity may have.

For example, the biggest decrease in expected pass ratio is the Houston Texans. However, the change in their coaching staff to Bill O’Brien and change in quarterbacks may depress these ratios even further. Last year the Texans NSPR was 58.1% which is about league average. However, let’s say we really think they want to pass at a 54% rate. This is a 4% decline in NSPR and if you multiply that by .4 it means their expected pass ratio will actually be 1.6% lower than that calculated above. Now we can simply subtract 1.6% from 58.1% to see their new expected pass ratio to be 56.5% which would move them all the way down to 24th. A far cry from their ranking of 7th last year.

Using this approach I will probably be staying away from Andre Johnson in most of my drafts. Arian Foster might represent good value on quantity alone but that means he would have to stay healthy for a full season as well as an increased work load. I am not so sure I would bet on that. This makes me like the prospects of Andre Brown as a good late round sleeper with some upside.

The projections above act as good starting points for all pass ratios but should be carefully adjusted for team personnel and coaching changes. When determining who you are going to draft this year I encourage everyone to really think about what their team’s offensive identity and how it will be affected as the season progresses. Remember you aren’t just drafting the player but also his teammates, coaches and expected game situations. Who do you think will change their offensive identity the most this year? Let me know on twitter @stevebuzzard.


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