by Aaron Schatz

The time has come for our annual preseason DVOA projections, updated from the projections that gave us the season forecasts in Football Outsiders Almanac 2015.

We must start with the requisite link to an explanation of DVOA. For anyone new to our site, DVOA stands for Defense-adjusted Value Over Average and measures a team's performance on every play of the season compared to league average in the same situation, adjusted for opponent. I know a lot of people may be coming here from various message boards and this is just going to look like a jumble of pointless numbers. Trust me, there is a method to the madness, and over the past dozen seasons past DVOA ratings -- as well as these multivariable-based DVOA projections -- have been a far more accurate predictor of future performance than wins or points.

Offense, defense, and special teams DVOA are all projected separately using a system based on looking at trends for teams over the past decade. This offseason, we overhauled and improved the team projection system for the first time in a few years. The new system starts by considering the team's DVOA over the past three seasons and, on offense, a separate projection for the starting quarterback. Then we look at a number of other variables which suggest when a team will be better or worse than would otherwise be expected due to standard regression towards the mean. The biggest change is that the new system does a much better job of incorporating personnel changes. For offense, this variable is based on DYAR for non-quarterbacks, while for defense, this variable is based on the net change in Pro Football Reference's Approximate Value stat over a replacement level set at 3 AV. Other factors include coaching experience, recent draft history, combined tenure on the offensive line, and certain players returning from injury (or, in the case of these preseason updates, certain players getting injured in the preseason).

(If you would like to hear more about the changes in this year's projection system and what they mean for this year's team projections, I'll be discussing that more on Thursday on the first edition of the new weekly "Off the Charts NFL Podcast" featuring myself with Sports Information Systems' Scott Spratt. Look on the homepage for a link on Thursday afternoon!)

The team projection system does a better job of forecasting the upcoming season than just looking at last year's numbers. The most accurate forecast incorporates the team's schedule to predict win totals, as you can see from the table below.

Correlation of Various Stats/Projections with Wins and DVOA, 2003-2014 Metric Correlation

with Wins Correlation

with DVOA Wins Y-1 .307 .414 Pythagorean Wins Y-1 .347 .412 DVOA Y-1 .370 .513 DVOA Projection .539 .642 Mean Wins Forecast from

DVOA Projection + Schedule .560 .624

(Note: I apologize for not having "old projection system" in this table for comparison's sake... I couldn't find all 12 years of projections from that system in one place to paste into a spreadsheet.)

The numbers we are presenting here are exactly what the projection system spit out. As we say every year: "A few of them will look strange to you. A few of them look strange to us." As always, the offensive projections come out in a wider range than defensive projections because offense performance tends to be easier to predict (and more consistent from year to year) than defensive performance. If you are looking for subjective projections, Thursday we will be running our usual staff predictions article where we all talk about where we think the numbers are wrong.

We've also done our first playoff odds report simulation based on these updated DVOA projections, and I've added the playoff odds and Super Bowl championship odds to the table below. In past years, we always manually tweaked our preseason simulations to get a set of win-loss records closer to what you see in the actual NFL. This year, we don't need to do this because what we're calling a "dynamic" season simulation. In real life, winning teams generally see their DVOA ratings go up because they played well, and losing teams generally see their DVOA ratings go down because they played poorly. So in our simulation of the future, we now boost a team's rating by 2.0% after each win and drop it by 2.0% after each loss. The original preseason projections are by their very nature conservative, but this dynamic simulation means that when we get to the end of the season, the best teams look as good as best teams usually look. To give an example, Seattle starts the simulation with a DVOA of 23.5%. Should the Seahawks go 14-2, the simulation starts the postseason matchups with the belief that Seattle has a DVOA of 47.5%. That's a rating much more in line with what we would see from a 14-2 team.

The resulting simulation still ends up looking very conservative as far as the average number of wins and losses projected for each team. Obviously, the NFL is going to have teams that are 11-5 or better, and it is going to have teams that are 5-11 or worse. But the actual range of seasons produced by this simulation looks much more like reality, with more simulated seasons ending with double-digit wins and fewer simulated seasons ending around 8-8.

There are still a couple of important differences between this simulation and the one we did for Football Outsiders Almanac 2015. For example, this simulation only uses one set of mean projected DVOA ratings, rather than using 1,000 different sets of ratings to represent that some teams have a higher standard deviation than others. More importantly, this simulation handles early-season suspensions and injuries differently. Obviously, suspensions were a bigger issue for a 2015 simulation than in any other year, because of Tom Brady, but we also considered a number of other suspensions in the simulation we did for the book. We also considered those suspensions and injuries in this simulation, but treated them differently. To create consistency with the playoff odds simulation in future weeks, we only wanted to use a single rating for each team. For example, we give the Dallas Cowboys a single defensive rating instead of simulating Dallas games with one defensive rating for Weeks 1-4 and another defensive rating for Weeks 5-17 once Greg Hardy and Rolando McClain return from suspensions. The projected defensive DVOA for Dallas is weighted to be 25 percent of what the projection would have been if McClain were gone and Hardy had never been signed, and 75 percent of what the projection is when we add Hardy to the roster and don't remove McClain.

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Making this change means that the simulation effectively "spreads" an injury or suspension over the entire year. In the simulation from the book, New England and San Francisco got a big extra advantage from playing Pittsburgh without Le'Veon Bell. In this simulation, every team that plays Pittsburgh this year faces a slightly weaker simulated Steelers offense. The differences here are small and should generally even out over the course of the year, and for the most part, this change doesn't really affect the playoff odds or mean win projections.

Note that the FO Premium picks against the spread will use DVOA ratings that fully incorporate early-season injuries and suspensions.

For those who may be curious about a handful of unsure player situations: Jason Pierre-Paul is projected to miss half the season, just as we did in the book. Brooks Reed and Arian Foster are projected to miss four games each. Dontari Poe is projected to miss three games to make things easier, since that let us consider his injury and Sean Smith's suspension together. We had no idea what to do about Kam Chancellor, so I penalized the Seahawks as if Chancellor would miss three games. I did not penalize the 49ers for losing Ahmad Brooks, since it looks like he's going to play despite his legal issues.

Projected division champions are colored in light yellow and projected wild card teams are colored in light purple, although the difference between Baltimore and Cincinnati is so close that it's a bit absurd to say we're picking one team over the other as AFC North champions.

TEAM TOTAL

DVOA TOTAL

RANK MEAN

WINS OFF.

DVOA OFF.

RANK DEF.

DVOA DEF.

RANK S.T.

DVOA S.T.

RANK SCHED SCHED

RANK NO. 1 PICK

ODDS PLAYOFF

ODDS S.B. WIN

ODDS SEA 23.5% 1 10.7 11.8% 3 -13.0% 1 -1.1% 23 0.9% 15 0.0% 78.4% 17.0% NE 18.4% 2 10.6 12.1% 2 -1.0% 11 5.3% 1 -2.9% 25 0.1% 75.3% 13.1% DEN 14.6% 3 9.4 9.7% 5 -7.8% 3 -2.8% 32 2.6% 8 0.4% 59.6% 7.8% GB 8.5% 4 8.8 12.2% 1 1.4% 20 -2.1% 30 2.5% 9 0.9% 50.1% 4.6% STL 7.7% 5 8.8 -0.3% 17 -6.7% 4 1.4% 7 0.8% 16 1.1% 47.0% 4.0% CIN 6.1% 6 8.6 3.5% 12 -2.5% 7 0.2% 11 1.6% 11 1.2% 46.2% 3.6% PIT 6.1% 7 8.2 10.9% 4 4.1% 26 -0.7% 20 4.0% 3 1.3% 40.1% 3.1% BAL 5.5% 8 8.5 -2.2% 20 -4.2% 6 3.6% 3 1.3% 13 1.5% 44.6% 3.4% SD 5.4% 9 8.6 8.7% 6 2.5% 22 -1.7% 27 1.0% 14 1.4% 44.4% 3.4% KC 5.2% 10 8.4 2.9% 13 0.0% 14 2.3% 5 1.4% 12 1.6% 40.8% 3.1% IND 5.2% 11 9.2 6.8% 9 1.8% 21 0.2% 10 -4.0% 29 0.7% 61.3% 5.1% MIN 4.4% 12 8.2 -1.9% 19 -2.1% 9 4.3% 2 2.9% 7 1.5% 39.5% 2.9% PHI 4.2% 13 8.8 3.6% 11 -1.0% 12 -0.3% 13 -2.7% 24 1.0% 50.2% 3.8% DAL 3.9% 14 8.6 7.3% 8 2.8% 23 -0.5% 17 -0.6% 19 1.0% 46.8% 3.5% DET 3.7% 15 8.0 2.8% 14 -1.3% 10 -0.4% 15 3.4% 5 1.6% 36.9% 2.4% ATL 2.7% 16 9.0 6.4% 10 6.4% 31 2.9% 4 -5.2% 32 1.0% 51.7% 3.6% TEAM TOTAL

DVOA TOTAL

RANK MEAN

WINS OFF.

DVOA OFF.

RANK DEF.

DVOA DEF.

RANK S.T.

DVOA S.T.

RANK SCHED SCHED

RANK NO. 1 PICK

ODDS PLAYOFF

ODDS S.B. WIN

ODDS NO 1.2% 17 8.8 7.8% 7 6.1% 30 -1.5% 26 -5.1% 31 1.3% 47.5% 3.1% NYJ 0.0% 18 8.5 -10.4% 27 -8.3% 2 2.2% 6 -3.2% 28 1.5% 40.9% 2.3% NYG -3.5% 19 7.8 1.2% 16 4.2% 27 -0.4% 16 -1.6% 21 2.6% 33.1% 1.5% CAR -3.7% 20 7.8 -2.7% 23 0.5% 17 -0.5% 18 -2.4% 23 2.4% 33.3% 1.6% SF -3.8% 21 7.0 -2.4% 22 0.7% 18 -0.6% 19 4.3% 2 4.2% 20.1% 0.8% MIA -3.9% 22 7.5 -1.2% 18 0.2% 15 -2.8% 31 -0.7% 20 3.2% 26.2% 1.0% ARI -5.0% 23 6.8 -2.4% 21 0.7% 19 -1.8% 28 4.8% 1 4.7% 18.3% 0.8% BUF -5.8% 24 7.4 -12.4% 30 -6.3% 5 0.4% 9 -0.5% 18 3.3% 25.3% 1.0% CHI -7.1% 25 6.8 2.3% 15 7.5% 32 -1.9% 29 2.3% 10 5.5% 19.9% 0.6% HOU -8.5% 26 7.3 -10.7% 28 -2.2% 8 0.1% 12 -2.9% 26 3.9% 29.1% 0.9% CLE -10.2% 27 6.3 -6.4% 24 3.4% 24 -0.3% 14 3.6% 4 7.7% 15.0% 0.4% OAK -10.3% 28 6.3 -8.7% 26 0.4% 16 -1.1% 24 3.3% 6 8.0% 14.7% 0.4% TEN -14.5% 29 6.8 -7.7% 25 6.0% 29 -0.8% 21 -4.2% 30 6.9% 21.6% 0.4% TB -15.2% 30 6.6 -16.3% 32 -0.7% 13 0.5% 8 -2.9% 27 7.4% 16.6% 0.3% JAC -16.5% 31 6.2 -10.7% 29 4.5% 28 -1.2% 25 -1.9% 22 9.4% 14.9% 0.2% WAS -18.2% 32 5.8 -13.5% 31 3.7% 25 -0.9% 22 0.2% 17 11.5% 10.6% 0.2%

Which teams saw the biggest drop in mean wins since the book, and why?

Green Bay fell by almost a full win because of the Jordy Nelson injury, though the Packers are still projected to win their division.

Buffalo fell by 0.6 mean wins because the quarterback-specific part of the projection system really, really doesn't like them starting Tyrod Taylor. It actually knows nothing about Taylor, because there's not much to go by to project quarterbacks with no track record. It simply knows he's a sixth-round pick (and thus has an estimated QBASE of zero) with almost no NFL experience. It likes Matt Cassel's history of mediocrity better than that gamble. Your own personal opinion may vary, but that's why we do the subjective staff predictions article on Thursday.

Minnesota fell by 0.3 mean wins because of Phil Loadholt.

Which teams saw the biggest rise in mean wins since the book, and why?

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Kansas City gained 0.5 mean wins because I didn't credit them in the book simulation for the return of Eric Berry, plus the age-related variables like some of the other changes in their expected starting lineup.

Tennessee gained 0.4 mean wins because the age-related variables like the younger expected starting lineup compared to what we had down in July.

Chicago gained 0.3 mean wins because of a few random things. Sorry, nothing specific that's easy to point to.

The New York Jets gained 0.3 wins because just as quarterback-specific part of the system likes Matt Cassel over Tyrod Taylor, so too does it like Ryan Fitzpatrick over Geno Smith... enough to move the Jets all the way up the amazing heights of 27TH PLACE in mean projected offense! But that's better than what we had before.

Finally, allow me to draw your attention to one team that curiously ends up with almost the same forecast it had a couple months ago. New England had 10.5 mean wins in the book, when we made Jimmy Garoppolo the starting quarterback in the first four games in 75 percent of the simulations. Now, Tom Brady plays all 16 games and... the Patriots are at 10.6 mean wins. Yes, that's strange. Penalizing them for being without Brandon LaFell for 5-6 games plays a small role, but also, I think this may be the one place where changing the methodology of handling suspensions matters, because of what it does to Pittsburgh's odds of winning the first two games in the simulation. If it's not that, I honestly have no idea how to explain it.