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Love the Bowl Championship Series or (more likely) hate it, tonight marks the end of college football’s 16-year BCS experiment. Designed to bring some measure of order to the chaotic state college football had been in under the Bowl Alliance/Coalition, the BCS did streamline the process of determining a national champion — though it was obviously not without its share of controversies either.

If various opinion polls conducted over the years are any indication, the public is ready to move on from the BCS to next season’s “plus-one”-style playoff system. But before it bids farewell forever, how does the BCS grade out relative to other playoff systems in terms of selecting the best team as a champion?

Back in 2008, I concluded that it didn’t really do much worse of a job than a plus-one system would have. But that was more of an unscientific survey of the 1992-2007 seasons than a truly rigorous study. Today, I plan to take a page from Doug’s book and use the power of Monte Carlo simulation to determine which playoff system sees the true best team win the national title most often.

(Note: If you just want the results and don’t want to get bogged down in the details, feel free to skip the next section.) [click to continue…]

{ 21 comments }

One of my favorite sabermetric baseball articles of all time was written by Sky Andrecheck in 2010 — part as a meditation on the purpose/meaning of playoffs, and part as a solution for some of the thorny logical concerns that arise from said mediation.

The basic conundrum for Andrecheck revolved around the very existence of a postseason tournament, since — logically speaking — such a thing should really only be invoked to resolve confusion over who the best team was during the regular season. To use a baseball example, if the Yankees win 114 games and no other AL team wins more than 92, we can say with near 100% certainty that the Yankees were the AL’s best team. There were 162 games’ worth of evidence; why make them then play the Rangers and Indians on top of that in order to confirm them as the AL’s representative in the World Series?

Andrecheck’s solution to this issue was to set each team’s pre-series odds equal to the difference in implied true talent between the teams from their regular-season records. If the Yankees have, say, a 98.6% probability of being better than the Indians from their respective regular-season records, then the ALCS should be structured such that New York has a 98.6% probability of winning the series — or at least close to it (spot the Yankees a 3-0 series lead and every home game from that point onward, and they have a 98.2% probability of winning, which is close enough). [click to continue…]

{ 8 comments }

The Simple Rating System is a many-splendored thing, but a known bug of the process is that huge outlier scoring margins can have undue influence on the rankings. Take the 2009 NFL season, for instance, during which the Patriots led the NFL in SRS in no small part because they annihilated the Titans 59-0 in a snowy October game that tied for the second-most lopsided margin of victory in NFL history. Outside of that single game, the Patriots’ PPG margin was +5.2, which wouldn’t have even ranked among the league’s top ten teams, but the SRS (particularly because it minimizes squared prediction errors between actual outcomes and those expected from team ratings) gave the 59-0 win a lot of weight, enough to propel New England to the #1 ranking. (A placement that looked downright laughable, I might add, when the Pats were crushed at home by Baltimore on Wild Card Weekend.)

One solution that is commonly proposed for this problem is to cap the margin of victory in a given game at a certain fixed number. This is especially popular in college football (in fact, Chase sort of uses a cap in his college SRS variant) because nonconference schedules will often see matchups between teams of incredibly disparate talent levels, games in which the powerhouse team can essentially choose the margin by which they want to steamroll their opponent. Within that context, it doesn’t really matter whether Florida State beats Idaho by 46 or by 66, because there’s a 0% chance Idaho is a better team than FSU — no new information is conveyed when they pile more and more points onto the game’s margin.

But what’s the right number to cap margin of victory at in the NFL? These are all professional teams, after all, so there’s plenty of evidence that in the NFL, blowing opponents out — even when they’re bad teams — says a lot about how good you are. Where do we draw the line, then, to find the point at which a team has clearly proven they’re better than the opponent, beyond which any extra MOV stops giving us information?

[click to continue…]

{ 12 comments }

Presented below, without comment, is a table of every matchup featuring Tom Brady & Peyton Manning as the starting quarterbacks. Enjoy:

Date
Home Team
Favorite
Patriots Passing
Colts/Broncos Passing
Advantage
Outcome
2001-09-30NWECLT -11.513-23, 159 yds, 0 TD, 0 Int, 6.63 ANYPA25-43, 240 yds, 1 TD, 3 Int, 2.72 ANYPA+3.91, NWE44-13, NWE
2001-10-21CLTCLT -10.517-21, 262 yds, 4 TD, 0 Int, 16.29 ANYPA22-34, 305 yds, 1 TD, 0 Int, 8.55 ANYPA+7.73, NWE38-17, NWE
2003-11-30CLTCLT -3.526-35, 226 yds, 2 TD, 2 Int, 4.76 ANYPA29-48, 272 yds, 4 TD, 1 Int, 6.14 ANYPA+1.38, CLT38-34, NWE
2004-01-18 (C)NWENWE -3.522-37, 237 yds, 1 TD, 1 Int, 5.73 ANYPA23-47, 208 yds, 1 TD, 4 Int, 0.94 ANYPA+4.79, NWE24-14, NWE
2004-09-09NWENWE -326-38, 320 yds, 3 TD, 1 Int, 8.38 ANYPA16-29, 244 yds, 2 TD, 1 Int, 7.97 ANYPA+0.41, NWE27-24, NWE
2005-01-16 (D)NWENWE -118-27, 115 yds, 1 TD, 0 Int, 4.50 ANYPA27-42, 230 yds, 0 TD, 1 Int, 4.30 ANYPA+0.20, NWE20-3, NWE
2005-11-07NWECLT -325-40, 254 yds, 3 TD, 0 Int, 7.48 ANYPA28-37, 321 yds, 3 TD, 1 Int, 9.08 ANYPA+1.60, CLT40-21, CLT
2006-11-05NWENWE -2.520-35, 201 yds, 0 TD, 4 Int, 0.60 ANYPA20-36, 301 yds, 2 TD, 1 Int, 7.59 ANYPA+6.99, CLT27-20, CLT
2007-01-21 (C)CLTCLT -321-34, 226 yds, 1 TD, 1 Int, 5.74 ANYPA27-47, 330 yds, 1 TD, 1 Int, 6.10 ANYPA+0.36, CLT38-34, CLT
2007-11-04CLTNWE -521-32, 237 yds, 3 TD, 2 Int, 6.09 ANYPA16-27, 210 yds, 1 TD, 1 Int, 6.17 ANYPA+0.08, CLT24-20, NWE
2009-11-15CLTCLT -1.529-42, 364 yds, 3 TD, 1 Int, 8.61 ANYPA28-44, 316 yds, 4 TD, 2 Int, 6.80 ANYPA+1.81, NWE35-34, CLT
2010-11-21NWENWE -4.519-25, 178 yds, 2 TD, 0 Int, 8.38 ANYPA38-52, 396 yds, 4 TD, 3 Int, 6.56 ANYPA+1.83, NWE31-28, NWE
2012-10-07NWENWE -623-31, 193 yds, 1 TD, 0 Int, 6.09 ANYPA31-44, 324 yds, 3 TD, 0 Int, 8.35 ANYPA+2.26, DEN31-21, NWE
2013-11-24NWEDEN -2.534-50, 324 yds, 3 TD, 0 Int, 7.25 ANYPA19-36, 132 yds, 2 TD, 1 Int, 3.34 ANYPA+3.90, NWE34-31, NWE
{ 8 comments }

(I originally posted this at the S-R Blog, but I thought it would be very appropriate here as well.)

Just a quick hit of a post to let you know that tonight’s MNF matchup between the 0-6 Giants and the 1-4 Vikings is, in fact, the worst ever this late in the season by combined winning percentage:

game_id
home
W
L
T
PF
PA
road
W
L
T
PF
PA
year_id
week_num
game_date
comb_wpct
comb_pt_diff
winner
201310210nygnyg060103209min1401251582013710/21/2013.091-12.6NULL
197512150sdgsdg1110148282nyj39022137819751312/15/1975.167-12.1sdg, 24-16
199411210otioti190147218nyg37017122019941211/21/1994.200-6.0nyg, 13-10
197211060nwenwe25092220clt160941451972811/6/1972.214-12.8clt, 24-17
197011020pitpit2407194cin150931561970711/2/1970.250-7.2pit, 21-10
198110190detdet240118126chi150891331981710/19/1981.250-4.3det, 48-17
199710200cltclt06088155buf3301221591997810/20/1997.250-8.7buf, 9-6
201211260phiphi370162252car28018424320121211/26/2012.250-7.5car, 30-22
200001030atlatl4110251351sfo41102664191999171/3/2000.267-8.4atl, 34-29
200512190ravrav490171253gnb310025525520051512/19/2005.269-3.2rav, 48-3
198310240crdcrd250137218nyg2501261561983810/24/1983.286-7.9tie (20-20)
201112120seasea570216246ram210014029620111412/12/2011.292-7.8sea, 30-13
201011290crdcrd370188292sfo37016021920101211/29/2010.300-8.2sfo, 27-6
200911160clecle17078209rav44020615420091011/16/2009.313-4.9rav, 16-0
198011240nornor0110181341ram74029422819801211/24/1980.318-4.3ram, 27-7
201112050jaxjax380138200sdg47024927520111312/5/2011.318-4.0sdg, 38-14
198312120tamtam2120212345gnb77039640719831512/12/1983.321-5.1gnb, 12-9
198711020daldal330135134nyg150991421987711/2/1987.333-3.5dal, 33-24
199910250pitpit33011793atl150741531999710/25/1999.333-4.6pit, 13-9
200211180ramram450194196chi27018223220021111/18/2002.333-2.9ram, 21-16
200511210gnbgnb270201184min45015422820051111/21/2005.333-3.2min, 20-17
200012040nwenwe390192253kan57028327420001412/4/2000.333-2.2nwe, 30-24
200412130otioti480231294kan48034132620041412/13/2004.333-2.0kan, 49-38
200712100atlatl390171272nor57026627920071412/10/2007.333-4.8nor, 34-14
199112090miamia760256275cin211021137419911512/9/1991.346-7.0mia, 37-13
197311260sfosfo370180232gnb35213819819731111/26/1973.350-5.6sfo, 20-6
197811130cincin190110184rai64019316419781111/13/1978.350-2.3rai, 34-21
200711260pitpit730269145mia010018327420071211/26/2007.350+1.7pit, 3-0
199311290cltclt370154233sdg46016419519931311/29/1993.350-5.5sdg, 31-0
198010270nyjnyj160114164mia430991441980810/27/1980.357-6.8nyj, 17-14
199710270miamia520143124chi0701011991997910/27/1997.357-5.6chi, 36-33
199811020phiphi16079162dal4301741151998911/2/1998.357-1.7dal, 34-0
201211050nornor250190216phi3401201552012911/5/2012.357-4.4nor, 28-13
198311070detdet450202188nyg26116621419831011/7/1983.361-1.9det, 15-9
199111250ramram380181256sfo56021815519911311/25/1991.364-0.5sfo, 33-10
199211300seasea110073218den74017520719921311/30/1992.364-8.0sea, 16-13
200812010htxhtx470252293jax47022424020081312/1/2008.364-2.6htx, 30-17
197111150sdgsdg350150179crd3501351491971911/15/1971.375-2.7sdg, 20-17
197910290atlatl350160181sea3501721811979910/29/1979.375-1.9sea, 31-28
200611130carcar440137163tam26010217320061011/13/2006.375-6.1car, 24-10
200711120seasea440167141sfo26010418620071011/12/2007.375-3.5sea, 24-0
201211120pitpit530191164kan17013324020121011/12/2012.375-5.0pit, 16-13
199010290pitpit340109128ram2401641731990810/29/1990.385-2.2pit, 41-10
201212170otioti490271386nyj67024530620121512/17/2012.385-6.8oti, 14-10
197012070otioti371177249cle56023623619701212/7/1970.386-3.3cle, 21-10
197711210waswas540126132gnb2708315219771011/21/1977.389-4.2was, 10-9
200311170sfosfo450202152pit36017621720031111/17/2003.389+0.5sfo, 30-14
201011220sdgsdg450239197den36020325220101111/22/2010.389-0.4sdg, 35-14
199410170denden140108146kan32090801994710/17/1994.400-2.8kan, 31-28
199911290sfosfo370163281gnb55019220919991211/29/1999.400-6.8gnb, 20-3

It is not, however, the worst by combined PPG margin. That honor belongs to this 1972 game between the 2-5 Patriots and the 1-6 Colts (Baltimore ended up winning 24-17):

game_id
home
W
L
T
PF
PA
road
W
L
T
PF
PA
year_id
week_num
game_date
comb_wpct
comb_pt_diff
winner
197211060nwenwe25092220clt160941451972811/6/1972.214-12.8clt, 24-17
201310210nygnyg060103209min1401251582013710/21/2013.091-12.6NULL
197512150sdgsdg1110148282nyj39022137819751312/15/1975.167-12.1sdg, 24-16
199710200cltclt06088155buf3301221591997810/20/1997.250-8.7buf, 9-6
200001030atlatl4110251351sfo41102664191999171/3/2000.267-8.4atl, 34-29
201011290crdcrd370188292sfo37016021920101211/29/2010.300-8.2sfo, 27-6
199211300seasea110073218den74017520719921311/30/1992.364-8.0sea, 16-13
198310240crdcrd250137218nyg2501261561983810/24/1983.286-7.9tie (20-20)
201112120seasea570216246ram210014029620111412/12/2011.292-7.8sea, 30-13
201211260phiphi370162252car28018424320121211/26/2012.250-7.5car, 30-22
197011020pitpit2407194cin150931561970711/2/1970.250-7.2pit, 21-10
199112090miamia760256275cin211021137419911512/9/1991.346-7.0mia, 37-13
198010270nyjnyj160114164mia430991441980810/27/1980.357-6.8nyj, 17-14
201212170otioti490271386nyj67024530620121512/17/2012.385-6.8oti, 14-10
199911290sfosfo370163281gnb55019220919991211/29/1999.400-6.8gnb, 20-3
197910150nyjnyj240128174min3301071421979710/15/1979.417-6.8nyj, 14-7
200611130carcar440137163tam26010217320061011/13/2006.375-6.1car, 24-10
199411210otioti190147218nyg37017122019941211/21/1994.200-6.0nyg, 13-10
200611060seasea430149177rai250921482006911/6/2006.429-6.0sea, 16-0
199710270miamia520143124chi0701011991997910/27/1997.357-5.6chi, 36-33
197311260sfosfo370180232gnb35213819819731111/26/1973.350-5.6sfo, 20-6
199311290cltclt370154233sdg46016419519931311/29/1993.350-5.5sdg, 31-0
198312120tamtam2120212345gnb77039640719831512/12/1983.321-5.1gnb, 12-9
201211120pitpit530191164kan17013324020121011/12/2012.375-5.0pit, 16-13
200911160clecle17078209rav44020615420091011/16/2009.313-4.9rav, 16-0
200712100atlatl390171272nor57026627920071412/10/2007.333-4.8nor, 34-14
199910250pitpit33011793atl150741531999710/25/1999.333-4.6pit, 13-9
201211050nornor250190216phi3401201552012911/5/2012.357-4.4nor, 28-13
198110190detdet240118126chi150891331981710/19/1981.250-4.3det, 48-17
198011240nornor0110181341ram74029422819801211/24/1980.318-4.3ram, 27-7
197711210waswas540126132gnb2708315219771011/21/1977.389-4.2was, 10-9
199510230nwenwe15069160buf510136951995810/23/1995.500-4.2nwe, 27-14
200611270seasea640203219gnb46018525220061211/27/2006.500-4.2sea, 34-24
201112050jaxjax380138200sdg47024927520111312/5/2011.318-4.0sdg, 38-14
200412060seasea650239223dal47019328920041312/6/2004.455-3.6dal, 43-39
198711020daldal330135134nyg150991421987711/2/1987.333-3.5dal, 33-24
200711120seasea440167141sfo26010418620071011/12/2007.375-3.5sea, 24-0
199010220clecle24098139cin4201541531990710/22/1990.500-3.3cin, 34-13
201110310kankan330105150sdg4201411362011810/31/2011.583-3.3kan, 23-20
197811060cltclt360120230was72018613519781011/6/1978.556-3.3clt, 21-17
200711190denden450153238oti63017815220071111/19/2007.556-3.3den, 34-20
197012070otioti371177249cle56023623619701212/7/1970.386-3.3cle, 21-10
200511210gnbgnb270201184min45015422820051111/21/2005.333-3.2min, 20-17
200512190ravrav490171253gnb310025525520051512/19/2005.269-3.2rav, 48-3
200512120atlatl750277237nor39018329520051412/12/2005.417-3.0atl, 36-17
200211180ramram450194196chi27018223220021111/18/2002.333-2.9ram, 21-16
199410170denden140108146kan32090801994710/17/1994.400-2.8kan, 31-28
197211130sdgsdg251152203cle5301411341972911/13/1972.469-2.8cle, 21-17
197710310crdcrd330124122nyg330911261977710/31/1977.500-2.8crd, 28-0
200512260nyjnyj3110189298nwe95032228920051612/26/2005.429-2.7nwe, 31-21
{ 9 comments }

Receiving WOWY Extended Back to 1950

A WOWY Superstar.

A WOWY Superstar.

Last week, we announced that our True Receiving Yards metric has now been calculated back to 1950, so it’s only fitting that we also compute WOWY (With Or Without You) for all of those receivers as well.

Skip the paragraph after this if you don’t care about the gory mathematical details, and just know that WOWY basically answers the question: “Did a receiver’s quarterbacks play better when they threw a lot to him, or not?”

For the brave souls who care about the calculation: WOWY starts by measuring the difference between a QB’s age-adjusted Relative Adjusted Net Yards Per Attempt in a given season and his combined age-adjusted RANY/A in every other season of his career. This is computed as an average for each team’s QB corps, using a combination of QB dropbacks during the season in question and the rest of his career as the weights (the exact formula is: weight = 1/(1/drpbk_year + 1/drpbk_other)). Finally, for each receiver we compute a weighted career average of the QB WOWY scores for the teams he played on, weighted by his True Receiving Yards in each season.

At any rate, the only players who don’t get a WOWY are those who either debuted before 1950, played with a QB who debuted before 1950, or played with a QB who ever threw to a receiver who debuted before 1950. Here are the career WOWY marks (when applicable), alongside TRY, for every 3,000-TRY receiver whose career started in 1950 or later:

[click to continue…]

{ 9 comments }

Brady needs to channel another Tom (Flores) this season

Brady needs to channel another Tom (Flores) this season

As Jason Lisk and I wrote about before the season, Tom Brady and Ben Roethlisberger have become something of the poster children so far this year when it comes to veteran QBs working with inexperienced and otherwise less-than-notable receiving groups. And, lo and behold, each has put up career-low RANY/A marks through 2 games. But how do their receiving corps rank relative to those of other teams so far this year, and how do they stack up historically?

To take a stab at answering these questions, I turned to True Receiving Yards. For each player who debuted in 1950 or later, I computed their Weighted Career True Receiving Yards for every year, as of the previous season, to get a sense of how experienced/accomplished they’d been going into the season in question. Then, I calculated a weighted averaged of those numbers for every receiver on a given team, using TRY during the season in question as the weights. For example, here are the 2013 Patriots receivers:

Player
Age
Debut
TRY
% of Tm
At-the-time WCTRY
Weighted Average560.7
Julian Edelman27200913938%615.7
Danny Amendola2820097220%1541.9
Kenbrell Thompkins2520135615%0.0
Shane Vereen2420114412%110.9
Aaron Dobson2220134312%0.0
Michael Hoomanawanui25201051%278.8
James Develin25201341%0.0

The way to read that is: Julian Edelman has accounted for 38 percent of the Pats’ TRY so far. Going into the season, he had a career Weighted TRY of 615.7, so he contributes to 38% of the 2013 Pats’ weighted average with his 615.7 previous career weighted TRY; Danny Amendola contributes to 20% of the team weighted average with his 1541.9 previous career weighted TRY; etc. Multiply each guy’s previous weighted career TRYs by the percentage of the team’s 2013 TRY he contributed, and you get a cumulative weighted average of 560.7 — meaning the average TRY of a 2013 Pats receiver has been gained by a guy who had a previous career weighted TRY of 560.7.

Is that a low number? Well, here are the numbers for all of the 2013 team receiving corps (not including Thursday night’s Eagles-Chiefs tilt), inversely sorted by weighted average (asterisks indicate rookies):

[click to continue…]

{ 3 comments }

This guy was pretty good.

This guy was pretty good.

About a month ago, Chase & I developed a stat called True Receiving Yards, which seeks to put all modern & historical receiving seasons on equal footing by adjusting for the league’s passing YPG environment & schedule length, plus the amount the player’s team passed (it’s easier to produce raw receiving stats on a team that throws a lot), with bonuses thrown in for touchdowns and receptions. It’s not perfect — what single stat in a sport with so many moving parts is? — but it does a pretty good job of measuring receiving productivity across different seasons and across teams with passing games that operated at vastly different volumes.

Anyway, today’s post is basically a data dump to let everyone know we’ve extended TRY data back to 1950 (before, it was only computed for post-merger seasons). Here are the new all-time career leaders among players who debuted in 1950 or later (see below for a key to the column abbreviations):
[click to continue…]

{ 16 comments }

Straight cash, homey.

Straight cash, homey.

In 1998, 21-year-old Randy Moss made a stunning NFL debut, racking up 17 touchdowns and 1,260 True Receiving Yards, the 2nd-best total in football that season. The Vikings’ primary quarterback that year, Randall Cunningham, was a former Pro Bowler and MVP, but all that seemed like a lifetime ago before the ’98 season. He’d been out of football entirely in 1996, and in 1997 he posted an Adjusted Net Yards per Attempt average that was 1.2 points below the league’s average (for reference’s sake, replacement level is usually around 0.75 below average). With Moss in ’98, though, Cunningham’s passing efficiency numbers exploded: he posted a career best +3.2 Relative Adjusted Net Yards per Attempt, miles ahead of his perfectly-average overall career mark. If we adjust for the fact that Cunningham was also 35 at the time (an age at which quarterbacks’ RANY/A rates tend to be 1.1 points below what they are at age 27), Cunningham’s 1998 rate was actually 4.3 points better than we’d expect from the rest of his career, a staggering outlier.

The following year, Jeff George took over as the Vikings primary quarterback, and he promptly posted a Relative ANY/A 2.2 points higher than expected based on his age and the rest of his career.1 George left Moss and Minnesota after the season, and he would throw only 236 passes the rest of his career, producing a cumulative -0.6 RANY/A in Washington before retiring.

From 2000-04, Moss was the primary target of Daunte Culpepper, whose RANY/A was 0.7 better than expected (based on Culpepper’s career numbers) when Moss was around.2 Although he’d enjoyed one of the best quarterback seasons in NFL history in 2004, Culpepper was never the same after Moss was traded to Oakland; in fact, he never even had another league-average passing season, producing a horrible -1.2 RANY/A from 2005 until his retirement in 2009.3

Moss’s stint with the Raiders was famously checkered — although Kerry Collins’ RANY/A was 0.6 better than expected in 2005, Aaron Brooks played 2.5 points of RANY/A below his previous standards in 2006 — but we all know what happened when he joined the Patriots in 2007. With Moss, Tom Brady’s RANY/A was a whopping 1.3 points higher than expected from the rest of his career, and Moss also played a big role in Matt Cassel’s RANY/A being +1.0 relative to expectations after Brady was lost for the season in 2008.

While Moss’s post-Pats career hasn’t exactly been the stuff of legends, the majority of his career (weighted by True Receiving Yards) saw him dramatically improve his quarterbacks’ play relative to the rest of their careers. In fact, his lifetime WOWY (With or Without You) mark of +1.1 age-adjusted RANY/A ranks 3rd among all receivers who: a) had at least 3,000 career TRY, b) started their careers after the merger, and c) played exclusively with quarterbacks who started their careers after the merger. And the first two names on the list are possibly explained by other means. The table below lists all 301 receivers with 3,000 career TRY. The table is fully sortable and searchable, and you can click on the arrows at the bottom of the table to scroll. The table is sorted by the QB WOWY column.
[click to continue…]

  1. Cunningham’s RANY/A was also 1.0 better than expected in limited action. []
  2. That number is an average weighted by the number of TRY Moss had in each season []
  3. To be fair, Culpepper tore his ACL, MCL, and PCL halfway through the 2005 season, which also was a factor in his decline. []
{ 7 comments }

Roethlisberger will be without his best targets this year.

Roethlisberger will be without his best targets this year.

While the state of the Steelers’ receiving corps isn’t as shaky as say, that of the New England Patriots, it could certainly be called an area of potential concern for Ben Roethlisberger and the Pittsburgh offense going into 2013. One of the biggest moves on the first day of free agency involved Mike Wallace departing for Miami; meanwhile, Heath Miller’s injury status — while more encouraging than previously thought — will cost him several games, and probably some effectiveness when he does eventually return. All of this comes on the heels of losing stealth HoFer Hines Ward (albeit an older, drastically less effective version) to retirement after the 2011 season.

For Roethlisberger, this downturn in the quality of his receivers is a pretty new phenomenon. In fact, by one measure of career receiving-corps talent (which I’ll explain below), Big Ben has been blessed with the fourth-most gifted receiving group among current starting quarterbacks with more than two years of experience (behind only Peyton Manning, Matt Ryan, and Tony Romo). In fact, Roethlisberger’s 16th-ranked receiving corps in 2012 was by far the least talented group of pass catchers he’s ever had to throw to.

How do you begin to measure the quality of a quarterback’s receiving corps, you ask? Well, pretty much any method is going to fraught with circular logic, especially if a quarterback consistently has the same receivers over several years. His successes are theirs, and vice-versa. However, here’s one stab at shedding at least some light on the issue.

For each team since the NFL-AFL merger, I:

  • Gathered all players with at least 1 catch for the team in the season.
  • Computed their True Receiving Yards in that season; I then determined what percentage of the team’s True Receiving Yards was accumulated by which receiver in each year. For example, Hines Ward had 1,029 TRY in 2009, which represented 25.9% of the 3,979 True Receiving Yards accumulated by all Steelers that year
  • Figured out the most TRY they ever had in a season, a number I’m calling each player’s peak TRY; for Ward, his peak TRY is equal to 1,279.
  • Calculated a weighted average (based on the percentage of team TRY gained by each receiver) of the receivers’ peak TRY (weighted by their TRY during the season in question).

(I also threw out all teams that had a receiver who debuted before 1970, since I don’t know what the real peak TRY of any pre-merger receiver was. I should eventually calculate TRY for pre-merger seasons, of course — thank you Chase & Don Maynard.)

As an example, here are the 2009 Steelers, the most talented corps of receivers Roethlisberger has had in his career:
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{ 11 comments }

This guy's 1982 Chargers sure come up a lot when we do lists like these.

This guy's 1982 Chargers sure come up a lot when we do lists like these.

More than a decade ago (on a side note: how is that possible?), Doug wrote a series of player comments highlighting specific topics as they related to the upcoming fantasy football season. I recommend that you read all of them, if for no other reason than the fact you should make it a policy to read everything Doug Drinen ever wrote about football, but today we’re going to focus on the Isaac Bruce comment, which asked/answered the question:

Is this Ram team the biggest fantasy juggernaut of all time?

“This Ram team,” of course, being the 1999, 2000, & 2001 Greatest Show on Turf St. Louis Rams. At the time, Doug determined that those Rams were not, in fact, the best real-life fantasy team ever assembled, by adding up the collective VBD for the entire roster. They ranked tenth since 1970; the top 10 were:

1. 1. 1975 Buffalo Bills – 550 Simpson (281) Ferguson (98) Braxton (83) Chandler (44) Hill (42)

2. 1982 San Diego Chargers – 542 Chandler (190) Fouts (126) Winslow (121) Muncie (92) Brooks (10) Joiner (1)

3. 1994 San Francisco 49ers – 514 Young (208) Rice (140) Watters (98) Jones (67)

4. 1995 Detroit Lions – 478 Mitchell (136) Moore (132) Sanders (121) Perriman (87)

5. 1984 Miami Dolphins – 470 Marino (243) Clayton (145) Duper (76) Nathan (6)

6. 1998 San Francisco 49ers – 467 Young (200) Hearst (137) Owens (81) Rice (46) Stokes (1)

7. 1986 Miami Dolphins – 456 Marino (210) Duper (94) Clayton (76) Hampton (61) Hardy (13)

8. 2000 Minnesota Vikings – 452 Culpepper (170) Moss (123) Smith (87) Carter (70)

9. 1991 Buffalo Bills – 449 Thomas (157) Kelly (143) Reed (80) Lofton (51) McKeller (17)

10. 1999 St. Louis Rams – 435 Faulk (184) Warner (179) Bruce (71)

As an extension of Chase’s recent post on the The Best Skill Position Groups Ever, we thought it might be useful to update Doug’s study in a weekend data-dump post. I modified the methodology a bit — instead of adding up VBD for the entire roster, for each team-season I isolated the team’s leading QB and top 5 non-QBs by fantasy points (using the same point system I employed when ranking the Biggest Fluke Fantasy Seasons Ever). I then added up the total VBD of just those players, to better treat each roster like it was a “real” fantasy team.

Anyway, here are the results. Remember as well that VBD is scaled up to a 16-game season, so as not to short-change dominant fantasy groups from strike-shortened seasons (:cough:1982 Chargers:cough:).
[click to continue…]

{ 2 comments }

Yesterday, I set up a method for ranking the flukiest fantasy football seasons since the NFL-AFL merger, finding players who had elite fantasy seasons that were completely out of step with the rest of their careers. I highlighted fluke years #21-30, so here’s a recap of the rankings thus far:

30. Lorenzo White, 1992
29. Dwight Clark, 1982
28. Willie Parker, 2006
27. Lynn Dickey, 1983
26. Robert Brooks, 1995
25. Ricky Williams, 2002
24. Jamal Lewis, 2003
23. Mark Brunell, 1996
22. Vinny Testaverde, 1996
21. Garrison Hearst, 1998

Now, let’s get to…

The Top Twenty

20. RB Natrone Means, 1994

Best Season
yeargrushrushydrushtdrecrecydrectdVBD
1994163431,35012392350103.0
2nd-Best Season
yeargrushrushydrushtdrecrecydrectdVBD
199714244823915104012.9

Big, bruising Natrone Means burst onto the scene in 1994 as a newly-minted starter for the Chargers’ eventual Super Bowl team, gaining 1,350 yards on the ground with 12 TDs. In the pantheon of massive backs, he was supposed to be the AFC’s answer to the Rams’ Jerome Bettis, but Means was slowed by a groin injury the following year and never really stayed healthy enough to recapture his old form. The best he could do was to post a pair of 800-yard rushing campaigns for the Jaguars & Chargers in 1997 & ’98 before retiring after the ’99 season.

19. WR Braylon Edwards, 2007

Best Season
yeargrecrecydrectdVBD
200716801,28916107.7
2nd-Best Season
yeargrecrecydrectdVBD
20101653904715.4

The 3rd overall pick in the 2005 Draft out of Michigan, Edwards seemingly had a breakout 2007 season catching passes from fellow Pro Bowler Derek Anderson. But both dropped off significantly the next season, and Edwards was sent packing to the Jets in 2009. He did post 904 yards as a legit starting fantasy wideout in 2010, but he has just 380 receiving yards over the past 2 seasons, and it’s not clear he’ll ever live up to those eye-popping 2007 numbers again.
[click to continue…]

{ 7 comments }

I prefer cooking in a Garrison  Hearst replica jersey

I prefer cooking in a Garrison Hearst replica jersey.

There’s nothing like a truly great fluke fantasy season. Because they can help carry you to a league championship (and therefore eternal bragging rights — flags fly forever, after all), a random player who unexpectedly has a great season will often have a special place in the heart of every winning owner. And even if you only use their jerseys as makeshift aprons to cook in, fluke fantasy greats are a part of the fabric of football fandom. That’s why this post is a tribute to the greatest, most bizarre, fluke fantasy seasons of all time (or at least since the 1970 NFL-AFL merger).

First, a bit about the methodology. I’m going to use a very basic fantasy scoring system for the purposes of this post:

  • 1 point for every 20 passing yards
  • 1 point for every 10 rushing or receiving yards
  • 6 points for every rushing or receiving TD
  • 4 points for every passing TD
  • -2 points for every passing INT

I’m also measuring players based on Value Based Drafting (VBD) points rather than raw points. In a nutshell, VBD measures true fantasy value by comparing a player to replacement level, defined here as the number of fantasy points scored by the least valuable starter in your league. For the purposes of this exercise, I’m basing VBD on a 12-team league with a starting lineup of one QB, two RBs, 2.5 WRs, and 1 TE. That means we’re comparing a player at a given position to the #12-ranked QB, the #24 RB, the #30 WR, or the #12 TE in each season. If a player’s VBD is below the replacement threshold at his position, he simply gets a VBD of zero for the year.
[click to continue…]

{ 3 comments }

No, Peyton, you are #1

No, Peyton, you are #1.

Back in March, Chase wrote a post investigating how quarterbacks age, finding that they peak at age 29 (with a generalized peak from 26-30) in terms of value over average. Today, I thought I’d quickly look at how quarterbacks age in terms of their performance rate — specifically, their Adjusted Net Yards per Attempt (ANY/A). For newer readers, ANY/A is based on the following formula: (Passing Yards + 20 * Passing TDs – 45 * INTs – Sack Yards Lost) / (Pass Attempts + Sacks).

First, I need to introduce a way of adjusting ANY/A for era: Relative ANY/A. Relative ANY/A is simply equal to:

QB_ANY/A – LgAvg_ANY/A

The table below lists the 30 single-season leaders in Relative ANY/A since the merger. You won’t be too surprised to see the 2004 version of Peyton Manning at the top. That year, Manning averaged 9.8 ANY/A, while the league average was just 5.6 ANY/A. That means Manning gets a Relative ANY/A grade of +4.1 (with the difference due to rounding).
[click to continue…]

{ 29 comments }

(I originally posted this at the S-R Blog, but I thought it would be very appropriate here as well.)

Here is a google doc containing every team-season in our database since 1957, including the Head Coach and offensive & defensive coordinators. It also specifies those coaches’ preferred offensive or defensive schemes (depending on which side of the ball they specialize in), and attempts to figure out the general offensive family (i.e. Air Coryell, Erhardt-Perkins, etc) each team-season fell into.

THIS IS BY NO MEANS COMPLETE. In fact, it’s very much incomplete at this stage — and that’s where you come in. In the comments of this post, or in an email, we’d love to hear corrections and/or additions to the data, if you see an entry about which you know more than we do (and it’s a good bet you do). Thanks in advance for your help, and hopefully we can assemble a more complete listing of teams’ systems/schemes, which will let us do things like compute splits vs. a certain type of offense or defense, analyze whether 4-3 or 3-4 defenses were better in a given season, etc.

So let those corrections/additions pour in!

{ 5 comments }

The Saints would dig Football Perspective

The Saints would dig Football Perspective.

Last week, Chase had a great post where he looked at what percentage of the points scored by a team in any given game is a function of the team, and what percentage is a function of the opponent. The answer, according to Chase’s method, was 58 percent for the offense and 42 percent for the defense (note that, in the context of posts like these, “offense” means “scoring ability, including defensive & special-teams scores”, and “defense” means “the ability to prevent the opponent from scoring”). Today I’m going to use a handy R extension to look at Chase’s question from a slightly different perspective, and see if it corroborates what he found.

My premise begins with every regular-season game played in the NFL since 1978. Why 1978? I’d love to tell you it was because that was the year the modern game truly emerged thanks to the liberalization of passing rules (which, incidentally, is true), but really it was because that was the most convenient dataset I had on hand with which to run this kind of study. Anyway, I took all of those games, and specifically focused on the number of points scored by each team in each game. I also came armed with offensive and defensive team SRS ratings for every season, which give me a good sense of the quality of both the team’s offense and their opponent’s defense in any given matchup.

If you know anything about me, you probably guessed that I want to run a regression here. My dependent variable is going to be the number of points scored by a team in a game, but I can’t just use raw SRS ratings as the independent variables. I need to add them to the league’s average number of points per game during the season in question to account for changing league PPG conditions, lest I falsely attribute some of the variation in scoring to the wrong side of the ball simply due to a change in scoring environment. This means for a given game, I now have the actual number points scored by a team, the number of points they’d be expected to score against an average team according to SRS, and the number of points their opponents would be expected to allow vs. an average team according to SRS.
[click to continue…]

{ 2 comments }

Now that we live in a world where Joe Flacco and Eli Manning have quarterbacked 3 of the last 6 Super Bowl-winning teams, you might be tempted to think that winning a Super Bowl as a QB doesn’t mean what it used to. After all, the playoffs are getting more random — as Aaron Schatz pointed out last night, four of the last six Super Bowl champs have finished the regular season with 10 or fewer wins. So it stands to reason that, as the championship teams themselves post less-remarkable seasons, so too would their quarterbacks not be the cream of the crop. And for all of his postseason brilliance, Flacco was just the league’s 17th-best quarterback during the regular season. Does his ascendancy, coming on the heels of Manning’s, signal a new trend?

To answer that question, I turned to a methodology I’ve used many times before. The basic premise is that, to put modern and historical quarterbacks on an even playing field (no pun intended), you must translate their stats into a common environment. To do this, you take the quarterback’s stats from a given season, pro-rate to 16 scheduled games, and multiply by the ratio of the league’s per-game average during the season in question to that of a common reference season. For instance, if I’m adjusting Terry Bradshaw’s 1977 passing yards to the 1991-2012 period, I would multiply his actual total of 2,523 by (16/14) to account for the shorter season that year, then multiply that by (225.1/162.2) to account for the change in the league’s passing environment, giving an adjusted total of 4,001 yards.

After doing that for every QB season since the merger, I then plugged the translated stats into a regression formula that predicts Football Outsiders’ Yards Above Replacement based on the QB’s box score stats (including the standard cmp/att/yds/td/int, plus sacks, fumbles, and rushing stats). This gives us Estimated Yards Above Replacement (eYAR) a measure of total value for each QB season, adjusted for schedule length and league passing conditions, which is perfect for historical analysis.

To get an idea of what we’re talking about, here are Flacco’s career translated stats and eYAR numbers:
[click to continue…]

{ 7 comments }

Those are some clutch shirts

Those are some clutch shirts.

Eight years ago — almost to the day — our old PFR colleague Doug Drinen wrote a Sabernomics post about “The Manning Index”, a metric designed to roughly gauge the clutchness (or chokeitude) of a given quarterback by looking at how he did relative to expectations (he revived this concept in version two, six years ago). In a nutshell, Doug used the location of the game and the win differential of the two teams involved to establish an expected winning percentage for each quarterback in a given matchup. He then added those up across all of a quarterback’s playoff starts, and compared to the number of wins he actually had. Therefore, quarterbacks who frequently exceeded expectations in playoff games could be considered “clutch” while those who often fell short (like the Index’s namesake, Peyton Manning) might just be inveterate chokers.

Doug ran that study in the midst of the 2004-05 playoffs, so it shouldn’t be surprising that Tom Brady (who was at the time 8-0 as a playoff starter and would run it to 10-0 before ever suffering a loss) came out on top, winning 3.5 more games than you’d expect from the particulars of the games he started. Fast-forward eight years, though, and you get this list of quarterbacks who debuted after 1977:
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{ 33 comments }

Presented without comment, the most current Simple Ratings, weighted for recency:

Rk
Team
pfr_id
Mean (SRS)
Std. Error
Upper
Lower
1Seattle Seahawkssea13.573.2219.927.23
2New England Patriotsnwe12.393.3518.985.81
3San Francisco 49erssfo10.233.3316.803.66
4Denver Broncosden9.543.3416.122.96
5Green Bay Packersgnb7.083.2813.540.62
6Atlanta Falconsatl6.603.3113.110.08
7New York Giantsnyg5.943.4312.70-0.82
8Chicago Bearschi5.763.4312.50-0.99
9Baltimore Ravensrav4.053.2410.42-2.33
10Washington Redskinswas3.523.3310.08-3.04
11Minnesota Vikingsmin3.303.379.94-3.34
12Cincinnati Bengalscin2.833.319.36-3.69
13New Orleans Saintsnor2.063.428.80-4.68
14Carolina Pantherscar1.503.428.23-5.23
15Houston Texanshtx1.483.247.85-4.90
16St Louis Ramsram0.753.437.50-6.00
17Dallas Cowboysdal-0.243.436.51-6.99
18Pittsburgh Steelerspit-0.783.435.98-7.53
19Tampa Bay Buccaneerstam-1.053.415.67-7.78
20Miami Dolphinsmia-2.843.443.93-9.62
21San Diego Chargerssdg-2.913.423.82-9.64
22Detroit Lionsdet-3.513.423.22-10.24
23Arizona Cardinalscrd-4.473.442.30-11.23
24Indianapolis Coltsclt-5.063.301.45-11.56
25Cleveland Brownscle-5.403.441.37-12.17
26Buffalo Billsbuf-6.703.440.07-13.46
27New York Jetsnyj-7.353.43-0.60-14.09
28Philadelphia Eaglesphi-9.883.43-3.12-16.65
29Oakland Raidersrai-10.523.42-3.78-17.26
30Tennessee Titansoti-10.603.45-3.81-17.38
31Jacksonville Jaguarsjax-13.943.43-7.19-20.69
32Kansas City Chiefskan-14.403.43-7.65-21.16

“Upper” and “Lower” are the 95% confidence intervals around each estimate. Roughly speaking, this means we can be 95% confident that, say, the 49ers’ “true” SRS rating is between 3.66 and 16.80.

{ 2 comments }

This is mostly a huge end-of-regular-season data dump, but I’ll explain a little before the table…

PFR’s Simple Rating System can be broken into offensive and defensive components, which represent the number of points per game the team scored/allowed per game compared to the league average, after adjusting for the strength of opposing offenses and defenses faced. If you want to derive an expected winning percentage from that, you have to “back out” to total points scored/allowed again. To do that, you just add OSRS (or subtract DSRS) to the league’s average PPG, then multiply by the number of games the team played. This will give you adjusted points scored/allowed totals for the season.

To get that into a winning percentage-like form, you then need to plug those totals into the Pythagorean Formula. It usually takes the form of

(Pts Scored ^ x) / (Pts Scored ^ x + Pts Allowed ^ x)

where x was determined to be around 2.4 for the NFL in the 1990s, when current Houston Rockets (yep, basketball) GM Daryl Morey researched it for STATS, Inc. Last year, Football Outsiders decided to employ a “floating” exponent that varies with the scoring environment in which a team played, recognizing that a single point is more important to winning in lower-scoring environments. To that end, they used what’s known as the “Pythagenport” method of determining the exponent, which is

1.5 * log10((PF + PA) / G)

I was poking around in the data the other day, though, and found that the so-called “Pythagenpat” variant actually correlates slightly better with teams’ actual won-lost records since the NFL-AFL merger. That formula suggests for each team an exponent of

((PF + PA) / G) ^ 0.2466

This gives you a 1.204 RMSE vs. wins since 1970, a very slight improvement over the 1.205 RMSE you get using FO’s formula.

At any rate, I applied the Pythagenpat exponent to each team’s schedule-adjusted points scored/allowed totals since 1970, and tweaked the pythagorean win/loss totals up/down at the league-season level to match actual league-wide win/loss totals. The result was a definitive set of pythagorean ratings for every team since the merger:

Year
Tm
pfr_id
Division
G
W
L
T
WPct
Pts
PtsO
OSRS
DSRS
adj_PF
adj_PA
adj_Pyth
2012Buffalo BillsbufAFC East1661000.375344435-0.9-5.8349.84456.840.3331
2012Miami DolphinsmiaAFC East167900.438288317-5.52.9276.24317.640.4174
2012New England PatriotsnweAFC East1612400.75055733112.20.5559.44356.040.7747
2012New York JetsnyjAFC East1661000.375281375-5.1-0.9282.64378.440.3267
2012Baltimore RavensravAFC North1610600.6253983441.91.0394.64348.040.5819
2012Cincinnati BengalscinAFC North1610600.6253913201.20.9383.44349.640.5606
2012Cleveland BrownscleAFC North1651100.313302368-4.6-0.7290.64375.240.3467
2012Pittsburgh SteelerspitAFC North168800.500336314-2.82.1319.44330.440.4806
2012Houston TexanshtxAFC South1612400.7504163311.81.7393.04336.840.5993
2012Indianapolis ColtscltAFC South1611500.688357387-1.9-2.8333.84408.840.3740
2012Jacksonville JaguarsjaxAFC South1621400.125255444-8.1-4.9234.64442.440.1700
2012Tennessee TitansotiAFC South1661000.375330471-2.6-7.4322.64482.440.2595
2012Denver BroncosdenAFC West1613300.8134812896.33.8465.04303.240.7539
2012Kansas City ChiefskanAFC West1621400.125211425-10.3-3.7199.44423.240.1368
2012Oakland RaidersraiAFC West1641200.250290443-4.6-6.2290.64463.240.2322
2012San Diego ChargerssdgAFC West167900.438350350-2.0-0.4332.24370.440.4329
2012Dallas CowboysdalNFC East168800.5003764001.4-1.2386.64383.240.5074
2012New York GiantsnygNFC East169700.5634293444.02.2428.24328.840.6662
2012Philadelphia EaglesphiNFC East1641200.250280444-5.3-3.6279.44421.640.2619
2012Washington RedskinswasNFC East1610600.6254363884.6-1.2437.84383.240.5887
2012Chicago BearschiNFC North1610600.6253752771.06.0380.24268.040.7066
2012Detroit LionsdetNFC North1641200.2503724372.1-4.4397.84434.440.4436
2012Green Bay PackersgnbNFC North1611500.6884333364.92.4442.64325.640.6911
2012Minnesota VikingsminNFC North1610600.6253793481.61.8389.84335.240.5971
2012Atlanta FalconsatlNFC South1613300.8134192992.04.5396.24292.040.6855
2012Carolina PantherscarNFC South167900.438357363-0.51.3356.24343.240.5252
2012New Orleans SaintsnorNFC South167900.4384614546.5-5.1468.24445.640.5351
2012Tampa Bay BuccaneerstamNFC South167900.4383893941.3-1.3385.04384.840.5020
2012Arizona CardinalscrdNFC West1651100.313250357-4.81.6287.44338.440.4022
2012San Francisco 49erssfoNFC West1611410.7193972733.56.7420.24256.840.7772
2012Seattle SeahawksseaNFC West1611500.6884122454.57.7436.24240.840.8186
2012St. Louis RamsramNFC West167810.469299348-2.12.4330.64325.640.5112
2011Buffalo BillsbufAFC East1661000.3753724341.2-4.5374.03426.930.4200
2011Miami DolphinsmiaAFC East1661000.375329313-2.23.2319.63303.730.5374
2011New England PatriotsnweAFC East1613300.8135133429.4-0.1505.23356.530.7234
2011New York JetsnyjAFC East168800.5003773631.2-0.3374.03359.730.5310
2011Baltimore RavensravAFC North1612400.7503782662.63.5396.43298.930.6776
2011Cincinnati BengalscinAFC North169700.5633443230.7-0.2366.03358.130.5199
2011Cleveland BrownscleAFC North1641200.250218307-7.21.8239.63326.130.3284
2011Pittsburgh SteelerspitAFC North1612400.750325227-0.86.0342.03258.930.6698
2011Houston TexanshtxAFC South1610600.6253812781.43.2377.23303.730.6393
2011Indianapolis ColtscltAFC South1621400.125243430-6.0-5.3258.83439.730.2127
2011Jacksonville JaguarsjaxAFC South1651100.313243329-7.11.4241.23332.530.3212
2011Tennessee TitansotiAFC South169700.563325317-2.21.2319.63335.730.4753
2011Denver BroncosdenAFC West168800.500309390-3.6-1.7297.23382.130.3527
2011Kansas City ChiefskanAFC West167900.438212338-10.22.1191.63321.330.2347
2011Oakland RaidersraiAFC West168800.5003594330.2-5.1358.03436.530.3790
2011San Diego ChargerssdgAFC West168800.5004063772.7-1.8398.03383.730.5298
2011Dallas CowboysdalNFC East168800.5003693470.70.9366.03340.530.5518
2011New York GiantsnygNFC East169700.5633944003.1-1.5404.43378.930.5484
2011Philadelphia EaglesphiNFC East168800.5003963283.11.7404.43327.730.6377
2011Washington RedskinswasNFC East1651100.313288367-4.30.2286.03351.730.3804
2011Chicago BearschiNFC North168800.500353341-1.53.1330.83305.330.5555
2011Detroit LionsdetNFC North1610600.6254743876.9-0.8465.23367.730.6569
2011Green Bay PackersgnbNFC North1615100.93856035911.5-0.1538.83356.530.7589
2011Minnesota VikingsminNFC North1631300.188340449-1.6-4.1329.23420.530.3530
2011Atlanta FalconsatlNFC South1610600.6254023501.81.7383.63327.730.6050
2011Carolina PantherscarNFC South1661000.3754064292.6-3.9396.43417.330.4722
2011New Orleans SaintsnorNFC South1613300.81354733910.60.8524.43342.130.7642
2011Tampa Bay BuccaneerstamNFC South1641200.250287494-4.0-6.6290.83460.530.2397
2011Arizona CardinalscrdNFC West168800.500312348-1.7-0.5327.63362.930.4416
2011San Francisco 49erssfoNFC West1613300.8133802291.76.6382.03249.330.7479
2011Seattle SeahawksseaNFC West167900.438321315-0.51.3346.83334.130.5295
2011St. Louis RamsramNFC West1621400.125193407-8.4-2.1220.43388.530.2053
2010Buffalo BillsbufAFC East1641200.250283425-1.5-3.1328.74402.040.3740
2010Miami DolphinsmiaAFC East167900.438273333-2.83.1307.94302.840.5106
2010New England PatriotsnweAFC East1614200.87551831312.62.8554.34307.640.8287
2010New York JetsnyjAFC East1611500.6883673042.24.2387.94285.240.6846
2010Baltimore RavensravAFC North1612400.7503572700.95.5367.14264.440.6929
2010Cincinnati BengalscinAFC North1641200.2503223950.4-1.9359.14382.840.4593
2010Cleveland BrownscleAFC North1651100.313271332-3.42.0298.34320.440.4565
2010Pittsburgh SteelerspitAFC North1612400.7503752322.57.7392.74229.240.7908
2010Houston TexanshtxAFC South1661000.3753904271.8-3.7381.54411.640.4508
2010Indianapolis ColtscltAFC South1610600.6254353883.7-0.9411.94366.840.5753
2010Jacksonville JaguarsjaxAFC South168800.500353419-1.2-3.3333.54405.240.3777
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2008Indianapolis ColtscltAFC South1612400.7503772982.63.9394.07290.070.6823
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2006Tennessee TitansotiAFC South168800.5003244001.0-2.3346.53367.280.4680
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2006New York GiantsnygNFC East168800.5003553621.2-1.1349.73348.080.5080
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2005Pittsburgh SteelerspitAFC North1611500.6883892583.84.0390.48265.830.7259
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2005Indianapolis ColtscltAFC South1614200.8754392475.65.2419.28246.630.7935
2005Jacksonville JaguarsjaxAFC South1612400.7503612691.13.7347.28270.630.6514
2005Tennessee TitansotiAFC South1641200.250299421-2.0-5.5297.68417.830.2988
2005Denver BroncosdenAFC West1613300.8133952585.05.8409.68237.030.7987
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2004Cleveland BrownscleAFC North1641200.250276390-1.5-1.9319.80374.200.4054
2004Pittsburgh SteelerspitAFC North1615100.9383722513.45.6398.20254.200.7576
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2004Indianapolis ColtscltAFC South1612400.75052235111.7-0.3531.00348.600.7595
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2004Tennessee TitansotiAFC South1651100.3133444390.3-4.6348.60417.400.3888
2004Denver BroncosdenAFC West1610600.6253813041.64.3369.40275.000.6794
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2004San Diego ChargerssdgAFC West1612400.7504463136.13.0441.40295.800.7404
2004Dallas CowboysdalNFC East1661000.375293405-3.0-4.8295.80420.600.2930
2004New York GiantsnygNFC East1661000.375303347-2.3-1.6307.00369.400.3893
2004Philadelphia EaglesphiNFC East1613300.8133862602.03.5375.80287.800.6648
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2004Chicago BearschiNFC North1651100.313231331-8.50.3207.80339.000.2407
2004Detroit LionsdetNFC North1661000.375296350-3.8-1.4283.00366.200.3484
2004Green Bay PackersgnbNFC North1610600.6254243804.3-4.0412.60407.800.5114
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2003Miami DolphinsmiaAFC East1610600.625311261-0.74.1322.06267.760.6138
2003New England PatriotsnweAFC East1614200.8753482382.14.9366.86254.960.7137
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2003Cleveland BrownscleAFC North1651100.313254322-5.32.4248.46294.960.4024
2003Pittsburgh SteelerspitAFC North1661000.375300327-2.51.4293.26310.960.4675
2003Houston TexanshtxAFC South1651100.313255380-4.4-1.5262.86357.360.3227
2003Indianapolis ColtscltAFC South1612400.7504473368.2-1.2464.46352.560.6775
2003Jacksonville JaguarsjaxAFC South1651100.313276331-3.30.9280.46318.960.4254
2003Tennessee TitansotiAFC South1612400.7504353247.0-0.5445.26341.360.6703
2003Denver BroncosdenAFC West1610600.6253813012.43.1371.66283.760.6658
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2003Oakland RaidersraiAFC West1641200.250270379-4.2-1.3266.06354.160.3341
2003San Diego ChargerssdgAFC West1641200.250313441-0.6-6.2323.66432.560.3241
2003Dallas CowboysdalNFC East1610600.625289260-3.22.7282.06290.160.4863
2003New York GiantsnygNFC East1641200.250243387-4.8-3.7256.46392.560.2605
2003Philadelphia EaglesphiNFC East1612400.7503742872.91.5379.66309.360.6300
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2003Chicago BearschiNFC North167900.438283346-4.20.7266.06322.160.3891
2003Detroit LionsdetNFC North1651100.313270379-4.3-1.6264.46358.960.3233
2003Green Bay PackersgnbNFC North1610600.6254423076.21.9432.46302.960.7173
2003Minnesota VikingsminNFC North169700.5634163534.0-1.1397.26350.960.5826
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2003San Francisco 49erssfoNFC West167900.4383843373.00.1381.26331.760.5911
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2001Arizona CardinalscrdNFC East167900.438295343-1.5-2.6299.41365.010.3772
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2000Indianapolis ColtscltAFC East1610600.6254293267.10.8444.32318.030.7013
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2000Chicago BearschiNFC Central1651100.313216355-6.40.1228.32329.230.2907
2000Detroit LionsdetNFC Central169700.563307307-0.11.5329.12306.830.5405
2000Green Bay PackersgnbNFC Central169700.5633533231.80.7359.52319.630.5707
2000Minnesota VikingsminNFC Central1611500.6883973714.3-2.3399.52367.630.5510
2000Tampa Bay BuccaneerstamNFC Central1610600.6253882693.43.9385.12268.430.7083
2000Arizona CardinalscrdNFC East1631300.188210443-7.2-8.1215.52460.430.1261
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2000New York GiantsnygNFC East1612400.750328246-1.33.8309.92270.030.5799
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2000Atlanta FalconsatlNFC West1641200.250252413-5.7-2.9239.52377.230.2436
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2000New Orleans SaintsnorNFC West1610600.625354305-1.22.1311.52297.230.5259
2000San Francisco 49erssfoNFC West1661000.3753884221.7-5.5357.92418.830.3962
2000St. Louis RamsramNFC West1610600.62554047112.6-9.5532.32482.830.5646
1999Baltimore RavensravAFC Central168800.500324277-1.62.5307.43293.080.5251
1999Cincinnati BengalscinAFC Central1641200.250283460-3.3-8.2280.23464.280.2099
1999Cleveland BrownscleAFC Central1621400.125217437-7.6-6.5211.43437.080.1367
1999Jacksonville JaguarsjaxAFC Central1614200.8753962171.35.1353.83251.480.6937
1999Pittsburgh SteelerspitAFC Central1661000.375317320-2.2-0.2297.83336.280.4213
1999Tennessee TitansotiAFC Central1613300.8133923242.0-1.0365.03349.080.5244
1999Buffalo BillsbufAFC East1611500.688320229-0.27.4329.83214.680.7319
1999Indianapolis ColtscltAFC East1613300.8134233336.6-0.5438.63341.080.6543
1999Miami DolphinsmiaAFC East169700.5633263361.20.7352.23321.880.5524
1999New England PatriotsnweAFC East168800.500299284-1.03.2317.03281.880.5672
1999New York JetsnyjAFC East168800.5003083090.22.9336.23286.680.5930
1999Denver BroncosdenAFC West1661000.3753143181.02.4349.03294.680.5997
1999Kansas City ChiefskanAFC West169700.5633903225.51.3421.03312.280.6789
1999Oakland RaidersraiAFC West168800.5003903295.71.0424.23317.080.6751
1999San Diego ChargerssdgAFC West168800.500269316-3.92.9270.63286.680.4614
1999Seattle SeahawksseaAFC West169700.5633382981.92.6363.43291.480.6303
1999Chicago BearschiNFC Central1661000.375272341-3.01.0285.03317.080.4312
1999Detroit LionsdetNFC Central168800.5003223230.11.8334.63304.280.5547
1999Green Bay PackersgnbNFC Central168800.5003573412.5-0.9373.03347.480.5412
1999Minnesota VikingsminNFC Central1610600.6253993355.2-0.8416.23345.880.6137
1999Tampa Bay BuccaneerstamNFC Central1611500.688270235-4.16.7267.43225.880.5930
1999Arizona CardinalscrdNFC East1661000.375245382-5.5-3.2245.03384.280.2433
1999Dallas CowboysdalNFC East168800.500352276-0.33.4328.23278.680.5949
1999New York GiantsnygNFC East167900.438299358-1.8-1.2304.23352.280.4054
1999Philadelphia EaglesphiNFC East1651100.313272357-3.3-0.9280.23347.480.3661
1999Washington RedskinswasNFC East1610600.6254433776.8-3.9441.83395.480.5689
1999Atlanta FalconsatlNFC West1651100.313285380-4.0-3.0269.03381.080.2916
1999Carolina PantherscarNFC West168800.5004213812.9-4.0379.43397.080.4664
1999New Orleans SaintsnorNFC West1631300.188260434-5.3-6.9248.23443.480.1831
1999San Francisco 49erssfoNFC West1641200.250295453-4.5-6.8261.03441.880.2038
1999St. Louis RamsramNFC West1613300.8135262428.73.2472.23281.880.7875
1998Baltimore RavensravAFC Central1661000.375269335-5.2-0.2257.35343.590.3389
1998Cincinnati BengalscinAFC Central1631300.188268452-4.5-6.5268.55444.390.2253
1998Jacksonville JaguarsjaxAFC Central1611500.6883923383.1-0.6390.15349.990.5780
1998Pittsburgh SteelerspitAFC Central167900.438263303-5.11.3258.95319.590.3838
1998Tennessee OilersotiAFC Central168800.500330320-0.50.5332.55332.390.5088
1998Buffalo BillsbufAFC East1610600.6254003334.40.7410.95329.190.6475
1998Indianapolis ColtscltAFC East1631300.188310444-0.1-5.3338.95425.190.3656
1998Miami DolphinsmiaAFC East1610600.625321265-0.76.5329.35236.390.6983
1998New England PatriotsnweAFC East169700.5633373291.12.8358.15295.590.6261
1998New York JetsnyjAFC East1612400.7504162665.45.8426.95247.590.8060
1998Denver BroncosdenAFC West1614200.8755013099.5-0.6492.55349.990.7211
1998Kansas City ChiefskanAFC West167900.438327363-0.7-2.4329.35378.790.4204
1998Oakland RaidersraiAFC West168800.500288356-4.1-1.5274.95364.390.3405
1998San Diego ChargerssdgAFC West1651100.313241342-7.7-0.7217.35351.590.2472
1998Seattle SeahawksseaAFC West168800.5003723100.90.6354.95330.790.5529
1998Chicago BearschiNFC Central1641200.250276368-3.4-0.3286.15345.190.3945
1998Detroit LionsdetNFC Central1651100.313306378-1.9-1.5310.15364.390.4085
1998Green Bay PackersgnbNFC Central1611500.6884083194.30.7409.35329.190.6451
1998Minnesota VikingsminNFC Central1615100.93855629613.21.6551.75314.790.8264
1998Tampa Bay BuccaneerstamNFC Central168800.500314295-2.33.5303.75284.390.5485
1998Arizona CardinalscrdNFC East169700.563325378-1.6-5.8314.95433.190.3137
1998Dallas CowboysdalNFC East1610600.6253812750.92.6354.95298.790.6144
1998New York GiantsnygNFC East168800.500287309-4.31.1271.75322.790.4051
1998Philadelphia EaglesphiNFC East1631300.188161344-12.3-0.5143.75348.390.1215
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1998Atlanta FalconsatlNFC West1614200.8754422897.03.0452.55292.390.7637
1998Carolina PantherscarNFC West1641200.2503364131.0-2.4356.55378.790.4697
1998New Orleans SaintsnorNFC West1661000.375305359-1.51.6316.55314.790.5120
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1997Baltimore RavensravAFC Central166910.406326345-0.4-0.2325.61335.050.4824
1997Cincinnati BengalscinAFC Central167900.4383554051.8-4.0360.81395.850.4406
1997Jacksonville JaguarsjaxAFC Central1611500.6883943184.41.0402.41315.850.6503
1997Pittsburgh SteelerspitAFC Central1611500.6883723072.72.6375.21290.250.6559
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1997Buffalo BillsbufAFC East1661000.375255367-4.0-0.5268.01339.850.3587
1997Indianapolis ColtscltAFC East1631300.188313401-0.6-3.8322.41392.650.3771
1997Miami DolphinsmiaAFC East169700.5633393271.00.3348.01327.050.5393
1997New England PatriotsnweAFC East1610600.6253692892.33.0368.81283.850.6581
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1997Denver BroncosdenAFC West1612400.7504722878.12.6461.61290.250.7686
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1997Oakland RaidersraiAFC West1641200.250324419-0.1-5.0330.41411.850.3621
1997San Diego ChargerssdgAFC West1641200.250266425-4.1-4.8266.41408.650.2545
1997Seattle SeahawksseaAFC West168800.5003653621.8-1.7360.81359.050.5034
1997Chicago BearschiNFC Central1641200.250263421-3.1-5.3282.41416.650.2718
1997Detroit LionsdetNFC Central169700.5633793063.00.9380.01317.450.6124
1997Green Bay PackersgnbNFC Central1613300.8134222825.32.4416.81293.450.7101
1997Minnesota VikingsminNFC Central169700.5633543591.7-1.8359.21360.650.4978
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1997Arizona CardinalscrdNFC East1641200.250283379-2.6-3.8290.41392.650.3187
1997Dallas CowboysdalNFC East1661000.375304314-1.71.0304.81315.850.4784
1997New York GiantsnygNFC East1610510.656307265-1.83.3303.21279.050.5505
1997Philadelphia EaglesphiNFC East166910.406317372-0.7-2.3320.81368.650.4133
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1997Atlanta FalconsatlNFC West167900.438320361-1.2-2.9312.81378.250.3824
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1997New Orleans SaintsnorNFC West1661000.375237327-6.8-0.6223.21341.450.2646
1997San Francisco 49erssfoNFC West1613300.8133752652.42.7370.41288.650.6514
1997St. Louis RamsramNFC West1651100.313299359-1.8-2.2303.21367.050.3826
1996Baltimore RavensravAFC Central1641200.2503714413.9-6.8389.18435.630.4254
1996Cincinnati BengalscinAFC Central168800.5003723692.5-2.6366.78368.430.4964
1996Houston OilersotiAFC Central168800.5003453190.10.8328.38314.030.5271
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1996Pittsburgh SteelerspitAFC Central1610600.6253442570.05.1326.78245.230.6661
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1996Indianapolis ColtscltAFC East169700.563317334-1.80.2297.98323.630.4486
1996Miami DolphinsmiaAFC East168800.5003393250.6-0.4336.38333.230.5052
1996New England PatriotsnweAFC East1611500.6884183134.90.2405.18323.630.6395
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1996Denver BroncosdenAFC West1613300.8133912753.73.9385.98264.430.7190
1996Kansas City ChiefskanAFC West169700.563297300-0.72.5315.58286.830.5575
1996Oakland RaidersraiAFC West167900.4383402930.32.1331.58293.230.5746
1996San Diego ChargerssdgAFC West168800.5003103760.4-2.1333.18360.430.4497
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1996Chicago BearschiNFC Central167900.438283305-1.41.2304.38307.630.4928
1996Detroit LionsdetNFC Central1651100.313302368-0.4-2.5320.38366.830.4146
1996Green Bay PackersgnbNFC Central1613300.8134562108.76.6465.98221.230.8673
1996Minnesota VikingsminNFC Central169700.563298315-0.90.7312.38315.630.4929
1996Tampa Bay BuccaneerstamNFC Central1661000.375221293-5.92.9232.38280.430.3906
1996Arizona CardinalscrdNFC East167900.438300397-1.7-5.8299.58419.630.2964
1996Dallas CowboysdalNFC East1610600.625286250-2.95.3280.38242.030.5853
1996New York GiantsnygNFC East1661000.375242297-5.71.5235.58302.830.3542
1996Philadelphia EaglesphiNFC East1610600.6253633412.1-1.6360.38352.430.5135
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1996Atlanta FalconsatlNFC West1631300.188309461-0.2-8.9323.58469.230.2736
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1996New Orleans SaintsnorNFC West1631300.188229339-7.1-1.0213.18342.830.2417
1996San Francisco 49erssfoNFC West1612400.7503982573.74.3385.98258.030.7307
1996St. Louis RamsramNFC West1661000.375303409-1.7-4.6299.58400.430.3231
1995Cincinnati BengalscinAFC Central167900.4383493740.1-1.9345.35374.040.4529
1995Cleveland BrownscleAFC Central1651100.313289356-3.2-0.6292.55353.240.3885
1995Houston OilersotiAFC Central167900.438348324-0.10.9342.15329.240.5279
1995Jacksonville JaguarsjaxAFC Central1641200.250275404-4.4-3.7273.35402.840.2774
1995Pittsburgh SteelerspitAFC Central1611500.6884073273.41.2398.15324.440.6318
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1995Indianapolis ColtscltAFC East169700.563331316-1.70.4316.55337.240.4643
1995Miami DolphinsmiaAFC East169700.5633983323.1-0.4393.35350.040.5783
1995New England PatriotsnweAFC East1661000.375294377-2.8-2.6298.95385.240.3489
1995New York JetsnyjAFC East1631300.188233384-7.9-3.3217.35396.440.1896
1995Denver BroncosdenAFC West168800.5003883453.3-0.2396.55346.840.5892
1995Kansas City ChiefskanAFC West1613300.8133582410.86.9356.55233.240.7412
1995Oakland RaidersraiAFC West168800.5003483321.11.0361.35327.640.5653
1995San Diego ChargerssdgAFC West169700.563321323-0.62.1334.15310.040.5502
1995Seattle SeahawksseaAFC West168800.5003633661.6-2.2369.35378.840.4874
1995Chicago BearschiNFC Central169700.5633923603.1-0.8393.35356.440.5670
1995Detroit LionsdetNFC Central1610600.6254363365.91.0438.15327.640.6839
1995Green Bay PackersgnbNFC Central1611500.6884043143.52.5399.75303.640.6717
1995Minnesota VikingsminNFC Central168800.5004123855.1-1.4425.35366.040.6007
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1992New England PatriotsnweAFC East1621400.125205363-6.5-4.5195.68371.740.1792
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1991Green Bay PackersgnbNFC Central1641200.250273313-3.3-1.4251.04326.190.3491
1991Minnesota VikingsminNFC Central168800.500301306-0.2-1.8300.64332.590.4402
1991Tampa Bay BuccaneerstamNFC Central1631300.188199365-7.0-4.2191.84370.990.1723
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1991Los Angeles RamsramNFC West1631300.188234390-2.6-3.4262.24358.190.3193
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1990Miami DolphinsmiaAFC East1612400.7503362420.54.2330.02254.710.6465
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1990New York JetsnyjAFC East1661000.375295345-2.0-2.6290.02363.510.3569
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1990Green Bay PackersgnbNFC Central1661000.375271347-3.7-0.4262.82328.310.3619
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1990New Orleans SaintsnorNFC West168800.500274275-4.73.4246.82267.510.4470
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1989Houston OilersotiAFC Central169700.5633654122.8-5.3374.46414.630.4329
1989Pittsburgh SteelerspitAFC Central169700.563265326-4.30.6260.86320.230.3773
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1989Indianapolis ColtscltAFC East168800.500298301-2.72.2286.46294.630.4821
1989Miami DolphinsmiaAFC East168800.500331379-0.6-4.2320.06397.030.3649
1989New England PatriotsnweAFC East1651100.313297391-3.0-4.0281.66393.830.2999
1989New York JetsnyjAFC East1641200.250253411-4.9-5.2251.26413.030.2226
1989Denver BroncosdenAFC West1611500.6883622263.75.7388.86238.630.7688
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1989Chicago BearschiNFC Central1661000.3753583771.8-1.8358.46358.630.4988
1989Detroit LionsdetNFC Central167900.438312364-1.7-1.8302.46358.630.3941
1989Green Bay PackersgnbNFC Central1610600.6253623561.0-2.0345.66361.830.4701
1989Minnesota VikingsminNFC Central1610600.6253512750.24.1332.86264.230.6364
1989Tampa Bay BuccaneerstamNFC Central1651100.313320419-0.4-4.7323.26405.030.3585
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1989New York GiantsnygNFC East1612400.7503482521.94.6360.06256.230.6970
1989Philadelphia EaglesphiNFC East1611500.6883422741.93.2360.06278.630.6531
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1989Atlanta FalconsatlNFC West1631300.188279437-3.2-5.9278.46424.230.2545
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1989San Francisco 49erssfoNFC West1614200.8754422535.84.9422.46251.430.7858
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1988Pittsburgh SteelerspitAFC Central1651100.3133364210.1-5.5325.65411.990.3567
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1988Miami DolphinsmiaAFC East1661000.3753193801.0-3.9340.05386.390.4223
1988New England PatriotsnweAFC East169700.563250284-4.54.4252.05253.590.4999
1988New York JetsnyjAFC East168710.5313723543.7-2.4383.25362.390.5395
1988Denver BroncosdenAFC West168800.5003273520.1-3.4325.65378.390.4092
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1988San Diego ChargerssdgAFC West1661000.375231332-7.1-0.2210.45327.190.2627
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1988Chicago BearschiNFC Central1612400.7503122150.06.6324.05218.390.7227
1988Detroit LionsdetNFC Central1641200.250220313-5.01.0244.05307.990.3677
1988Green Bay PackersgnbNFC Central1641200.250240315-3.90.6261.65314.390.3942
1988Minnesota VikingsminNFC Central1611500.6884062336.54.3428.05255.190.7902
1988Tampa Bay BuccaneerstamNFC Central1651100.313261350-1.9-2.2293.65359.190.3803
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1988New York GiantsnygNFC East1610600.6253593041.50.7348.05312.790.5699
1988Philadelphia EaglesphiNFC East1610600.6253793192.31.1360.85306.390.6046
1988Phoenix CardinalscrdNFC East167900.4383443980.5-3.7332.05383.190.4131
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1988Atlanta FalconsatlNFC West1651100.313244315-5.00.5244.05315.990.3531
1988Los Angeles RamsramNFC West1610600.6254072935.60.9413.65309.590.6808
1988New Orleans SaintsnorNFC West1610600.625312283-1.02.6308.05282.390.5562
1988San Francisco 49erssfoNFC West1610600.6253692943.11.7373.65296.790.6442
1987Cincinnati BengalscinAFC Central1541100.267285370-1.8-1.9296.96352.520.3952
1987Cleveland BrownscleAFC Central1510500.6673902395.45.6404.96240.020.7916
1987Houston OilersotiAFC Central159600.6003453493.3-1.1373.46340.520.5616
1987Pittsburgh SteelerspitAFC Central158700.533285299-2.13.0292.46279.020.5309
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1987Indianapolis ColtscltAFC East159600.600300238-1.35.4304.46243.020.6356
1987Miami DolphinsmiaAFC East158700.5333623353.2-1.1371.96340.520.5590
1987New England PatriotsnweAFC East158700.5333202930.42.5329.96286.520.5894
1987New York JetsnyjAFC East156900.4003343601.2-2.8341.96366.020.4582
1987Denver BroncosdenAFC West1510410.7003792883.10.8370.46312.020.6103
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1987Los Angeles RaidersraiAFC West1551000.333301289-1.81.6296.96300.020.4957
1987San Diego ChargerssdgAFC West158700.533253317-4.20.6260.96315.020.3884
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1987Chicago BearschiNFC Central1511400.7333562821.42.2344.96291.020.6076
1987Detroit LionsdetNFC Central1541100.267269384-4.5-5.3256.46403.520.2421
1987Green Bay PackersgnbNFC Central155910.367255300-5.41.8242.96297.020.3829
1987Minnesota VikingsminNFC Central158700.533336335-0.2-2.6320.96363.020.4238
1987Tampa Bay BuccaneerstamNFC Central1541100.267286360-3.0-2.6278.96363.020.3417
1987Dallas CowboysdalNFC East157800.467340348-0.2-1.7320.96349.520.4479
1987New York GiantsnygNFC East156900.400280312-2.92.1280.46292.520.4763
1987Philadelphia EaglesphiNFC East157800.4673373801.2-3.4341.96375.020.4425
1987St. Louis CardinalscrdNFC East157800.4673623682.3-3.0358.46369.020.4832
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1987Atlanta FalconsatlNFC West1531200.200205436-7.9-5.9205.46412.520.1509
1987Los Angeles RamsramNFC West156900.400317361-0.5-1.1316.46340.520.4557
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1987San Francisco 49erssfoNFC West1513200.8674592539.14.5460.46256.520.8223
1986Cincinnati BengalscinAFC Central1610600.6254093945.7-4.2419.46395.460.5357
1986Cleveland BrownscleAFC Central1612400.7503913102.90.7374.66317.060.6011
1986Houston OilersotiAFC Central1651100.313274329-4.40.4257.86321.860.3658
1986Pittsburgh SteelerspitAFC Central1661000.375307336-1.30.3307.46323.460.4657
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1980Minnesota VikingsminNFC Central169700.5633173080.0-0.3327.61332.410.4906
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1979Cincinnati BengalscinAFC Central1641200.2503374213.1-4.4370.75391.320.4667
1979Cleveland BrownscleAFC Central169700.5633593522.90.1367.55319.320.5896
1979Houston OilersotiAFC Central1611500.6883623312.91.4367.55298.520.6292
1979Pittsburgh SteelerspitAFC Central1612400.7504162626.85.1429.95239.320.8149
1979Baltimore ColtscltAFC East1651100.313271351-2.0-1.5289.15344.920.3941
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1979Miami DolphinsmiaAFC East1610600.6253412570.34.0325.95256.920.6422
1979New England PatriotsnweAFC East169700.5634113266.1-1.1418.75338.520.6359
1979New York JetsnyjAFC East168800.5003373831.1-4.0338.75384.920.4206
1979Denver BroncosdenAFC West1610600.625289262-1.65.7295.55229.720.6464
1979Kansas City ChiefskanAFC West167900.438238262-4.05.4257.15234.520.5550
1979Oakland RaidersraiAFC West169700.5633653373.80.0381.95320.920.6105
1979San Diego ChargerssdgAFC West1612400.7504112466.05.7417.15229.720.8170
1979Seattle SeahawksseaAFC West169700.5633783724.9-2.3399.55357.720.5727
1979Chicago BearschiNFC Central1610600.625306249-2.22.1285.95287.320.4988
1979Detroit LionsdetNFC Central1621400.125219365-6.6-4.4215.55391.320.1899
1979Green Bay PackersgnbNFC Central1651100.313246316-5.3-1.5236.35344.920.2873
1979Minnesota VikingsminNFC Central167900.438259337-4.5-3.1249.15370.520.2750
1979Tampa Bay BuccaneerstamNFC Central1610600.625273237-4.61.8247.55292.120.4044
1979Dallas CowboysdalNFC East1611500.6883713133.00.3369.15316.120.5983
1979New York GiantsnygNFC East1661000.375237323-5.4-0.8234.75333.720.3014
1979Philadelphia EaglesphiNFC East1611500.6883392820.32.4325.95282.520.5884
1979St. Louis CardinalscrdNFC East1651100.313307358-0.7-2.0309.95352.920.4211
1979Washington RedskinswasNFC East1610600.6253482950.91.5335.55296.920.5768
1979Atlanta FalconsatlNFC West1661000.375300388-1.9-4.7290.75396.120.3157
1979Los Angeles RamsramNFC West169700.563323309-1.20.6301.95311.320.4829
1979New Orleans SaintsnorNFC West168800.5003703602.3-3.5357.95376.920.4685
1979San Francisco 49erssfoNFC West1621400.125308416-1.3-6.1300.35418.520.3015
1978Cincinnati BengalscinAFC Central1641200.250252284-2.71.3250.06272.520.4499
1978Cleveland BrownscleAFC Central168800.5003343562.5-4.5333.26365.320.4425
1978Houston OilersotiAFC Central1610600.625283298-0.60.2283.66290.120.4869
1978Pittsburgh SteelerspitAFC Central1614200.8753561952.65.6334.86203.720.7660
1978Baltimore ColtscltAFC East1651100.313239421-2.8-6.0248.46389.320.2476
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1978Miami DolphinsmiaAFC East1611500.6883722544.23.3360.46240.520.7295
1978New England PatriotsnweAFC East1611500.6883582863.61.4350.86270.920.6547
1978New York JetsnyjAFC East168800.5003593644.4-3.3363.66346.120.5320
1978Denver BroncosdenAFC West1610600.625282198-1.96.9262.86182.920.6955
1978Kansas City ChiefskanAFC West1641200.250243327-3.0-1.4245.26315.720.3533
1978Oakland RaidersraiAFC West169700.5633112831.61.1318.86275.720.5882
1978San Diego ChargerssdgAFC West169700.5633553094.6-0.9366.86307.720.6093
1978Seattle SeahawksseaAFC West169700.5633453583.4-3.7347.66352.520.4917
1978Chicago BearschiNFC Central167900.438253274-2.70.3250.06288.520.4162
1978Detroit LionsdetNFC Central167900.438290300-0.1-1.7291.66320.520.4429
1978Green Bay PackersgnbNFC Central168710.531249269-2.21.6258.06267.720.4788
1978Minnesota VikingsminNFC Central168710.5312943061.0-1.1309.26310.920.4972
1978Tampa Bay BuccaneerstamNFC Central1651100.313241259-4.10.8227.66280.520.3805
1978Dallas CowboysdalNFC East1612400.7503842085.85.2386.06210.120.8158
1978New York GiantsnygNFC East1661000.375264298-1.1-1.3275.66314.120.4217
1978Philadelphia EaglesphiNFC East169700.563270250-1.13.4275.66238.920.5839
1978St. Louis CardinalscrdNFC East1661000.375248296-2.60.0251.66293.320.4101
1978Washington RedskinswasNFC East168800.500273283-1.10.7275.66282.120.4866
1978Atlanta FalconsatlNFC West169700.563240290-3.7-0.9234.06307.720.3430
1978Los Angeles RamsramNFC West1612400.7503162451.31.9314.06262.920.6065
1978New Orleans SaintsnorNFC West167900.438281298-0.5-1.2285.26312.520.4450
1978San Francisco 49erssfoNFC West1621400.125219350-4.6-4.5219.66365.320.2257
1977Cincinnati BengalscinAFC Central148600.5712382350.61.6248.86218.060.5811
1977Cleveland BrownscleAFC Central146800.4292692672.40.1274.06239.060.5856
1977Houston OilersotiAFC Central148600.5712992304.42.3302.06208.260.7148
1977Pittsburgh SteelerspitAFC Central149500.6432832433.92.3295.06208.260.7023
1977Baltimore ColtscltAFC East1410400.7142952213.61.5290.86219.460.6679
1977Buffalo BillsbufAFC East1431100.214160313-4.3-3.7180.26292.260.2437
1977Miami DolphinsmiaAFC East1410400.7143131974.43.5302.06191.460.7531
1977New England PatriotsnweAFC East149500.6432782172.51.5275.46219.460.6369
1977New York JetsnyjAFC East1431100.214191300-3.8-2.0187.26268.460.3027
1977Denver BroncosdenAFC West1412200.8572741481.99.4267.06108.860.8862
1977Kansas City ChiefskanAFC West1421200.1432253490.9-5.8253.06321.660.3578
1977Oakland RaidersraiAFC West1411300.7863512308.81.6363.66218.060.7861
1977San Diego ChargerssdgAFC West147700.500222205-1.45.2220.86167.660.6548
1977Seattle SeahawksseaAFC West145900.3572823734.2-8.6299.26360.860.3846
1977Chicago BearschiNFC Central149500.6432552530.0-3.6240.46290.860.3888
1977Detroit LionsdetNFC Central146800.429183252-4.4-3.0178.86282.460.2565
1977Green Bay PackersgnbNFC Central1441000.286134219-9.10.4113.06234.860.1693
1977Minnesota VikingsminNFC Central149500.643231227-1.0-0.6226.46248.860.4473
1977Tampa Bay BuccaneerstamNFC Central1421200.143103223-11.10.385.06236.260.1020
1977Dallas CowboysdalNFC East1412200.8573452127.60.2346.86237.660.7242
1977New York GiantsnygNFC East145900.357181265-4.0-2.2184.46271.260.2903
1977Philadelphia EaglesphiNFC East145900.357220207-2.51.9205.46213.860.4802
1977St. Louis CardinalscrdNFC East147700.5002722872.6-4.0276.86296.460.4607
1977Washington RedskinswasNFC East149500.643196189-3.12.6197.06204.060.4834
1977Atlanta FalconsatlNFC West147700.500179129-6.16.6155.06148.060.5279
1977Los Angeles RamsramNFC West1410400.7143021463.84.6293.66176.060.7748
1977New Orleans SaintsnorNFC West1431100.2142323360.3-9.4244.66372.060.2595
1977San Francisco 49erssfoNFC West145900.357220260-1.1-2.7225.06278.260.3778
1976Cincinnati BengalscinAFC Central1410400.7143352105.05.1338.14196.690.7840
1976Cleveland BrownscleAFC Central149500.6432672870.7-2.4277.94301.690.4419
1976Houston OilersotiAFC Central145900.357222273-2.71.0230.34254.090.4346
1976Pittsburgh SteelerspitAFC Central1410400.7143421385.79.6347.94133.690.9010
1976Baltimore ColtscltAFC East1411300.7864172469.30.4398.34262.490.7394
1976Buffalo BillsbufAFC East1421200.143245363-2.5-6.7233.14361.890.2413
1976Miami DolphinsmiaAFC East146800.429263264-1.81.7242.94244.290.4898
1976New England PatriotsnweAFC East1411300.7863762366.52.2359.14237.290.7330
1976New York JetsnyjAFC East1431100.214169383-7.1-6.8168.74363.290.1254
1976Denver BroncosdenAFC West149500.6433152062.54.4303.14206.490.7105
1976Kansas City ChiefskanAFC West145900.3572903762.2-6.9298.94364.690.3672
1976Oakland RaidersraiAFC West1413100.9293502376.32.2356.34237.290.7289
1976San Diego ChargerssdgAFC West146800.429248285-1.70.5244.34261.090.4531
1976Tampa Bay BuccaneerstamAFC West1401400.000125412-10.8-8.8116.94391.290.0439
1976Chicago BearschiNFC Central147700.5002532160.34.2272.34209.290.6456
1976Detroit LionsdetNFC Central146800.429262220-0.63.4259.74220.490.5898
1976Green Bay PackersgnbNFC Central145900.357218299-2.5-2.0233.14296.090.3508
1976Minnesota VikingsminNFC Central1411210.8213051763.75.7319.94188.290.7765
1976Dallas CowboysdalNFC East1411300.7862961940.54.0275.14212.090.6444
1976New York GiantsnygNFC East1431100.214170250-5.52.1191.14238.690.3667
1976Philadelphia EaglesphiNFC East1441000.286165286-7.3-1.4165.94287.690.2077
1976St. Louis CardinalscrdNFC East1410400.7143092673.5-1.0317.14282.090.5665
1976Washington RedskinswasNFC East1410400.7142912171.21.8284.94242.890.5896
1976Atlanta FalconsatlNFC West1441000.286172312-6.7-3.5174.34317.090.1849
1976Los Angeles RamsramNFC West1410310.7503511905.94.5350.74205.090.7840
1976New Orleans SaintsnorNFC West1441000.286253346-0.6-5.5259.74345.090.3207
1976San Francisco 49erssfoNFC West148600.571270190-1.35.0249.94198.090.6264
1976Seattle SeahawksseaNFC West1421200.143229429-2.2-12.9237.34448.690.1525
1975Cincinnati BengalscinAFC Central1411300.7863402464.02.9344.27247.560.6979
1975Cleveland BrownscleAFC Central1431100.214218372-2.6-4.2251.87346.960.3097
1975Houston OilersotiAFC Central1410400.7142932261.25.2305.07215.360.7019
1975Pittsburgh SteelerspitAFC Central1412200.8573731625.48.8363.87164.960.8755
1975Baltimore ColtscltAFC East1410400.7143952696.32.2376.47257.360.7274
1975Buffalo BillsbufAFC East148600.57142035510.4-3.4433.87335.760.6671
1975Miami DolphinsmiaAFC East1410400.7143572223.36.0334.47204.160.7725
1975New England PatriotsnweAFC East1431100.214258358-2.3-2.0256.07316.160.3729
1975New York JetsnyjAFC East1431100.214258433-1.2-7.1271.47387.560.2864
1975Denver BroncosdenAFC West146800.429254307-2.7-0.7250.47297.960.3959
1975Kansas City ChiefskanAFC West145900.357282341-0.6-3.7279.87339.960.3801
1975Oakland RaidersraiAFC West1411300.7863752555.51.3365.27269.960.6860
1975San Diego ChargerssdgAFC West1421200.143189345-6.0-3.4204.27335.760.2289
1975Chicago BearschiNFC Central1441000.286191379-4.9-6.6219.67380.560.2012
1975Detroit LionsdetNFC Central147700.500245262-3.21.3243.47269.960.4391
1975Green Bay PackersgnbNFC Central1441000.286226285-3.3-0.9242.07300.760.3708
1975Minnesota VikingsminNFC Central1412200.8573771803.55.4337.27212.560.7594
1975Dallas CowboysdalNFC East1410400.7143502684.00.1344.27286.760.6163
1975New York GiantsnygNFC East145900.357216306-6.2-0.6201.47296.560.2839
1975Philadelphia EaglesphiNFC East1441000.286225302-4.0-0.6232.27296.560.3562
1975St. Louis CardinalscrdNFC East1411300.7863562763.40.1335.87286.760.6009
1975Washington RedskinswasNFC East148600.5713252762.60.2324.67285.360.5826
1975Atlanta FalconsatlNFC West1441000.286240289-2.7-0.8250.47299.360.3930
1975Los Angeles RamsramNFC West1412200.8573121350.58.7295.27166.360.7970
1975New Orleans SaintsnorNFC West1421200.143165360-8.2-5.9173.47370.760.1347
1975San Francisco 49erssfoNFC West145900.357255286-2.2-2.5257.47323.160.3629
1974Cincinnati BengalscinAFC Central147700.5002832591.90.2281.14251.740.5704
1974Cleveland BrownscleAFC Central1441000.2862513440.5-4.7261.54320.340.3787
1974Houston OilersotiAFC Central147700.500236282-1.3-0.6236.34262.940.4391
1974Pittsburgh SteelerspitAFC Central1410310.7503051892.34.5286.74191.540.7270
1974Baltimore ColtscltAFC East1421200.143190329-4.6-4.1190.14311.940.2352
1974Buffalo BillsbufAFC East149500.643264244-0.31.4250.34234.940.5411
1974Miami DolphinsmiaAFC East1411300.7863272163.92.3309.14222.340.6948
1974New England PatriotsnweAFC East147700.5003482896.6-1.8346.94279.740.6372
1974New York JetsnyjAFC East147700.5002793001.1-3.7269.94306.340.4247
1974Denver BroncosdenAFC West147610.5363022943.4-2.8302.14293.740.5209
1974Kansas City ChiefskanAFC West145900.357233293-1.3-1.1236.34269.940.4233
1974Oakland RaidersraiAFC West1412200.8573552286.42.7344.14216.740.7624
1974San Diego ChargerssdgAFC West145900.357212285-3.5-1.3205.54272.740.3403
1974Chicago BearschiNFC Central1441000.286152279-7.4-2.9150.94295.140.1747
1974Detroit LionsdetNFC Central147700.5002562700.5-2.1261.54283.940.4526
1974Green Bay PackersgnbNFC Central146800.429210206-3.22.0209.74226.540.4582
1974Minnesota VikingsminNFC Central1410400.7143101953.62.5304.94219.540.6937
1974Dallas CowboysdalNFC East148600.5712972354.01.9310.54227.940.6846
1974New York GiantsnygNFC East1421200.143195299-3.5-2.1205.54283.940.3182
1974Philadelphia EaglesphiNFC East147700.5002422170.03.0254.54212.540.6085
1974St. Louis CardinalscrdNFC East1410400.7142852182.83.0293.74212.540.6896
1974Washington RedskinswasNFC East1410400.7143201965.74.5334.34191.540.7992
1974Atlanta FalconsatlNFC West1431100.214111271-9.7-2.6118.74290.940.1161
1974Los Angeles RamsramNFC West1410400.714263181-0.14.0253.14198.540.6424
1974New Orleans SaintsnorNFC West145900.357166263-5.7-1.7174.74278.340.2533
1974San Francisco 49erssfoNFC West146800.429226236-2.1-0.5225.14261.540.4142
1973Cincinnati BengalscinAFC Central1410400.7142862310.12.9273.69231.750.5939
1973Cleveland BrownscleAFC Central147520.571234255-2.71.3234.49254.150.4463
1973Houston OilersotiAFC Central1411300.071199447-4.4-12.4210.69445.950.1205
1973Pittsburgh SteelerspitAFC Central1410400.7143472104.23.5331.09223.350.7207
1973Baltimore ColtscltAFC East1441000.286226341-2.4-5.9238.69354.950.2635
1973Buffalo BillsbufAFC East149500.643259230-1.72.2248.49241.550.5115
1973Miami DolphinsmiaAFC East1412200.8573431505.18.3343.69156.150.8650
1973New England PatriotsnweAFC East145900.357258300-2.5-3.4237.29319.950.3172
1973New York JetsnyjAFC East1441000.286240306-1.5-1.3251.29290.550.4061
1973Denver BroncosdenAFC West147520.5713542965.4-2.5347.89307.350.5738
1973Kansas City ChiefskanAFC West147520.571231192-4.25.1213.49200.950.5293
1973Oakland RaidersraiAFC West149410.6792921750.06.6272.29179.950.7208
1973San Diego ChargerssdgAFC West1421110.179188386-3.7-8.4220.49389.950.1851
1973Chicago BearschiNFC Central1431100.214195334-4.4-4.4210.69333.950.2376
1973Detroit LionsdetNFC Central146710.4642712470.81.2283.49255.550.5580
1973Green Bay PackersgnbNFC Central145720.429202259-5.00.2202.29269.550.3300
1973Minnesota VikingsminNFC Central1412200.8572961681.76.9296.09175.750.7704
1973Dallas CowboysdalNFC East1410400.7143822036.86.0367.49188.350.8343
1973New York GiantsnygNFC East1421110.179226362-2.8-4.7233.09338.150.2777
1973Philadelphia EaglesphiNFC East145810.3933103933.5-7.7321.29380.150.3859
1973St. Louis CardinalscrdNFC East144910.3212863651.8-5.1297.49343.750.4028
1973Washington RedskinswasNFC East1410400.7143251981.26.1289.09186.950.7334
1973Atlanta FalconsatlNFC West149500.6433182242.33.6304.49221.950.6787
1973Los Angeles RamsramNFC West1412200.8573881788.15.2385.69199.550.8340
1973New Orleans SaintsnorNFC West145900.357163312-7.0-1.7174.29296.150.2153
1973San Francisco 49erssfoNFC West145900.3572623191.4-1.6291.89294.750.4884
1972Cincinnati BengalscinAFC Central148600.5712992291.23.0300.34241.590.6239
1972Cleveland BrownscleAFC Central1410400.714268249-1.61.9261.14256.990.5028
1972Houston OilersotiAFC Central1411300.071164380-7.1-5.9184.14366.190.1475
1972Pittsburgh SteelerspitAFC Central1411300.7863431753.46.6331.14191.190.7856
1972Baltimore ColtscltAFC East145900.357235252-5.12.8212.14244.390.4102
1972Buffalo BillsbufAFC East144910.321257377-1.6-5.6261.14361.990.2961
1972Miami DolphinsmiaAFC East1414001.0003851714.56.5346.54192.590.8023
1972New England PatriotsnweAFC East1431100.214192446-6.3-11.1195.34438.990.1047
1972New York JetsnyjAFC East147700.5003673244.7-4.2349.34342.390.5061
1972Denver BroncosdenAFC West145900.3573253502.9-4.7324.14349.390.4444
1972Kansas City ChiefskanAFC West148600.5712872540.42.2289.14252.790.5750
1972Oakland RaidersraiAFC West1410310.7503652485.03.0353.54241.590.7161
1972San Diego ChargerssdgAFC West144910.321264344-0.2-2.6280.74319.990.4111
1972Chicago BearschiNFC Central144910.321225275-2.81.5244.34262.590.4495
1972Detroit LionsdetNFC Central148510.6073392904.40.0345.14283.590.6159
1972Green Bay PackersgnbNFC Central1410400.7143042262.34.7315.74217.790.7063
1972Minnesota VikingsminNFC Central147700.5003012522.63.4319.94235.990.6731
1972Dallas CowboysdalNFC East1410400.7143192403.42.7331.14245.790.6712
1972New York GiantsnygNFC East148600.5713312473.52.4332.54249.990.6646
1972Philadelphia EaglesphiNFC East1421110.179145352-9.2-5.3154.74357.790.1084
1972St. Louis CardinalscrdNFC East144910.321193303-4.5-1.2220.54300.390.3130
1972Washington RedskinswasNFC East1411300.7863362182.73.7321.34231.790.6849
1972Atlanta FalconsatlNFC West147700.500269274-2.30.4251.34277.990.4316
1972Los Angeles RamsramNFC West146710.4642912860.1-0.6284.94291.990.4777
1972New Orleans SaintsnorNFC West1421110.179215361-5.3-5.0209.34353.590.2066
1972San Francisco 49erssfoNFC West148510.6073532494.91.5352.14262.590.6712
1971Cincinnati BengalscinAFC Central1441000.286284265-0.4-0.1265.37272.640.4793
1971Cleveland BrownscleAFC Central149500.6432852731.1-0.8286.37282.440.5046
1971Houston OilersotiAFC Central144910.321251330-3.0-3.8228.97324.440.2927
1971Pittsburgh SteelerspitAFC Central146800.429246292-1.8-1.8245.77296.440.3825
1971Baltimore ColtscltAFC East1410400.7143131402.08.5298.97152.240.8264
1971Buffalo BillsbufAFC East1411300.071184394-4.5-8.9207.97395.840.1600
1971Miami DolphinsmiaAFC East1410310.7503151742.45.3304.57197.040.7372
1971New England PatriotsnweAFC East146800.429238325-2.1-3.0241.57313.240.3404
1971New York JetsnyjAFC East146800.429212299-3.7-1.6219.17293.640.3254
1971Denver BroncosdenAFC West144910.321203275-5.51.0193.97257.240.3356
1971Kansas City ChiefskanAFC West1410310.7503022080.64.5279.37208.240.6653
1971Oakland RaidersraiAFC West148420.6433442784.8-1.1338.17286.640.5999
1971San Diego ChargerssdgAFC West146800.4293113412.6-6.6307.37363.640.3885
1971Chicago BearschiNFC Central146800.429185276-4.41.1209.37255.840.3793
1971Detroit LionsdetNFC Central147610.5363412866.5-2.4361.97304.840.6055
1971Green Bay PackersgnbNFC Central144820.3572742981.4-2.3290.57303.440.4687
1971Minnesota VikingsminNFC Central1411300.786245139-3.49.9223.37132.640.7568
1971Dallas CowboysdalNFC East1411300.7864062228.31.6387.17248.840.7523
1971New York GiantsnygNFC East1441000.286228362-1.7-6.3247.17359.440.2751
1971Philadelphia EaglesphiNFC East146710.464221302-2.7-1.6233.17293.640.3586
1971St. Louis CardinalscrdNFC East144910.321231279-4.0-0.2214.97274.040.3542
1971Washington RedskinswasNFC East149410.679276190-0.65.8262.57190.040.6777
1971Atlanta FalconsatlNFC West147610.5362742770.31.0275.17257.240.5372
1971Los Angeles RamsramNFC West148510.6073132604.22.4329.77237.640.6894
1971New Orleans SaintsnorNFC West144820.3572663471.4-4.1290.57328.640.4182
1971San Francisco 49erssfoNFC West149500.6433002162.43.8304.57218.040.6893
1970Cincinnati BengalscinAFC Central148600.5713122551.2-0.6286.47278.120.5201
1970Cleveland BrownscleAFC Central147700.5002862650.50.1276.67268.320.5206
1970Houston OilersotiAFC Central1431010.250217352-3.5-5.8220.67350.920.2408
1970Pittsburgh SteelerspitAFC Central145900.357210272-5.9-1.5187.07290.720.2604
1970Baltimore ColtscltAFC East1411210.8213212340.9-0.5282.27276.720.5140
1970Boston PatriotsnweAFC East1421200.143149361-9.1-6.8142.27364.920.0943
1970Buffalo BillsbufAFC East1431010.250204337-5.8-6.5188.47360.720.1691
1970Miami DolphinsmiaAFC East1410400.714297228-1.30.0251.47269.720.4591
1970New York JetsnyjAFC East1441000.286255286-2.2-2.7238.87307.520.3507
1970Denver BroncosdenAFC West145810.393253264-2.5-0.1234.67271.120.4152
1970Kansas City ChiefskanAFC West147520.571272244-0.11.6268.27247.320.5510
1970Oakland RaidersraiAFC West148420.6433002932.3-1.3301.87287.920.5314
1970San Diego ChargerssdgAFC West145630.4642822780.5-1.2276.67286.520.4800
1970Chicago BearschiNFC Central146800.4292562610.41.8275.27244.520.5734
1970Detroit LionsdetNFC Central1410400.7143472027.46.6373.27177.320.8647
1970Green Bay PackersgnbNFC Central146800.429196293-2.40.6236.07261.320.4407
1970Minnesota VikingsminNFC Central1412200.8573351435.99.2352.27140.920.9024
1970Dallas CowboysdalNFC East1410400.7142992212.54.5304.67206.720.7212
1970New York GiantsnygNFC East149500.6433012701.90.1296.27268.320.5630
1970Philadelphia EaglesphiNFC East1431010.250241332-0.9-3.6257.07320.120.3678
1970St. Louis CardinalscrdNFC East148510.6073252283.83.6322.87219.320.7234
1970Washington RedskinswasNFC East146800.4292973143.6-0.6320.07278.120.5894
1970Atlanta FalconsatlNFC West144820.357206261-3.51.2220.67252.920.4211
1970Los Angeles RamsramNFC West149410.6793252024.45.3331.27195.520.7858
1970New Orleans SaintsnorNFC West1421110.179172347-4.6-3.3205.27315.920.2606
1970San Francisco 49erssfoNFC West1410310.7503522676.40.1359.27268.320.6799


Now, as an aside, I wouldn’t go plugging those directly into the log5 formula to predict this weekend’s games just yet. You first need to regress to the mean to account for the uncertainty we see in any observed result. To do that, just add about 17.65 games of .500 performance to each team’s pythagorean Wpct, and you’ll get a “true talent” number that should yield more accurate probabilities regarding future outcomes.

{ 8 comments }

I’ve been on a major QB kick lately, and there’s no reason to stop now. Today, I want to look at a method that might tease out a quarterback’s “true talent” better than if we simply use his raw stats from the season.

Three years ago, our colleague Jason Lisk had a post on the old PFR Blog about which rate stats stay consistent when a QB changes teams. Basically, he grabbed QBs who were still in their primes and changed teams, looking at how their key rate stats correlated from one year to the next. Here’s what Jason found:

[...]I looked at the correlation coefficient for our group of 48 passers, for the year N advanced passing score compared to the year N+1 advanced passing score in each category. This should tell us whether the passers who were good in a performance area (or bad) tended to be the ones who remained good in that performance area the following season, even with the uncertainty of team changes (some positive, some negative for the quarterback).

Sack Percentage:  0.31
Completion Percentage: 0.25
Yards Per Attempt:  0.20
Touchdown Percentage: 0.12
Interception Percentage: 0.10

What do those correlations mean, exactly? Well, take sack percentage as an example. In general, a correlation of 0.31 means you can expect 31% of a QB’s difference from the mean to be repeated next year when he changes teams. In other words, you have to regress the QB’s sack rate 69% towards the mean to get the true rate that “belongs” to him. If the average sack rate is 6.1%, and a QB has a rate of 4.0% (like, say, Drew Brees this year), his “true” sack rate is probably something like 5.4% — 31% of the distance between .061 and .040.

The same concept applies to the other stats listed above. Tony Romo’s observed 66.7% completion percentage is really more like 62.5% after regressing to the mean, and so forth. Do that for every QB who had a reasonable number of attempts this year, and you get these rate stats:

Player
Tm
G
GS
Att
A-Sk%
A-Cmp%
A-YPA
A-TD%
A-INT%
R-Sk%
R-Cmp%
R-YPA
R-TD%
R-INT%
Matthew StaffordDET14146294.359.56.82.72.45.560.77.04.02.6
Drew BreesNOR14145744.062.07.66.33.15.461.47.24.52.7
Tony RomoDAL14145685.366.77.53.92.85.962.57.24.22.7
Andrew LuckIND14135646.254.67.13.53.26.159.57.14.12.7
Carson PalmerOAK14145624.460.97.13.92.55.661.17.14.22.6
Tom BradyNWE14145603.963.47.65.41.15.461.77.24.42.5
Matt RyanATL14145394.468.57.85.02.65.663.07.24.32.7
Peyton ManningDEN14145113.967.97.96.12.05.462.87.24.42.6
Brandon WeedenCLE14144985.057.26.62.83.45.760.27.04.02.7
Philip RiversSDG14144888.164.36.74.53.16.762.07.04.22.7
Joe FlaccoBAL14144876.559.17.14.12.16.260.77.14.22.6
Eli ManningNYG14144873.060.47.44.13.15.161.07.14.22.7
Sam BradfordSTL14144826.860.26.83.72.36.360.97.04.22.6
Matt SchaubHOU14134764.064.77.54.62.15.562.07.24.32.6
Aaron RodgersGNB14144748.766.77.66.81.76.962.57.24.52.6
Andy DaltonCIN14144727.562.57.05.53.06.561.57.14.42.7
Josh FreemanTAM14144694.354.87.45.32.65.559.67.14.32.7
Ryan FitzpatrickBUF14134445.961.76.65.03.46.061.37.04.32.7
Christian PonderMIN14144256.663.15.93.32.86.261.66.94.12.7
Ryan TannehillMIA14144245.858.76.92.42.86.060.57.04.02.7
Cam NewtonCAR14144237.258.28.24.32.46.460.47.34.22.6
Mark SanchezNYJ14144187.354.86.43.14.16.559.66.94.12.8
Ben RoethlisbergerPIT11113985.764.17.35.51.56.061.97.14.42.6
Jay CutlerCHI13133778.559.77.04.53.76.860.87.14.22.8
Russell WilsonSEA14143536.962.97.65.92.56.361.67.24.42.7
Robert Griffin IIIWAS13133517.466.48.35.11.16.562.57.34.32.5
Michael VickPHI993167.958.56.93.52.86.660.57.04.12.7
Blaine GabbertJAX10102787.358.36.03.22.26.560.46.94.12.6
Matt CasselKAN982776.458.16.52.24.36.260.47.04.02.8
Jake LockerTEN992695.657.67.03.33.35.960.37.14.12.7
Matt HasselbeckTEN852216.062.46.23.22.36.161.56.94.12.6
Nick FolesPHI652176.559.46.22.31.86.260.76.94.02.6
Alex SmithSFO9921710.070.08.06.02.37.363.47.34.42.6
Chad HenneJAX842168.551.96.73.72.36.858.87.04.22.6
John SkeltonARI762016.954.25.61.04.56.459.46.83.82.8
Kevin KolbARI6518312.959.66.44.41.68.260.86.94.22.6
Brady QuinnKAN861599.159.75.81.33.87.060.86.83.92.8
Colin KaepernickSFO1151548.365.68.44.51.36.862.37.34.32.5
Ryan LindleyARI631416.651.14.30.04.36.358.66.53.72.8

(“A-” before a stat means the actual observed rate; “R-” means the regressed rate.)

Now we just need to reconstruct the player’s raw passing line as though he posted those rate stats instead of his actual rates. Cmp%, YPA, TD%, and INT% are easy (just multiply by attempts), and Sack% can be derived via simple algebra:

Sacks_new = (-reg_sk% * Attempts) / (reg_sk% – 1)

(Sack yards can be assumed by multiplying raw sack yards per sack by the new sack total.)

Finally, we plug the new totals into the Adjusted Net Yards Per Attempt formula, and we have a QB stat that is sort of like baseball’s Fielding Independent Pitching (FIP), which also seeks to reduce the noise and teammate interactions in a pitcher’s ERA by reducing his performance to only those elements he has control over — strikeouts, walks, and home runs.

Here are the 2012 leaders in QB-FIP (along with their regressed totals):

Rk
Player
Age
Tm
G
GS
Cmp
Att
Yds
TD
Int
Sk
SkYds
QB-FIP
1Peyton Manning36DEN141432151136972313291916.22
2Tom Brady35NWE141434656040272414322146.20
3Robert Griffin III22WAS13132193512568159241666.15
4Colin Kaepernick25SFO1159615411307411766.11
5Ben Roethlisberger30PIT111124639828361710251416.11
6Matt Ryan27ATL141433953938932314322256.11
7Eli Manning31NYG141429748734762013261656.09
8Drew Brees33NOR141435257441182616332376.08
9Josh Freeman24TAM141427946933502012271816.08
10Aaron Rodgers29GNB141429647434022112351976.06
11Cam Newton23CAR141425642330861811292036.05
12Russell Wilson24SEA14142173532539169241486.05
13Alex Smith28SFO99138217157510617976.04
14Matt Schaub31HOU141329547634072012272286.01
15Tony Romo32DAL141435556840712415352555.97
16Andy Dalton25CIN141429047233362113331765.94
17Joe Flacco27BAL141429548734532013322095.93
18Carson Palmer33OAK141434356239802315332545.92
19Ryan Fitzpatrick30BUF141327244431011912291515.89
20Andrew Luck23IND141333656439902315372305.89
21Matthew Stafford24DET141438262944132517372565.88
22Jake Locker24TEN991622691900117171085.88
23Michael Vick32PHI991913162223138221245.84
24Sam Bradford25STL141429448233802013322165.82
25Brandon Weeden29CLE141430049834772014301985.80
26Ryan Tannehill24MIA141425742429871711272055.78
27Chad Henne27JAX8412721615119616945.78
28Philip Rivers31SDG141430248834222113352215.77
29Matt Cassel30KAN98167277192811818975.75
30Jay Cutler29CHI131322937726611610281875.74
31Christian Ponder24MIN141426242529121711281645.70
32Nick Foles23PHI65132217150096141025.70
33Matt Hasselbeck37TEN85136221152596141055.70
34Mark Sanchez26NYJ141424941829031712291785.68
35Blaine Gabbert23JAX10101682781907117191385.61
36Kevin Kolb28ARI6511118312708516965.61
37Brady Quinn28KAN869715910866412705.50
38John Skelton24ARI7611920113658614895.46
39Ryan Lindley23ARI6383141921549725.15
Lg Average5.92
{ 3 comments }

These guys are pretty good.

These guys are pretty good.

After posting about SRS-style quarterback ratings on Monday, I was thinking about other things we can do with game-by-game data like that. In his QBGOAT series, Chase likes to compare QBs to the league average, which makes a lot of sense for all-time ratings — you want to reward guys who are at least above-average in a ranking like that. However, if we want seasonal value, perhaps average is too high a baseline.

Over at Football Outsiders, Aaron Schatz has always compared to “replacement level”, borrowing a concept from baseball. I like that approach, but replacement level can be hard to empirically determine. So for the purposes of this post, I wanted to come up with a quick-and-dirty baseline to which we can compare QBs.

To that end, I looked at all players who were not their team’s primary passer in each game since 2010. Weighted by recency and the number of dropbacks by each passer, they performed at roughly a 4.4 Adjusted Net Yards Per Attempt level. This is not necessarily the replacement level, but it does seem to be the “bench level” — i.e., the ANYPA you could expect from a backup-caliber QB across the league.

Using 4.4 ANYPA as the baseline, we get the following values for 2012:

Quarterback
QBYAB
Tom Brady1888.1
Peyton Manning1708.2
Matt Ryan1453.4
Drew Brees1441.8
Aaron Rodgers1337.4
Robert Griffin III1226.6
Matt Schaub1205.1
Josh Freeman1140.1
Cam Newton1128.2
Tony Romo1120.2
Ben Roethlisberger1082.8
Carson Palmer1011.9
Eli Manning1002.9
Joe Flacco914.8
Russell Wilson890.5
Matthew Stafford834.1
Andy Dalton756.9
Andrew Luck691.6
Sam Bradford616.3
Alex Smith558.5
Colin Kaepernick506.5
Ryan Fitzpatrick481.1
Philip Rivers447.7
Ryan Tannehill409.6
Brandon Weeden320.4
Michael Vick317.5
Jake Locker316.9
Jay Cutler293.8
Chad Henne217.4
Kirk Cousins156.8
Nick Foles152.5
Shaun Hill151.9
Matt Hasselbeck134.0
Kevin Kolb121.4
Blaine Gabbert92.2
Christian Ponder91.0
Mohamed Sanu87.7
Kyle Orton62.8
Matt Moore52.5
Derek Anderson30.1
Matt Flynn23.7
Dan Orlovsky17.6
Greg McElroy11.4
Tyrod Taylor9.2
Rusty Smith9.1
Chase Daniel5.6
Tyler Thigpen2.7
Graham Harrell-1.6
Terrelle Pryor-4.4
Matt Leinart-5.1
David Carr-5.9
Tim Tebow-6.3
Mark Sanchez-13.3
Charlie Batch-17.8
Kellen Clemens-22.4
Ryan Mallett-45.9
Byron Leftwich-46.6
Matt Cassel-47.7
Brad Smith-50.0
T.J. Yates-55.1
Jason Campbell-88.4
Brady Quinn-146.4
John Skelton-309.2
Ryan Lindley-382.0

If we weigh each game by how recent the results took place, we get this list:

Quarterback
Wgtd QBYAB
Tom Brady1527.6
Drew Brees1205.4
Peyton Manning1202.0
Matt Ryan1129.8
Aaron Rodgers1109.4
Tony Romo961.1
Cam Newton936.6
Matt Schaub900.3
Robert Griffin III869.5
Eli Manning795.5
Ben Roethlisberger793.9
Josh Freeman790.3
Carson Palmer760.4
Russell Wilson722.9
Matthew Stafford687.5
Joe Flacco666.3
Andy Dalton520.4
Andrew Luck479.9
Sam Bradford459.9
Colin Kaepernick443.0
Alex Smith399.3
Philip Rivers384.9
Ryan Fitzpatrick324.0
Ryan Tannehill313.1
Brandon Weeden266.5
Michael Vick249.9
Jay Cutler236.8
Jake Locker192.4
Chad Henne178.7
Kirk Cousins158.7
Nick Foles150.5
Matt Hasselbeck133.1
Shaun Hill84.4
Kevin Kolb70.6
Matt Moore64.8
Kyle Orton59.8
Mohamed Sanu47.4
Matt Flynn47.4
Blaine Gabbert39.9
Dan Orlovsky26.3
Tim Tebow16.3
Derek Anderson16.3
Greg McElroy10.3
Chase Daniel5.1
Rusty Smith4.8
Tyrod Taylor4.0
Tyler Thigpen-0.8
Graham Harrell-1.4
Matt Leinart-2.8
David Carr-3.3
Terrelle Pryor-4.4
Charlie Batch-7.6
Kellen Clemens-14.0
Matt Cassel-24.2
T.J. Yates-29.7
Brad Smith-33.2
Byron Leftwich-38.3
Christian Ponder-39.4
Ryan Mallett-44.6
Jason Campbell-51.1
Mark Sanchez-91.0
Brady Quinn-113.4
John Skelton-263.6
Ryan Lindley-340.5

This kind of thing isn’t exactly the most advanced stat in the world, but it’s pretty good if you want to sort QBs into general groups based on how good they are (the assumption being that a player who never plays is implicitly a bench-level player by definition).

{ 2 comments }

Here’s a quick set of quarterback ratings I was messing around with, based on Doug’s Simple Rating System. The basic setup: I took every passer-game (Att > 0) since the 2010 season, weighting for recency according to Wayne Winston’s method. I ran the data through the SRS to adjust for the quality of opponent pass defenses, creating a predicted Net YPA rate for each passer in each game via the following formula:

Predicted NYPA = League Constant + Home-Field Advantage + Passer Rating – Opponent Pass D Rating

The league constant in this case was a Net YPA of 6.24; the homefield component (which was positive while at home, negative on the road, and 0 in Super Bowls) was 0.05. Minimize the sum of squared errors between predicted and actual NYPA for each passer-game (weighted by recency and how many dropbacks the passer had in the game), and you’ve got a set of opponent-adjusted, recency-weighted QB ratings.

Throwing out the Brett Favres and Curtis Painters of the world who haven’t been active this year, here are the full ratings:

Player
Wgtd Drpbks
NYPA Rating
Mohamed Sanu0.660.94
Brian Hoyer0.29.80
Shaun Hill8.85.13
Derek Anderson2.93.96
Kirk Cousins47.22.37
Chase Daniel1.22.02
Matt Flynn13.21.74
Tom Brady467.01.23
Kyle Orton20.71.16
Russell Wilson266.00.95
Robert Griffin III257.70.92
Peyton Manning382.90.78
Drew Brees472.30.75
Matt Ryan440.80.73
Colin Kaepernick147.40.72
Eli Manning399.10.64
Cam Newton359.50.64
Tony Romo468.30.54
Aaron Rodgers394.90.50
Kellen Clemens9.70.45
Matt Schaub376.30.39
Alex Smith176.10.39
Charlie Batch66.10.37
Josh Freeman395.00.28
Ben Roethlisberger324.60.23
Carson Palmer445.60.15
Matthew Stafford521.20.10
Joe Flacco410.40.00
Rusty Smith3.3-0.03
Tyrod Taylor2.5-0.10
Andrew Luck433.0-0.11
Jake Locker192.4-0.11
Sam Bradford399.1-0.15
Josh McCown4.7-0.17
Matt Moore36.9-0.18
Tarvaris Jackson26.4-0.20
Ryan Tannehill320.8-0.20
Rex Grossman27.3-0.21
Jordan Palmer0.0-0.27
Michael Vick231.4-0.30
Brandon Weeden367.1-0.40
Andy Dalton395.9-0.40
Dennis Dixon0.1-0.41
Ryan Fitzpatrick374.6-0.43
Jay Cutler307.5-0.43
Philip Rivers419.2-0.49
Drew Stanton0.5-0.56
Matt Hasselbeck181.8-0.58
Chad Henne212.4-0.59
Matt Cassel196.9-0.62
Mark Sanchez318.0-0.69
Dan Orlovsky21.3-0.76
Joe Webb5.3-0.77
Tyler Thigpen6.2-0.88
Colt McCoy24.7-0.90
Tim Tebow28.3-0.96
John Skelton172.9-1.04
Bruce Gradkowski1.4-1.09
Nick Foles205.3-1.16
Blaine Gabbert213.0-1.23
Christian Ponder334.1-1.23
T.J. Yates22.1-1.24
Kevin Kolb132.1-1.38
Jason Campbell55.1-1.46
Brady Quinn147.2-1.49
Charlie Whitehurst3.3-1.91
Byron Leftwich45.4-2.13
Trent Edwards0.4-2.13
Luke McCown2.5-2.14
Jimmy Clausen1.3-2.17
Ryan Mallett3.1-2.26
Caleb Hanie7.9-2.30
Ryan Lindley133.6-2.37
Greg McElroy7.0-2.51
Graham Harrell2.0-2.79
David Carr1.2-4.09
Matt Leinart1.6-4.71
Brad Smith0.7-6.04
Terrelle Pryor1.0-7.09

[click to continue…]

{ 1 comment }

On November 15, 2010, Michael Vick humiliated the Washington Redskins before a national television audience on Monday Night Football. The big news right before kickoff was that the ‘Skins had signed former Eagle QB Donovan McNabb to a five-year, $78 million contract extension, an ironic note that wouldn’t be lost on observers as McNabb’s replacement, Vick, put together one of the greatest all-around quarterbacking performances in NFL history:

Date
Tm
 
Opp
Result
Cmp
Att
Cmp%
Yds
TD
Int
Rate
Sk
SkYd
ANYPA
Rush
R_Yds
R_TD
11/15/2010PHI@WASW 59-28202871.4%33340150.71114.28802

McNabb, on the other hand, was mediocre, going 17-31 with 295 yards and 2 TDs, but also 3 picks. More damning, Washington’s offense produced zero points until the Redskins were in a 35-0 hole and the game was essentially over.

Fast-forward 735 days, though, and the Redskins got their payback. An injured Vick was on the shelf, the Eagles entered the game on a 5-game losing streak, and electrifying rookie Robert Griffin III extended it to six with a brilliant performance of his own:

Date
Tm
 
Opp
Result
Cmp
Att
Cmp%
Yds
TD
Int
Rate
Sk
SkYd
ANYPA
Rush
R_Yds
R_TD
11/18/2012WASPHIW 31-6141593.3%20040158.32816.012840

So, which performance was better, Vick’s original or RGIII’s remix? Vick threw for 133 more yards, rushed for practically the same amount as Griffin (on 4 fewer carries), and produced more total TDs. Then again, Griffin completed 93% of his passes and put together the vaunted 158.3 “perfect” QB rating. It’s a tough call.

Sound off with your opinion below!


{ 9 comments }

Here’s my weekly set of power ratings, according to a weighted version of the Simple Rating System:

Rk
Team
W
L
Talent
PWAG
Off
Def
SRS
wpa_loc
wpa_veg
wpa_1st
wpa_2nd
wpa_3rd
wpa_4ot
1Chicago Bears710.65867.7%6.0-6.012.00.0000.9220.802-0.3490.4581.167
2San Francisco 49ers620.60562.3%0.3-11.511.70.0001.2350.0880.1890.576-0.087
3New England Patriots530.55366.7%9.6-1.010.7-0.0671.4080.7120.3040.368-1.724
4Houston Texans710.65864.1%4.1-5.69.70.1351.6480.0740.7800.2540.110
5Denver Broncos530.55350.1%6.0-3.39.30.0000.213-0.398-0.6040.2081.581
6New York Giants630.57560.4%7.8-0.68.40.0670.5280.2520.450-0.2070.410
7Green Bay Packers630.57566.0%6.6-0.67.20.0671.4310.7560.011-0.463-0.302
8Atlanta Falcons800.71175.4%3.8-3.27.10.0000.6880.7350.4840.4181.675
9Seattle Seahawks540.52550.1%-1.2-6.14.9-0.067-0.2920.770-0.4240.1510.362
10Tampa Bay Buccaneers440.50048.4%4.30.63.70.000-0.5540.831-0.2201.095-1.153
11Pittsburgh Steelers530.55363.6%-1.8-4.02.2-0.1350.6250.2480.2460.0070.010
12Dallas Cowboys350.44742.7%-1.5-3.62.1-0.1350.160-0.322-0.5750.531-0.658
13Carolina Panthers260.39531.6%-2.6-3.81.20.000-0.439-0.7660.8030.153-1.751
14Miami Dolphins440.50046.8%-2.4-3.41.0-0.135-0.3710.1581.2010.945-1.799
15Detroit Lions440.50049.6%2.82.00.8-0.1350.335-0.426-0.728-0.4811.435
16Baltimore Ravens620.60568.0%1.41.5-0.10.0000.678-0.1170.429-0.0371.047
17New Orleans Saints350.44753.8%4.45.3-0.90.0000.425-0.152-0.350-0.078-0.846
18Minnesota Vikings540.52544.9%-0.10.9-1.10.0670.2380.301-0.344-0.2830.520
19Washington Redskins360.42537.7%1.83.0-1.2-0.067-0.410-0.2210.3281.173-2.303
20New York Jets350.44748.7%0.11.8-1.70.135-0.642-0.396-0.278-0.5150.695
21San Diego Chargers440.50051.2%-3.3-1.4-1.90.0000.5380.6390.2740.319-1.771
22Arizona Cardinals450.47544.0%-5.7-3.2-2.50.067-0.935-0.356-0.125-0.3561.205
23St Louis Rams350.44738.0%-4.2-1.4-2.80.067-1.223-0.1240.027-0.2950.548
24Cincinnati Bengals350.44741.3%1.26.3-5.10.000-0.0440.6590.152-1.164-0.604
25Philadelphia Eagles350.44747.5%-6.1-0.6-5.50.0000.452-1.300-0.5860.675-0.241
26Indianapolis Colts530.55351.3%-4.11.7-5.80.135-0.8120.623-0.7970.3391.512
27Cleveland Browns270.37531.5%-6.80.4-7.20.067-1.406-0.514-0.7010.163-0.109
28Oakland Raiders350.44745.1%-0.37.3-7.50.000-0.298-0.1940.182-1.9581.268
29Buffalo Bills350.44738.4%-0.28.6-8.8-0.135-0.4750.1910.1150.035-0.731
30Jacksonville Jaguars170.34232.8%-9.12.6-11.70.000-1.202-0.242-0.449-0.187-0.921
31Tennessee Titans360.42541.6%-2.010.2-12.10.067-1.401-0.9790.688-0.7050.830
32Kansas City Chiefs170.34236.9%-8.86.3-15.00.000-1.021-1.332-0.132-1.1400.626


KEY:
Talent - Regressed WPct talent for 2012; Talent = (W + 5.5) / (G + 11)
PWAG - Probability of Winning Any Game
Off - Offensive SRS (positive = better)
Def - Defensive SRS (negative = better)
SRS - Simple Rating System (Off + Def)
wpa_loc - Win Probability Added from location of games
wpa_veg - Win Probability Added from Vegas lines
wpa_1st - Win Probability Added in 1st quarter
wpa_2nd - Win Probability Added in 2nd quarter
wpa_3rd - Win Probability Added in 3rd quarter
wpa_4ot - Win Probability Added in 4th qtr/overtime

{ 7 comments }

In Tuesday’s post, I outlined a method of regressing a team’s record to the mean to estimate its “true winning percentage talent” (the trick is to add eleven games of .500 ball to their record, at any point in the season). In the comments, FP reader Dave wondered if we could incorporate last year’s true WPct talent into our talent assessment for this season, so I thought I’d run a quick regression to look at that.

My dataset was simply every game from 2003-2012 (including Monday night’s game). For each game, I recorded:

  • Whether the game was a win, loss, or tie for the team in question. Wins got you a “1”, ties a “0.5”, losses a “0”.
  • The team’s WPct talent estimate going into the game. So in the first game of the season, that’s (0+5.5)/(0+11)=0.500 for everybody; meanwhile, for an 11-4 team going into the final game of the season, it’s (11+5.5)/(15+11)=0.635.
  • The team’s WPct talent estimate from the previous season.

I then set up a logistic regression to predict whether the game was a win or a loss based on the two WPct talent variables, this year and last year:

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7686  -1.1489   0.1616   1.1429   1.7072  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)    -2.6936     0.1982 -13.589  < 2e-16 ***
currenttalent   4.0297     0.3509  11.485  < 2e-16 ***
prevtalent      1.3571     0.2666   5.091 3.57e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 6712.4  on 4843  degrees of freedom
Residual deviance: 6508.0  on 4841  degrees of freedom
AIC: 6516.1

Number of Fisher Scoring iterations: 4

That means to predict your likelihood of winning any given game, you plug your WPct talent numbers from this season and last season into this formula:

WPct ~ 1 / (1 + EXP(2.693606 - 4.029688*(Current_Talent) - 1.357123*(Prev_Talent)))

It's important to note the size of the coefficients here -- the current WPct talent coefficient is three times as big as that of last season's WPct talent, so it has much more bearing on the prediction.

At any rate, here are the probabilities of winning any given game that this formula implies for this year's teams:

Year
Team
Games
Wins
Current_Talent
Prev_Talent
p(W_any_gm)
2012atl770.6940.57470.8%
2012sfo860.6050.68566.3%
2012htx760.6390.57465.9%
2012gnb850.5530.75963.7%
2012chi760.6390.50063.6%
2012rav750.5830.64863.1%
2012nyg860.6050.53761.6%
2012nwe850.5530.68561.4%
2012pit740.5280.64857.8%
2012den740.5280.50052.8%
2012mia740.5280.42650.3%
2012crd840.5000.50050.0%
2012det730.4720.57449.7%
2012min850.5530.31549.0%
2012sea840.5000.46348.7%
2012cin730.4720.53748.5%
2012nor720.4170.68547.9%
2012dal730.4720.50047.2%
2012phi730.4720.50047.2%
2012rai730.4720.50047.2%
2012sdg730.4720.50047.2%
2012oti830.4470.53746.0%
2012clt740.5280.27845.3%
2012nyj830.4470.50044.7%
2012buf730.4720.42644.7%
2012tam730.4720.35242.2%
2012was830.4470.38941.0%
2012ram830.4470.27837.4%
2012kan710.3610.46335.2%
2012cle820.3950.35234.9%
2012car710.3610.42634.1%
2012jax710.3610.38932.9%
{ 11 comments }

(I originally posted this at the S-R Blog, but I thought it would be very appropriate here as well.)

WARNING: Math post.

PFR user Brad emailed over the weekend with an interesting question:

“Wondering if you’ve ever tracked or how it would be possible to find records vs. records statistics….for instance a 3-4 team vs. a 5-2 team…which record wins how often? but for every record matchup in every week.”

That’s a cool concept, and one that I could answer historically with a query when I get the time. But in the meantime, here’s what I believe is a valid way to estimate that probability…

  1. Add eleven games of .500 ball to the team’s current record (at any point in the season). So if a team is 3-4, their “true” wpct talent is (3 + 5.5) / (7 + 11) = .472. If their opponent is 5-2, it would be (5 + 5.5) / (7 + 11) = .583.
  2. Use the following equation to estimate the probability of Team A beating Team B at a neutral site:

    p(Team A Win) = Team A true_win% *(1 – Team B true_win%)/(Team A true_win% * (1 – Team B true_win%) + (1 – Team A true_win%) * Team B true_win%)

  3. You can even factor in home-field advantage like so:

    p (Team A Win) = [(Team A true_win%) * (1 - Team B true_win%) * HFA]/[(Team A true_win%) * (1 - Team B true_win%) * HFA +(1 - Team A true_win%) * (Team B true_win%) * (1 - HFA)]

    In the NFL, home teams win roughly 57% of the time, so HFA = 0.57.

This means in Brad’s hypothetical matchup of a 5-2 team vs. a 3-4 team, we would expect the 5-2 team to win .583 *(1 – .472)/(.583 * (1 – .472) + (1 – .583) * .472) = 61% of the time at a neutral site.

Really Technical Stuff:

Now, you may be wondering where I came up with the “add 11 games of .500 ball” part. That comes from this Tangotiger post about true talent levels for sports leagues.

Since the NFL expanded to 32 teams in 2002, the yearly standard deviation of team winning percentage is, on average, 0.195. This means var(observed) = 0.195^2 = 0.038. The random standard deviation of NFL records in a 16-game season would be sqrt(0.5*0.5/16) = 0.125, meaning var(random) = 0.125^2 = 0.016.

var(true) = var(observed) – var(random), so in this case var(true) = 0.038 – 0.016 = 0.022. The square root of 0.022 is 0.15, so 0.15 is stdev(true), the standard deviation of true winning percentage talent in the current NFL.

Armed with that number, we can calculate the number of games a season would need to contain in order for var(true) to equal var(random) using:

0.25/stdev(true)^2

In the NFL, that number is 11 (more accurately, it’s 11.1583, but it’s easier to just use 11). So when you want to regress an NFL team’s W-L record to the mean, at any point during the season, take eleven games of .500 ball (5.5-5.5), and add them to the actual record. This will give you the best estimate of the team’s “true” winning percentage talent going forward.

That’s why you use the “true” wpct number to plug into Bill James’ log5 formula (see step 2 above), instead of the teams’ actual winning percentages. Even a 16-0 team doesn’t have a 100% probability of winning going forward — instead, their expected true wpct talent is something like (16 + 5.5) / (16 + 11) = .796.

(For more info, see this post, and for a proof of this method, read what Phil Birnbaum wrote in 2011.)

{ 19 comments }

Here are the current SRS Ratings, weighted for the recency of each game, along with each team’s quarter-by-quarter Win Probability Added (WPA) so far this season:

Rank
Team
pfr_id
OSRS
OSOS
DSRS
DSOS
SRS
SOS
wpa_loc
wpa_vegas
wpa_1st
wpa_2nd
wpa_3rd
wpa_4th/ot
1Chicago Bearschi7.91.08.0-1.415.9-0.4-0.0680.4760.1280.0170.7290.219
2San Francisco 49erssfo-0.2-0.111.42.211.22.00.0680.956-0.0660.0590.566-0.083
3Houston Texanshtx6.9-1.04.2-0.211.1-1.20.0681.419-0.0380.9430.127-0.020
4Green Bay Packersgnb7.53.92.81.910.45.7-0.0680.9260.596-0.011-0.446-0.498
5New York Giantsnyg7.71.62.3-1.610.0-0.10.0680.351-0.0600.2780.0290.834
6Denver Broncosden5.2-0.11.61.56.81.40.000-0.060-0.644-0.8160.1831.338
7New England Patriotsnwe7.4-0.4-0.7-0.36.7-0.7-0.0681.2090.737-0.0170.363-1.724
8Seattle Seahawkssea-2.74.18.70.75.94.8-0.068-0.3350.708-0.588-0.0940.877
9Atlanta Falconsatl2.5-2.53.2-1.25.7-3.60.0000.6320.5250.1990.4041.240
10St Louis Ramsram-2.22.65.21.93.04.60.068-1.039-0.1430.348-0.2830.550
11Minnesota Vikingsmin-0.6-1.33.2-1.32.6-2.60.0680.2110.835-0.3620.0750.673
12Washington Redskinswas5.80.6-3.21.92.62.5-0.068-0.3810.1720.6971.235-2.155
13Dallas Cowboysdal-0.73.32.51.51.74.9-0.1360.365-0.216-0.3590.0510.295
14Arizona Cardinalscrd-4.61.25.0-1.10.40.10.068-0.452-0.0850.088-0.3311.212
15Miami Dolphinsmia-2.90.43.1-0.90.2-0.50.000-0.549-0.0110.7261.272-1.436
16Tampa Bay Buccaneerstam-0.6-2.70.80.50.1-2.20.136-0.5310.583-0.2760.276-1.188
17New York Jetsnyj0.00.60.11.00.11.50.068-0.605-0.080-0.084-0.4950.695
18Baltimore Ravensrav0.2-0.8-1.5-1.1-1.3-1.90.0680.536-0.4520.5870.0570.705
19New Orleans Saintsnor5.6-0.7-7.2-0.4-1.6-1.20.0000.552-0.193-0.476-0.019-0.864
20Detroit Lionsdet4.92.9-6.5-2.4-1.60.5-0.0680.249-0.313-0.833-0.3530.819
21Pittsburgh Steelerspit-2.5-2.60.1-1.3-2.4-3.9-0.1360.700-0.0270.2900.163-0.991
22San Diego Chargerssdg-2.3-3.8-0.4-0.1-2.7-3.90.0000.2020.7530.2140.294-1.463
23Carolina Pantherscar-4.11.50.71.4-3.42.90.136-0.246-0.8330.124-0.212-0.968
24Philadelphia Eaglesphi-6.6-0.62.50.3-4.0-0.40.0000.466-0.675-0.2960.729-0.224
25Indianapolis Coltsclt-3.20.7-3.1-0.4-6.30.30.136-0.5920.446-0.291-0.0660.367
26Cincinnati Bengalscin-0.9-1.2-5.7-2.6-6.7-3.8-0.0680.0090.7620.304-1.197-0.311
27Cleveland Brownscle-5.1-3.0-2.6-0.1-7.7-3.1-0.068-1.153-0.411-0.8000.161-0.229
28Oakland Raidersrai-3.40.6-6.6-1.5-10.0-0.90.000-0.368-0.3900.014-1.5291.272
29Buffalo Billsbuf1.10.0-11.1-1.9-10.0-1.9-0.068-0.3030.313-0.0340.180-0.589
30Tennessee Titansoti-3.0-1.6-8.42.1-11.40.5-0.068-1.194-0.5960.449-0.6671.576
31Jacksonville Jaguarsjax-8.8-0.3-3.31.0-12.10.80.000-0.647-0.229-0.144-0.227-0.754
32Kansas City Chiefskan-8.9-2.6-6.11.0-15.0-1.60.000-0.802-1.0940.049-0.9770.823
{ 7 comments }

Here are the current SRS Ratings, using the recency-weighted system I described on Monday:

Rank
Team
pfr_id
OFFSRS
OFFSOS
DEFSRS
DEFSOS
SRS
SOS
1Chicago Bearschi7.50.27.6-1.915.1-1.8
2New York Giantsnyg9.93.12.8-1.712.71.4
3San Francisco 49erssfo-0.10.110.92.010.82.1
4New England Patriotsnwe8.90.2-0.3-0.68.6-0.4
5Green Bay Packersgnb5.32.02.01.67.33.7
6Seattle Seahawkssea-2.63.99.11.16.45.0
7Houston Texanshtx4.4-1.31.5-1.55.9-2.8
8Denver Broncosden4.5-1.01.01.05.50.0
9Atlanta Falconsatl1.8-3.33.5-0.75.2-4.0
10Tampa Bay Buccaneerstam-0.6-1.84.41.63.8-0.2
11St Louis Ramsram-2.82.26.61.53.83.7
12Dallas Cowboysdal1.45.12.03.03.58.1
13Baltimore Ravensrav1.4-1.52.0-0.93.4-2.5
14Arizona Cardinalscrd-3.71.75.5-0.81.80.8
15Washington Redskinswas7.41.0-5.8-0.21.60.8
16Minnesota Vikingsmin-2.3-3.72.9-0.50.7-4.2
17Miami Dolphinsmia-2.70.42.3-1.5-0.4-1.1
18Detroit Lionsdet5.32.9-7.5-3.5-2.2-0.6
19Carolina Pantherscar-2.32.7-0.12.1-2.34.8
20New York Jetsnyj-2.1-0.5-0.7-0.2-2.8-0.8
21Philadelphia Eaglesphi-6.5-0.73.71.6-2.81.0
22San Diego Chargerssdg-2.4-4.0-1.6-1.1-4.0-5.1
23Cincinnati Bengalscin0.2-1.9-4.9-1.6-4.7-3.5
24Pittsburgh Steelerspit-2.1-2.4-2.7-3.1-4.8-5.5
25New Orleans Saintsnor2.6-2.3-7.60.0-5.0-2.3
26Cleveland Brownscle-2.1-1.8-3.11.3-5.2-0.5
27Buffalo Billsbuf1.01.3-9.4-1.0-8.40.4
28Indianapolis Coltsclt-2.01.5-6.7-0.5-8.61.0
29Oakland Raidersrai-4.60.6-6.20.0-10.90.6
30Kansas City Chiefskan-7.4-1.2-6.60.6-14.0-0.7
31Jacksonville Jaguarsjax-10.60.1-3.41.7-14.01.8
32Tennessee Titansoti-5.1-1.2-8.91.7-14.00.5


Also, just for fun, here’s how SRS sees this weekend’s games going (with the Vegas lines and over/unders for comparison’s sake):

game_id
year_id
game_date
home_team
srs
away_team
srs
vegas_line
vegas_o/u
srs_line
srs_o/u
201210220chi201210/22/2012chi15.1det-2.2-5.047.5-19.658.9
201210210nyg201210/21/2012nyg12.7was1.6-6.550.0-13.566.5
201210210buf201210/21/2012buf-8.4oti-14.0-3.046.5-7.960.3
201210210tam201210/21/2012tam3.8nor-5.03.049.5-11.151.4
201210210min201210/21/2012min0.7crd1.8-6.040.5-1.231.8
201210210clt201210/21/2012clt-8.7cle-5.2-3.045.01.251.9
201210210cin201210/21/2012cin-4.7pit-4.82.546.0-2.452.0
201210210nwe201210/21/2012nwe8.6nyj-2.8-10.547.5-13.854.0
201210210ram201210/21/2012ram3.8gnb7.35.544.51.240.1
201210210rai201210/21/2012rai-10.9jax-14.0-4.043.0-5.540.7
201210210car201210/21/2012car-2.4dal3.52.045.53.543.5
201210210htx201210/21/2012htx5.9rav3.4-6.048.0-4.848.5
{ 7 comments }

Here’s a quick Monday data dump… I ran the Simple Rating System (for offense and defense) on this year’s NFL results, but instead of weighing each game equally, I used Wayne Winston’s method of giving more weight to recent outcomes. Winston’s system is simply to give each game a weight of:

λ ^ (weeks ago)

In the NFL’s case, a λ of 0.95 works best for predicting future outcomes. The games from yesterday were (6 – week 6) = 0 weeks ago, so they get a weight of .95 ^ 0, or 1.00. Last week’s games were (6 – week 5) = 1 week ago, and get a weight of .95 ^ 1 = 0.95; the opening-week games were (6 – week 1) = 5 weeks ago, and get a weight of .95 ^ 5 = 0.77. See how it works?

Using this weighted form of SRS, here are the rankings going into tonight’s game (NOTE: For defenses, negative SRS numbers are better):

Rk
Team
Gms
W
L
Off
Def
SRS
wpa_loc
wpa_vegas
wpa_1st
wpa_2nd
wpa_3rd
wpa_4th/ot
1Chicago Bears5417.6-7.615.2-0.070.480.130.020.730.22
2New York Giants6429.8-3.012.80.000.260.060.210.050.43
3San Francisco 49ers6420.5-10.410.90.000.80-0.040.210.30-0.28
4New England Patriots6339.00.48.6-0.141.000.620.000.27-1.75
5Green Bay Packers6334.9-2.57.50.000.670.440.06-0.64-0.53
6Seattle Seahawks642-2.0-8.76.60.00-0.200.68-0.740.191.07
7Houston Texans6514.6-1.25.80.001.34-0.190.760.12-0.02
8Atlanta Falcons6602.3-3.25.40.000.610.520.200.411.25
9Denver Broncos5232.5-2.24.70.07-0.13-0.34-0.630.120.41
10Tampa Bay Buccaneers523-0.5-4.54.00.07-0.420.350.200.36-1.06
11St Louis Rams633-2.8-6.73.90.00-0.770.020.27-0.100.58
12Dallas Cowboys5231.4-2.33.6-0.070.23-0.21-0.18-0.400.13
13Baltimore Ravens6511.4-2.13.50.140.65-0.310.760.060.70