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Brady likes the second half of the season

Brady likes the second half of the season

When we think about the most dominant teams of all time, the New England Patriots of the last few years don’t leap immediately to mind. Yet, their performance late in the year has been mind-bogglingly good. From 2010-13, New England went 29-3 in the final eight games of each season, a record that no other team since 1960 can match over any four-year period. Including their three games this year, the Patriots are on a 32-3 run in regular-season games in the second half of the season. From 2010-2013, the Patriots also have the biggest four-year point differential in second-half games in the history of football.

Part of that huge point differential comes from the higher point totals that teams have than they did in the past, and from New England’s offensive-centric philosophy. As a result, when we look at Pythagenpat records, the Patriots are not as dominant.1 Here are the hundred best late-season teams over any four-year period, according to Pythagenpat record. The Patriots from 2010-13 rank only 38th on the list, behind four other recent Patriots’ runs, some of those overlapping with 2010-13. The Patriots have been great and it is an unlikely outcome that they’d have no Super Bowls in the decade so far, but they also have not been quite as strong in terms of their true strength as their second-half records would suggest. As a high-scoring team, we would have expected them to lose more of their regular season games than they have. [click to continue…]

  1. I used 0.251 as the value in the Pythagenpat formula to find exponents for each team-year. []
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These two men look important

The two best regular season quarterbacks of all time?

Yesterday, I explained the methodology behind the formula involved in ranking every quarterback season since 1960. Today, I’m going to present the career results. Converting season value to career value isn’t as simple as it might seem. Generally, we don’t want a player who was very good for 12 years to rank ahead of a quarterback who was elite for ten. Additionally, we don’t want to give significant penalties to players who struggled as rookies or hung around too long; we’re mostly concerned with the peak value of the player.

What I’ve historically done — and done here — is to give each quarterback 100% of his value or score from his best season, 95% of his score in his second best season, 90% of his score in his third best season, and so on. This rewards quarterbacks who played really well for a long time and doesn’t kill players with really poor rookie years or seasons late in their career. It also helps to prevent the quarterbacks who were compilers from dominating the top of the list. For visibility reasons, the table below displays only the top 25 quarterbacks initially, but you can change that number in the filter or click on the right arrow to see the remaining quarterbacks.1

Here’s how to read the table. Manning’s first year was in 1998, and his last in 2013. He’s had 8,740 “dropbacks” in his career, which include pass attempts, sacks, and rushing touchdowns. His career value — using the 100/95/90 formula2 is 12,769, putting him at number one. His strength of schedule has been perfectly average over his career; as a reminder, the SOS column is shown just for reference, as SOS is already incorporated into these numbers (so while Tom Brady has had a schedule that’s 0.25 ANY/A tougher than average, that’s already incorporated into his 10,063 grade). Manning is not yet eligible for the Hall of Fame, of course, but I’ve listed the HOF status of each quarterback in the table. Note that I only have quarterback records going back to 1960; therefore, for quarterbacks who played before and during (or after) 1960, only their post-1960 record is displayed. In addition, SOS adjustments are only for the years beginning in 1960. [click to continue…]

  1. Note that while yesterday’s list was just from 1960 to 2013, the career list reflects every season in history, using the same methodology as used in GQBOAT IV. []
  2. And including negative seasons. []
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Can you spot the GOAT?

Can you spot the GOAT?

In 2006, I took a stab at ranking every quarterback in NFL history. Two years later, I acquired more data and made enough improvements to merit publishing an updated and more accurate list of the best quarterbacks the league has ever seen. In 2009, I tweaked the formula again, and published a set of career rankings, along with a set of strength of schedule, era and weather adjustments, and finally career rankings which include those adjustments and playoff performances.  And two years ago, I revised the formula and produced a new set of career rankings.

This time around, I’m not going to tweak the formula much (that’s for GQBOAT VI), but I do have one big change that I suspect will be well-received.  Let’s review the methodology.

Methodology

We start with plain old yards per attempt. I then incorporate sack data by removing sack yards from the numerator and adding sacks to the denominator.1 To include touchdowns and interceptions, I gave a quarterback 20 yards for each passing touchdown and subtracted 45 yards for each interception. This calculation — (Pass Yards + 20 * PTD – 45 * INT – Sack Yards Lost) / (Sacks + Pass Attempts) forms the basis for Adjusted Net Yards per Attempt, one of the key metrics I use to evaluate quarterbacks. For purposes of this study, I did some further tweaking. I’m including rushing touchdowns, because our goal is to measure quarterbacks as players. There’s no reason to separate rushing and passing touchdowns from a value standpoint, so all passing and rushing touchdowns are worth 20 yards and are calculated in the numerator of Adjusted Net Yards per Attempt. To be consistent, I also include rushing touchdowns in the denominator of the equation. This won’t change anything for most quarterbacks, but feels right to me. A touchdown is a touchdown.

Now, here comes the twist.  In past year, I’ve compared each quarterback’s “ANY/A” — I put that term in quotes because what we’re really using is ANY/A with a rushing touchdowns modifier — and then calculated a value over average statistic after comparing that rate to the league average. For example, if a QB has an “ANY/A” of 7.0 and the NFL average “ANY/A” is 5.0, and the quarterback has 500 “dropbacks” — i.e., pass attempts plus sacks plus rushing touchdowns — then the quarterback gets credit for 1,000 yards above average. [click to continue…]

  1. I have individual game sack data for every quarterback back to 2008. For seasons between 1969 and 2007, I have season sack data and team game sack data, so I was able to derive best-fit estimates for each quarterback in each game. For seasons between 1960 and 1969, I gave each quarterback an approximate number of sacks, giving him the pro-rated portion of sacks allowed by the percentage of pass attempts he threw for the team. []
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Even for Football Perspective, this is a very math-heavy post. I’ve explained all the dirty work and fine details behind this system, but if you want to skip to the results section, I’ll understand. Heck, it might even make more sense to start there and then work your way back to the top.

Background

In 2012, Neil Paine wrote a fascinating article on championship leverage in the NBA, building on Tom Tango’s work on the same topic in Major League Baseball. Championship Leverage was borne out of the desire to quantify the relative importance of any particular playoff game. Truth be told, this philosophy has more practical application in sports where each playoff round consists of a series of games. But Neil applied this system to the NFL playoffs and crunched all the data for every playoff game since 1965. Then he was kind enough to send it my way, and I thought this data would make for a good post.

The best way to explain Championship Leverage is through an example. For purposes of this exercise, we assume that every game is a 50/50 proposition. At the start of the playoffs, the four teams playing on Wild Card weekend all have a 1-in-16 chance of winning the Super Bowl (assuming a 50% chance of winning each of four games). This means after the regular season ended, the Colts had a 6.25% chance of winning the Super Bowl. After beating Kansas City, Indianapolis’ win probability doubled to 12.5%. Win or lose, the Colts’ Super Bowl probability was going to move by 6.25%, a number known as the Expected Delta.

New England, by virtue of a first round bye, began the playoffs with a 12.5% chance of winning the Lombardi. With a win over Indianapolis, the Patriots’ probability of winning the Super Bowl jumped 12.5% to 25%; had New England lost, the odds would have moved from 12.5% to zero. Therefore, the Expected Delta in a Division round game is 12.5%. [click to continue…]

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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…]

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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. []
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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…]

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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…]

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On Monday, I explained my methodology for ranking every wide receiver in football history, and yesterday, I presented a list of the best single seasons of all time. Today the career list of the top 150 wide receivers. As usual, I implemented a 100/95/90 formula, giving a player credit for 100% of his production in his best season, 95% of his value in his second-best season, 90% in his third year, and so on. The table below is fully sortable and lists the first and last year each person played wide receiver1; you can use the search feature to find the best receiver to ever play for each team (for example, typing ‘ram’ for the Rams ‘clt’ for the Colts.)
[click to continue…]

  1. Note that I have excluded seasons where a wide receiver played running back or tight end. This is generally not a big deal, but does hurt someone like Lenny Moore. []
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Yesterday, I explained my methodology for ranking every wide receiver in football history. Today I’m going to present a list of single-season leaders, which presents some problems.

I think the method I described yesterday does a good job adjusting for era, as receivers are only given credit for their yards above the baseline, which is different each season. But there are some other complicating factors unique to football. Seasons have had varying lengths: a receiver who plays 12 games in a 12-game season can’t be penalized the way you would penalize a receiver who only plays in 12 games now. Since older receivers are generally at a disadvantage for many reasons, I decided to simply pro-rate the value for all non-16 game seasons as if it was a 16-game season. However, I have also included downward adjustments for players in other leagues and during World War II.1

The table below lists the top 200 wide receiver seasons of all-time.
[click to continue…]

  1. The fine print: For players in 1943, 1944, and 1945, and for players in the AAFC, I only gave the receivers credit for 60% of the value they created. For the AFL, I gave players 60% of their value in 1960 and 1961, 70% in 1962, 80% in 1963, 90% in 1964, and 100% in 1965 through 1969. In case it wasn’t obvious, all of these adjustments are arbitrary. []
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We know how this story will end.

We know how this story will end.

Regular readers know that one of my projects this off-season is to come up with a better way to grade wide receivers. I first attempted to rank every wide receiver four years ago. That study, which I will reproduce this week, has some positives and negatives. My goal is to eventually come up with four or five different ranking systems, so consider the series this week to be the first of several ranking systems to come.

The first step in this system is to combine the three main stats — receptions, receiving yards and receiving touchdowns — into one stat: Adjusted Catch Yards. We know that a passing touchdown is worth about 20 yards, so I’m crediting a receiver with 20 yards for every touchdown reception. Next, we need to decide on an appropriate bonus for each reception.

We want to give receivers credit for receptions because, all else being equal, a receiver with more receptions is providing more value because he’s likely generating more first downs. I looked at all receivers over a 12-year period who picked up at least 30 receiving first downs. I then used the number of receptions and receiving yards those players had as inputs, and performed a regression to estimate how many first downs should be expected. The best-fit formula was:
[click to continue…]

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The original standard for postseason success.

On Wednesday, I explained the methodology for grading each quarterback in each season. Yesterday, I came up with an all-time career list of the best quarterbacks based on their regular season play. Today, a look at playoff performances.

Using the same formula, we can grade each quarterback in each game and adjust for era1. However, it should be obvious that the sample sizes here are incredibly small, and the stats are even less likely to tell the true story when looking at just one game. Strength of schedule becomes a significant factor here, as well. But, caveats aside, there’s a lot we can do with playoff data. For example, we can rank every quarterback performance in Super Bowl history:

RkQBTmOppSBW/LAttPydTDINTCYCYPVAL
1Joe MontanaSFOMIA19W353313040611272
2Steve YoungSFOSDG29W363256043111.1264
3Troy AikmanDALBUF27W302734038112.3258
4Joe MontanaSFODEN24W292975039713.3256
5Kurt WarnerSTLTEN34W45414204479.7225
6Jim PlunkettOAKPHI15W212613032014.5219
7Phil SimmsNYGDEN21W252683032312.4216
8Doug WilliamsWASDEN22W293404135811.6211
9John ElwayDENATL33W293361133111181
10Jim McMahonCHINWE20W202560028411.6174
11Joe MontanaSFOCIN23W36357203519165
12Jake DelhommeCARNWE38L33323303258.8146
13Tom BradyNWECAR38W48354313697.7141
14Terry BradshawPITDAL13W30318412667.8140
15Mark RypienWASBUF26W33292212878.7128
16Terry BradshawPITRAM14W213092321410.2123
17Bart StarrGNBKAN1W23250212248.7121
18Terry BradshawPITDAL10W19209202009.5121
19Aaron RodgersGNBPIT45W39304303488.3118
20Brett FavreGNBNWE31W27246202688.1111
21Drew BreesNORIND44W39288203218107
22Ken StablerOAKMIN11W19180101838.7103
23Troy AikmanDALPIT30W23209102188.791
24Kurt WarnerARIPIT43L43377313297.387
25John ElwayDENNYG21L37304112706.581
26Bart StarrGNBOAK2W24202101826.579
27Joe MontanaSFOCIN16W22157101887.876
28Tom BradyNWEPHI39W33236202597.475
29Joe NamathNYJBAL3W28206001956.568
30Peyton ManningINDNOR44L45333113086.867
31Ken AndersonCINSFO16L34300222546.467
32Jeff HostetlerNYGBUF25W32222102346.966
33Bob LeeMINOAK11L9811010010.965
34Roger StaubachDALMIA6W19119201406.763
35Steve McNairTENSTL34L36214002085.661
36Eli ManningNYGNWE46W4029610302761
37Terry BradshawPITMIN9W1496101046.559
38Kurt WarnerSTLNWE36L4436512287655
39Roger StaubachDALPIT13L30228311614.653
40Jim KellyBUFNYG25L30212002056.652
41Jim PlunkettRAIWAS18W25172101746.448
42Roger StaubachDALDEN12W25183101424.845
43Brad JohnsonTAMOAK37W34215212106.245
44Earl MorrallBALDAL5W15147011026.843
45Ben RoethlisbergerPITARI43W30256112096.537
46Bob GrieseMIAMIN8W77300637.934
47Brett FavreGNBDEN32L42256312405.634
48Daryle LamonicaOAKGNB2L34208211814.932
49Fran TarkentonMINMIA8L28182011414.531
50Gary KubiakDENNYG21L448004510.225
51Troy AikmanDALBUF28W27207011595.521
52Tom BradyNWENYG46L41276212616.120
53Len DawsonKANGNB1L27211111354.219
54Trent DilferBALNYG35W25153101545.517
55Tom BradyNWESTL36W27145101545.314
56Len DawsonKANMIN4W1714211974.910
57Gary KubiakDENSFO24L32800256.87
58Frank ReichBUFWAS26L11100109.45
59Steve YoungSFODEN24W32000206.55
60Vince FerragamoRAMPIT14L25212011274.44
61Danny WhiteDALDEN12W250031.14
62Matt HasselbeckSEAPIT40L49273112344.53
63Ben RoethlisbergerPITGNB45L40263222115.12
64Bill MusgraveSFOSDG29W160065.20
65Fran TarkentonMINOAK11L35205121323.7-4
66Babe ParilliNYJBAL3W10000-0.4-5
67Zeke BratkowskiGNBKAN1W1000-1-0.8-5
68Jay SchroederWASDEN22W1000-1-0.6-6
69Pete BeathardKANGNB1L5170071.3-6
70Tony BanksBALNYG35W1000-1-0.7-6
71Eli ManningNYGNWE42W34255211824.9-8
72Bob GrieseMIAWAS7W118811443.4-8
73Peyton ManningINDCHI41W38247111844.7-10
74John ElwayDENGNB32W2212301984.3-12
75Don StrockMIAWAS17L3000-3-0.9-17
76Steve FullerCHINWE20W4000-3-0.6-23
77Ron JaworskiPHIOAK15L38291131463.8-28
78Joe TheismannWASMIA17W2314322742.8-33
79Dan MarinoMIASFO19L50318122194.1-33
80Elvis GrbacSFOSDG29W1000-30-28.2-36
81Johnny UnitasBALNYJ3L2411001652.7-37
82David WoodleyMIAWAS17L149711281.9-37
83Donovan McNabbPHINWE39L51357332494.5-40
84Norris WeeseDENDAL12L102200-18-1.6-42
85Gale GilbertSDGSFO29L63001-17-2.7-44
86Gary CuozzoMINKAN4L31601-32-9.6-46
87Johnny UnitasBALDAL5W98812-12-1.3-47
88Tom BradyNWENYG42L48266102194.1-53
89Bob GrieseMIADAL6L2313401301.3-58
90Boomer EsiasonCINSFO23L2514401782.6-65
91Jim KellyBUFDAL28L50260011823.4-66
92Stan HumphriesSDGSFO29L49275121893.7-67
93Ben RoethlisbergerPITSEA40W2112302452-67
94Tony EasonNWECHI20L6000-40-5.6-72
95Roger StaubachDALPIT10L2420423371.2-78
96Chris ChandlerATLDEN33L3521913912.5-79
97Joe KappMINKAN4L2518302391.4-81
98John ElwayDENWAS22L3825713932.2-90
99Rex GrossmanCHIIND41L2816512541.9-90
100Steve GroganNWECHI20L3017712561.6-105
101Jim KellyBUFDAL27L78202-72-9.3-107
102Joe TheismannWASRAI18L3524302731.8-112
103Craig MortonDALBAL5L2612713-2-0.1-112
104Fran TarkentonMINPIT9L2610203-33-1.3-127
105Earl MorrallBALNYJ3L177103-64-3.8-136
106Frank ReichBUFDAL27L3119412160.5-137
107Neil O'DonnellPITDAL30L49239131222.3-147
108Billy KilmerWASMIA7L2810403-48-1.6-159
109Drew BledsoeNWEGNB31L4825324741.4-178
110John ElwayDENSFO24L2610802-22-0.7-182
111Rich GannonOAKTAM37L4427225350.7-212
112Craig MortonDENDAL12L153904-157-9-214
113Jim KellyBUFWAS26L5827524300.5-269
114Kerry CollinsNYGBAL35L3911204-124-2.9-335

If you type Montana’s name into the search box, you can see that he has the 1st, 4th, 11th and 27th best performance in Super Bowl history. The best performance in a losing effort goes to Jake Delhomme, who shredded the Patriots secondary in the second half of Super Bowl XXXVIII (he began the game 1 for 9 for 1 yard). The worst performance in a winning effort, unsurprisingly, goes to Ben Roethlisberger in Super Bowl XL, although Joe Theismann against the Dolphins gets an honorable mention. Worst performance overall goes to Kerry Collins, although Craig Morton’s 4 interceptions and 39 yards on 15 attempts against his former team in Super Bowl XII could give Collins a run for his money.

What about best championship game performances in the pre-Super Bowl era?

RkQBTmOppYearW/LAttPydTDINTCYCYPVAL
1Tobin RoteDETCLE1957W192804038019304
2Sid LuckmanCHIWAS1943W262865038614.8248
3Otto GrahamCLERAM1950W33298412927.7236
4Sammy BaughWASCHI1937W33335313209.7228
5Harry NewmanNYGCHI1933L192092120410.7197
6Charlie ConerlyNYGCHI1956W101952023222.1192
7Bart StarrGNBNYG1961W171643022413.2152
8Otto GrahamCLEDET1954W121633219312.9135
9Frank RyanCLEBAL1964W182063121211.2132
10Norm Van BrocklinRAMCLE1951W61281014824.7129
11Tobin RoteSDGBOS1963W151732022613.1127
12Sid LuckmanCHINYG1941W121600016013.3125
13George BlandaHOULAC1960W313013036111.6123
14Charlie ConerlyNYGBAL1958W141871019011.6122
15Arnie HerberGNBNYG1938L141231014310.2117
16Johnny UnitasBALNYG1959W29264202677.4115
17Charlie O'RourkeCHIWAS1942L71280012818.3105

[click to continue…]

  1. Note that I do not have individual playoff sack data prior to 2008, so I am using pro-rated sack numbers based on team sack data. []
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Yesterday, I explained the methodology behind the formula involved in ranking every quarterback season in football history. Today, I’m going to present the career results. Converting season value to career value isn’t as simple as it might seem. Generally, we don’t want a player who was very good for 12 years to rank ahead of a quarterback who was elite for ten. Additionally, we don’t want to give significant penalties to players who struggled as rookies or hung around too long; we’re mostly concerned with the peak value of the player.

What I’ve historically done — and done here — is to give each quarterback 100% of his value or score from his best season, 95% of his score in his second best season, 90% of his score in his third best season, and so on. This rewards quarterbacks who played really well for a long time and doesn’t kill players with really poor rookie years or seasons late in their career. It also helps to prevent the quarterbacks who were compilers from dominating the top of the list. The table below shows the top 150 regular season QBs in NFL history using that formula, along with the first and last years of their careers, their number of career attempts (including sacks and rushing touchdowns), and their career records and winning percentages (each since 1950). For visibility reasons, I’ve shown the top 30 quarterbacks below, but you can change that number in the filter or click on the right arrow to see the remaining quarterbacks.
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In 2006, I took a stab at ranking every quarterback in NFL history. Two years later, I acquired more data and made enough improvements to merit publishing an updated and more accurate list of the best quarterbacks the league has ever seen. In 2009, I tweaked the formula again, and published a set of career rankings, along with a set of strength of schedule, era and weather adjustments, and finally career rankings which include those adjustments and playoff performances.

If nothing else, that was three years ago, so the series was due for an update. I’ve also acquired more data, enabling me to tweak the formula to better reflect player performance. But let’s start today with an explanation of the methodology I’m using. To rank a group of players, you need to decide which metric you’re ordering the list by. I’ll get to all of the criteria I’m not using in a little bit, but the formula does use each of the following: pass attempts, passing touchdowns, passing yards, interceptions, sacks, sack yards lost, fumbles, fumbles recovered, rush attempts, rushing yards and rushing touchdowns. Most importantly, the formula is adjusted for era and league.

Two of the best quarterbacks ever.

So where do we begin? We start with plain old yards per attempt. I then incorporate sack data by removing sack yards from the numerator and adding sacks to the denominator1. To include touchdowns and pass attempts, I gave a quarterback 20 yards for each passing touchdown and subtracted 45 yards for each interception. This calculation — (Pass Yards + 20 * PTD – 45 * INT – Sack Yards Lost) / (Sacks + Pass Attempts) forms the basis for Adjusted Net Yards per Attempt, one of the key metrics I use to evaluate quarterbacks.

For purposes of this study, I did some further tweaking. I’m including rushing touchdowns, because our goal is to measure quarterbacks as players. There’s no reason to separate rushing and passing touchdowns from a value standpoint, so all passing and rushing touchdowns are worth 20 yards and are calculated in the numerator of Adjusted Net Yards per Attempt. To be consistent, I also include rushing touchdowns in the denominator of the equation. This won’t change anything for most quarterbacks, but feels right to me. A touchdown is a touchdown.
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  1. I have individual sack data for every quarterback since 1969. For seasons before then, I have team sack data going back to 1949. For seasons before 1950, I ignored sacks; for seasons between 1950 and 1969, I gave each quarterback an approximate number of sacks, giving him the pro-rated portion of sacks allowed by the percentage of pass attempts he threw for the team. While imperfect, I thought this “fix” to be better than to ignore the data completely, especially for years where one quarterback was responsible for the vast majority of his team’s pass attempts. []
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Thirty years ago, the NFL began officially recording defensive player sacks. Prior to 1982, all teams kept their own individual sack data, but those records (with few exceptions) have never been verified. As a result, it’s an unfortunate reality that for much of NFL history, we simply do not have reliable sack data for individual defensive players.

Three times, Deacon Jones produced 20+ unofficial sacks in the 1960s.1 In 1967, Raiders defensive end Ben Davidson had 17 sacks2 in the AFL. Jack Youngblood and Jim Katcavage both led the league in sacks on two different occasions in the pre-1982 era.3 Cincinnati Bengal Coy Bacon has been credited with 21.5 unofficial sacks during in 1976. The first team to record 60 sacks in a season was the ’57 Bears, and we can be sure that Doug Atkins recorded more than his fair share of that number. For players like Gino Marchetti, Norm Willey, and Len Ford, even unofficial records weren’t kept during their time, leaving us unsure as to who is the true sack king.

It’s important to remember that just because we don’t have official sack data before 1982 doesn’t mean there were great sack artists before then. But that’s a topic for another today. So while we can’t precisely measure how the forefathers of the game played, we do have official data for the last 30 years. So who has been the best pass rusher of the last three decades?

Brett, are you SURE you're okay?

Using total sacks isn’t a fair method to current players, or to those players who chose to retire instead of sticking around to compile six-sack seasons. So if we want to measure sack dominance, we can’t simply look at total sacks any more than we can grade running backs by looking at career rushing yards. One method I like that I’ve used before is sacks over one-half sack per game. This makes 8 sacks in a 16-game season the bar; a player only gets credit for their production over that level. This means that 12 sacks in a 16-game season brings a value of +4.00, while 16 sacks is twice as valuable at +8.00.There’s no great reason to choose 8 over 6 or 10 or any other number. I chose 8 because it feels right, but I don’t claim that it’s based on anything other than my personal, subjective preference.

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  1. According to research done by John Turney. []
  2. According to Nate Webster. []
  3. Source: Turney/Webster []
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