Jameis Winston has played 15 games so far this season, and he’s thrown for 4,908 yards, with 31 touchdowns and 28 interceptions.  He is going to run away with the INT crown, is a good favorite to win the passing yards crown, and will likely finish anywhere from 1st to 3rd in passing touchdowns. Winston also is in a tight race with two half-season guys — Matthew Stafford and Ryan Tannehill — to win the yards per completion crown.  Oh, and he leads the NFL right now in sacks taken, too.

Some of this is a function of him leading the NFL in pass attempts (he’s also played 15 games, while most teams play their 15th game today).   But Winston is also just extreme in almost every fun stat, which is why I decided to revisit my Fun QB Index with some tweaks.

Here are the rules for being fun:

• Passing a lot.
• A high yards per completion average.
• Touchdowns.
• Interceptions.

So here’s the simple formula I came up with:

Fun = [ (Yards per Completion – 10.0) * Completions, plus 45 x INTs, plus 45 x TDs ] / Games Played

This isn’t a rate stat, which means you get more credit for passing more often…. except when it comes to yards per completion.  There, you only get credit for your yards per completion above 10.0.

Let’s use this formula for Winston.  He’s completed 367 of 602 passes for 4,908 yards, a 13.37 yards per completion average.  So he gets credit in the YPC component of the formula for (3.37 * 367), or 1,237 yards.  With 59 combined TDs/INTs, that gets him another 2,655 yards.  That gives him 3,892 fun yards, which is an average of 259.5 yards per game of fun.

Now, that probably means nothing to you in the abstract. But here is how the 2019 quarterbacks look when you run them through the same formula: [continue reading…]

I like to run a contest where I ask you 38 questions, you answer them, and we all look silly after the season. This year, Adam Steele did the lion’s share of the work, and we thank him for that.

Below you will find 38 pairs of numbers. In each case, you tell me which number will be bigger. One point for each correct answer. Most points wins.

Ties — and I expect there to be a nontrivial number of them — go to the side that had fewer votes. For example, here is a pair:

Number of wins by the Lions
Number of wins by the Ravens

Let’s say 49 people take the Lions and 44 take the Ravens. If the Lions and Ravens end up with the same number of wins, then each Ravens-backer will get a point and each Lions-backer will not. Last year, JimZornsLemma won with 25 correct guesses out of 38; the average was just 19 correct guesses. Thanks to Jeremy De Shetler for an assist on some of this year’s questions.

GRAND PRIZE: the main prizes will be (1) honor and (2) glory. There may also be some sort of trinket to be named later. By the time this thing is over, more than five months will have passed, so that gives me some time to scrape something together. But you probably shouldn’t enter unless honor and glory are sufficient.

MORE RULES:

1. Everyone is limited to one entry per person. This will be enforced by the honor system. If caught breaking this rule, you, your children, and your children’s children will be banned from all future FP contests.

2. I won’t enter the contest myself, which will allow me to arbitrate any dispute impartially. Any ambiguity in the rules will be clarified by me in whatever way causes me the least amount of hassle.

3. While there are quite a few items that refer in some way to the NFL postseason, unless specifically stated, all the items below refer to regular season totals only.

5. You may enter until 1:00 p.m. Eastern time on Sunday, September 9th, 2018. However, there’s an incentive to entering early because…

6. In the event that the contest ends in a tie, the winner will be the person whose entry was submitted first. [continue reading…]

For the majority of the NFL, the 2017 season feels pretty similar to the 1978 season.

In 1978, the Rams won 75% of their games and the Redskins went .500.
In 2017, the Rams have won 75% of their games and the Redskins are .500.

In 1978, the Colts had a superstar quarterback (Bert Jones) in the prime of his career, but he was hurt, and the team finished with the worst points differential in the NFL. In 2017, the Colts have a superstar quarterback in the prime of his career (Andrew Luck), but he is hurt, and the team has the worst points differential in the NFL.

The 1978 49ers had the worst record in the NFL. The 2017 49ers have the worst record in the NFL.

In 1978, the Packers won 8 games with a Hall of Fame QB on the sidelines in head coach Bart Starr; the 2017 Packers are 4-4 with a Hall of Fame QB on the sidelines in Aaron Rodgers.

The 1978 Giants finished in last place in the NFC East. The 2017 Giants are going to finish in last place in the NFC East.

The 1978 Jets went 8-8; with a win over the Bucs this Sunday, the 2017 Jets will be 5-5.

Speaking of Tampa Bay, the ’78 Bucs finished last in their division by two games; the ’17 Bucs are currently last in their division by two games.

But you know who was good in 1978? The Patriots, Steelers, Cowboys, and Eagles.

In 1978, New England started 6-2 and won the AFC East. In 2017, New England is 6-2 and in first place in the AFC East.

In 1978, Pittsburgh had a top-5 defense and a Hall of Fame quarterback in Terry Bradshaw; in 2017, Pottsburgh has a top-5 defense and a Hall of Fame quarterback in Ben Roethlisberger.

In 1978, Philadelphia made the playoffs with Ron Jaworski in his second season as quarterback of the Eagles; in 2017, Philadelphia will make the playoffs with Carson Wentz in his second season as quarterback of the Eagles.

In 1978, Dallas made the playoffs with Pro Bowlers at QB, RB, WR, and TE in Roger Staubach, Tony Dorsett, Tony Hill, and Billy Joe DuPree; in 2017, Dallas is 5-3 and on the road to the postseason thanks to star QB Dak Prescott, star RB Ezekiel Elliott, star WR Dez Bryant, and star TE Jason Witten.

In 1978, the Vikings won their division; in 2017, the Vikings are in first place in their division.

In 1978, the Bengals and their 29-year-old franchise quarterback Ken Anderson had a down year; Anderson went 4-8 as the starter. In 2017, the Bengals and their 30-year-old franchise quarterback Andy Dalton is having a down year; Dalton is 3-5 as a starter.

In 1978, the Bears had a losing record and yet still ranked last in pass attempts; in 2017, the Bears have a losing record and yet still rank last in pass attempts.

In 1978, the Falcons were a year removed from greatness on one side of the ball, and sputtered around .500; in 2017, the Falcons are a year removed from greatness on one side of the ball, and are sputtering around .500.

In 1978, the Lions finished 7-9 with an average offense and an average defense; in 2017, the Lions are 4-4 with an average offense and an average defense.

In 1978, the Cardinals had a losing record and an ugly offense; in 2017, the Cardinals have a losing record and an ugly offense.

In 1978, the Raiders disappointed; after an 11-3 season with a great quarterback in Ken Stabler, the team missed the playoffs. In 2017, the Raiders are disappointing; after a 12-3 season with Derek Carr in 2016, the Raiders are on track to miss the postseason.

In 1978, the Chargers had a Hall of Fame quarterback in Dan Fouts, but a 1-4 start and a tough division kept them out of the playoffs. In 2017, the Chargers have a Hall of Fame caliber quarterback in Philip Rivers, but a 1-4 start and a tough division might is likely to leave them out of the playoffs.

The 2017 Browns have lost 8 games, just like the 1978 Browns.

Not all teams are playing like it’s 1978, of course. The Ravens, Jaguars, Panthers, and Texans didn’t exist. The Saints were 7-9 that year, while this year’s Saints are 6-2 and look to be one of the better and more balanced (!) teams in the league. The Broncos were 10-6 with a great defense and a pretty good offense; this year’s Broncos are 3-5 with a terrible offense and a pretty good (and sometimes great) defense. This year’s Seahawks team is 6-3 and has a good chance of making the playoffs thanks to a good offense and a good defense; the ’78 Seahawks had a very good offense but a terrible defense, finishing 9-7 and out of the playoffs. Kansas City was one of the worst teams in the NFL in 1978, but one of the best in 2017. And the Bills were bad in ’78 but good in ’17, while the Dolphins are the reverse.

The normal way to measure a franchise’s winning percentage is the simplest. All you need to do is take the team’s total number of wins, add to that number the total number of ties divided by two, and then take that sum and divide it by the team’s total number of games.

That’s simplest and makes sense and is perfectly correct. However, it also means games in 1960 are given the same weight as games in 2016. And depending on what you want to measure, that may not be what you want to do. If you want to measure something like the amount of pleasure a fanbase receives from its team ^[1]Why would you want to do this? I have no idea., you want to put more weight on recent seasons. [continue reading…]

There have been 44 quarterbacks in NFL history to throw for at least 30,000 yards. Given enough time, you could probably guess that Drew Bledsoe, Jim Kelly, and Steve McNair are three of them. All three have something else in common: they were all born on February 14th.

If we drop the cut-off to 16,000 yards, we jump to 137 quarterbacks but get to include David Garrard, another Valentine’s Day baby. But wait, there’s more: If we drop the threshold to 3,500 passing yards, we get to include Patrick Ramsey and Anthony Wright. Those guys may not impress you, but consider that only 334 players have thrown for 3,500 yards. That means dozens of days have zero quarterbacks with 3,500 yards — including New Year’s Day, Cinco De Mayo, Halloween, and Christmas Eve — so slotting in Ramsey and Wright as QB5 and QB6 on your birthday dream team is pretty damn good. [continue reading…]

The Washington Redskins have made five Super Bowls in their history:

• To conclude the 1972 season, with Republican Richard Nixon in office as President of the United States;
• At the end of the 1982, 1983, and 1987 seasons, with Republican Ronald Reagan as the sitting POTUS;
• In January 1992, during the final year of Republican George H. W. Bush’s presidency.

During the Super Bowl era, Washington has gone 272-180-3 while a Republican is in office. That translates to a .601 winning percentage, the best of any team.

But the Redskins have been a lot worse with a Democrat in office. In fact, Washington has a lowly 150-201-5, a 0.428 winning percentage that is the fourth worst of any team during the Super Bowl era. Washington’s best years came under Reagan, Bush, Ford, and Nixon, while the franchise’s worst years have been under Obama, Clinton, and LBJ.  Take a look: [continue reading…]

A fun article at Five Thirty Eight last week noted how insanely top-heavy the NBA is this year. Just about everyone knows that the Warriors set the record for wins in a season with 73, as part of a wire-to-wire display of dominance. But the Spurs were nearly as good. In fact, based on Elo Ratings, San Antonio was the best second-best team in the league in league history. And the Oklahoma City Thunder? They’re currently (since Elo is constantly updating) the strongest 3rd-best team in NBA history. And LeBron James and the Cavs? They’re the toughest 4th-best team in NBA history, thanks in part to a scorched earth run through the Eastern Conference in the playoffs.

Which made me wonder: what NFL season was most comparable to the 2015-2016 NBA season? There are some good candidates out there:

• In 2012, the Seahawks, 49ers, Patriots, and Broncos were far and away the best teams in the NFL. They were no flukes in this bunch: those four teams met in the conference championship games the next year, after another set of strong regular seasons.

[continue reading…]

In the comments to this post, Ryan noted that Mike Alstott led the Bucs with 557 receiving yards in 1996, but it was the fewest yards of any player who led his team in receiving yards that season. And in 1997, Karl Williams led Tampa Bay with just 486 receiving yards, also the fewest of any player who led his team in receiving yards that year.

Which made Ryan wonder: why isn’t there a list of the lowest team-leading receiving total across the league for each season? That’s a good question, so I went ahead and generated it for every season since 1950. For example, Jacksonville’s undrafted rookie Allen Hurns led the Jaguars with 677 receiving yards in 2014, but every other team had at least one player with more yards. [continue reading…]

There have been 35 quarterbacks in NFL history to throw for at least 30,000 yards. Given enough time, you could probably guess that Drew Bledsoe, Jim Kelly, and Steve McNair are three of them. All three have something else in common: they were all born on February 14th.

If we drop the cut-off to 16,000 yards, we jump to 130 quarterbacks but get to include David Garrard, another Valentine’s Day baby. But wait, there’s more: If we drop the threshold to 3,500 passing yards, we get to include Patrick Ramsey and Anthony Wright. Those guys may not impress you, but consider that only 322 players have thrown for 3,500 yards. That means dozens of days have zero quarterbacks with 3,500 yards, so slotting in Ramsey and Wright as QB5 and QB6 on your birthday dream team is pretty damn good.

By now, regular readers will have picked up on the fact that this post is a blatant ripoff of Doug’s original post back in 2008, which I updated two years ago. In terms of total career passing yards through the entire history of the league, today has an enormous lead on the second-best birthday, March 24, which consists entirely of Peyton Manning, Aaron Brooks, and Scott Brunner.  Put simply, passing yardage is for lovers, with maybe an exception or two for a certain linebacker or running back.

If you’re looking to give birth to an NFL quarterback, let me give you a word of comfort: one need not be so precise with their delivery dates. That’s because tomorrow, February 15th, is another outstanding day for quarterbacks, ranking in the top five for passing yards. It’s the birthday of Football Perspective love icon John Hadl, a (perhaps) one-day-Hall-of-Famer in Ken Anderson, and former Raider Marc Wilson. So my parenting advice to you is to circle May 14 and May 15 on your calendars, to give yourself a little wiggle room.

Oh, and happy 22nd birthday to one man who is most certainly not a lover of quarterbacks: Jadeveon Clowney.

The Super Bowl is still 18 days away, but that doesn’t mean it’s too early to write a preview of The Big Game. In fact, only suckers wait until the conclusion of the conference championship games to write their preview article. So prepare yourself for your first Super Bowl XLVIII Preview:

Super Bowl XLVIII Promises To Be A Classic

The NFL was at its best on championship Sunday, providing us with a delicious appetizer in preparation of Super Bowl XLVIII. In the early game, Tom Brady and Peyton Manning staged yet another all-time classic, and then Russell Wilson and Colin Kaepernick gave us a glimpse of the next great quarterback rivalry in the late game. One thing’s for sure: after two great battles following yet another remarkable season, the two best teams in the league will be meeting this year at MetLife Stadium.

After winning games in Denver and Seattle to get here, I don’t think either team is going to be afraid of the elements in two weeks. You will hear many doomsday predictions for the weather in this game, but truth be told, neither team is at a disadvantage in the first cold-weather Super Bowl. Many narratives will be written about this year’s game, so let me be the first to remind you that this game features [a matchup of two former Jets head coaches in the stadium where the Jets play their home games/rematch of Super Bowl XXIV/Brady against the team he grew up loving/the teams from the only two states in the country to legalize recreational marijuana/]!

One topic of discussion you’ll certainly hear this week: if victorious, many would conclude that OLDQB is the greatest quarterback in NFL history. With multiple MVP awards and multiple Super Bowl rings on top of some pretty incredible statistical accomplishments, it would be hard to argue otherwise. And consider: [Manning would become the first quarterback to win Super Bowls with two different teams and the first quarterback to win the Super Bowl and lead the league in passing yards in the same season./Brady, who win or lose will to become the first quarterback to ever start in six Super Bowls, surpassing John Elway, would join Joe Montana and Terry Bradshaw in the four ring club with a win. Brady would also become the only quarterback to ever win Super Bowls more than a decade apart, an incredible accomplishment.] And while the stakes may not quite be the same for YOUNGQB, a Super Bowl victory would perhaps be the foundation of a Hall of Fame career.

[continue reading…]

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). [continue reading…]

New Orlean’s Drew Brees is officially listed as six feet tall. Seattle’s Russell Wilson is officially listed as 5’11. That means the average height of the starting quarterbacks in tonight’s game is 71.5 inches, tied for the shortest average in any game since 1964. In fact, it’s been twelve years since a game has featured two quarterbacks of such short stature, when in week two of the 2001 season, Doug Flutie (5’10) and the Chargers beat Anthony Wright (6’1) and the Cowboys.

The other two games since 1990 where the average height of the starting quarterbacks was below six feet also involved Flutie facing a 73-inch quarterback: a 24-21 win in 1999 against Pittsburgh and Kordell Stewart and a 17-16 win year earlier against Mark Brunell and the Jags.

Twenty-five years ago, two other Flutie vs. 6’1 Quarterback games make the list: this game against Jim McMahon and this one against Dave Krieg.

You have to go back to 1978 to find a game before tonight where (1) the average height of the starting quarterbacks was under six feet and (2) Doug Flutie was not involved. Fran Tarkenton (6’0) and Pat Haden (5’11) met five times in the mid-to-late ’70s, and Billy Kilmer (6’0) also faced Haden in the final game of the 1977 season.

Kilmer and 5’11 Bob Berry met three times in the early ’70s, and Sonny Jurgensen (5’11) faced Gary Cuozzo (6’0) and Tarkenton twice each. The only other games of the post-merger era were Len Dawson (6’0) vs. Berry in 1972 and Bill Nelsen and Edd Hargett in 1971. [continue reading…]

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
2001-09-30	NWE	CLT -11.5	13-23, 159 yds, 0 TD, 0 Int, 6.63 ANYPA	25-43, 240 yds, 1 TD, 3 Int, 2.72 ANYPA	+3.91, NWE	44-13, NWE
2001-10-21	CLT	CLT -10.5	17-21, 262 yds, 4 TD, 0 Int, 16.29 ANYPA	22-34, 305 yds, 1 TD, 0 Int, 8.55 ANYPA	+7.73, NWE	38-17, NWE
2003-11-30	CLT	CLT -3.5	26-35, 226 yds, 2 TD, 2 Int, 4.76 ANYPA	29-48, 272 yds, 4 TD, 1 Int, 6.14 ANYPA	+1.38, CLT	38-34, NWE
2004-01-18 (C)	NWE	NWE -3.5	22-37, 237 yds, 1 TD, 1 Int, 5.73 ANYPA	23-47, 208 yds, 1 TD, 4 Int, 0.94 ANYPA	+4.79, NWE	24-14, NWE
2004-09-09	NWE	NWE -3	26-38, 320 yds, 3 TD, 1 Int, 8.38 ANYPA	16-29, 244 yds, 2 TD, 1 Int, 7.97 ANYPA	+0.41, NWE	27-24, NWE
2005-01-16 (D)	NWE	NWE -1	18-27, 115 yds, 1 TD, 0 Int, 4.50 ANYPA	27-42, 230 yds, 0 TD, 1 Int, 4.30 ANYPA	+0.20, NWE	20-3, NWE
2005-11-07	NWE	CLT -3	25-40, 254 yds, 3 TD, 0 Int, 7.48 ANYPA	28-37, 321 yds, 3 TD, 1 Int, 9.08 ANYPA	+1.60, CLT	40-21, CLT
2006-11-05	NWE	NWE -2.5	20-35, 201 yds, 0 TD, 4 Int, 0.60 ANYPA	20-36, 301 yds, 2 TD, 1 Int, 7.59 ANYPA	+6.99, CLT	27-20, CLT
2007-01-21 (C)	CLT	CLT -3	21-34, 226 yds, 1 TD, 1 Int, 5.74 ANYPA	27-47, 330 yds, 1 TD, 1 Int, 6.10 ANYPA	+0.36, CLT	38-34, CLT
2007-11-04	CLT	NWE -5	21-32, 237 yds, 3 TD, 2 Int, 6.09 ANYPA	16-27, 210 yds, 1 TD, 1 Int, 6.17 ANYPA	+0.08, CLT	24-20, NWE
2009-11-15	CLT	CLT -1.5	29-42, 364 yds, 3 TD, 1 Int, 8.61 ANYPA	28-44, 316 yds, 4 TD, 2 Int, 6.80 ANYPA	+1.81, NWE	35-34, CLT
2010-11-21	NWE	NWE -4.5	19-25, 178 yds, 2 TD, 0 Int, 8.38 ANYPA	38-52, 396 yds, 4 TD, 3 Int, 6.56 ANYPA	+1.83, NWE	31-28, NWE
2012-10-07	NWE	NWE -6	23-31, 193 yds, 1 TD, 0 Int, 6.09 ANYPA	31-44, 324 yds, 3 TD, 0 Int, 8.35 ANYPA	+2.26, DEN	31-21, NWE
2013-11-24	NWE	DEN -2.5	34-50, 324 yds, 3 TD, 0 Int, 7.25 ANYPA	19-36, 132 yds, 2 TD, 1 Int, 3.34 ANYPA	+3.90, NWE	34-31, NWE

(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
201310210nyg	nyg	0	6	0	103	209	min	1	4	0	125	158	2013	7	10/21/2013	.091	-12.6	NULL
197512150sdg	sdg	1	11	0	148	282	nyj	3	9	0	221	378	1975	13	12/15/1975	.167	-12.1	sdg, 24-16
199411210oti	oti	1	9	0	147	218	nyg	3	7	0	171	220	1994	12	11/21/1994	.200	-6.0	nyg, 13-10
197211060nwe	nwe	2	5	0	92	220	clt	1	6	0	94	145	1972	8	11/6/1972	.214	-12.8	clt, 24-17
197011020pit	pit	2	4	0	71	94	cin	1	5	0	93	156	1970	7	11/2/1970	.250	-7.2	pit, 21-10
198110190det	det	2	4	0	118	126	chi	1	5	0	89	133	1981	7	10/19/1981	.250	-4.3	det, 48-17
199710200clt	clt	0	6	0	88	155	buf	3	3	0	122	159	1997	8	10/20/1997	.250	-8.7	buf, 9-6
201211260phi	phi	3	7	0	162	252	car	2	8	0	184	243	2012	12	11/26/2012	.250	-7.5	car, 30-22
200001030atl	atl	4	11	0	251	351	sfo	4	11	0	266	419	1999	17	1/3/2000	.267	-8.4	atl, 34-29
200512190rav	rav	4	9	0	171	253	gnb	3	10	0	255	255	2005	15	12/19/2005	.269	-3.2	rav, 48-3
198310240crd	crd	2	5	0	137	218	nyg	2	5	0	126	156	1983	8	10/24/1983	.286	-7.9	tie (20-20)
201112120sea	sea	5	7	0	216	246	ram	2	10	0	140	296	2011	14	12/12/2011	.292	-7.8	sea, 30-13
201011290crd	crd	3	7	0	188	292	sfo	3	7	0	160	219	2010	12	11/29/2010	.300	-8.2	sfo, 27-6
200911160cle	cle	1	7	0	78	209	rav	4	4	0	206	154	2009	10	11/16/2009	.313	-4.9	rav, 16-0
198011240nor	nor	0	11	0	181	341	ram	7	4	0	294	228	1980	12	11/24/1980	.318	-4.3	ram, 27-7
201112050jax	jax	3	8	0	138	200	sdg	4	7	0	249	275	2011	13	12/5/2011	.318	-4.0	sdg, 38-14
198312120tam	tam	2	12	0	212	345	gnb	7	7	0	396	407	1983	15	12/12/1983	.321	-5.1	gnb, 12-9
198711020dal	dal	3	3	0	135	134	nyg	1	5	0	99	142	1987	7	11/2/1987	.333	-3.5	dal, 33-24
199910250pit	pit	3	3	0	117	93	atl	1	5	0	74	153	1999	7	10/25/1999	.333	-4.6	pit, 13-9
200211180ram	ram	4	5	0	194	196	chi	2	7	0	182	232	2002	11	11/18/2002	.333	-2.9	ram, 21-16
200511210gnb	gnb	2	7	0	201	184	min	4	5	0	154	228	2005	11	11/21/2005	.333	-3.2	min, 20-17
200012040nwe	nwe	3	9	0	192	253	kan	5	7	0	283	274	2000	14	12/4/2000	.333	-2.2	nwe, 30-24
200412130oti	oti	4	8	0	231	294	kan	4	8	0	341	326	2004	14	12/13/2004	.333	-2.0	kan, 49-38
200712100atl	atl	3	9	0	171	272	nor	5	7	0	266	279	2007	14	12/10/2007	.333	-4.8	nor, 34-14
199112090mia	mia	7	6	0	256	275	cin	2	11	0	211	374	1991	15	12/9/1991	.346	-7.0	mia, 37-13
197311260sfo	sfo	3	7	0	180	232	gnb	3	5	2	138	198	1973	11	11/26/1973	.350	-5.6	sfo, 20-6
197811130cin	cin	1	9	0	110	184	rai	6	4	0	193	164	1978	11	11/13/1978	.350	-2.3	rai, 34-21
200711260pit	pit	7	3	0	269	145	mia	0	10	0	183	274	2007	12	11/26/2007	.350	+1.7	pit, 3-0
199311290clt	clt	3	7	0	154	233	sdg	4	6	0	164	195	1993	13	11/29/1993	.350	-5.5	sdg, 31-0
198010270nyj	nyj	1	6	0	114	164	mia	4	3	0	99	144	1980	8	10/27/1980	.357	-6.8	nyj, 17-14
199710270mia	mia	5	2	0	143	124	chi	0	7	0	101	199	1997	9	10/27/1997	.357	-5.6	chi, 36-33
199811020phi	phi	1	6	0	79	162	dal	4	3	0	174	115	1998	9	11/2/1998	.357	-1.7	dal, 34-0
201211050nor	nor	2	5	0	190	216	phi	3	4	0	120	155	2012	9	11/5/2012	.357	-4.4	nor, 28-13
198311070det	det	4	5	0	202	188	nyg	2	6	1	166	214	1983	10	11/7/1983	.361	-1.9	det, 15-9
199111250ram	ram	3	8	0	181	256	sfo	5	6	0	218	155	1991	13	11/25/1991	.364	-0.5	sfo, 33-10
199211300sea	sea	1	10	0	73	218	den	7	4	0	175	207	1992	13	11/30/1992	.364	-8.0	sea, 16-13
200812010htx	htx	4	7	0	252	293	jax	4	7	0	224	240	2008	13	12/1/2008	.364	-2.6	htx, 30-17
197111150sdg	sdg	3	5	0	150	179	crd	3	5	0	135	149	1971	9	11/15/1971	.375	-2.7	sdg, 20-17
197910290atl	atl	3	5	0	160	181	sea	3	5	0	172	181	1979	9	10/29/1979	.375	-1.9	sea, 31-28
200611130car	car	4	4	0	137	163	tam	2	6	0	102	173	2006	10	11/13/2006	.375	-6.1	car, 24-10
200711120sea	sea	4	4	0	167	141	sfo	2	6	0	104	186	2007	10	11/12/2007	.375	-3.5	sea, 24-0
201211120pit	pit	5	3	0	191	164	kan	1	7	0	133	240	2012	10	11/12/2012	.375	-5.0	pit, 16-13
199010290pit	pit	3	4	0	109	128	ram	2	4	0	164	173	1990	8	10/29/1990	.385	-2.2	pit, 41-10
201212170oti	oti	4	9	0	271	386	nyj	6	7	0	245	306	2012	15	12/17/2012	.385	-6.8	oti, 14-10
197012070oti	oti	3	7	1	177	249	cle	5	6	0	236	236	1970	12	12/7/1970	.386	-3.3	cle, 21-10
197711210was	was	5	4	0	126	132	gnb	2	7	0	83	152	1977	10	11/21/1977	.389	-4.2	was, 10-9
200311170sfo	sfo	4	5	0	202	152	pit	3	6	0	176	217	2003	11	11/17/2003	.389	+0.5	sfo, 30-14
201011220sdg	sdg	4	5	0	239	197	den	3	6	0	203	252	2010	11	11/22/2010	.389	-0.4	sdg, 35-14
199410170den	den	1	4	0	108	146	kan	3	2	0	90	80	1994	7	10/17/1994	.400	-2.8	kan, 31-28
199911290sfo	sfo	3	7	0	163	281	gnb	5	5	0	192	209	1999	12	11/29/1999	.400	-6.8	gnb, 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
197211060nwe	nwe	2	5	0	92	220	clt	1	6	0	94	145	1972	8	11/6/1972	.214	-12.8	clt, 24-17
201310210nyg	nyg	0	6	0	103	209	min	1	4	0	125	158	2013	7	10/21/2013	.091	-12.6	NULL
197512150sdg	sdg	1	11	0	148	282	nyj	3	9	0	221	378	1975	13	12/15/1975	.167	-12.1	sdg, 24-16
199710200clt	clt	0	6	0	88	155	buf	3	3	0	122	159	1997	8	10/20/1997	.250	-8.7	buf, 9-6
200001030atl	atl	4	11	0	251	351	sfo	4	11	0	266	419	1999	17	1/3/2000	.267	-8.4	atl, 34-29
201011290crd	crd	3	7	0	188	292	sfo	3	7	0	160	219	2010	12	11/29/2010	.300	-8.2	sfo, 27-6
199211300sea	sea	1	10	0	73	218	den	7	4	0	175	207	1992	13	11/30/1992	.364	-8.0	sea, 16-13
198310240crd	crd	2	5	0	137	218	nyg	2	5	0	126	156	1983	8	10/24/1983	.286	-7.9	tie (20-20)
201112120sea	sea	5	7	0	216	246	ram	2	10	0	140	296	2011	14	12/12/2011	.292	-7.8	sea, 30-13
201211260phi	phi	3	7	0	162	252	car	2	8	0	184	243	2012	12	11/26/2012	.250	-7.5	car, 30-22
197011020pit	pit	2	4	0	71	94	cin	1	5	0	93	156	1970	7	11/2/1970	.250	-7.2	pit, 21-10
199112090mia	mia	7	6	0	256	275	cin	2	11	0	211	374	1991	15	12/9/1991	.346	-7.0	mia, 37-13
198010270nyj	nyj	1	6	0	114	164	mia	4	3	0	99	144	1980	8	10/27/1980	.357	-6.8	nyj, 17-14
201212170oti	oti	4	9	0	271	386	nyj	6	7	0	245	306	2012	15	12/17/2012	.385	-6.8	oti, 14-10
199911290sfo	sfo	3	7	0	163	281	gnb	5	5	0	192	209	1999	12	11/29/1999	.400	-6.8	gnb, 20-3
197910150nyj	nyj	2	4	0	128	174	min	3	3	0	107	142	1979	7	10/15/1979	.417	-6.8	nyj, 14-7
200611130car	car	4	4	0	137	163	tam	2	6	0	102	173	2006	10	11/13/2006	.375	-6.1	car, 24-10
199411210oti	oti	1	9	0	147	218	nyg	3	7	0	171	220	1994	12	11/21/1994	.200	-6.0	nyg, 13-10
200611060sea	sea	4	3	0	149	177	rai	2	5	0	92	148	2006	9	11/6/2006	.429	-6.0	sea, 16-0
199710270mia	mia	5	2	0	143	124	chi	0	7	0	101	199	1997	9	10/27/1997	.357	-5.6	chi, 36-33
197311260sfo	sfo	3	7	0	180	232	gnb	3	5	2	138	198	1973	11	11/26/1973	.350	-5.6	sfo, 20-6
199311290clt	clt	3	7	0	154	233	sdg	4	6	0	164	195	1993	13	11/29/1993	.350	-5.5	sdg, 31-0
198312120tam	tam	2	12	0	212	345	gnb	7	7	0	396	407	1983	15	12/12/1983	.321	-5.1	gnb, 12-9
201211120pit	pit	5	3	0	191	164	kan	1	7	0	133	240	2012	10	11/12/2012	.375	-5.0	pit, 16-13
200911160cle	cle	1	7	0	78	209	rav	4	4	0	206	154	2009	10	11/16/2009	.313	-4.9	rav, 16-0
200712100atl	atl	3	9	0	171	272	nor	5	7	0	266	279	2007	14	12/10/2007	.333	-4.8	nor, 34-14
199910250pit	pit	3	3	0	117	93	atl	1	5	0	74	153	1999	7	10/25/1999	.333	-4.6	pit, 13-9
201211050nor	nor	2	5	0	190	216	phi	3	4	0	120	155	2012	9	11/5/2012	.357	-4.4	nor, 28-13
198110190det	det	2	4	0	118	126	chi	1	5	0	89	133	1981	7	10/19/1981	.250	-4.3	det, 48-17
198011240nor	nor	0	11	0	181	341	ram	7	4	0	294	228	1980	12	11/24/1980	.318	-4.3	ram, 27-7
197711210was	was	5	4	0	126	132	gnb	2	7	0	83	152	1977	10	11/21/1977	.389	-4.2	was, 10-9
199510230nwe	nwe	1	5	0	69	160	buf	5	1	0	136	95	1995	8	10/23/1995	.500	-4.2	nwe, 27-14
200611270sea	sea	6	4	0	203	219	gnb	4	6	0	185	252	2006	12	11/27/2006	.500	-4.2	sea, 34-24
201112050jax	jax	3	8	0	138	200	sdg	4	7	0	249	275	2011	13	12/5/2011	.318	-4.0	sdg, 38-14
200412060sea	sea	6	5	0	239	223	dal	4	7	0	193	289	2004	13	12/6/2004	.455	-3.6	dal, 43-39
198711020dal	dal	3	3	0	135	134	nyg	1	5	0	99	142	1987	7	11/2/1987	.333	-3.5	dal, 33-24
200711120sea	sea	4	4	0	167	141	sfo	2	6	0	104	186	2007	10	11/12/2007	.375	-3.5	sea, 24-0
199010220cle	cle	2	4	0	98	139	cin	4	2	0	154	153	1990	7	10/22/1990	.500	-3.3	cin, 34-13
201110310kan	kan	3	3	0	105	150	sdg	4	2	0	141	136	2011	8	10/31/2011	.583	-3.3	kan, 23-20
197811060clt	clt	3	6	0	120	230	was	7	2	0	186	135	1978	10	11/6/1978	.556	-3.3	clt, 21-17
200711190den	den	4	5	0	153	238	oti	6	3	0	178	152	2007	11	11/19/2007	.556	-3.3	den, 34-20
197012070oti	oti	3	7	1	177	249	cle	5	6	0	236	236	1970	12	12/7/1970	.386	-3.3	cle, 21-10
200511210gnb	gnb	2	7	0	201	184	min	4	5	0	154	228	2005	11	11/21/2005	.333	-3.2	min, 20-17
200512190rav	rav	4	9	0	171	253	gnb	3	10	0	255	255	2005	15	12/19/2005	.269	-3.2	rav, 48-3
200512120atl	atl	7	5	0	277	237	nor	3	9	0	183	295	2005	14	12/12/2005	.417	-3.0	atl, 36-17
200211180ram	ram	4	5	0	194	196	chi	2	7	0	182	232	2002	11	11/18/2002	.333	-2.9	ram, 21-16
199410170den	den	1	4	0	108	146	kan	3	2	0	90	80	1994	7	10/17/1994	.400	-2.8	kan, 31-28
197211130sdg	sdg	2	5	1	152	203	cle	5	3	0	141	134	1972	9	11/13/1972	.469	-2.8	cle, 21-17
197710310crd	crd	3	3	0	124	122	nyg	3	3	0	91	126	1977	7	10/31/1977	.500	-2.8	crd, 28-0
200512260nyj	nyj	3	11	0	189	298	nwe	9	5	0	322	289	2005	16	12/26/2005	.429	-2.7	nwe, 31-21

Bill Barnwell wrote an interesting article about the hurdles the NFL must jump to successfully place a team in London. We don’t know whether the NFL would move an existing team or place an expansion team in London (presumably with the introduction of a second expansion team, likely in Los Angeles, as well). Thirty-four teams is an unwieldy number, so expansion might even bring us to 36 teams (with six teams in three divisions in each conference).

Anecdotally, it seems like most fans are against placing a team in London, or expansion, or change of any kind. For me, the most interesting hurdle to analyze is how to come up with a schedule for a London team. And I think I’ve figured out a decent solution (there are no perfect options). I’m going to use the Buffalo Bills 2013 slate of opponents as a model just because I need to pick some team’s schedule as a model for London’s franchise (I’m going to self-appoint them the Monarchs for purposes of this post). Here are my thoughts:

Week: 1 – Cincinnati Bengals (Home – Friday at 2:30 Eastern)
Week: 2 – Baltimore Ravens (Home)

The big hurdle in creating the schedule is limiting the number of cross-Atlantic trips for the Monarchs.  As a result, you need to bunch together home and road games. And since we want to have the London games in the middle of the season — so we can give the opponents in those games a bye after playing in London — that necessitates a couple of home games early in the year.

The NFL can use the time zone difference to its advantage here, and create a March Madness-style of Friday afternoon fun. On Thursday Night, we have the opener featuring last year’s Super Bowl champion.  And then on Friday afternoon — at 7:30 PM in London — we get another huge event. There would be a Super Bowl-type of atmosphere at the “Week 1 Kickoff Extravaganza” in London on Friday night, and you can be sure that it would get monstrous ratings in England. The NFL doesn’t schedule Friday night games because it doesn’t want to compete with high school football, but the time zone difference works to everyone’s advantage here. The Bengals can fly home after the game and be back in Cincinnati early in the morning on Saturday — which gives them two extra days to prepare for week two.

It also makes sense to have a team that played on Thursday Night in the NFL opener play in London in week two. This year, that would be the Ravens, but it can also be the non-Super Bowl champion appearing in that game.  Baltimore can fly out to London early in the week, using those extra days to help ease some of the travel burden. If they like, the Ravens (or whomever is in the week two slot) can get a Monday Night game in week three.
[continue reading…]

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
year	g	rush	rushyd	rushtd	rec	recyd	rectd	VBD
1994	16	343	1,350	12	39	235	0	103.0
2nd-Best Season
year	g	rush	rushyd	rushtd	rec	recyd	rectd	VBD
1997	14	244	823	9	15	104	0	12.9

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
year	g	rec	recyd	rectd	VBD
2007	16	80	1,289	16	107.7
2nd-Best Season
year	g	rec	recyd	rectd	VBD
2010	16	53	904	7	15.4

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.
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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.
[continue reading…]

For three straight years, NFL Network has produced a list of the Top 100 Players of 20xx. Many people have criticized the results, and this summary from Bill Barnwell hits on some of the main issues. But my issue isn’t with the mistakes the players may be making in the voting booth, but the mistakes made in tabulating the votes. I want to suggest to the fine folks at NFL Network an alternative method for deriving a list of the top 100 players. This method has three big advantages over the current process:

(1) It will take players only a few minutes — or as long as they like — to participate.

(2) More players will be part of the judging, since the time commitment will be lessened.

(3) The results will be more accurate.

Instead of asking players to write down a bunch of names from memory, my suggested method would involve asking them a bunch of simple and straightforward questions. Imagine a player sitting in front of a computer screen, and asked to pick an answer to each of the following:

“Who should be ranked higher: Adrian Peterson or Max Unger?” [Clicks Peterson.]
“Who should be ranked higher: Tyvon Branch or Ben Roethlisberger?” [Clicks Roethlisberger.]
“Who should be ranked higher: Reggie Bush or Andy Dalton?” [Thinks for a second… picks Bush.]
“Who should be ranked higher: Patrick Willis or Joe Flacco?” [Thinks…. picks Willis.]
“Who should be ranked higher: Jimmy Graham or Jacoby Jones?” [Clicks Graham.]

That’s a lot better than the current system, described below by Mike Florio of Pro Football Talk:
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Yesterday, Joe Fortenbaugh canonized Mike Lombardi for discovering and emphasizing one of the game’s great hidden stats: the number of rushing attempts plus completions a team has in a game. If you hit 50, you’re in great shape. Fortenbaugh reminds us that Lombardi, whose last team went 2-14, “possesses a vast range of knowledge spanning from management to game theory.” Fortenbaugh does the math for us, noting that the “top-10 teams in rushing attempts + completions combined to post a record of 101-59 (.631) in 2012, with seven of those ten organizations advancing to the postseason. On the opposite end of the spectrum, the bottom-10 teams combined for a 62-97-1 (.387) mark, with zero total playoff berths.” Then, he blows us away with the prize-winning line:

If you take only the teams that averaged 50.0 or more rushing attempts + completions per game over the last five years, you get a combined regular season record of 339-189 (.642), with 22 of 33 (66%) teams qualifying for the postseason. That winning percentage puts a team in between 10 and 11 wins per season.

The headline to the article reads: Average a combined total of 50 rushing attempts and completions per game and a winning season will likely follow. I’ll do the article one better: From 2008 to 2012, including playoffs, teams with 50+ rushes + completions have a record of 819-325-3, giving them a .715 winning percentage.

After reading that article and getting an inside look into Lombardi’s wisdom, I had considered the code to producing a winning season cracked. But I’ve got a robust database, so I thought maybe I could do even better than that .715 winning percentage Lombardi’s stat produces. The following information is based on the results from every game, regular and postseason, since 2008:
[continue reading…]

With the Jets releasing Tim Tebow, it appears that his NFL career may be over. If that’s the case, it’s time to reflect on a great career that never ceased to captivate the nation. While there are many ways to grade a quarterback, probably the best and simplest measure would be “production in last home start.” After all, the NFL is a ‘what have you done for me lately’ league.

The table below lists the final home start for over 400 retired (or close to retired) quarterbacks (the default is to show just 25 quarterbacks, but the table is fully sortable and searchable, and you can change the number of players displayed by using the drop-down box on the left). For each quarterback, I have provided a link to the boxscore from that game, the result of the game, and the quarterback’s passing and rushing statistics. If Tebow is in fact retired, he will have finished with the highest Adjusted Yards per Attempt (minimum 10 attempts) of any player in his last home start:
[continue reading…]

[In case you missed it, earlier this week, I created an NFL Draft Pick Value Calculator and provided wallet-sized and iPhone-style copies of the 2013 NFL Schedule.]

I find old newspaper articles very entertaining, so I decided to see how the Boston Globe documented the selection of Tom Brady in the sixth round of the 2000 Draft.

On April 17th, the day after the draft concluded, the Globe provided a full summary of each player. Here’s how they described the 199th pick:

6, 199 – Tom Brady, QB, Michigan

A pocket passer who will compete for a practice squad spot with the Patriots . . . Drafted as a catcher by the Montreal Expos in 1995 out of Serra (San Mateo, Calif.) HS . . . Completed 62.8 percent of his passes with 20 TDs and six interceptions. Only Elvis Grbac had more TD tosses in a season for the Wolverines . . . Throws a great slant . . . At almost 6-4, 214 pounds, has some mobility . . . Platooned with sophomore Drew Henson . . . Was projected to go in the third round, but dropped quickly.

[continue reading…]

I have no inside information and I’m not a draftnik, but that won’t stop me from trying to read the tea leaves. I’m of the opinion that Geno Smith will fall to the late first round of the draft next week. I’m not a huge Smith fan — a passer-friendly system boosted his admittedly impressive numbers — but I wouldn’t label myself a Smith hater, either. So why do I think he’ll slide? Part of the reason is that Smith simply isn’t a slam dunk pick, much to the chagrin of several teams in the top five. Another factor is that following the delusional quarterback carousel last month, no team has a pressing need for a quarterback to start in week 1, a stark contrast to where the Redskins and Colts were this time last year. Finally, while Smith may be the top quarterback prospect on most boards, I’m sure some teams think selecting Matt Barkley, E.J. Manuel, Ryan Nassib, Tyler Wilson, Mike Glennon, Tyler Bray, or Landry Jones in rounds two, three, or four, is preferable to spending a high first round pick on Smith. If those picks miss, the cost is lower, and team won’t feel the immediate need to thrust them into the lineup like they would with Smith.

Looking at the teams drafting in the top ten, and I don’t see any landing spot that is particularly likely for Smith. Consider:
[continue reading…]

Five years ago, Doug wrote an interesting post about game-winning touchdowns. Let’s be clear: tracking things like game-winning touchdowns is only interesting in a trivial sort way, but hey, it’s April.

Football doesn’t have a statistic like “game-winning RBIs” the way baseball does, although my friend Scott Kacsmar has been doing a great job tracking 4th quarter comebacks and game-winning drives for quarterbacks. I was wondering which players have scored the most game-winning touchdowns in the 4th quarter or overtime, and fortunately I have the data to answer that pretty easily. I looked at all games, regular and postseason, in all leagues, from 1940 to 2012, and counted all touchdowns scored that put the player’s team ahead for good (with one exception: I did not count touchdowns scored when down by 7 and the team successfully went for two afterwards).

The table below lists all players with at least five such touchdowns and the teams for which they scored those touchdowns.

[continue reading…]

There have been 35 quarterbacks in NFL history to throw for at least 30,000 yards. Given enough time, you could probably guess that Drew Bledsoe, Jim Kelly, and Steve McNair are three of them. All three have something else in common: they were all born on February 14th.

If we drop the cut-off to 16,000 yards, we jump to 125 quarterbacks but get to include David Garrard, another Valentine’s Day baby. With 366 possible birthdays, it’s pretty incredible that four out of a random group of 125 people would have the same birthday. Consider that no one born on any the following seven days — January 30th, March 4th and 30th, April 9th and 13th, and June 12th and 21st — has ever gained a single passing yard in NFL history. ^[1]Even February 29th has a couple of representatives, led by Dick Wood and his 7,153 passing yards.

But wait, there’s more: If we drop the threshold to 3,500 passing yards, we get to include Patrick Ramsey and Anthony Wright. Those guys may not impress you, but consider that only 310 players have thrown for 3,500 yards. That means dozens of days have zero quarterbacks with 3,500 yards, so slotting in Ramsey and Wright as QB5 and QB6 on your birthday dream team is pretty damn good. February 13th, for example, has Jim Youel as its top passer, and he only collected 849 yards. Yesterday’s number two slot goes to the greatest receiver of all-time to ever play with Aaron Brooks (106 yards), outpacing Drew Henson and his 98 career yards. Clearly, passing yardage is for lovers. ^[2]Long-time readers will recognize that this post is a blatant ripoff of Doug’s classic Passing Yardage is for Lovers, published five years ago to the day.
[continue reading…]

In 2009, Doug produced a Super Bowl Squares post, itself a revival of his old Sabernomics post eight years ago. In those posts, Doug derived the probability of winning a squares pool for each given square (or set of numbers). Unsurprisingly, he found that those lucky souls holding the ‘7/0’ squares were in good shape, while those left holding the ‘2/2’ ticket were screwed. You can download the Sports-Reference Super Bowl Squares app here, which is free, and should help you taunt your guests at your Super Bowl party.

Let’s say that this year, your Super Bowl squares pool allows you to either pick or trade squares: if that’s the case, this post is for you. I looked at every regular season and postseason game from the last ten years. The table below shows the likelihood of each score after each quarter, along with three final columns that show the expected value of a $100 prize pool under three different payout systems. The “10/” column shows the payout in a pool where 10% of the prize money is given out after each of the first three quarters and 70% after the end of the game; the next column is for pools that give out 12.5% of the pool after the first and third quarters, 25% at halftime, and 50% for the score at the end of the game. The final column is for pools that give out 25% of the pot after each quarter — since I think that is the most common pool structure, I’ve sorted the table by that column, but you can sort by any column you like.
[continue reading…]

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 Stafford	DET	14	14	629	4.3	59.5	6.8	2.7	2.4	5.5	60.7	7.0	4.0	2.6
Drew Brees	NOR	14	14	574	4.0	62.0	7.6	6.3	3.1	5.4	61.4	7.2	4.5	2.7
Tony Romo	DAL	14	14	568	5.3	66.7	7.5	3.9	2.8	5.9	62.5	7.2	4.2	2.7
Andrew Luck	IND	14	13	564	6.2	54.6	7.1	3.5	3.2	6.1	59.5	7.1	4.1	2.7
Carson Palmer	OAK	14	14	562	4.4	60.9	7.1	3.9	2.5	5.6	61.1	7.1	4.2	2.6
Tom Brady	NWE	14	14	560	3.9	63.4	7.6	5.4	1.1	5.4	61.7	7.2	4.4	2.5
Matt Ryan	ATL	14	14	539	4.4	68.5	7.8	5.0	2.6	5.6	63.0	7.2	4.3	2.7
Peyton Manning	DEN	14	14	511	3.9	67.9	7.9	6.1	2.0	5.4	62.8	7.2	4.4	2.6
Brandon Weeden	CLE	14	14	498	5.0	57.2	6.6	2.8	3.4	5.7	60.2	7.0	4.0	2.7
Philip Rivers	SDG	14	14	488	8.1	64.3	6.7	4.5	3.1	6.7	62.0	7.0	4.2	2.7
Joe Flacco	BAL	14	14	487	6.5	59.1	7.1	4.1	2.1	6.2	60.7	7.1	4.2	2.6
Eli Manning	NYG	14	14	487	3.0	60.4	7.4	4.1	3.1	5.1	61.0	7.1	4.2	2.7
Sam Bradford	STL	14	14	482	6.8	60.2	6.8	3.7	2.3	6.3	60.9	7.0	4.2	2.6
Matt Schaub	HOU	14	13	476	4.0	64.7	7.5	4.6	2.1	5.5	62.0	7.2	4.3	2.6
Aaron Rodgers	GNB	14	14	474	8.7	66.7	7.6	6.8	1.7	6.9	62.5	7.2	4.5	2.6
Andy Dalton	CIN	14	14	472	7.5	62.5	7.0	5.5	3.0	6.5	61.5	7.1	4.4	2.7
Josh Freeman	TAM	14	14	469	4.3	54.8	7.4	5.3	2.6	5.5	59.6	7.1	4.3	2.7
Ryan Fitzpatrick	BUF	14	13	444	5.9	61.7	6.6	5.0	3.4	6.0	61.3	7.0	4.3	2.7
Christian Ponder	MIN	14	14	425	6.6	63.1	5.9	3.3	2.8	6.2	61.6	6.9	4.1	2.7
Ryan Tannehill	MIA	14	14	424	5.8	58.7	6.9	2.4	2.8	6.0	60.5	7.0	4.0	2.7
Cam Newton	CAR	14	14	423	7.2	58.2	8.2	4.3	2.4	6.4	60.4	7.3	4.2	2.6
Mark Sanchez	NYJ	14	14	418	7.3	54.8	6.4	3.1	4.1	6.5	59.6	6.9	4.1	2.8
Ben Roethlisberger	PIT	11	11	398	5.7	64.1	7.3	5.5	1.5	6.0	61.9	7.1	4.4	2.6
Jay Cutler	CHI	13	13	377	8.5	59.7	7.0	4.5	3.7	6.8	60.8	7.1	4.2	2.8
Russell Wilson	SEA	14	14	353	6.9	62.9	7.6	5.9	2.5	6.3	61.6	7.2	4.4	2.7
Robert Griffin III	WAS	13	13	351	7.4	66.4	8.3	5.1	1.1	6.5	62.5	7.3	4.3	2.5
Michael Vick	PHI	9	9	316	7.9	58.5	6.9	3.5	2.8	6.6	60.5	7.0	4.1	2.7
Blaine Gabbert	JAX	10	10	278	7.3	58.3	6.0	3.2	2.2	6.5	60.4	6.9	4.1	2.6
Matt Cassel	KAN	9	8	277	6.4	58.1	6.5	2.2	4.3	6.2	60.4	7.0	4.0	2.8
Jake Locker	TEN	9	9	269	5.6	57.6	7.0	3.3	3.3	5.9	60.3	7.1	4.1	2.7
Matt Hasselbeck	TEN	8	5	221	6.0	62.4	6.2	3.2	2.3	6.1	61.5	6.9	4.1	2.6
Nick Foles	PHI	6	5	217	6.5	59.4	6.2	2.3	1.8	6.2	60.7	6.9	4.0	2.6
Alex Smith	SFO	9	9	217	10.0	70.0	8.0	6.0	2.3	7.3	63.4	7.3	4.4	2.6
Chad Henne	JAX	8	4	216	8.5	51.9	6.7	3.7	2.3	6.8	58.8	7.0	4.2	2.6
John Skelton	ARI	7	6	201	6.9	54.2	5.6	1.0	4.5	6.4	59.4	6.8	3.8	2.8
Kevin Kolb	ARI	6	5	183	12.9	59.6	6.4	4.4	1.6	8.2	60.8	6.9	4.2	2.6
Brady Quinn	KAN	8	6	159	9.1	59.7	5.8	1.3	3.8	7.0	60.8	6.8	3.9	2.8
Colin Kaepernick	SFO	11	5	154	8.3	65.6	8.4	4.5	1.3	6.8	62.3	7.3	4.3	2.5
Ryan Lindley	ARI	6	3	141	6.6	51.1	4.3	0.0	4.3	6.3	58.6	6.5	3.7	2.8

(“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
1	Peyton Manning	36	DEN	14	14	321	511	3697	23	13	29	191	6.22
2	Tom Brady	35	NWE	14	14	346	560	4027	24	14	32	214	6.20
3	Robert Griffin III	22	WAS	13	13	219	351	2568	15	9	24	166	6.15
4	Colin Kaepernick	25	SFO	11	5	96	154	1130	7	4	11	76	6.11
5	Ben Roethlisberger	30	PIT	11	11	246	398	2836	17	10	25	141	6.11
6	Matt Ryan	27	ATL	14	14	339	539	3893	23	14	32	225	6.11
7	Eli Manning	31	NYG	14	14	297	487	3476	20	13	26	165	6.09
8	Drew Brees	33	NOR	14	14	352	574	4118	26	16	33	237	6.08
9	Josh Freeman	24	TAM	14	14	279	469	3350	20	12	27	181	6.08
10	Aaron Rodgers	29	GNB	14	14	296	474	3402	21	12	35	197	6.06
11	Cam Newton	23	CAR	14	14	256	423	3086	18	11	29	203	6.05
12	Russell Wilson	24	SEA	14	14	217	353	2539	16	9	24	148	6.05
13	Alex Smith	28	SFO	9	9	138	217	1575	10	6	17	97	6.04
14	Matt Schaub	31	HOU	14	13	295	476	3407	20	12	27	228	6.01
15	Tony Romo	32	DAL	14	14	355	568	4071	24	15	35	255	5.97
16	Andy Dalton	25	CIN	14	14	290	472	3336	21	13	33	176	5.94
17	Joe Flacco	27	BAL	14	14	295	487	3453	20	13	32	209	5.93
18	Carson Palmer	33	OAK	14	14	343	562	3980	23	15	33	254	5.92
19	Ryan Fitzpatrick	30	BUF	14	13	272	444	3101	19	12	29	151	5.89
20	Andrew Luck	23	IND	14	13	336	564	3990	23	15	37	230	5.89
21	Matthew Stafford	24	DET	14	14	382	629	4413	25	17	37	256	5.88
22	Jake Locker	24	TEN	9	9	162	269	1900	11	7	17	108	5.88
23	Michael Vick	32	PHI	9	9	191	316	2223	13	8	22	124	5.84
24	Sam Bradford	25	STL	14	14	294	482	3380	20	13	32	216	5.82
25	Brandon Weeden	29	CLE	14	14	300	498	3477	20	14	30	198	5.80
26	Ryan Tannehill	24	MIA	14	14	257	424	2987	17	11	27	205	5.78
27	Chad Henne	27	JAX	8	4	127	216	1511	9	6	16	94	5.78
28	Philip Rivers	31	SDG	14	14	302	488	3422	21	13	35	221	5.77
29	Matt Cassel	30	KAN	9	8	167	277	1928	11	8	18	97	5.75
30	Jay Cutler	29	CHI	13	13	229	377	2661	16	10	28	187	5.74
31	Christian Ponder	24	MIN	14	14	262	425	2912	17	11	28	164	5.70
32	Nick Foles	23	PHI	6	5	132	217	1500	9	6	14	102	5.70
33	Matt Hasselbeck	37	TEN	8	5	136	221	1525	9	6	14	105	5.70
34	Mark Sanchez	26	NYJ	14	14	249	418	2903	17	12	29	178	5.68
35	Blaine Gabbert	23	JAX	10	10	168	278	1907	11	7	19	138	5.61
36	Kevin Kolb	28	ARI	6	5	111	183	1270	8	5	16	96	5.61
37	Brady Quinn	28	KAN	8	6	97	159	1086	6	4	12	70	5.50
38	John Skelton	24	ARI	7	6	119	201	1365	8	6	14	89	5.46
39	Ryan Lindley	23	ARI	6	3	83	141	921	5	4	9	72	5.15
	Lg Average												5.92

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:

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

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).

If Friday (or sometimes Thursday) is rant day at Football Perspective, then perhaps the first Tuesday in December can be fantasy day. Since everyone else does it, allow me to describe to you my preferred college playoff system.

• A modified 8-team playoff that’s actually a 10-team playoff with a four-team “play-in” round. A playoff system should not undermine the regular season, and my system places significant emphasis on success in the regular season: the most-accomplished teams get to clear the lowest hurdles due to byes, home-field, and weaker opponents.
• Notre Dame and the conference champions from the SEC, B12, P12, B10, and ACC earn automatic berths to the playoffs but only if they finish in the top 14.
• A selection committee (or a BCS-style ranking system) is used to select the remaining teams. There is a limit of 4 teams per conference. The “play-in” round and the first round of the playoffs are held at the higher seed’s location the week after the conference championship games (i.e., next weekend).

Therefore, Alabama (#2 in the BCS), Kansas State (#5), Stanford (#6), and Florida State (#12) would be guaranteed spots by virtue of being conference champions. Notre Dame (#1) is also guaranteed a slot.

That leaves five at-large selections:

— Florida (#3)
— Oregon (#4)
— two of Georgia (#7), LSU (#8), Texas A&M (#9), and South Carolina (#10)
— Oklahoma (#11)

Basically the only teams that could complain about this system are the leftover SEC teams, but I’m okay with that since they would be considered the 5th and 6th best teams from their conference. I suppose 12-1 MAC Champion Northern Illinois might be bothered, but they lost to Iowa and had a cupcake schedule. Had Nebraska defeated Wisconsin, or had Ohio State been eligible, Oklahoma or a 4th SEC team would have been left out.

For purposes of this post, I will say Georgia and A&M would be the committee’s picks.

Here’s how the playoff system would work. The three highest-ranked conference champions (Alabama, K-State, Stanford) get byes along with the next three highest-ranked teams — Notre Dame, Florida, and Oregon. The other four teams play at the site of the higher ranked team ^[1]I would have the committee involved in this step of the process as well and instruct them to avoid rematches if possible. This year it is not an issue. this Saturday (December 8th). So we would have:

Play-in round

#13 Florida State @ #7 Georgia
#11 Oklahoma @ #9 Texas A&M

— Had UGA beaten Alabama, the Bulldogs would have earned a bye followed by a home playoff game. By losing, they have to play a play-in game and then win a road playoff game. So the SEC Championship was a critical game — and there’s a good chance the committee would have simply selected LSU instead because of the loss.
— Had Florida State beaten Florida, they would have had likely received a bye and possibly a home playoff game. Now they have to win two road games. Of course, the ACC Championship Game, which no one cared about on Saturday night, would have been significantly more relevant.
— Ditto the Big 10 Championship Game, well, at least for the first half. Meanwhile, Oklahoma would have known they would have needed to run the table to get in after losing to Notre Dame. Instead of the season essentially being meaningless, think how much more exciting the Sooners last-second wins over West Virginia and Oklahoma State would have been?
— And of course, think how much more exciting the rest of the season for A&M and Johnny Manziel would have been if they had a chance to make the playoffs?

First round of the playoffs:

The three conference winners and the top-seeded at large receive home playoff games, which would be played on Saturday, December 15th:

#1 Notre Dame hosts the worst remaining seed (either FSU or the A&M/Oklahoma winner)
#2 Alabama hosts the 2nd worst seed remaining (Georgia or the A&M/Oklahoma winner).
#5 Kansas State hosts the 3rd worst seed remaining (Florida)
#6 Stanford hosts the 4th worst seed remaining (Oregon)

[Note: If you want to have the committee switch the bottom two games so that Stanford does not “have to beat Oregon again” I am fine with that. KSU-Oregon and Stanford-Florida are just as acceptable to me, and I am willing to do the same to avoid a UGA or A&M rematch with Alabama if events unfolded that way.]

Notre Dame and Alabama had the best two regular seasons, and look at their rewards: they get a bye, they get a home game, and they play a team coming off a play-in game.

KSU and Stanford both won their conferences, so they are rewarded with byes and home playoff games. Florida State won their conference but their lackluster regular season did not merit the same reward. Wisconsin won the Big 10 and got nothing. So the regular season remains vitally important. Oregon and Florida had great regular seasons, and thanks to a second bite at the apple (which is instead only reserved for Alabama this year), their seasons weren’t meaningless after one loss. And the lowest two seeds played their win into the playoff.

This playoff format makes the regular season much more meaningful for many more teams, while only slightly taking away from the value of certain games (admittedly, the Kansas State and Oregon upsets on November 17th would have been less meaningful in this format; I’m okay with that — the perfect should not be the enemy of the good.)

The Final Four

We now break until the first weekend of January. At this point, we have four very deserving teams. They would play in a four-team playoff, rotated among the bowl sites, as currently envisioned. We might see Notre Dame and Oregon in the Rose Bowl and Alabama and Kansas State in the Sugar Bowl. Or perhaps A&M and Oregon in the Cotton Bowl and Florida and Notre Dame in the Orange Bowl. Who knows. The good news is that unless there are 11 deserving teams, everyone gets a shot (and even if all 5 autobids finish in the top 14, 4 non-conference champs are still eligible; no worthy undefeated team should get left out in this system).

[Five years ago, my friend and Pro-Football-Reference.com founder Doug Drinen wrote the predecessor to todaay’s article, but refused to go with this title. The principles remain fundamental to advanced analysis of any sport, so today I’ll be revisiting them with current examples.]

Our brains are really good at making connections and finding patterns. In The Believing Brain, Michael Shermer argued that we’ve made it to where we are today precisely because of our ability to do just that:

A human ancestor hears a rustle in the grass. Is it the wind or a lion? If he assumes it’s the wind and the rustling turns out to be a lion, then he’s not an ancestor anymore. Since early man had only a split second to make such decisions, Mr. Shermer says, we are descendants of ancestors whose “default position is to assume that all patterns are real; that is, assume that all rustles in the grass are dangerous predators and not the wind.”

Of course, not all patterns are real, and sometimes that rustle is just the sound of the wind. Just because you see a surprising split — maybe a player dominated the second half of the season after a slow start — doesn’t mean that the “trend” is real. For example, here are some splits from the 2011 season:

Reggie Wayne was much better against teams that wear the color blue than when facing teams that have no blue in their uniforms. Here is his weekly production (the last column represents his fantasy points) when playing against teams that do not have blue as a color in their uniform:

Week	Opp	Rec	Yd	TD	FP
17	jax	8	73	0	15.3
5	kan	4	77	0	11.7
6	cin	5	58	0	10.8
2	cle	4	66	0	10.6
4	tam	4	59	0	9.9
14	rav	4	41	0	8.1
9	atl	4	30	0	7.0
7	nor	3	36	0	6.6
3	pit	3	24	0	5.4
10	jax	3	13	0	4.3
Avg		4.2	47.7	0	9.0

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