On Sunday, Calvin Johnson became just the second player in NFL history to gain at least 125 receiving yards in five consecutive games. He’s also the second Lion to hit those marks.

As you’ve probably figured it out by the title, the only player with 125+ receiving yards in five straight games prior to Johnson was Patrick Lewis Studstill Jr., who had an 11-year career with the Lions, Rams, and Patriots. Studstill was a first-team All-Pro wide receiver in 1966, when he led the NFL in receiving yards. Beginning in week 5 of that season, Studstill topped 115 receiving yards in six straight weeks. Those were six highest single-game yardage totals of his career.

Studstill was a receiver and punter at the University of Houston in the late ’50s, but his senior season was a disaster. Head coach Hal Lahar was your typical coach of the era when it came to rules, and Studstill violated a major one — by getting married. Lahar benched Studstill for his entire senior year, and as a result, Studstill went undrafted. One of the Cougars assistant coaches, Red Conkright, was a former NFL player who would later become the last head coach of the Oakland Raiders before Al Davis. Conkright vouched for Studstill, who received a tryout in Detroit. Because of his versatility and speed, he was able to make the Lions roster in 1961.

Studstill didn’t see the field much during his rookie season, but did manage a 100-yard kickoff return touchdown against the Bears. An injury to Terry Barr in 1962 opened the door for Studstill to begin making a contribution on offense. In the last 9 games of the season, Sutdstill caught 31 passes for 446 yards, second on the team in both categories to Pro Bowler Gail Cogdill.
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On November 15, 2010, Michael Vick humiliated the Washington Redskins before a national television audience on Monday Night Football. The big news right before kickoff was that the ‘Skins had signed former Eagle QB Donovan McNabb to a five-year, $78 million contract extension, an ironic note that wouldn’t be lost on observers as McNabb’s replacement, Vick, put together one of the greatest all-around quarterbacking performances in NFL history:

Date	Tm		Opp	Result	Cmp	Att	Cmp%	Yds	TD	Int	Rate	Sk	SkYd	ANYPA	Rush	R_Yds	R_TD
11/15/2010	PHI	@	WAS	W 59-28	20	28	71.4%	333	4	0	150.7	1	1	14.2	8	80	2

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

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

Date	Tm		Opp	Result	Cmp	Att	Cmp%	Yds	TD	Int	Rate	Sk	SkYd	ANYPA	Rush	R_Yds	R_TD
11/18/2012	WAS		PHI	W 31-6	14	15	93.3%	200	4	0	158.3	2	8	16.0	12	84	0

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

Sound off with your opinion below!

One of the most difficult decisions an organization has to make is when to admit its mistakes. The Jaguars drafted Blaine Gabbert with the 10th overall pick in 2011, and his lack of success is even more striking when compared to the rest of the top dozen selections:

Last year, there were three legitimate excuses the Jaguars could proffer to defend Gabbert’s play: he was a rookie, the lockout prevented him from getting proper training, and Jacksonville had the worst set of receivers in the league. Giving up on a first round quarterback after just one season would be silly, especially one where the expectations were that the rookies would struggle. And the cupboard was bare: Jacksonville became the first team since the 2004 Ravens and only the 5th team in the previous 20 seasons to not have a 500-yard wide receiver, so it’s not like Gabbert had a lot to work with. ^[1]Of course, there is the obvious “chicken or the egg” question involved there. The other four teams on that list? The 2004 Ravens (Kyle Boller), 2003 Lions (Joey Harrington), 1997 … Continue reading

But through five games, little has changed in Jacksonville. The Jaguars should wait to evaluate Gabbert’s career — five games into his second season isn’t a fair sample size — but his production so far have been extremely disappointing:

• Gabbert is currently 28th in ESPN’s Total QBR, a disappointing start after he ranked last (34th) in that metric in 2011.
• Gabbert ranks last in Net Passing Yards per Attempt with an anemic 4.3 average; last season, he also ranked last with a 4.2 NY/A average.

A few years ago, Jason Lisk wrote this post on when the Lions should have given upon Joey Harrington. One of the most relevant points of that article was Lisk’s supposition

that teams are far more likely to commit errors of holding on to a quarterback for too long, while rarely giving up on a quarterback too early — once they have seen him play any amount of time in a real NFL game. I can think of examples of quarterbacks who were drafted, never started for their original team, and found success elsewhere, but its relatively rare to find a quarterback who started but never had success with his original team, and moved elsewhere to have his first breakout.

There were 70 quarterbacks selected in the first round of NFL drafts between 1978 and 2010. How often did a team give up too early on a good quarterback? ^[2]Note that for purposes of this post, I am considering Eli Manning, Philip Rivers, Jim Everett, and John Elway as being drafted by the Giants, Chargers, Rams and Broncos. Vinny Testaverde had success outside of Tampa Bay, but the Bucs didn’t give up “early” on him by any means; he played for six years in Tampa with with varying levels of success. The team did give up too early on Steve Young, although he wasn’t included in this study because he was selected in the supplemental draft. Jim Harbaugh had success in Indianapolis, but it’s not like the Bears didn’t know what they had: Harbaugh was in Chicago for the first seven years of his career.

Jeff George had good years outside of Indianapolis, but I wouldn’t say the Colts gave up early on him. He was inconsistent for four years and caused problems off the field; he was finally traded in connection with a holdout. Mike Vick has had success in Philadelphia, but the Falcons obviously had their hands forced when they gave up on him. Ditto Kerry Collins, whose off the field issues left the Panthers with little choice.

With the exception of Steve Young, who Tampa traded after two years — and who may not have ever turned into a star quarterback in Tampa Bay — you’d be hard pressed to find any examples of teams giving up on first round picks too early (with the exception of those released/traded for nonfootball reasons). Chad Pennington had one great year in Miami, but that was after a long career in New York. Doug Williams and Trent Dilfer won Super Bowls with other teams, but Tampa Bay didn’t give up on either quarterback too early by any reasonable definition of the phrase. The reality is, teams will do just about everything before giving up on a first round quarterback too early and as a result, take way too long to move on from a bad investment. And while teams are (understandably) deathly afraid of giving up on a highly drafted quarterback too early, they’re more likely to harm themselves by waiting to move on for too long on a bad investment.

Through six weeks, NFL teams are averaging 6.44 NY/A, meaning Gabbert is averaging only 67% as many net yards per attempt as the average passer. How does that compare historically? The table below shows all drafted quarterbacks who threw at least 250 passes in their second season, and lists their NY/A and NY/A relative to league average during their sophomore years:

If your quarterback plays poorly in his second year, you’re basically hoping he’s Phil Simms (who had his first strong season at age 30) or the good version of Jeff George. Maybe Sam Bradford or [gasp] Tim Tebow, will also become solid starters in the NFL one day. But that’s only one part of the equation, and it’s the minor half. You could have the next Akili Smith or Kyle Boller or David Klingler or Colt McCoy or Rick Mirer or Cade McNown or Joey Harrington, too.

You might think it’s far better to wait a year too long with a first round investment than to cut bait a year too early. Tell that to the Ravens, who after two years of Kyle Boller, chose to wait it out in the 2005 draft and selected Mark Clayton over Aaron Rodgers (why take Rodgers, Cal quarterbacks are terrible!). Detroit selected Joey Harrington with the third pick in the 2002 draft, but as Lisk noted, Detroit could have reasonably “given up” (more on this in a second) on Harrington by the end of the 2003 season. The Lions did not, and selected Roy Williams in the 2004 draft instead of say, Ben Roethlisberger.

And “give up” doesn’t necessarily mean cut or spend a first round pick on another quarterback. Assuming Joe Flacco re-signs with Baltimore, there won’t be any real options in free agency for the Jaguars to address the quarterback position (Jason Campbell is probably the best of the bunch). But they can certainly address the issue in the draft. If a quarterback the Jaguars’ scouts view as elite is available with their (potentially very high) first round pick, then I don’t think you can simply say “let’s give Blaine one more year.” But at a minimum, the Jaguars must spend a pick on a quarterback in the 2013 draft if Gabbert doesn’t improve over the rest of 2012.

NFL offenses and defenses are not mirror images of each other. The gap between the best and worst offenses is generally bigger than the spread on the defensive side of the ball. And strength of schedule is more likely to play a key role when it comes to determine the best and worst defenses, too. Today’s post is a two-parter: in Part I, we look at some data on the best and worst teams in the modern era, while Part II analyzes the above claims.

Ranking offenses (or defenses) isn’t easy. I don’t like using yards, which is misleading in a lot of ways. Points scored sounds good, but non-offensive scores and other big plays on defense and special teams can make that metric less telling. There are some very good advanced metrics, but they don’t help us if we want to go back to the 1970s. So I simply used offensive touchdowns scored to rank the offenses and offensive touchdowns allowed to rank the defenses. And since I’m going to go back to 1970, I’ll be comparing each unit to the league average in that season.

Part I – Team Rankings

In addition, I’m going to adjust the offenses and defenses for strength of schedule. I’ll be doing this in an iterative way just like I do with the SRS. Listed below are the top 100 teams since 1970 in terms of offensive touchdowns per game over average (and in addition to adjusting for strength of schedule, I’ve pro-rated the non-16 game seasons to 16 games). The first line shows the 2007 Patriots, who scored 67 offensive touchdowns when the league average was 34.6. Therefore, NE gets credit for being 32.4 touchdowns over average. The Patriots’ schedule was actually difficult (once you adjust for the fact that their opponents faced New England) — it cost the offense nearly 2 touchdowns — so their final rating is +34.4.
[continue reading…]

Career statistics can be very misleading, since a player can hang around for a bunch of meaningless years but really pad his totals. Six years ago, Doug came up with a system that only counted the receiving yards a player recorded after his first 1,000 receiving yards each season.

I’m going to do something similar for running backs, but instead will focus on individual game performances. I have game logs for every running back (post-season included) for every game since 1960. What I did was zero out all rushing yards in games where a player had 50 or fewer rushing yards; in the remaining games, I only gave those runners credit for the rushing yards they gained after their first 50 rushing yards. The “RYov50” column shows the running back’s career rushing yards after removing the first 50 rushing yards he had in every game; the next column shows each player’s career rushing yards (since 1960, including post-season), and the first “Perc%” column shows the ratio of the “RYov50” column to the career rushing yards. A higher percentage means the player spent most of his time as the lead back for his team, while a lower percentage indicates that the player spent significant time in a committee and/or stuck around for several years past his prime. Obviously for still active players, the percentage column could be misleading as they may not have entered the decline portion of their careers just yet.

The #50YG column shows how many games the player had over 50 rushing yards, and the next column shows what percentage of games the running back gained over 50 yards. For players like Jim Brown, this study only includes his seasons starting in 1960, and for active players, 2012 data is *not* included:
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Five weeks in, you start to hear NFL experts trade their preseason overconfidence for regular season overconfidence. It’s tempting to fall into the trap thinking that with over a quarter of the season in the books, now we have an idea of how the rest of the regular season will unfold.

It’s tempting, but it’s not really true. The best way to measure whether someone knows what they’re talking about is to see if their predictions come true. Fortunately for us historians, each game a group of experts predict what will happen every week — it’s called the point spread.

Assuming we actually learn something each week, then the point spreads should reflect the actual results as the leaves change colors. But do they?

I looked at the point spread for all regular season games from 1988 to 2011. Now the question becomes how do we measure if a point spread was “right”? If the Texans are favored by 7 to beat the Bengals, and they win by 10, is that a “good” projection?

The simplest way to test this is to see the difference between the actual result and the projected result. The line that was most “off” in the database came two years ago. Likely due to the genius of Josh McDaniels, the Raiders were 7-point underdogs in Denver in 2010, but won the game 59-14. So in that game, the line was off by 52 points. Last year, the 49ers were 3-point favorites at home against Tampa Bay, but won by 45 points; that line was “off” by 42 points.

One interesting sidenote: you might think that big lines are more likely to be off by bigger margins than small lines. But that’s not really the case. The standard deviation of “how much a line is off by” is roughly 8 points regardless of the spread. It’s not exact, of course, but for our purposes, we can work under the assumption that lines are generally equally likely to be off by the same amount regardless of the spread.

Anyway, back to the point of the post. How accurate are lines early in the year? In week 1, the spreads are generally a little tighter than they are the rest of the way; perhaps the oddsmakers are just as unsure as the rest of us. But no matter what week it is, the lines are always off by about 10 points per week. Take a look. The table below shows how much the lines were off by, on average, in weeks 1 through 17 from 1988 to 2011. In the last column, I’ve shown the percentage of games where the line was within 10 points of the actual result.

It’s tempting to think we know more once we see more, but that’s unfortunately not the case. Of course, if it was, it would be easy to make money gambling on football.

For those curious, week 12 of the 2003 season was as close as Vegas has ever come to perfection. Look at how close these lines were to the actual results (as always, the boxscores are clickable):

What about the other side of the coin? Vegas was really, really, really off in week 17 of the 1993 season. Just so you know, 1993 was the year the NFL tried the double-bye week approach resulting in an 18-game season, so this was really like a week 16 most years:

For a seven-year stretch beginning in 1988, the San Francisco 49ers or Buffalo Bills were in each Super Bowl. And every year for an even longer stretch, Chris Berman would predict that the 49ers and Bills would meet in that year’s Super Bowl.

The teams never met in a Super Bowl, but it was a historic performance by San Francisco when the teams met on Sunday. The 49ers passed for 310 yards and rushed for 311 yards, becoming the first team in NFL history to top the 300-yard mark both through the air and on the ground.

In fact, before this season, only 46 teams had ever hit 250 rushing yards and passing yards in the same game, with the 2010 Eagles being the most recent team. The first two teams to set this mark were in the AAFC, and it wasn’t until 1948 that an NFL team crossed those thresholds. Three times in the playoffs, including one Super Bowl, a team gained at least 250 yards passing and rushing.

The table below lists all such games, and has linkable boxscores in the “Date” column.

Tm	Yr	wk	Opp	Date	PF	PA	ATT	PYD	RSH	RYD	Min
SFO	2012	5	BUF	10/7/2012	45	3	25	310	38	311	310
PHI	2010	10	WAS	11/15/2010	59	28	28	332	38	260	260
NYG	2008	5	SEA	10/5/2008	44	6	26	269	36	254	254
DEN	2005	8	PHI	10/30/2005	49	21	35	309	36	255	255
CIN	2004	12	CLE	11/28/2004	58	48	29	251	32	253	251
KAN	2004	7	ATL	10/24/2004	56	10	28	269	49	271	269
JAX	1999	19	MIA	1/15/2000	62	7	20	263	46	257	257
PHI	1995	6	WAS	10/8/1995	37	34	45	252	38	272	252
GNB	1994	15	CHI	12/11/1994	40	3	34	259	46	257	257
KAN	1990	6	DET	10/14/1990	43	24	26	256	43	310	256
RAM	1988	6	ATL	10/9/1988	33	0	26	249	45	252	252
NYJ	1988	5	KAN	10/2/1988	17	17	48	270	46	272	270
WAS	1987	19	DEN	1/31/1988	42	10	30	322	40	280	280
CIN	1986	14	NWE	12/7/1986	31	7	31	284	42	300	284
NOR	1986	14	MIA	12/7/1986	27	31	34	269	36	257	257
CIN	1985	14	DAL	12/8/1985	50	24	33	296	42	274	274
SEA	1983	13	KAN	11/27/1983	51	48	32	251	47	280	251
PIT	1982	9	CLE	1/2/1983	37	21	24	260	49	261	260
CHI	1980	14	GNB	12/7/1980	61	7	24	327	48	267	267
PIT	1979	13	CLE	11/25/1979	33	30	44	351	45	255	255
ATL	1979	1	NOR	9/2/1979	40	34	38	295	47	257	257
DAL	1978	1	BAL	9/4/1978	38	0	27	305	45	278	278
CHI	1976	13	SEA	12/5/1976	34	7	28	250	48	259	250
DEN	1976	2	NYJ	9/19/1976	46	3	28	292	44	251	251
OAK	1975	8	NOR	11/9/1975	48	10	27	263	53	260	260
DAL	1973	3	STL	9/30/1973	45	10	25	328	42	250	250
SDG	1963	16	BOS	1/5/1964	51	10	26	292	32	318	292
GNB	1962	9	PHI	11/11/1962	49	0	31	334	55	294	294
HOU	1961	13	NYT	12/10/1961	48	21	38	307	35	266	266
SFO	1961	4	RAM	10/8/1961	35	0	26	262	40	259	259
CRD	1959	1	WAS	9/27/1959	49	21	28	319	26	250	250
SFO	1958	9	GNB	11/23/1958	33	12	35	283	42	256	256
RAM	1958	7	SFO	11/9/1958	56	7	27	253	39	324	253
CRD	1958	2	WAS	10/4/1958	37	10	32	270	39	261	261
RAM	1957	11	GNB	12/8/1957	42	17	31	297	42	302	297
RAM	1956	12	GNB	12/16/1956	49	21	27	297	49	314	297
BAL	1956	8	RAM	11/25/1956	56	21	26	316	39	258	258
SFO	1953	12	BAL	12/13/1953	45	14	44	345	33	252	252
RAM	1950	10	NYY	11/19/1950	43	35	49	370	38	266	266
CRD	1950	2	BAL	10/2/1950	55	13	30	281	51	272	272
PHI	1949	8	WAS	11/13/1949	44	21	28	294	55	256	256
CRD	1949	8	NYY	11/13/1949	65	20	36	253	47	319	253
PHI	1948	12	DET	12/12/1948	45	21	24	258	50	262	258
SFO	1948	5	BUF	9/26/1948	38	28	25	268	0	268	268
CRD	1948	4	NYG	10/17/1948	63	35	20	280	34	299	280
LAD	1946	12	BUF	12/1/1946	62	14	19	337	0	288	288

A couple of weeks ago, I wrote about a method of calculating a team’s win probability at the end of any given quarter, given the pregame Vegas line and the score margin of the game after the quarter in question. Today, I want to break down those numbers in more detail by looking at which teams (and quarterbacks) added the most Win Probability in each stage of the game.

To compute Win Probability Added (WPA) for the purposes of this post, you look at how much the team’s chances of winning changed from one quarter to the next. For instance, here’s how I’d deconstruct Monday night’s game for the winning Bears:

Date	Tm		Opp	QB	WPA_loc	WPA_vegas	WPA_1st	WPA_2nd	WPA_3rd	WPA_4th	WPA_ot	WPA_tot
10/1/2012	Chi	@	Dal	Jay Cutler	-0.079	-0.021	+0.013	+0.137	+0.420	+0.029	+0.000	+0.500

WPA_loc and WPA_vegas are the two components that make up the pregame win expectancy. Chicago was on the road here, which typically deducts about 8% from a team’s base 50% WP right from the get-go (or roughly 2.5-3.0 points of spread), and on top of that they were 3.5-point underdogs, which put their pregame WP another 2.1% lower than you’d expect from an evenly-matched road team. All told, before the opening kickoff, they were already down about 10% in terms of WP.

Then both teams had a scoreless first quarter, which added 1.3% to Chicago’s total under the WPA_1st banner. This happened because, even though they were still tied, there was less time remaining in the game during which Dallas could exert their theoretical talent advantage (the variance of the future was likely to be higher, which always favors the underdog).

Chicago took a 10-7 lead in the 2nd quarter, which tacked on 13.7% of WP, as seen under WPA_2nd. By this point, they had erased their early 10% deficit and were actually favored to win with a WP of 55.1%. A 14-3 3rd period was the killer, though, adding 42% of WP in the WPA_3rd column. Going into the final quarter with a 24-10 lead, the Bears had a 97.1% chance of winning; when they didn’t relinquish that lead, the remaining 2.9% of WP were added under WPA_4th, since the game was over.

And as is the case with every winning team ever, their WPA_tot for the game was +0.500.

See how it works? By using WPA in this manner, we can detect when in the course of the game a team adds or subtracts the most from its chances of victory. We can also add these WPA numbers up across games at the season level, or even for entire careers.

[continue reading…]

This post is intended to be more exhaustive than groundbreaking, more like an encyclopedia than a fiction novel. If you’re at this website, you’re probably into advanced statistics, and if you’re into advanced stats, you probably know that turnover rates are generally pretty inconsistent from time period to time period. But I wanted to get more granular than that, and to break down turnover rates into three specific components. The all-encompassing word ‘turnovers’ in itself is not helpful, because there are three types of turnovers:

— Interceptions
— Offensive fumbles
— Special teams/defensive fumbles

The word turnovers makes sense from an explanatory standpoint. If a team has three turnovers, it doesn’t matter all that much how they occurred. An interception returned for a touchdown is the same as a fumble returned for a touchdown. But when it comes to analyzing turnovers from a predictive standpoint, the word itself only serves to confuse. I’m going to put interceptions to the side for now — I wrote about them last Monday — and today focus on offensive fumbles.

The six types of offensive fumbles

We’ve known for awhile that fumbles are pretty random for year to year, particularly on the defensive side of the ball (see this article by Jim Armstrong showing that forcing fumbles was almost entirely a function of luck and not skill). Even the word “fumbles” is too broad from a predictive standpoint, as there are many different types of fumbles. On offense alone, I grouped all fumbles from 2000 to 2011 into one of six types:

— Quarterback/center exchange fumbles

— Quarterback sack fumbles

— Quarterback fumbles on running plays

— Quarterback fumbles on non-sack plays behind the line of scrimmage (this could be on bad handoffs, simply dropping the ball, etc.)

— Fumbles on running plays (non-QB)

— Fumbles following completed passes

In addition, there are four possibilities following a fumble. The ball could harmless go out of bounds ^[1]Technically, it could go harmfully go out of bounds, too. There were a few examples where the ball went out of bounds for a safety, or a team driving for a touchdown fumbled out of the other … Continue reading, it could be recovered by the fumbler, it could be recovered by one of the other ten players on offense, or it could be recovered by the defense. And as you might expect, the recovery rates are different depending on the type of fumble. I’ve done all the heavy lifting for you. The graph below shows the recovery rates associated with each of the six different types of offensive fumbles. The stacked columns are color coded: yellow represents fumbles that go out of bounds, blue is for fumbles recovered by the fumbler (“RBF”), blue is for plays where a different offensive player recovered, and red shows when the defense gained possession.

I like to see things in graphs, but for the more numbers-oriented folks out there:

As an example, look at the second to last row which shows fumbles following completed pass plays. This tells us that 21% of all offensive fumbles come on these types of plays. A good chunk of them go out of bounds (17%), and one in ten are recovered by the fumbler. The far right column tells us that of the remaining fumbles that are recovered but not by the fumbler — Fumbles In Play — 82% of them are recovered by the defense.

Again, this isn’t meant to be “shocking” as it is to be informative. The goal here is to understand how fumbles generally operate to better understand how likely specific events are to be predictive. A receiver fumbling downfield who then has a teammate recover the fumble is a lot luckier than a quarterback who fumbles the snap and then recovers it. To me, knowing exactly how much luckier is valuable information.

For example, there were 5 offensive fumbles in the Chargers-Chiefs game yesterday. We can analyze them using the above info:

• In the first quarter, Jamaal Charles fumbled on a run, and San Diego’s Shaun Phillips recovered. So San Diego recovered 1.0 fumbles, instead of the 0.60 fumbles usually recovered by the defense on a rushing play.
• Early in the second quarter, Phillips sacked Matt Cassel, but Cassel recovered. The quarterback only recovers the fumble on 14% of sacks, so this was atypical of most fumbles. The defense recovers the fumble 51% of the time, and San Diego recovered 0.0 fumbles instead of the expected 0.51 fumbles.
• On Kansas City’s next drive, Charles fumbled again on a run, and Corey Liuget recovered. Again, San Diego gets credit for 1.0 fumbles instead of the expected 0.60 fumbles. On these three Kansas City fumbles, San Diego recovered 2.0 fumbles when we would expect them to recover 1.71 fumbles in these situations. So we could argue that they were lucky to recover an extra 0.29 fumbles.
• On the next play, Philip Rivers had an aborted snap and fumbled out of bounds (assuming the game book is accurate) — a very rare play (2.6% of the time). But in general, we would expect San Diego to retain possession on 76% of aborted snaps, so they are +0.24 fumbles on this play, and +0.53 on the day.
• The last fumble came in the fourth quarter, when Shaun Draughn fumbled and Atari Bigby of the Chargers recovered. Again, this is another +0.40 fumble situation for the Chargers.

In total, San Diego recovered four of the five offensive fumbles on the day, and that represents 0.93 more fumbles than we would expect. That’s an example of how I envision people using the above table.

Roy Jefferson isn’t well-remembered today, but he was one of the top receivers at the start of the Super Bowl era. Jefferson was a second round pick of both the Steelers and Chargers in 1965, back when the leagues held separate drafts. Jefferson chose to sign with Pittsburgh, and in his second season, he led the NFL with a 24.1 yards per reception average. In 1968, Jefferson led the NFL in receiving yards and scored 11 touchdowns, one behind Paul Warfield for the lead. Jefferson matched his production the next year and was a unanimous first-team All-Pro selection. But for Jefferson, personal glory was the only success he would see in Pittsburgh, as the Steelers went just 7-33-2 from ’67 to ’69.

Jefferson’s 1969 performance was interesting for another reason. He gained 44% of his team’s receiving yards, and since then, only a few other players have reached that mark:

As Steelers fans know, 1969 was a key year in the franchise’s history. It was Chuck Noll’s first season, and his first draft selection was Joe Greene. After finishing with the league’s worst record in 1969, Pittsburgh won the rights to draft Terry Bradshaw. On the field, Jefferson was the best player in Noll’s first season. But that doesn’t mean Noll and Jefferson got along.

[continue reading…]

The Tennessee-Detroit game was an instant classic today, with one of the wildest fourth quarters anyone will ever see. The 46 points scored were the second most in NFL history, trailing another recent game involving the Lions.

The scoring was crazy. Tommie Campbell had a 65-yard punt return at the end of the first quarter; a few minutes later, Jared Cook caught a 61-yard touchdown, and both were more incredible than I’m describing. But that was about it until the 4th quarter, save a one-yard Mikel Leshoure touchdown. Then, in the 4th, Nate Burleson (3 yards), Darius Reynaud (105), Nate Washington (71), Alterraun Verner (72), Calvin Johnson (3) and Titus Young (46) scored touchdowns, the last coming on a Hail Mary.

All told, there were 9 touchdowns scored in the game, and those touchdowns covered a total of 427 yards. The Titans became the first team in NFL history to score five touchdowns of 60 or more yards. But that 427-yard mark? That just sneaks into the top 10 all-time for yards on touchdowns in a game (click on any of the boxscores below to take you to that game):

That top game was one of the most memorable games of the ’60s and remains the game with the most points ever scored in an NFL game.

Of course, a lot of the craziness was coming from Tennessee, which managed to gain 374 yards on their touchdowns. That’s good enough for 2nd place — in the Redskins-Giants game, Washington’s touchdowns covered 403 yards. Here’s a list of the single teams to cover at least 300 yards on their touchdowns in a game:

[Today is a two-post day at Football Perspective. Check here for my week 2 power rankings, while Neil provides an innovative look at the biggest comebacks of the last 35 years in this post. — Chase

In my last post, I introduced a method of estimating the home team’s pre-game win probability in Excel using the Vegas spread:

p(W) = (1-NORMDIST(0.5,-(home_line),13.86,TRUE)) + 0.5*(NORMDIST(0.5,-(home_line),13.86,TRUE)-NORMDIST(-0.5,-(home_line),13.86,TRUE))

Let me explain the rationale behind the scary-looking equation. The first part represents the probability that the home team ends regulation time with a lead of 1 point or more, using Hal Stern’s finding that the home team’s final margin of victory can be approximated by a normal random variable with a mean of the Vegas line and a standard deviation of 13.86. The second part is the probability that regulation ends in a tie, multiplied by 0.5 (this assumes each team has roughly a 50-50 chance of winning in overtime).

With a small twist, we can also apply this formula within games, to the line-score data for every quarter. Within a game, the home team’s probability becomes:

p(W) = (1-NORMDIST(away_margin+0.5,-home_line*(minleft/60),13.86/SQRT(60/minleft),TRUE))+0.5*(NORMDIST(away_margin+0.5,-home_line*(minleft/60),13.86/SQRT(60/minleft),TRUE)-NORMDIST(away_margin-0.5,-home_line*(minleft/60),13.86/SQRT(60/minleft),TRUE))

This is the same equation as before, but we’re adding in Home_Margin (home team pts minus road team pts for the game, through the end of the quarter in question), reducing the effect of the home Vegas line linearly based on how much time remains in the game, and changing the standard deviation of scoring margin to become:

Stdev = 13.86 / sqrt(60 / n)

where n = the number of minutes remaining in the game.

These changes will help us estimate a team’s chances of winning at the end of each quarter. For instance, Monday night’s game — where the Falcons were a 3-point home favorite over the Broncos — goes from:

To this:

[continue reading…]

The San Francisco 49ers were the breakout team of the 2011 season, going from 6-10 in 2010 to 13-3 last year. The Cincinnati Bengals were the surprise team of the AFC, jumping from four to nine wins and earning a playoff berth. The Houston Texans, Detroit Lions, and Denver Broncos all made the playoffs after notching four more wins in 2011 than they had in 2010. But could you have known after just one week that those teams were on track for such breakout seasons? Let’s review.

San Francisco 49ers 33, Seattle 17

This was an odd game, and Jason McIntyre explains why:

San Francisco had just 12 first downs and 209 yards … but still beat the Seahawks by 16 points. The real reason the 49ers won was because Ted Ginn ran a punt and kickoff back for touchdowns in a span of 59 seconds. But offensively, Frank Gore averaged 2.7 ypc and Alex Smith threw for just 124 yards. San Francisco had the ball for 31 minutes and mustered only 12 first downs. As bad as the 49ers looked offensively, the defense did sack T-Jack five times and generate three turnovers. But if the 49ers couldn’t move the ball against a mediocre Seahawks defense … what will they be able to do against the Cowboys, which annihilated the Jets’ offensive line Sunday night? The 49ers have opened as 3-point dogs against Dallas next weekend … I humbly suggest loading up on Romo in that one. No word if San Fran will be without Michael Crabtree.

The 49ers offense wasn’t impressive, but San Francisco’s defense and special teams were dominant. That formula proved to work all season, although few expected it to work against teams better than Seattle. At the time Seattle was considered one of the worst teams in the league (they were a 14-point underdog in Pittsburgh the following week), which made the victory look even less impressive. After week one, Jason Lisk unveiled his Week 1 Power rankings, placing NFL teams into seven tiers. The 49ers were placed in Tier 6 with the comment “When you need two returns by Ted Ginn to put away the Seahawks, you are not good.” ESPN’s power rankings placed San Francisco at #22.

Cincinnati 27, Cleveland 17
This was an ugly game that caught almost nobody’s attention. The big stories of the game were the officiating and the performance of Bruce Gradkowski, who came and led Cincinnati to a win after Andy Dalton was injured. Many in Cleveland blamed the referees, as the Browns were flagged for 11 penalties, compared to just three for Cincinnati. And on the game’s pivotal play — a 41-yard touchdown to A.J. Green — the Browns were still in their defensive huddle at the start of the play, and later argued that the quick snap wasn’t a legal play. The Bengals defense was excellent, shutting down Peyton Hillis and Colt McCoy, but many thought that was simply a product of the schedule. Suffice it to say, no one was boarding the Bengals’ bandwagon after week one. Lisk placed Cincinnati in Tier 5: “Still not sold here, especially if Bruce Gradkowski is QB. Haden shut down Green until the play where he was uncovered at the snap, and Benson’s numbers boosted by a late TD run.” ESPN ranked the Bengals 30th… and the Browns 32nd. The Bengals would lose in Denver and in San Francisco the next two weeks, dropping to 1-2.
[continue reading…]

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 era ^[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.. 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:

Rk	QB	Tm	Opp	SB	W/L	Att	Pyd	TD	INT	CY	CYP	VAL
1	Joe Montana	SFO	MIA	19	W	35	331	3	0	406	11	272
2	Steve Young	SFO	SDG	29	W	36	325	6	0	431	11.1	264
3	Troy Aikman	DAL	BUF	27	W	30	273	4	0	381	12.3	258
4	Joe Montana	SFO	DEN	24	W	29	297	5	0	397	13.3	256
5	Kurt Warner	STL	TEN	34	W	45	414	2	0	447	9.7	225
6	Jim Plunkett	OAK	PHI	15	W	21	261	3	0	320	14.5	219
7	Phil Simms	NYG	DEN	21	W	25	268	3	0	323	12.4	216
8	Doug Williams	WAS	DEN	22	W	29	340	4	1	358	11.6	211
9	John Elway	DEN	ATL	33	W	29	336	1	1	331	11	181
10	Jim McMahon	CHI	NWE	20	W	20	256	0	0	284	11.6	174
11	Joe Montana	SFO	CIN	23	W	36	357	2	0	351	9	165
12	Jake Delhomme	CAR	NWE	38	L	33	323	3	0	325	8.8	146
13	Tom Brady	NWE	CAR	38	W	48	354	3	1	369	7.7	141
14	Terry Bradshaw	PIT	DAL	13	W	30	318	4	1	266	7.8	140
15	Mark Rypien	WAS	BUF	26	W	33	292	2	1	287	8.7	128
16	Terry Bradshaw	PIT	RAM	14	W	21	309	2	3	214	10.2	123
17	Bart Starr	GNB	KAN	1	W	23	250	2	1	224	8.7	121
18	Terry Bradshaw	PIT	DAL	10	W	19	209	2	0	200	9.5	121
19	Aaron Rodgers	GNB	PIT	45	W	39	304	3	0	348	8.3	118
20	Brett Favre	GNB	NWE	31	W	27	246	2	0	268	8.1	111
21	Drew Brees	NOR	IND	44	W	39	288	2	0	321	8	107
22	Ken Stabler	OAK	MIN	11	W	19	180	1	0	183	8.7	103
23	Troy Aikman	DAL	PIT	30	W	23	209	1	0	218	8.7	91
24	Kurt Warner	ARI	PIT	43	L	43	377	3	1	329	7.3	87
25	John Elway	DEN	NYG	21	L	37	304	1	1	270	6.5	81
26	Bart Starr	GNB	OAK	2	W	24	202	1	0	182	6.5	79
27	Joe Montana	SFO	CIN	16	W	22	157	1	0	188	7.8	76
28	Tom Brady	NWE	PHI	39	W	33	236	2	0	259	7.4	75
29	Joe Namath	NYJ	BAL	3	W	28	206	0	0	195	6.5	68
30	Peyton Manning	IND	NOR	44	L	45	333	1	1	308	6.8	67
31	Ken Anderson	CIN	SFO	16	L	34	300	2	2	254	6.4	67
32	Jeff Hostetler	NYG	BUF	25	W	32	222	1	0	234	6.9	66
33	Bob Lee	MIN	OAK	11	L	9	81	1	0	100	10.9	65
34	Roger Staubach	DAL	MIA	6	W	19	119	2	0	140	6.7	63
35	Steve McNair	TEN	STL	34	L	36	214	0	0	208	5.6	61
36	Eli Manning	NYG	NWE	46	W	40	296	1	0	302	7	61
37	Terry Bradshaw	PIT	MIN	9	W	14	96	1	0	104	6.5	59
38	Kurt Warner	STL	NWE	36	L	44	365	1	2	287	6	55
39	Roger Staubach	DAL	PIT	13	L	30	228	3	1	161	4.6	53
40	Jim Kelly	BUF	NYG	25	L	30	212	0	0	205	6.6	52
41	Jim Plunkett	RAI	WAS	18	W	25	172	1	0	174	6.4	48
42	Roger Staubach	DAL	DEN	12	W	25	183	1	0	142	4.8	45
43	Brad Johnson	TAM	OAK	37	W	34	215	2	1	210	6.2	45
44	Earl Morrall	BAL	DAL	5	W	15	147	0	1	102	6.8	43
45	Ben Roethlisberger	PIT	ARI	43	W	30	256	1	1	209	6.5	37
46	Bob Griese	MIA	MIN	8	W	7	73	0	0	63	7.9	34
47	Brett Favre	GNB	DEN	32	L	42	256	3	1	240	5.6	34
48	Daryle Lamonica	OAK	GNB	2	L	34	208	2	1	181	4.9	32
49	Fran Tarkenton	MIN	MIA	8	L	28	182	0	1	141	4.5	31
50	Gary Kubiak	DEN	NYG	21	L	4	48	0	0	45	10.2	25
51	Troy Aikman	DAL	BUF	28	W	27	207	0	1	159	5.5	21
52	Tom Brady	NWE	NYG	46	L	41	276	2	1	261	6.1	20
53	Len Dawson	KAN	GNB	1	L	27	211	1	1	135	4.2	19
54	Trent Dilfer	BAL	NYG	35	W	25	153	1	0	154	5.5	17
55	Tom Brady	NWE	STL	36	W	27	145	1	0	154	5.3	14
56	Len Dawson	KAN	MIN	4	W	17	142	1	1	97	4.9	10
57	Gary Kubiak	DEN	SFO	24	L	3	28	0	0	25	6.8	7
58	Frank Reich	BUF	WAS	26	L	1	11	0	0	10	9.4	5
59	Steve Young	SFO	DEN	24	W	3	20	0	0	20	6.5	5
60	Vince Ferragamo	RAM	PIT	14	L	25	212	0	1	127	4.4	4
61	Danny White	DAL	DEN	12	W	2	5	0	0	3	1.1	4
62	Matt Hasselbeck	SEA	PIT	40	L	49	273	1	1	234	4.5	3
63	Ben Roethlisberger	PIT	GNB	45	L	40	263	2	2	211	5.1	2
64	Bill Musgrave	SFO	SDG	29	W	1	6	0	0	6	5.2	0
65	Fran Tarkenton	MIN	OAK	11	L	35	205	1	2	132	3.7	-4
66	Babe Parilli	NYJ	BAL	3	W	1	0	0	0	0	-0.4	-5
67	Zeke Bratkowski	GNB	KAN	1	W	1	0	0	0	-1	-0.8	-5
68	Jay Schroeder	WAS	DEN	22	W	1	0	0	0	-1	-0.6	-6
69	Pete Beathard	KAN	GNB	1	L	5	17	0	0	7	1.3	-6
70	Tony Banks	BAL	NYG	35	W	1	0	0	0	-1	-0.7	-6
71	Eli Manning	NYG	NWE	42	W	34	255	2	1	182	4.9	-8
72	Bob Griese	MIA	WAS	7	W	11	88	1	1	44	3.4	-8
73	Peyton Manning	IND	CHI	41	W	38	247	1	1	184	4.7	-10
74	John Elway	DEN	GNB	32	W	22	123	0	1	98	4.3	-12
75	Don Strock	MIA	WAS	17	L	3	0	0	0	-3	-0.9	-17
76	Steve Fuller	CHI	NWE	20	W	4	0	0	0	-3	-0.6	-23
77	Ron Jaworski	PHI	OAK	15	L	38	291	1	3	146	3.8	-28
78	Joe Theismann	WAS	MIA	17	W	23	143	2	2	74	2.8	-33
79	Dan Marino	MIA	SFO	19	L	50	318	1	2	219	4.1	-33
80	Elvis Grbac	SFO	SDG	29	W	1	0	0	0	-30	-28.2	-36
81	Johnny Unitas	BAL	NYJ	3	L	24	110	0	1	65	2.7	-37
82	David Woodley	MIA	WAS	17	L	14	97	1	1	28	1.9	-37
83	Donovan McNabb	PHI	NWE	39	L	51	357	3	3	249	4.5	-40
84	Norris Weese	DEN	DAL	12	L	10	22	0	0	-18	-1.6	-42
85	Gale Gilbert	SDG	SFO	29	L	6	30	0	1	-17	-2.7	-44
86	Gary Cuozzo	MIN	KAN	4	L	3	16	0	1	-32	-9.6	-46
87	Johnny Unitas	BAL	DAL	5	W	9	88	1	2	-12	-1.3	-47
88	Tom Brady	NWE	NYG	42	L	48	266	1	0	219	4.1	-53
89	Bob Griese	MIA	DAL	6	L	23	134	0	1	30	1.3	-58
90	Boomer Esiason	CIN	SFO	23	L	25	144	0	1	78	2.6	-65
91	Jim Kelly	BUF	DAL	28	L	50	260	0	1	182	3.4	-66
92	Stan Humphries	SDG	SFO	29	L	49	275	1	2	189	3.7	-67
93	Ben Roethlisberger	PIT	SEA	40	W	21	123	0	2	45	2	-67
94	Tony Eason	NWE	CHI	20	L	6	0	0	0	-40	-5.6	-72
95	Roger Staubach	DAL	PIT	10	L	24	204	2	3	37	1.2	-78
96	Chris Chandler	ATL	DEN	33	L	35	219	1	3	91	2.5	-79
97	Joe Kapp	MIN	KAN	4	L	25	183	0	2	39	1.4	-81
98	John Elway	DEN	WAS	22	L	38	257	1	3	93	2.2	-90
99	Rex Grossman	CHI	IND	41	L	28	165	1	2	54	1.9	-90
100	Steve Grogan	NWE	CHI	20	L	30	177	1	2	56	1.6	-105
101	Jim Kelly	BUF	DAL	27	L	7	82	0	2	-72	-9.3	-107
102	Joe Theismann	WAS	RAI	18	L	35	243	0	2	73	1.8	-112
103	Craig Morton	DAL	BAL	5	L	26	127	1	3	-2	-0.1	-112
104	Fran Tarkenton	MIN	PIT	9	L	26	102	0	3	-33	-1.3	-127
105	Earl Morrall	BAL	NYJ	3	L	17	71	0	3	-64	-3.8	-136
106	Frank Reich	BUF	DAL	27	L	31	194	1	2	16	0.5	-137
107	Neil O'Donnell	PIT	DAL	30	L	49	239	1	3	122	2.3	-147
108	Billy Kilmer	WAS	MIA	7	L	28	104	0	3	-48	-1.6	-159
109	Drew Bledsoe	NWE	GNB	31	L	48	253	2	4	74	1.4	-178
110	John Elway	DEN	SFO	24	L	26	108	0	2	-22	-0.7	-182
111	Rich Gannon	OAK	TAM	37	L	44	272	2	5	35	0.7	-212
112	Craig Morton	DEN	DAL	12	L	15	39	0	4	-157	-9	-214
113	Jim Kelly	BUF	WAS	26	L	58	275	2	4	30	0.5	-269
114	Kerry Collins	NYG	BAL	35	L	39	112	0	4	-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?

Rk	QB	Tm	Opp	Year	W/L	Att	Pyd	TD	INT	CY	CYP	VAL
1	Tobin Rote	DET	CLE	1957	W	19	280	4	0	380	19	304
2	Sid Luckman	CHI	WAS	1943	W	26	286	5	0	386	14.8	248
3	Otto Graham	CLE	RAM	1950	W	33	298	4	1	292	7.7	236
4	Sammy Baugh	WAS	CHI	1937	W	33	335	3	1	320	9.7	228
5	Harry Newman	NYG	CHI	1933	L	19	209	2	1	204	10.7	197
6	Charlie Conerly	NYG	CHI	1956	W	10	195	2	0	232	22.1	192
7	Bart Starr	GNB	NYG	1961	W	17	164	3	0	224	13.2	152
8	Otto Graham	CLE	DET	1954	W	12	163	3	2	193	12.9	135
9	Frank Ryan	CLE	BAL	1964	W	18	206	3	1	212	11.2	132
10	Norm Van Brocklin	RAM	CLE	1951	W	6	128	1	0	148	24.7	129
11	Tobin Rote	SDG	BOS	1963	W	15	173	2	0	226	13.1	127
12	Sid Luckman	CHI	NYG	1941	W	12	160	0	0	160	13.3	125
13	George Blanda	HOU	LAC	1960	W	31	301	3	0	361	11.6	123
14	Charlie Conerly	NYG	BAL	1958	W	14	187	1	0	190	11.6	122
15	Arnie Herber	GNB	NYG	1938	L	14	123	1	0	143	10.2	117
16	Johnny Unitas	BAL	NYG	1959	W	29	264	2	0	267	7.4	115
17	Charlie O'Rourke	CHI	WAS	1942	L	7	128	0	0	128	18.3	105

[continue reading…]

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.
[continue reading…]

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.

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 denominator ^[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 … Continue reading. 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.
[continue reading…]

Bill Simmons wrote about LaDainian Tomlinson last month and called him the best fantasy football player of all-time. “Greatest ever” debates are always subjective, but at least when it comes to fantasy football, we can get pretty close to declaring a definitive answer. Joe Bryant’s landmark “Value Base Drafting” system explained that the “value of a player is determined not by the number of points he scores, but by how much he outscores his peers at his particular position.” Bryant came up with the concept of calculating a ‘VBD’ number for each player to measure their value.

A player’s VBD is easy to calculate. Each player’s VBD score is the difference between the amount of fantasy points he scored and the fantasy points scored by the worst starter (at his position) in your fantasy league. A player who scores fewer fantasy points than the worst starter has a VBD of 0. There is no standard scoring system for fantasy leagues, so a player’s fantasy points total will depend on the specific league’s scoring rules. ^[1]I’ve decided to use a blend of the most common scoring options: 1 point per 20 yards passing, 5 points per passing touchdown, -2 points per interception, 6 points for rushing/receiving … Continue reading And, of course, his VBD score will change depending on the number of starters at each position in the league. ^[2]Again, I’m using a blend here, but for baseline purposes I’m using QB12, RB24, WR32 and TE12, since the standard 12-team league starts 1 QB, 2 RBs, 2-3 WRs and 1 TE.

That said, once you pick a scoring system and a set of rules, it’s easy to calculate career VBD scores for every player since 1950 ^[3]I’ve pro-rated production for those players who were part of seasons when the NFL did not have a 16-game schedule; I also changed the baseline numbers depending on the number of teams in the … Continue reading. Let’s start with the quarterbacks:

Player	Years	POS	Teams	VBD	OVR RK	POS RK
Peyton Manning	1998--2010	QB	clt	1071	9	1
Brett Favre	1992--2010	QB	atl-gnb-nyj-min	1061	10	2
Dan Marino	1983--1999	QB	mia	988	14	3
Fran Tarkenton	1961--1978	QB	min-nyg	921	15	4
Steve Young	1985--1999	QB	tam-sfo	774	24	5
Joe Montana	1979--1994	QB	sfo-kan	727	33	6
Randall Cunningham	1985--2001	QB	phi-min-dal-rav	723	35	7
Tom Brady	2000--2011	QB	nwe	720	36	8
Drew Brees	2001--2011	QB	sdg-nor	688	38	9
John Elway	1983--1998	QB	den	660	40	10
Roger Staubach	1969--1979	QB	dal	630	44	11
Johnny Unitas	1956--1973	QB	clt-sdg	625	47	12
Warren Moon	1984--2000	QB	oti-min-sea-kan	592	57	13
Ken Anderson	1971--1986	QB	cin	539	74	14
Sonny Jurgensen	1957--1974	QB	phi-was	528	77	15
Dan Fouts	1973--1987	QB	sdg	526	78	16
Daunte Culpepper	1999--2009	QB	min-mia-rai-det	515	80	17
Aaron Rodgers	2005--2011	QB	gnb	507	83	18
Tobin Rote	1950--1964	QB	gnb-det-sdg-den	494	88	19
Roman Gabriel	1962--1977	QB	ram-phi	404	130	20
Rich Gannon	1988--2004	QB	min-was-kan-rai	396	135	21
Kurt Warner	1998--2009	QB	ram-nyg-crd	396	136	22
Bobby Layne	1950--1962	QB	chi-nyy-det-pit	385	140	23
Y.A. Tittle	1950--1964	QB	bcl-sfo-nyg	384	141	24
Daryle Lamonica	1963--1973	QB	buf-rai	368	153	25

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I broke down each of the NFL and AFL champions since 1950 into three categories:

• Pass Efficiency, measured by a modified version of ANY/A. The formula was (Passing Yards + 10*TD – 22.5*INT – Sack Yards)/(Pass Attempts + Sacks). This strikes a middle ground between traditional ANY/A and NY/A.
• Rushing Success, according to the following formula: (Rushing Yards + 10*RTD + 5*Rushing1stDowns)/(Carries).
• Defensive Rating, based on the number of offensive touchdowns scored by their opponents.

There are ways to quibble with those categories, and I won’t begrudge anyone who does. After giving each team a rating in each category, I calculated how they compared to the league average in each season. In all cases, the average is 100%, and a number higher than 100% means better.

Here’s what each of the columns mean, from left to right. In 2011, the New York Giants won the Super Bowl; they allowed 43 touchdowns to opposing offenses, averaged 7.6 in my modified version of ANY/A, and averaged 4.9 adjusted yards per carry. The next three columns show how New York ranked relative to league average. By allowing 43 scores, the Giants D was well below average, putting them at 83% of the average mark; they were 25% better than average at passing, but only 86% of league average efficiency in the running game. Since the Giants highest rating came in the passing category, they are listed in the Identity column as a Passing team.
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A couple of years ago, my colleague Jason Lisk explained why Joe Namath is a legitimate Hall of Famer. With each passing year, it seems as though Namath’s career gets more misunderstood, particularly by those who look at his career stats without context. One of the main pieces of evidence that sounds damning: among Hall of Fame quarterbacks who began their careers after 1950, Namath ranks last in both touchdown/interception differential and passer rating:

But analyzing a player by his career numbers is too broad a brush for advanced analysis. Brandon Jacobs is 107 yards away from matching Gale Sayers’ career rushing total. Plaxico Burress and Jeremy Shockey have caught more passes than Lance Alworth and Kellen Winslow. At quarterback, comparing players across eras by their raw numbers is a pointless exercise. Byron Leftwich, Kyle Orton and Aaron Brooks have higher career passer ratings than Johnny Unitas. As always, we can only compare players by how they compared to their peers.

Namath’s career is misunderstood for several reasons. Younger fans think he’s famous because of The Guarantee, but he would have been an elite quarterback (and was acknowledged as one by his contemporaries) even if he never won a Super Bowl. He was among the best ever at avoiding sacks, an often overlooked but key element of effective quarterback play. He played in one of the worst eras for quarterbacks to compile strong passing stats, which is why his numbers don’t compare to modern quarterbacks. And his career arc was unusual, which further makes the use of career numbers an inappropriate way to understand Namath’s career.

There are 17 Hall of Fame quarterbacks to enter the league since 1950, and we can add Brett Favre, Peyton Manning and Tom Brady to get to an even twenty. Through age 26, Namath was outstanding, and was the second most productive quarterback of the twenty behind Dan Marino during those years. The table below ^[1]This list is sorted by how much Adjusted Net Yards over average each quarterback produced each season. This is calculated by taking each quarterback’s ANY/A, comparing it to league average, … Continue reading shows how much value was added by each of the twenty quarterbacks through the age of 26:
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Twice in four years, the Buffalo Bills have teased their fans. In 2008, the Bills started 5-1, and Peter King named Trent Edwards his MVP after the first quarter of the season. Edwards had led fourth-quarter comebacks and game-winning drives in the second and third weeks of the year, and he ranked in the top-five in AY/A after seven weeks.

The former Stanford Cardinal would rank 24th in AY/A the rest of the season while Buffalo finished the year with a 2-8 record.

The Bills won just ten games the next two seasons, but made some noise at the start of the 2011 season. Going back to the nerd well at quarterback, Buffalo raced out to a 5-2 start. Fitzpatrick led two fourth-quarter comebacks/game-winning drives and ranked 11th in AY/A after eight weeks. Unfortunately, the former Harvard star ranked 32nd in that metric over the last nine weeks of the season, and the Bills finished the season 1-8.

How unusual is it for a team to have such a hot start and cold finish? It’s simple enough to look at first-half/second-half splits, but I prefer a more nuanced approach by weighing each team game based on when it occurred; e.g., game 1 counts 16 times as much as game 16, game 2 counts 15 times as much as game 16, game 3 counts 14 times as much as game 16, and so on. I looked at each team since 1990 and calculated their actual winning percentage and their “weighted” winning percentage.

The 2011 Bills had a 0.375 winning percentage last year, but by placing greater weight on games earlier in the season, Buffalo had a 0.507 weighted winning percentage. In 2008, the Bills had a 0.438 actual winning percentage and a 0.566 weighted winning percentage. As it turns out, those were two of the five “strongest-starting” teams of the last five years. The table below lists the “strongest-starting teams” since 1990, along with their actual and weighted winning percentages. The last column represents the difference between the two winning percentages.
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Last year, I noted that Drew Brees had thrown a touchdown in 37 consecutive games and examined his chances of breaking the NFL record. The current mark is held by Johnny Unitas, who threw a touchdown in 47 consecutive games from 1956 to 1960. By the end of the 2011 season, Brees had upped his streak to 43 games, which positions Brees to break the record in week 5 of this season, against his former team, the San Diego Chargers, on Sunday Night Football.

Assuming Brees breaks the record, we can expect a four-hour telecast devoted to the greatness of Drew Brees, which is largely warranted. Brees is a future Hall of Famer and one of the most accurate quarterbacks in the history of the game. And he’ll be breaking one of the oldest records in football, one currently held by the standard bearer at the position.

If you’ve been at Football Perspective for long, you probably know where this is going. How impressive will it be for Brees to break this record? The short answer is, probably not as impressive as you might think.

What are the odds of throwing a touchdown in 47 straight games ^[1]Assuming independence and a consistent rate per game? Brees deserves all of the credit and praise he gets for being an elite quarterback, and a Blaine Gabbert-type is obviously not going to be the one to break this record. But the real question we want to ask is what are the odds of a star quarterback throwing a touchdown in 47 consecutive games. We can get a pretty good estimate of that.

In 44 games from 2002 to 2005, Marc Bulger threw a touchdown in 93% of his games, or 41 of 44 games. And in two of the games where he did not throw a touchdown, he threw fewer than five passes. In 73 games from 2000 to 2004, Daunte Culpepper threw a touchdown in 86% of his games. Brett Favre, from 2001 to 2004, threw a touchdown in 95% of his games. Eli Manning, from 2005 to 2011, threw a touchdown in 86% of his starts and was booed by Giants fans in just as many. Peyton Manning, excluding his rookie year, threw a touchdown in 87% of his games with the Colts. Philip Rivers once threw a touchdown in 50 of 54 straight games. Aaron Rodgers has thrown a touchdown in 50 of his last 53 games, with one of his zeroes coming in a partial game against the Lions. Tony Romo has thrown a touchdown in 90% of his games since 2007, and 92% of those games if you exclude two games he did not finish. Matt Ryan has thrown a touchdown in 30 of his last 31 games. Matthew Stafford has thrown a touchdown in 28 of his last 30 games, with one of his shutouts coming in a game he did not finish due to injury. From ’99 to ’01, Kurt Warner threw a touchdown in 93% of his games.

And then there’s Tom Brady. Since 2007, Brady has thrown a touchdown in 92% of his starts, or 94% if you exclude his game against the Chiefs when he tore his ACL in the first quarter. Brady has also thrown a touchdown in each regular season game the past two seasons, which means he could also break Unitas’ mark in 2012.

There is obviously an upper limit to the question ‘what is the likelihood of an elite quarterback playing at an elite level throwing a touchdown in any given game?’ Last year, I speculated that Brees’ likelihood was around 89-91%, which in retrospect, might be a little low. The upper limit is probably closer to 96 or 97%, although obviously very few quarterbacks could get there. If we assume complete games — i.e., that the quarterback won’t get injured or get benched or rested — maybe a star quarterback in today’s game has a 94% chance of throwing a touchdown in any given game.

In that case, such a quarterback has a 5% chance of throwing a touchdown in 48 consecutive games. In some ways, of course, this is a “what are the odds of that” sort of question. Yes, Brees is at 43 in a row, but he’s not alone. Brady has thrown a touchdown in every game the last two seasons. Stafford has done it in 18 straight games, Rodgers for 17, and Ryan is at 15. And, of course, players like Kurt Warner and Brett Favre and Peyton Manning have played at elite levels for stretches just like Brees.

Perhaps the better question is, assuming 14 elite quarterbacks playing at elite levels play in 48 straight games, and each has a 94% chance of throwing a touchdown in any given game, what are the odds that none of them go 48/48? The answer to that: 48%. In other words, it is more likely than not that some quarterback would break the record.

Brees deserves all the praise in the world for essentially putting himself at the upper limit of elite quarterback play. He deserves credit for having a quick release and excelling at pre-snap coverage, which limits the amount of hits he takes. On the other hand, he’s fortunate to have almost entirely avoided playing in poor weather. He’s fortunate to have avoided injury on the hits he has taken, and to have not played for a coach that chose to bench him for a meaningless game after a drive or two (in fact, he missed week 17 of the 2009 season entirely, keeping his streak alive). He’s also fortunate that he’s thrown a touchdown in 43 games and not 41 or 42 out of 43, like many other elite quarterbacks. Brees has had bad games during this streak — in 7 of them, he’s averaged 4.8 AY/A or fewer — but he always managed to throw at least one touchdown. That’s less skill than luck, and you can read about some of Brees’ near misses here.

The skill involved for Brees is getting himself to that upper limit. Given enough quarterbacks playing at elite levels for enough years, Unitas’ record was bound to fall. Brees happens to be one of those quarterbacks.

In 1983, the Washington Redskins set an NFL record by scoring 541 points. Fifteen years later, the Minnesota Vikings broke that mark by going 15-1 and scoring 556 points. Then, in 2007, the New England Patriots topped Minnesota by going undefeated and scoring 589 points, the most in NFL history.

Last year’s Green Bay Packers scored 560 points, preventing Randy Moss from being a star on the two highest scoring teams in NFL history. But it’s not the Patriots, Vikings or Redskins that hold the mark for most points scored per game in NFL history. New England averaged 36.8 points per game in 2007, but one NFL team in the pre-modern era scored 38.83 points per game.

They’re the subject of today’s trivia question. Can you name the highest scoring team in NFL history?

Yesterday, I looked at how frequently the highest drafted rookie running back ended leading his draft class in rushing yards. Today, we’ll examine how often the best rookie running back ends up being having the most career rushing yards among the members from his class.

I performed this same exercise at wide receiver, and concluded that as great as A.J. Green was last season, the odds were stacked against him leading the 2011 rookie receiver class in career receiving yards. ^[1]From 1978 to 2008, only three of the 31 wide receivers with the best rookie seasons ended up with the most receiving yards from their class. For whatever reason, there simply is not a strong correlation between rookie performance and career performance for wide receivers. Is the same true at the running back position?

There was an eleven-year stretch from ’92 to ’02, when Ricky Watters, Jerome Bettis, Marshall Faulk, Curtis Martin, Eddie George, Corey Dillon, Fred Taylor, Edgerrin James, LaDainian Tomlinson, and Clinton Portis each led their class in rushing yards both as rookies and over the course of their careers. The lone exception came in 2000, when Mike Anderson nudged by Jamal Lewis to lead the ’00 class in rookie rushing yards, while Lewis ended with the most career rushing yards. If I had written this article a decade ago, I would have thought that unlike at the receiver position, there was an extremely strong correlation between rookie and career performance for the top running backs.

But since then, things have changed. Domanick Williams (Larry Johnson), Kevin Jones (Steven Jackson), Cadillac Williams (Frank Gore), Joseph Addai (Maurice Jones-Drew), Steve Slaton (Chris Johnson), and Knowshon Moreno (Arian Foster) led all rookies in rushing yards but have been passed in the career category by another back from the same rookie class. It’s too early to get a handle on the last two draft classes, although I certainly wouldn’t take even odds on either Ben Tate or LeGarrette Blount finishing with the most career rushing yards of any running back who entered the league in either 2010 or 2011.

The table below shows the top rookie running backs and the top career running backs from each class since 1978.
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Last month, I wondered whether previous Justin Blackmons — i.e., the first receiver selected in the draft — lived up to their lofty draft status. There have been 42 drafts since 1970, but only five times has the highest selected wideout gained the most receiving yards among all rookie receivers. The long-term odds were slightly better, though, as nearly a quarter of those highest selected receivers ended up with the most receiving yards in his draft class.

So how do things look at the running back position? The Bills drafted Willis McGahee knowing he would miss his entire rookie season, while Bo Jackson chose to play baseball instead of playing with the Buccaneers. Ki-Jana Carter and Larry Stegent suffered season-ending injuries in the pre-season of their rookie years, making them inapplicable for our purposes, assuming Richardson stays healthy. What about the other 38 running backs who were the first at their position selected in the draft since the merger?

Over 40% of those highest drafted running backs led their class ^[1]Note that this only includes drafted running backs. in rushing yards as a rookie, while exactly half gained at least 75% as many rushing yards as the the most productive rookie running back. That may not be particularly impressive in the abstract, but represents a much better track record than we saw at the wide receiver position. On the other hand, recent history has not been particularly great: in the past 10 years, arguably every top rookie running back outside of Adrian Peterson disappointed, as even Moreno failed to meet the expectations of many.

Year	Running Back	Team	Pick	College	Rush Yds	% of Leader	Top Rookie
2011	Mark Ingram	NOR	28	Alabama	474	0.53	DeMarco Murray
2010	C.J. Spiller	BUF	9	Clemson	283	0.42	Ryan Mathews
2009	Knowshon Moreno	DEN	12	Georgia	947	1.00	Knowshon Moreno
2008	Darren McFadden	OAK	4	Arkansas	499	0.39	Steve Slaton
2007	Adrian Peterson	MIN	7	Oklahoma	1341	1.00	Adrian Peterson
2006	Reggie Bush	NOR	2	USC	565	0.52	Joseph Addai
2005	Ronnie Brown	MIA	2	Auburn	907	0.77	Cadillac Williams
2004	Steven Jackson	STL	24	Oregon St.	673	0.59	Kevin Jones
2003	Willis McGahee	BUF	23	Miami (FL)	--	--	Domanick Williams
2002	William Green	CLE	16	Boston Col.	887	0.59	Clinton Portis
2001	LaDainian Tomlinson	SDG	5	TCU	1236	1.00	LaDainian Tomlinson
2000	Jamal Lewis	BAL	5	Tennessee	1364	0.92	Mike Anderson
1999	Edgerrin James	IND	4	Miami (FL)	1553	1.00	Edgerrin James
1998	Curtis Enis	CHI	5	Penn St.	497	0.41	Fred Taylor
1997	Warrick Dunn	TAM	12	Florida St.	978	0.87	Corey Dillon
1996	Lawrence Phillips	STL	6	Nebraska	632	0.46	Eddie George
1995	Ki-Jana Carter	CIN	1	Penn St.	--	--	Curtis Martin
1994	Marshall Faulk	IND	2	San Diego St.	1282	1.00	Marshall Faulk
1993	Garrison Hearst	PHO	3	Georgia	264	0.18	Jerome Bettis
1992	Tommy Vardell	CLE	9	Stanford	369	0.65	Vaughn Dunbar
1991	Leonard Russell	NWE	14	Arizona St.	959	1.00	Leonard Russell
1990	Blair Thomas	NYJ	2	Penn St.	620	0.66	Emmitt Smith
1989	Barry Sanders	DET	3	Oklahoma St.	1470	1.00	Barry Sanders
1988	Gaston Green	RAM	14	UCLA	117	0.10	John Stephens
1987	Alonzo Highsmith	HOU	3	Miami (FL)	106	0.16	Christian Okoye
1986	Bo Jackson	TAM	1	Auburn	--	--	Rueben Mayes
1985	George Adams	NYG	19	Kentucky	498	1.00	George Adams
1984	Greg Bell	BUF	26	Notre Dame	1100	1.00	Greg Bell
1983	Eric Dickerson	RAM	2	SMU	1808	1.00	Eric Dickerson
1982	Darrin Nelson	MIN	7	Stanford	136	0.20	Marcus Allen
1981	George Rogers	NOR	1	South Carolina	1674	1.00	George Rogers
1980	Billy Sims	DET	1	Oklahoma	1303	1.00	Billy Sims
1979	Ottis Anderson	STL	8	Miami (FL)	1605	1.00	Ottis Anderson
1978	Earl Campbell	HOU	1	Texas	1450	1.00	Earl Campbell
1977	Ricky Bell	TAM	1	USC	436	0.43	Tony Dorsett
1976	Chuck Muncie	NOR	3	California	659	1.00	Chuck Muncie
1975	Walter Payton	CHI	4	Jackson St.	679	0.74	Mike Thomas
1974	Bo Matthews	SDG	2	Colorado	328	0.28	Don Woods
1973	Otis Armstrong	DEN	9	Purdue	90	0.09	Boobie Clark
1972	Franco Harris	PIT	13	Penn St.	1055	1.00	Franco Harris
1971	John Riggins	NYJ	6	Kansas	769	0.70	John Brockington
1970	Larry Stegent	STL	8	Texas A&M	--	--	Duane Thomas

Richardson is the favorite to be the most productive rookie running back, although “the field” appears to be a more enticing proposition. But Cleveland drafted Richardson for what he can do for the next five or ten years, which will ultimately be much more significant than how he performs in 2012. Even if the highest drafted running back is unlikely to lead his draft class in rushing yards as a rookie, is he more likely (than the field) to lead his draft class in career rushing yards?
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In light of the Ichiro Suzuki trade to the Yankees, Jason Lisk wrote about other prominent star baseball players who switched teams late in their career. I’m going to do the same for football.

The table below shows all players who accumulated at least 100 points of career AV with one team and then switched teams. Len Dawson played for the Steelers and Browns before embarking on a Hall of Fame career with the Chiefs, but he won’t make this list since he never switched teams after his tenure in Kansas City. Johnny Unitas does because he finished his career with the Chargers. Brett Favre’s stint with the Falcons doesn’t count, but his time with the Jets and Vikings does. ^[1]Note that pass rushers Chris Doleman, Jason Taylor and Richard Dent all returned to their original teams after a stint with another franchise; for them, I’m only including their AV during their … Continue reading

As you might suspect, the top of the list is dominated by quarterbacks. Peyton Manning will join Johnny Unitas, Joe Namath, Joe Montana and Brett Favre as Hall of Fame quarterbacks that looked a bit out of place donning other colors. But it’s actually quarterback Jim Hart who spent the most seasons with one team, the St. Louis Cardinals, before a one-year stint with the Redskins.
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I’ll be spending the weekend in Cortland, New York, covering Jets training camp. The big story there, of course, will be how the Mark Sanchez/Tim Tebow drama unfolds. The party line among media members is that the duo is doomed to fail, because a team with two quarterbacks doesn’t have one.

Last year, Mark Sanchez ranked 27th in Net Yards per Attempt, so the Jets were behind the 8-ball at the quarterback position well before the Tebow trade. Not that he’ll necessarily help things: Tebow averaged even fewer net yards per attempt than Sanchez in 2011, although arguably his numbers should be viewed in a more positive light.

In my view, the Tebow trade simply gives the Jets more chances to succeed, not unlike when a team throws multiple late round picks at the same position. The most tired complaint regarding the situation is that if Sanchez has a bad drive, quarter or game, fans will call for Sanchez’ head and the Jets will bring in Tebow. But such analysis never goes beyond that. If the Jets do make Tebow the starting quarterback, and he does well, that’s a good thing. If the Jets bring in Tebow, and he fails, New York can go back to Sanchez. At that point, even if Sanchez has some struggles, the calls for Tebow will be muted. However, some will argue that if Sanchez is benched even once his confidence will be shot.

You may find it absurd to suggest that benching a professional athlete may be enough to derail a great career; in fact, that’s what I originally thought. But after combing through the annals of NFL history, I’m unable to find any proof in the other direction. Truth be told, I do think having two quarterbacks is essentially the football kiss of death. Don’t believe me? Let’s take a stroll down memory lane.

In the early ’50s, the Los Angeles Rams alternated Norm Van Brocklin and Bob Waterfield as their quarterbacks. In 1950, the team averaged 38.8 points per game while each quarterback started six games, and Los Angeles won the championship the next season. But while both Van Brocklin and Waterfield would end up in the Hall of Fame, neither player is well known today by most fans.

A few years later, the Giants would have Don Heinrich as the nominal starter for the first series or two before having Charlie Conerly come in and replace him one the coaching staff had a better read on the opposing defense. Sure the team won the NFL championship in 1956 using this method, but New York ultimately lost the championship to Baltimore in both ’58 and ’59, and neither Heinrich nor Conerly were able to slow down Johnny Unitas in either gmae. In John Eisenberg’s great book on the late ’50s Green Bay Packers, he explained how Vince Lombardi treated Bart Starr like a yo-yo, inserting him and out of the lineup. And while Starr would achieve some success in the ’60s, he ultimately failed as head coach of the Packers in the ’70s and ’80s, going 52-76-3 in 9 uneventful seasons.
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Bill Barnwell wrote an interesting article where he tried to identify the best running back in football. His article made me wonder: which player will gain the most rushing yards over the next decade?

It probably makes sense to start with a look at history. I suspect you would have been able to guess that LaDainian Tomlinson had the most rushing yards from 2002 to 2011, but what about from 1982 to 1991? Or from 1960 to 1969? The table below shows each leader in rushing yards for every ten year period, along with their age and NFL experience during their first season during the relevant period.

Tomlinson entered the league in 2001, but he was so productive in his first nine years that he also led the league in rushing yards gained from 2000 to 2009. O.J. Simpson, Eric Dickerson and Barry Sanders each led the league in rushing yards for ten year periods … when they spent the first three seasons of those decades playing college ball. Jim Brown was even more impressive, as he led the NFL in rushing yards from 1953 to 1962, even though he was just 17 years old in 1953 and did not enter the league until 1957.

But Steve Van Buren has them all beat: he entered the league in 1944, but led all players in rushing from 1938 to 1947. As you may recall, he’s still the Eagles franchise leader in rushing touchdowns. We can also look at the leaders over the last nine seasons, although obviously the ten-year windows are not closed in these cases:

So what can we make of the results? The average running back was just a hair under 22 at the start of his ten year period. Nearly half of all running backs were not yet in the NFL at the start of their ten year run, although that is likely to change now. Those players were in other football leagues, serving their country, or in college, but all three of those factors are less prevalent now. Star running backs leave college a year or two earlier than they did a generation ago, which will make it slightly less likely that a player will not be in the NFL at the start of the next ten-year run.

Fourteen players were rookies at the start of their great stretch, and another 10 were second year players, making nearly 80% of the players having just one year or less of experience in the summer before the start of their streak. What does that mean for the stretch from 2012 to 2021? Trent Richardson is the ideal candidate, as the new Browns running back just turned 21. Last year’s Alabama running sensation, Mark Ingram, was 22 in 2011, while Dion Lewis and Jacquizz Rodgers were the top 21-year-old running backs last season.

No running back started his 10-year stretch atop the leaderboard at the age of 26, and only Hall of Famers Steve Van Buren, Joe Perry, Jim Brown, Walter Payton, Eric Dickerson and Emmitt Smith were 25 at the start of a streak. That makes it pretty easy to rule out Maurice Jones-Drew, Matt Forte, Adrian Peterson and Chris Johnson, all of whom will be 27 in 2012. Ray Rice (25 in 2012), Arian Foster (26), Marshawn Lynch (26) and Ryan Mathews (25) are probably suckers’ bets, too.

LeSean McCoy, Beanie Wells and DeMarco Murray all are entering their age 24 season, making them perhaps the best hope among the young runners with NFL experience. On the other hand, along with Richardson, Doug Martin, David Wilson, Ronnie Hillman and Lamar Miller made the 2012 draft strong at the position. In the NFC West, Isaiah Pead and Kendall Hunter (or LaMichael James) could be the future for their teams for the next decade. As always, it’s too early to say.

In the collegiate ranks, South Carolina’s Marcus Lattimore is expected to be the cream of the 2013 class, with Auburn transfer Michael Dyer and Wisconsin’s Montee Ball also in the mix. And based on past history, we can’t count out sophomores Malcolm Brown or De’Anthony Thomas. If you had to pick which player will lead the league in rushing yards from 2012 to 2021, Trent Richardson is the obvious choice. After him, I’d probably be pretty evenly split among McCoy, Martin and Lattimore.