The game preview of the 1940 season finale between a pair of NFC East rivals

In the early days of the NFL, a player needed to be at least five yards behind the line of scrimmage in order to be eligible to pass.  Beginning in 1933, that rule was eliminated, making a pass legal at any point behind the line of scrimmage.  The next year, a slimmer and more aerodynamic football was introduced to make life easier for passers.

In the 1937 NFL championship game, trailing for much of the game, the Redskins and Sammy Baugh set a single-game record with 40 pass attempts against the great Chicago Bears.  Baugh led the team on a great comeback and secured the title for Washington in a 28-21 victory.

But playoff games have a tendency to make teams move outside of their comfort zone; in the regular season no team even hit the 35-pass attempt mark until 1939.  On October 15th of that season, the Chicago Cardinals were obliterated by the Chicago Bears, 44-7. Playing with a terrible game script, the Cardinals finished 10 of 37 for 162 yards with no touchdowns and 6 interceptions. Hardly a blueprint for future offenses,  it was a record-setting game nonetheless.  The next season, the Detroit Lions also threw 37 times in a loss to the Bears in mid-November. The following week, the Philadelphia Eagles, led by Davey O’Brien, faced that same dominant Bears team and threw a (regular season) record 38 times in a losing effort.  In case you haven’t picked up on it, the Bears were very good in the late ’30s.

By 1940, the passing game began to take off, at least compared to the ground-and-pound days of the 1930s.  In the Eagles opener, the team threw 40 times in a loss to the Packers, setting a new record in the regular season. A month later, as the Rams trailed the Packers, the team threw a record 42 times!  This was a real shootout: Green Bay won by throwing 37 passes of their own, with remarkable success.

Two weeks later, O’Brien’s Eagles matched that number in a loss to Brooklyn.  Another two weeks later, Brooklyn faced Baugh’s Redskins and jumped out to an early lead.  Washington responded with — are you sitting down? — 47 passes in a comeback that fell just short.  It was a historic performance: Baugh set a new record with 23 completions on 44 attempts.

As the 1940 season concluded, the Redskins looked like the best team in the NFL.  They were 9-2 entering the final game of the season, and had just defeated the second-best team (the Bears) two weeks earlier.  The worst team in the NFL?  That would be Davey O’Brien’s Philadelphia Eagles, who began the season 0-9, and then eeked by with a 7-3 victory against the struggling Pittsburgh Steelers. [continue reading…]

Scoring soared in the aftermath of World War II, but quickly dropped off in the middle of the 1950s. Scoring fell to its nadir in 1977, prompting the 1978 rules changes regarding pass blocking and pass coverage. After another lull in the early nineties, scoring has steadily increased over the last twenty years. It reached a peak in 2013 and nearly matched that again in 2018, before a a slight dip in 2019. Take a look at the average points per game for all NFL teams (i.e., excluding the AFL) since 1950:

You might think that the increase in scoring is due to the passing game becoming more dominant in modern times, but that’s hardly the full story. There are more passing touchdowns now, but they have also to some extent just taken touchdowns that would have otherwise been rushing touchdowns. Over the last 5 years, teams have scored about 16.6 points per game on passing plus rushing touchdowns, if we assign 7 points to each touchdown. That’s noticeably higher than how things were in the ’90s and ’00s, and much higher than the ’70s, but it’s lower than NFL life was in the ’50s and much of the ’60s.

One undeniable fact of life is that field goals have become a much bigger part of the game. The graph below assigns 7 points to all passing, rushing, and other touchdowns, and 3 points to all field goals. It then shows how many points per team game have come from each of those four categories. [continue reading…]

Every season, I like to look at each team’s fumble recovery data. The evidence suggests that when the ball is on the ground, teams aren’t better or worse at recovering those fumbles; in other words, the recovery is driven more by randomness than skill.

Let’s begin with looking at fumbles from the perspective of the offensive team. The New Orleans Saints had the fewest fumbles in the league last season, at just 9. Even more impressive, the Saints lost just two fumbles all year! Of course, that luck didn’t hold up in the postseason: New Orleans lost a critical fumble in the team’s opening playoff game, contributing to the surprise loss to the Vikings.

On average, the fumbling team recovered (or the ball went out of bounds, so the fumbling team retained possession) 53.5% of all fumbles in 2019. No team was “luckier” at recovering their own fumbles than the Saints, but the Broncos actually gained the biggest advantage due to having fumbled much more often. Denver fumbled 21 times last season; that means we would “expect” the Broncos to lose 9.75 of those fumbles. In reality, the team lost just 6 fumbles, meaning Denver recovered 3.75 more fumbles than we would have been expected. Non-QBs for the Broncos fumbled 10 times, but they lost just 2 of those fumbles.

The least fortunate team was the Colts. In 2019, Indianapolis fumbled 18 times, and lost 11 of them! Jacoby Brissett himself lost 5 of 7 fumbles. The table below shows the full fumble data for each offense in 2019: [continue reading…]

In March of 2011, the NFL voted to move kickoffs up from the 30 to the 35-yard line. That impact has been significant, and the league responded by placing a greater emphasis on kickers who can boom kickoffs into the opposing end zone.

In 2016, the NFL moved up where offenses would start following a touchback from the 20 to the 25-yard line, which made returners more likely to just take a touchback. That impact has been marginal.

The graph below shows the percentage of kickoffs that resulted in a touchback in each of the last 25 seasons. [continue reading…]

The Lions win the award for weirdest home/road pre-game splits.

But there’s something pretty unusual in those splits. In 8 home games, the Lions were underdogs by an average of 4.6 points. In 8 road games, the Lions were underdogs by an average of 3.9 points. Given that home field advantage is usually worth 3 points, you’d expect a team to be favored by about 6 more points — on average — in home games than in road games. But the 2019 Lions were actually favored to do better on the road than at home! That is exceedingly rare: it has only happened a handful of times in the last 40 years.

So, what happened? You might think this has something to do with Stafford, but that’s not really the case: he was healthy for 4 home and 4 road games. Here are the full season results: [continue reading…]

In general, more passing yards should be better than fewer passing yards. But we know that due to Game Script, teams that are trailing late in games throw much more frequently — and can rack up the passing yards — than teams with a lead.

So, if you know nothing else other than that a team threw for more passing yards than its opponent, would you guess that team won or lost?

And how would you guess that answer would change over time?

For the first question, let’s look at 2019. Last season, the team that threw for more gross passing yards went 136-119-1, so that’s a small but clear edge for the team that threw for more passing yards.  In the graph below, I’ve shown the number of passing yards by each winning team (in blue) and its opponent (in orange) in each game.  The X-Axis shows the difference between the passing yards for the winning team and the passing yards for the losing team. There are a few more dots to the right side of the graph than the left, which is because the winning team more often than not threw for more yards.  This is a fun graph, because it also lets you see how many games are in each category based on the size of the difference.

[continue reading…]

If you know nothing else about a game other than the quarterback threw for over 300 yards, would you bet that the team won the game?

On one hand, passing yards is correlated with production: all else being equal, more yards are better than fewer yards. On the other hand, we know that Game Scripts call for teams with a lead to throw less frequently than teams that trail; for the same reason that “teams that run 30+ times usually win”, you might be suspect about the fortunes of a team that threw for 300 yards.

And what about historically? Has the league-wide winning percentage changed over time for when a quarterback throws for 300 yards? Great questions! Let’s get some answers. [continue reading…]

Saints WR Michael Thomas had another dominant season in 2019.  He easily led the NFL in receiving yards with 1,725, and he also was responsible for 38.9% of all Saints receiving yards.  That was the largest percentage of a team’s receiving pie for any one player in 2019, followed by Bronco Courtland Sutton in a distant second place (32.7%), and Bears WR Allen Robinson (32.1%); only three other players (Buffalo’s John Brown, Cleveland’s Jarvis Landry, and Minnesota’s Stefon Diggs) topped 30%.

Regular readers know that I like to calculate something called the Concentration Index for passing offenses: it’s relatively simple to calculate, and it measures how concentrated a team’s passing offense is among a small or large number of players.  To calculate, you simple take each player’s receiving yards, divide that by his team’s total receiving yards, square that result, and then add that number for each player on the offense.  For the Saints, Thomas is at 38.9%; the square of that is 15.2%, so that’s the number we use.  Jared Cook was second on the team with 705 yards, or 15.9% of the team’s receiving yards; the square of that number is 2.5%.  Do this for every player, and the Saints have a total Concentration Index of 21.1%… which is highly concentrated. [continue reading…]

The NFL is looking at adding a 7th team to the playoff field in each conference, which would represent a significant change in the current structure. Moving forward, only the #1 seed would have a bye. How would this chance things?

Wild Card Round

There would now be three games played here in each conference: as before, the 6 seed would travel to face the 3 seed, and the 5 seed would go on the road against the 4 seed. And the 1 seed would have a bye. But the 2 seed and 7 seed would now play each other, as opposed to both teams being off that week (with the 7 seed missing the playoffs).

My assumptions throughout this post are (1) home field advantage matters, and (2) the stronger seed is the better team, with the exception of 4 vs. 5. With 4-team divisions, the best team to not win its division — that is, often, the 2nd best team in the division with a very good division winner — is more often than not a better team than the worst division winner.

Let’s assume the 2 seed has a 65% chance of beating the 7 seed, the 3 seed has a 60% chance of winning, and the 4 seed has a 55% chance of winning.  In the Division Round, the 1 seed will face the weakest remaining seed, while  the strongest-seeded winner that won on Wild Card weekend would be home against the other remaining winner from Wild Card weekend.  I simulated 32,000 playoff seasons to see which matchups are most likely in the Division Round.

As it turns out, the 1 seed has at least a 15% chance of facing any of the 4-7 seeds, with the 7 seed being its most likely opponent (because the 7 seed always plays the 1 seed when it wins). The 2 seed is the overwhelming favorite to be the other host team in the Division Round, although now the 3 seed has a 1-in-5 chance to do so (currently, it has a 0-in-5 chance of hosting a Division Round game). And heck, even the 5 seed has an opportunity to host a Division Round game, if all three road teams win on Wild Card weekend. [continue reading…]

Let’s look at each round of the NFL playoffs,

Wild Card Round

There are two games played here in each conference: the 6 seed travels to on the road to face the 3 seed, and the 5 seed visits the 4 seed. The 1 and 2 seeds have byes.

My assumptions throughout this post are (1) home field advantage matters, and (2) the stronger seed is the better team, with the exception of 4 vs. 5. With 4-team divisions, the best team to not win its division — that is, often, the 2nd best team in the division with a very good division winner — is more often than not a better team than the worst division winner.

Still, home field advantage matters. So I am assuming that the 3 seed has a 60% chance of winning its game, while the 4 seed has a 55% chance of winning its game (this is lower than the general rule that the home team wins about 57% of games).

This means, after the wild card round, there’s a 100% chance that the 1 seed remains, a 100% chance that the 2 seed remains, a 60% chance that the 3 seed remains, a 55% chance that the 4 seed remains, a 45% chance that the 5 seed remains, and a 40% chance that the 6 seed remains. If you want to change these percentages, that’s very easy; more on that at the end of this post.

Division Round

Who will the 1 and 2 seeds face in the Division Round? The 1 seed has a 33% chance of facing the 4 seed, a 27% chance of facing the 5 seed, and a 40% chance of facing the 6 seed. This is because the 1 seed always plays the 6 seed when the 6 seed wins in the Wild Card round (40% chance), and faces the 4/5 winner 60% of the time. The 2 seed has a 60% chance of facing the 3 seed (when the 3 seed beats the 6 seed), a 22% chance of facing the 4 seed, and an 18% chance of facing the 5 seed.

So what will happen in the Division round?  Again, we need to come up with some probability; I took a stab at that below.  If you don’t like them, you can change them letter!

These seem reasonable to me; maybe you want to give the home team a bigger edge, but they’re close enough (and simple!) for our purposes.  So how likely is each seed to make the conference championship game using these numbers?

The 1 seed can make it by beating the 6 seed (40% chance that game happens, 80% chance of winning, therefore a 32% chance the 1 seed makes the Conference Championship Game by beating the 6 seed), the 5 seed (27%, 70%, 19%) or the 4 seed (33%, 75%, 25%): therefore, the 1 seed has a 76% chance of getting to host the title game.

The 2 seed can make it by beating the 5 seed (18% chance, 65% conditional win probability, 12% chance the 2 seed makes it by beating the 5 seed), the 4 seed (22%, 70%, 15%), or the 3 seed (60%, 60%, 36%), for a 63% chance.

You can do this calculation for all the seeds.  The 6 seed, for example, only has an 8% chance (40% chance in the Wild Card round, 20% chance in the Divisional Round) of getting to the CCG.  The 3 seed has a 24% chance, while the 4 and 5 seeds each have around a 14-15% chance.

In fact, the 5 seed has a slightly better chance of making it to the CCG than the 4 seed, because of the assumption that it is the better team.  This is offset, of course, by being on the road in the Wild Card round.

Conference Championship Game

With 6 teams in the playoff field, there are 30 possible combinations (6 x 5) for the conference champinoship game.  Of course, only half of those truly exist because home field automatically goes to the better seed.  And 4 of those 15 combinations are impossible — 1 can’t play 6 and 2 can’t play 3, since it would automatically play in the Division Round, while 3/6 and 4/5 can’t meat in the CCG since they meet in the Wild Card round.  The table below shows the chance of each combination happening, along with my projection of the likelihood that the home team wins.

Again, if you disagree with any of these results, you will be able to change them! Just keep reading.

Conference Champion

If you perform all of the calculations using the assumptions in this post, you’ll see that there’s a roughly 48% chance the 1 seed wins the conference, a ~30% chance the 2 seed makes it to the Super Bowl, and the percentages drop to ~10%, 4-5%, 5-6%, and 2-3% for the 3, 4, 5, and 6 seeds.

Monte Carlo Simulation

One way to re-create the above is by performing a Monte Carlo simulation.  You can download the Excel file that I created here. This file simulates 32,000 NFL postseasons with random results, weighted based on the percentage chance the home team has of winning each game.

Here’s how to read/use this sheet. On the Wild Card sheet, the pre-game win probabilities are in cells V11 and V12, which are highlighted in yellow. Let’s say you think the 5 seed in a given season is really good and/or the 4 seed is really weak; in that case, let’s change the home team win probability from 55% to 40%. Well, this still only shifts the Conference Championship odds (in Column S on the “ccg” sheet) a little bit; the 5 seed jumps from just over 5% to just over 7%, while the 4 seed drops to about 3.5%.

Let’s go to the “div” sheet. Let’s say you think the 1 seed is really strong, and should have a 90% chance of winning no matter its opponent in the Division Round. Even still, this only jumps its odds of winning the conference to about 57%.

What do you think?

Arizona running back David Johnson is one of the better receiving backs in the NFL, but he’s also the most unique. Most teams use their running back as a last resort on passing plays; on average, passes to running backs are right at the line of scrimmage.  But with Johnson, he’s actually thrown passes down the field, rather than just as a checkdown option.

The graph below shows each running back with 40+ targets.  The X-Axis shows the average number air yards on each reception by that running back; the Y-Axis shows the average number of yards gained after the catch.  Most running backs will be up (high YAC) and to the far left (low Air Yards) on this chart.  Johnson, however, is a bit of an outlier.  Arizona frequently lines him up in the slot, and even throws him the occasional deep pass.

The other notable outliers at running back are Austin Ekeler (10.2 YAC per reception) and Dalvin Cook (11.2 YAC per reception): [continue reading…]

Fritz Pollard, the first African American coach and quarterback in the NFL.

Twelve years ago, I wrote a four part series detailing the history of the black quarterback.

Six years ago, I updated that article; today, a further update as the NFL just concluded its 100th season. And while for the last 52 of those seasons, at least one black quarterback was in the NFL, the roles and treatment of black quarterbacks have varied greatly over the last five decades.

The history of black quarterbacks in professional football is complicated. The New York Giants did not have a black quarterback throw a pass until 2007, when Anthony Wright became the first to do so; 10 years later, Geno Smith became the first and only black quarterback to start a game for the Giants. But as far back as 1920, Frederick Douglass “Fritz” Pollard was the tailback of the Akron Pros; a year later, he was promoted to player/coach, and became the first black head coach in NFL history. Pollard helped the Pros win the championship in the NFL’s inaugural season. ^[1]At the time, the NFL went by the name the American Professional Football Association. It was not known as the NFL until 1922. The Pros ran the single-wing, and Pollard was the player lined up behind the center who received the snaps. At the time the forward pass was practically outlawed, so Pollard barely resembles the modern quarterback outside of the fact that he threw a few touchdown passes during his career. ^[2]In addition to his NFL exploits, Pollard also achieved a great deal of fame for leading Brown to back-to-back road wins over the powerhouse schools of the time, Yale and Harvard, in 1916. He would … Continue reading And, of course, it was a time of significant discrimination: Pollard and end Bobby Marshall were the first two black players in professional football history.

As told by Sean Lahman, at least one African American played in the NFL in every year from 1920 to 1933, although Pollard was the only one resembling a quarterback. ^[3]It wasn’t just African Americans that had full access during this era: Jim Thorpe coached and starred in a team composed entirely of Native Americans called the Oorang Indians in 1922 and 1923. Beginning in 1934, that there was an informal ban on black athletes largely championed by Washington Redskins owner George Marshall. It wasn’t until 1946 that black players were re-admitted to the world of professional football, when UCLA’s Kenny Washington ^[4]Who occupied the same backfield with the Bruins as Jackie Robinson. and Woody Strode were signed by the Los Angeles Rams; in the AAFC, Bill Willis and Marion Motley were signed by Paul Brown’s Cleveland Browns that same season.
[continue reading…]

Yesterday, I wrote that the range of passer ratings is getting smaller.  Today, let’s investigate why.  As you know, passer rating is made up of four variables: completion percentage, yards per attempt, touchdown rate, and interception rate.

For each of the four variables, I calculated the standard deviation in that metric for all of the teams in the league in that season.  Last year, for example, the standard deviation in completion percentage was about 3.5%.  That’s on the low end historically, although not the absolute lowest mark.  But in general, it’s fair to say that the league-wide completion percentages are getting more compressed.  Last season, the Saints completed 72% of the team’s passes, and the Bengals were last at 58%. But in 1976, the Raiders were at 64%, while the Bills were at 41%.  That In 1994, the 49ers were a big outlier as they completed 70% of their passes at a time when two teams (Washington, Houston) completed just under 50% of their passes.

With a much higher floor now — the league average completion percentage was 58% in 1994, the same as what the 32nd-ranked Bengals did in 2019 — completion percentages as a whole are simply more compressed.

When it comes to yards per attempt, there isn’t much of a trend.  The variation was a bit higher in the ’70s, but over the last 40 years, the standard deviation is around 0.7 yards per attempt each season.

[continue reading…]

In 1988, the passer rating for the entire NFL was 72.9. In 2019, every single team had a passer rating higher than that mark! Last season, the Carolina Panthers finished with a 74.7 passer rating, which was both the lowest in the 2019 NFL season and also the highest mark in history by a team that ranked last in that statistic.

This is part of two general trends: passer ratings are going up, but also, the variance in passer ratings is declining. [continue reading…]

In what is becoming a yearly tradition, today I am going to post the era-adjusted passer ratings from the 2019 season.

Passer rating is made up of four variables: completion percentage, yards per attempt, touchdown percentage, and interception percentage. The reason passer rating needs to be adjusted for era? Well, that’s pretty simple to explain.

When the formula was derived in the early ’70s, an average rating in each variable was achieved with a 50% completion rate, averaging 7.0 yards per pass attempt, a 5% touchdown rate, and a 5.5% interception rate. Since those numbers are wildly out of date, I came up with a formula that perfectly matches the intent of passer rating but ties the variables to the league average in any given season. You can get the formulas and read more background in the linked posts.

In 2019, the four averages were 63.5%, 7.22, 4.46%, and 2.30%, respectively. The big changes, of course, are in completion percentage and interception rate; yards per attempt is much more stable throughout history, while touchdown rate is actually slightly lower than it was in the ’70s.

One thing to keep in mind: these adjustments will not change the order of passer ratings in a given season. So Ryan Tannehill, Drew Brees, Lamar Jackson, Kirk Cousins, and Russell Wilson will remain your top 5 leaders; the way the formula works, it simply subtracts a fixed amount from each passer’s actual passer rating. In 2019, that amount was a whopping 23.7 points from each passer.

Below are the 2019 passer ratings: [continue reading…]

There aren’t many shared birthdays among NFL starting quarterbacks. Teddy Bridgewater and Drew Lock were born four years apart on November 10th, making them the only shared birthday among players with 1,000 passing yards last season. The March 2nd birthday has been held by Ben Roethlisberger alone for a long time, but Tua Tagovailoa — both on the same date 16 years later — arrives just in time to carry that date’s mantle. And watch out: the next decade of the NFL could be defined by Kyler Murray (born August 7th, 1997) and Jalen Hurts (born August 7th, 1998). As for the presumptive number one pick in the 2020 Draft?  Well, Joe Burrow may wind up being the career leader in passing yards by a player born on December 10th by the end of his rookie season.

But when it comes to NFL birthdays, there’s no date that can compare to today.  Drew Bledsoe — born on Valentine’s Day, 1972 — ranks 16th on the all-time passing yards list, and he’s only the third best quarterback born on this date.  Hall of Famer Jim Kelly was born a dozen years before Bledsoe, and Steve McNair was born February 14th, 1973.  There are only 47 quarterbacks with 30,000 passing yards, and three of them were born today.  David Garrard ranks 142nd on the all-time passing yardage list with over 16,000, which is pretty darn good for the 4th best quarterback born on a calendar date.  In fact, no other calendar date has four passers of note (May 17th is the only other day to give us four quarterbacks who hit even 7,500 yards).

And Patrick Ramsey, who ranks as the 5th best February 14th passer, has more yards than any other player who ranks fifth on his birthday’s passing list. The same is true of Anthony Wright at #6, although that’s where the fun stops. With Christian Hackenberg — yes, he celebrating his 25th birthday today — failing to gain any traction in the NFL, May 11th remains the only birthday with seven 1,000-yard passers.

The graph below shows the career passing yards for each birthday for all of NFL history. With over 137,000 passing yards, February 14th is easily the leader: [continue reading…]

I’m going to take a little vacation: both in the physical sense and from writing every day. Given how tight this community is, I wanted to let everyone know that I probably won’t be updating this blog for the next week or two, but don’t worry about me.

In the meantime, please leave any ideas, thoughts, or anything on your mind in the comments. As we begin the 2020 offseason, what are you interesting in reading about? Writing about? Studying? Debating?

Thanks,

Chase

The Kansas City Chiefs are Super Bowl champions. And the Chiefs did it in one of the most remarkable ways possible: by coming back from double digit deficits in all three games.

Kansas City trailed 24-0 early in the 2nd quarter of the Division Round playoff game with the Texans. The Chiefs responded with four touchdown drives to somehow grab a 28-24 lead heading into the locker room.

In the AFC Championship Game, the Titans jumped out to a 10-0 lead 10 minutes into the game, and held a 17-7 lead with 5 minutes left in the half. Once again, Kansas City score two quick touchdowns to take a lead into the locker room, 21-17.

Then, last night in the Super Bowl, the 49ers took a 20-10 lead into the 4th quarter. The low point was probably with 7:13 left in the game, as the Chiefs trailed 20-10 and faced a 3rd-and-15 from the Kansas City 35-yard line. [continue reading…]

Two of the best coaches of the last 20 years. Belichick is 3-0 against Reid in the postseason.

How do you get to the Pro Football Hall of Fame as a head coach? Here’s a helpful flow chart.

Did you win 3 rings? If so, proceed to Canton (9)

If you win three championships as a head coach in the NFL, you are going to make the Hall of Fame. There are only 10 men who can make that claim, and nine of them are already enshrined in Canton: George Halas, Curly Lambeau, Paul Brown, Chuck Noll, Joe Gibbs, Weeb Ewbank, Vince Lombardi, Bill Walsh, and Guy Chamberlin. The 10th, of course, is Bill Belichick, who will at some point retire and then be a first ballot inductee.

Did you win 2 rings plus have a third appearance? If so, proceed to Canton (3)

Two of the best head coaches ever — Don Shula and Tom Landry — fall into this category. Both are also in the top four in all-time wins. The third is Bill Parcells, who also took two different teams, and three different quarterbacks, to the Super Bowl. [continue reading…]

The last running back to win the Super Bowl MVP was Terrell Davis, which happened over 20 Super Bowls ago. One reason for that is a decline in big rushing games in the Super Bowl, particularly with respect to the winning team. Just once in the last 16 Super Bowls — Dominic Rhodes back in SuperBowl XLI — has the winning team had a 100-yard rusher. Perhaps more interesting is that in the last 9 Super Bowls, the losing team had the game’s leading rusher more than half the time.

The graph below shows the leading rusher for both the winning and losing teams in the Super Bowl. The winning team’s leading rusher is in a full black circle, while the losing team’s leading rusher is in a white circle with a black outline. In addition, in the 13 of 53 Super Bowls where the game’s leading rusher was on the losing team, I’ve put that in a white circle with a red outline. [continue reading…]

It’s safe to say that the Chiefs rely on their quarterback, Patrick Mahomes, a lot more than the 49ers rely on Jimmy Garoppolo. More often than not, these Super Bowls have ended with the better team winning, and the better quarterback losing.

The most extreme example is probably Super Bowl XLVIII between the Seahawks and Broncos. Seattle had a great defense and a very good running game, with an efficient but low-volume quarterback. Denver had the best quarterback in the NFL. Does that sound familiar? Of course, as we all know, the Seahawks blew out the Broncos.

Five years earlier, in Super Bowl XLIII, the Steelers were a much more balanced team than the Cardinals. Arizona had Kurt Warner, Larry Fitzgerald, and Anquan Boldin, but Pittsburgh was driven by its top-ranked defense. Ben Roethlisberger had been interception prone during the season, and the Steelers passing attack was average at best for most of 2008. Still, Pittsburgh emerged victorious.

And while you likely don’t remember it, Super Bowl IX is another good comparison to this year’s game. Minnesota had Fran Tarkenton, who was arguably the top quarterback in the NFC in 1974, but the defense had fallen from its golden days of the late ’60s and early ’70s. Meanwhile, Pittsburgh ranked 1st or 2nd in most of the key defensive categories but had a young and unproven quarterback in Terry Bradshaw. The Steelers were not very reliant on their quarterback, while Minnesota was: and in the Super Bowl, the dominant defense carried the day.

A counter example comes from 2006 in Super Bowl XLI. The Bears had an interception prone quarterback in Rex Grossman and an outstanding defense, while the Colts were obviously carried by Peyton Manning. This time, the dominant quarterback’s team won, although it was the running game and the defense (or maybe the absence of a passing game for Chicago) that really led Indianapolis to victory.

How about one of the greatest upsets in pro football history, in Super Bowl XLII? We don’t often think of this game as a “great QB vs. a balanced team” sort of game, because New England was just so much better than New York during the regular season. But the Giants passing game was below-average during the regular season and the defense was better than average, while the Patriots were defined by their passing game. In the Super Bowl, the Giants defensive line dominated the game, and led to a huge upset.

Another lopsided game was Super Bowl XXIV between the ’89 49ers and ’89 Broncos. While San Francisco was the better team overall, and 13-point favorites, the Broncos were certainly the more balanced team. The 49ers passing offense was off the charts good during the regular season, while Denver’s defense finished 1st in points allowed and 3rd in yards allowed. The 49ers blew out the Broncos in the most one-sided game in Super Bowl history.

Let’s close with two more games that featured upsets by the “balanced/defense” team over the “star QB team”. In Super Bowl 50, the Broncos had a great defense while the Panthers had the league MVP at quarterback. And in Super Bowl XXXVII, the Bucs had a great defense while the Raiders had the league MVP at quarterback. In both games, the dominant defense stole the show.

Will Super Bowl LIV follow a similar trend? I’m a bit surprised to see the Chiefs as 1.5-point favorites in this game. While no game is a perfect mirror of any other game, there are many similarities between this 49ers/Chiefs game and several of the games on the above list. And we know that the 49ers were the better team overall this season. Even Super Bowl XXV, between the Giants and Bills, matches this trend. That game didn’t make the list because the Giants passing attack during the regular season was very good, but that was mostly with Phil Simms; if you consider the Giants team that made the Super Bowl as a balanced/defense-heavy team going up against a high-octane offense, that’s another mark in favor of the 49ers on Sunday.

So what methodology did I use to come up with these results? The full explanation below. [continue reading…]

Last year, the Kansas City Chiefs were the #1 seed in the AFC, while the 49ers had the second-worst record in the NFL. This year, the two teams are in the Super Bowl. Today, let’s look at how each of the 108 Super Bowl teams fared in Year N-1 — that is, the year before making the Super Bowl.

For each team, I calculated their Pythagenpat record in Year N-1. The best Super Bowl team by Year N-1 Pythagenpat record was, unsurprisingly, the 1973 Dolphins. As we all know, the year before Miami went 14-0 and won the Super Bowl.

The 2019 Chiefs don’t fare all that well in this regard, mostly because the 2018 team ranked 24th in points allowed. Kansas City was the #1 seed, but it was not a dominant team by these standards. The 49ers, however, do stand out as particularly bad (although I’ll note that San Francisco, despite finishing with the 2nd pick in the 2019 Draft, “only” had the 6th worst Pythagenpat rating of 2018). The 2018 49ers are one of the worst 5 teams to make the Super Bowl the following season. [continue reading…]

In 2019, the Vikings averaged 7.13 yards per pass play, while the Chargers averaged 7.01 yards per pass play. Yards per pass play here is just Net Yards per Attempt, which is team passing yards (net of sacks) divided by pass attempts (including sacks).

In 2019, the Vikings averaged 4.48 yards per carry, while the Chargers averaged only 3.97 yards per carry.

But in 2019, despite Minnesota being more efficient both at passing and rushing than Los Angeles, the Vikings averaged fewer yards per offensive play than the Chargers: Minnesota averaged 5.83 Y/P, while Los Angeles averaged 5.90 Y/P. Regular readers here know that this is the result of Simpson’s Paradox, a counter-intuitive phenomenon in which a trend appears in different sets of data but reverses when these data are combined.

How does this happen? Because overall, passing is more efficient than running when it comes to yards per play, and the Chargers passed much more frequently than the Vikings. That’s one reason why I don’t like “yards per play” as a statistic to measure offensive production: it’s biased in favor of pass-happy teams.

Instead, I like to use a modified version called adjusted yards per play, which takes 60% of the team’s Yards per Pass average and 40% of the team’s Yards per Carry average. This eliminates any Simpson’s Paradox issues and gives a better sense of which are the most efficient offensive teams.

And here’s your Super Bowl LIV tie-in: the Chiefs averaged more yards per play this year than the 49ers, but San Francisco averaged more adjusted yards per play. Here’s how to read the table below. San Francisco had 514 pass plays for 3,792 yards, averaging 7.38 Net Yards per Attempt. The 49ers had 498 rushing plays for 2,305 yards, averaging 4.63 yards per carry. Overall, this means the 49ers averaged 6.02 yards per play, but San Francisco passed on only 50.8% of plays this season. As a result, the 49ers averaged 6.28 Adjusted Yards per Play (60% of 7.38 plus 40% of 4.63), which was the third-best average this season. The Chiefs rank 5th in this metric: Kansas City averaged 6.22 yards per play and 6.16 adjusted yards per play. And, if you removed the three Matt Moore games (Denver, Green Bay, Minnesota), the Chiefs in the 13 Patrick Mahomes games averaged 6.34 yards per play and 6.29 Adjusted Yards per Play.

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During the regular season, Jimmy Garoppolo had 143 pass plays on third down. On 13 of those pass plays, he took a sack. On the other 130 third down pass plays, Garoppolo gained a first down on exactly half of them. On Saturday, commenter LightsOut85 pointed out that he thought Garoppolo’s great third down performance was “more an indicator of SF’s offensive scheme (namely YAC) than his passing ability.”

Is that true? I decided to investigate. Of Garoppolo’s 130 third down pass plays that were not sacks:

• • 41 of them (31.5%) were thrown at or beyond the first down marker (i.e., past the sticks) and completed for a first down.
  • 30 of them (23.1%) were thrown past the sticks but fell incomplete.
  • 24 of them (18.5%) were thrown short of the sticks but still picked up a first down (this is the category LightsOut85 was focused on).
  • 35 of them (26.9%) were thrown short of the sticks and did not pick up a first down.

The graph below shows each of his 130 pass attempts. It is color-coded to make it easier to read, but let’s explain.  The X-Axis shows the distance — i.e., it was 3rd-and-X.  The Y-Axis shows the amount of air yards for the throw.  For example, at the top right, you will see a 3rd-and-16 pass that went for 41 air yards, which was one of the more memorable plays of the 49ers season.

If the pass was thrown at or beyond the sticks, the bubble is blue.  If it was converted for a first down, the dot is fully colored in blue; if it was not, it is a white bubble with a blue outline.  If the pass was thrown short of the sticks, the bubble is red.  If it was converted for a first down, the dot is fully colored in red; if it was not, it is a white bubble with a red outline. [continue reading…]

Super Bowl LIV features the two best tight ends in the game: Kansas City’s Travis Kelce and San Francisco’s George Kittle. Over the last six seasons, Kelce has averaged 1,078 receiving yards per season, and he’s posted four straight 1,000-yard years. But it was Kittle who was the top tight end this year according to the AP, and the top player overall according to Pro Football Focus.

Kittle is an excellent blocker and an outstanding receiver, but today, I want to focus on his receiving numbers. While Kittle put together a great season working with Jimmy Garoppolo, he ranked “only” 3rd in receiving yards among tight ends. That’s because the 49ers were one of the most run-heavy teams this year: Kelce’s Chiefs threw 97 more passes than the 49ers, while Oakland’s Darren Waller played on a team that threw 45 more passes than San Francisco.

In terms of pure receiving yards per team pass attempt, Kittle was best in the league.
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In 2018, in an article about the weird 2017 season that Jameis Winston had (if I only knew what the future would hold!), there was one tidbit you might have missed: in  2017 season, Jimmy Garoppolo had the best 3rd down conversion rate in the league.  After being traded to the 49ers in midseason, Garoppolo picked up a first down on exactly half of his dropbacks (28 of 56).

Garoppolo did not repeat this success in limited playing time in 2018 (6 of 24); you won’t be surprised to learn that Patrick Mahomes (48%) led the NFL in this metric last season. [continue reading…]

On the surface, it is very easy to paint Super Bowl LIV as yet another matchup between a great defense and a great offense, similar to Super Bowl 50 (Panthers/Broncos), Super Bowl XLVIII (Broncos/Seahawks), any of the first three Super Bowls, or many of the great ones in between.

The Chiefs have the reigning MVP of the NFL in Patrick Mahomes, and Kansas City is averaging 4.3 points (!) and 48 yards per drive this postseason. The 49ers had a dominant defense in the first half of the season, becoming just the 9th team since 2002 to allow 102 or fewer points through 8 games. In the playoffs, San Francisco held Minnesota to just 10 points and then shut out the Packers in the first half (while forcing two turnovers), effectively clinching a Super Bowl berth by halftime courtesy of a 27-0 lead.

But here’s the interesting thing: did you know that the 49ers scored more points this year than the Chiefs? And that Kansas City allowed fewer points this year than San Francisco? Strange, but true. In fact, San Francisco scored more points and gained more yards than Kansas City.

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Can you name the winner and loser from each Super Bowl?

How about the Super Bowl MVP? The final score? The points spread? The stadium?

Test your knowledge below, by filling in as much as you can. Answers appear after the jump.

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Mahomes can beat you deep — or with a flip.

Against the Titans in the 2019 AFC Championship Game, Patrick Mahomes threw for 14 first downs on 35 passes, while also taking two sacks. That’s a passing first down rate of 37.8%. Note that this ignores that Mahomes scrambled 6 times and picked up a first down on four of them, so his true first down rate was 41.9% (the Chiefs also just dropped two passes here).

Let’s ignore the advanced stats for a minute (scrambles, drops) and just focus on his passing numbers: Mahomes has picked up 30 first downs through the air on 72 dropbacks this postseason, a 42% rate. That’s amazing, but it isn’t as amazing the standard way we analyze quarterbacks here: Mahomes is averaging 10.74 ANY/A this postseason! [continue reading…]