The table below shows each Super Bowl champion since 1970 and its rank in various categories. At the top, I’ve included an average of the ranks of the teams over the last 10 years and since 1970, and each team is hyperlinked to its Pro-Football-Reference team page. The categories in this first table are record, points for, points allowed, Pythagenpat record, offensive yards, defensive yards, yards differential, offensive pass yards, offensive rushing yards, defensive passing yards (i.e., passing yards allowed), and defensive rushing yards. [continue reading…]

In a couple of weeks, the newest class of the Pro Football Hall of Fame will be announced. Only five modern-era wide receivers have been selected enshrinement on their first ballot: Jerry Rice, Paul Warfield, Steve Largent, Raymond Berry, and Lance Alworth. This year, in his first year of eligibility, Marvin Harrison is one of 15 finalists for the Pro Football Hall of Fame. I suspect the majority will view Harrison as a first-ballot Hall of Famer, but there are a few minority voices who disagree.

As best as I can surmise, there are three primary reasons why Harrison shouldn’t be selected in 2014. Two of those reasons can be addressed rather easily, but let’s start with the more complicated issue to analyze.

Harrison’s numbers are inflated because of Peyton Manning

Jerry Rice is the greatest wide receiver of all time. Rice was probably better at his position than any football player has ever been at theirs. Rice might be the most dominant sportsman of his generation. Rice probably isn’t in the discussion of greatest athletes in the history of mankind, which is about the only negative thing I’m willing to say about him. All of that is important background to say, being worse than Jerry Rice is not a negative, but just a fact of life as a wide receiver.
[continue reading…]

When it comes Patriots/Colts, it’s easy to want to focus on Tom Brady vs. Andrew Luck. Or to marvel at the sheer number of star players these teams have lost in the last 12 months. If you played college in the state of Florida, you’re probably not going to be playing in this game: T.Y. Hilton is the last star standing with Vince Wilfork, Aaron Hernandez, Brandon Spikes, and Reggie Wayne gone. The Patriots also have placed Rob Gronkowski, Sebastian Vollmer, Jerod Mayo, Tommy Kelly and Adrian Wilson on injured reserve, while Devin McCourty and Alfonzo Dennard are both questionable. Also, of course, Brady is probable with a shoulder.

The Colts just put defensive starters Gregory Toler and Fili Moala on injured reserve, adding to a list that already included Wayne, Ahmad Bradshaw, Vick Ballard, Dwayne Allen, Donald Thomas, Montori Hughes, and Pat Angerer.  LaRon Landry and Darrius Heyward-Bey are both questionable, and the latter’s injury caused the team to sign ex-Patriot Deion Branch.

All the injuries and changing parts make this a pretty tough game to analyze. So I’m not going to, at least not from the usual perspective. Instead, I want to take a 30,000 foot view of the game. According to Football Outsiders, the Patriots were the most consistent team in the league this season, while the Colts were the fourth least consistent team. Rivers McCown was kind enough to send me the single-game DVOA grades for both teams this season, and I’ve placed those numbers in the graph below with the Colts in light blue and the Patriots in red. The graph displays each team’s single-game DVOA score for each game this season, depicted from worst (left) to best (right). For Indianapolis, the graph spans the full chart, from the worst game (against St. Louis) to the best (against Denver). As you can see, the portion of the graph occupied by New England is much narrower, stretching from Cincinnati to Pittsburgh. [continue reading…]

Steve Buzzard has agreed to write another guest post for us. And I think it’s a very good one. Steve is a lifelong Colts fan and long time fantasy football aficionado. He spends most of his free time applying advanced statistical techniques to football to better understand the game he loves and improve his prediction models.

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Last month, I wrote about how to project pass/run ratios using offensive Pass Identities and the point spread. However, this methodology only considers one side of the ball. Can we actually improve our projections model using both offensive and defensive Pass Identities? As it turns out the answer is yes.

First, I started off by creating defensive Pass Identities using the same methodology found here. The first thing I noticed was the standard deviation of pass ratios for defenses was only 3.0% compared to 5.1% for offenses. This led me to believe that offenses control how much passing goes on in a game more than defenses. I was glad to see this as it confirmed most of my previous research as well. Given this, it wasn’t appropriate to use a standard deviation of 3.0% for defenses in my projection while using a standard deviation of 5.1% for offenses. Instead, I used the combined standard deviation of all 64 offensive and defensive pass ratios, which turned out to be 4.17%. This doesn’t change the order of passer identities much but obviously does increase the deviation from the mean for the offensive side of the ball and decrease it for the defensive side. [Chase note: Determining the best way to handle the differing spreads between offensive and defensive pass ratios is a good off-season project; in the interest of time, I advised Steve to split the difference and move ahead with the analysis.]

Now that we have a Pass Identity grades for both sides of the ball, we can add a strength of schedule adjustment, too. To make the SOS adjustment, I simply took the average of the defensive Pass Identities played by each offensive unit and the average of the offensive Pass Identities played by each defensive unit. As expected the SOS adjustments had a much larger impact on the defensive Pass Identities than the offensive Pass Identities.
[continue reading…]

After 15 weeks, I wrote that Seattle’s pass defense looked to be one of the most dominant since the merger. With the regular season now over, and the Seahawks getting ready for their first playoff game, I wanted to revisit this question and slightly tweak the methodology.

We begin with the base statistic to measure pass defenses, Adjusted Net Yards per Attempt.  Team passing yards and team passing yards allowed, unlike individual passing yards, count sack yards lost against a team’s passing yards total. So to calculate ANY/A on the team level, we use the formula (Passing Yards + 20*TD – 45*INT) divided by (Attempts + Sacks).  The Seahawks allowed just 3.19 ANY/A this year, which was 1.20 ANY/A better than any other defense this season.  In fact, it was so good that it enabled Seattle to easily post the best ANY/A differential (offensive ANY/A minus defensive ANY/A) in the league, too.  The Seahawks 3.19 average is the 4th best average in the least 20 years (behind only the 1996 Packers, 2002 Bucs, and 2008 Steelers). But what makes Seattle’s accomplishment more impressive is the passing environment of the NFL in 2013.

When I graded the Seahawks three weeks ago, I defined the league average ANY/A in the customary way: the ANY/A average of the passing totals of the league as a whole. This time around, I decided it would be more appropriate to (1) exclude each team’s own pass defense when calculating the league average, and (2) take an average of the other team’s ANY/A ratings, as opposed to taking an average of the totals. In 2013, the other 31 pass defenses allowed an average of 5.98 Adjusted Net Yards per Attempt. That means Seattle allowed 2.79 fewer ANY/A than the average team this year: that’s better than every defense since 1990 other than the 2002 Bucs.

Next, I calculated the Z-Score for each pass defense. The Z-Score simply tells us how many standard deviations from average a pass defense was. The standard deviation of the 32 pass defenses in 2013 was 0.95, which means the Seahawks were 2.93 standard deviations above average. That’s the 4th best of any defense since 1950.
[continue reading…]

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

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

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

Every week this season, I’ve written about the Game Scripts from the previous weekend. For new readers, the term Game Script is just shorthand for the average points differential for a team over every second of each game. You can check out the updated Game Scripts page, which shows the results of all 256 games this year. Week 17 saw some big blowouts and some tight finishes: Peyton Manning, Andrew Luck, and Drew Brees all led their teams to convincing wins against overmatched opponents, while Green Bay and Philadelphia clinched playoff berths with close wins.

Week 17 was unremarkable from a Game Scripts perspective, although I’ll note that Denver’s win over Oakland produced a Game Script of 21.6, the fifth highest average margin of the year (and the best by the Broncos this year). On the comeback side, only three teams won with negative Game Scripts, and two of those wins (Green Bay, Carolina) were back-and-forth contests. That means we should all take a moment to reflect on the resolve and grit of the San Diego Chargers, who overcame an average deficit of 4.6 points (in regulation) to force overtime and eventually defeat the Chiefs B team.

The full Game Scripts data from week 17: [continue reading…]

Did you happen to notice the stat line produced by Ryan Tannehill last week? He completed just 10 of 27 passes for only 82 yards in a 19-0 loss to the Bills. A 37% completion rate and a 3.0 yards per attempt average are ugly numbers in their own right, but Tannehill was also sacked seven times for 46 yards. That means on 34 dropbacks, he produced…. 36 yards.

Tannehill did not throw an interception in the 19-0 shutout, so perhaps that’s why this game has gone under the radar. But a quarterback does not get to fare so poorly and avoid coverage of it at Football Perspective. Can you imagine if Tony Romo or Jay Cutler had a game like this? Why aren’t people talking about this? Tannehill averaged One Net Yard per Attempt over THIRTY FOUR DROPBACKS!?! Tannehill’s NY/A average dropped from 5.72 to 5.46, an unheard of drop this late in the season.

To be fair, Tannehill’s lack of interceptions does make the performance less horrible. But today, I want to just focus on yards produced on pass attempts (including sacks). Lots of good quarterbacks have had bad days when it comes to interceptions, but how often does a quarterback struggle so much on nearly every play for 34 plays?

Let’s provide some context. This season, the average pass play (including sacks) has produced 6.217 net yards, which means you would expect 34 dropbacks to produce 211.4 yards. That means Tannehill’s performance produced 175.4 net yards under average. Among quarterbacks with at least 15 pass attempts in a game, that’s the 25th worst performance since 1960, and the 7th worst performance since 2000.

The table below shows the worst 250 performances since 1960, although the only game I calculated for 2013 was Tannehill’s. The worst performance using this formula goes to Green Bay’s Lynn Dickey in 1981 against the Jets in week 16. He completed just 12 of 33 passes for 96 yards (I’ve included the TD and INT numbers even though they are not part of the calculation), and was sacked an incredible 9 times for 57 yards (Mark Gastineau, Joe Klecko, and Marty Lyons each had multiple sacks). So on 42 dropbacks, Dickey gained 39 yards, for an average of 0.9 NY/A. The NFL average that season was 6.02 NY/A, which means Dickey produced 214 Net Yards below average.
[continue reading…]

With one game remaining, Peyton Manning has already set the new single-season record with 51 passing touchdowns (two months ago, I projected Manning to finish the season with 52 touchdowns). But all records must be viewed in their environment, and NFL teams are averaging 1.58 touchdown passes per team game this year, the highest average since 1948. In 1984, the year Dan Marino threw 48 touchdowns, teams averaged 1.37 touchdown passes per game.

So which season is more impressive? That’s a complicated question, and one that could be answered in many ways. In my view, the question boils down to which performance was more outstanding; in mathematical terms, we could define that as which season was farthest from the mean.

To make life a little simpler, I’m going to analyze this question on the team level, meaning we will compare “Denver 2013” to “Miami 1984.” Of course, this approach is preferable in many ways, since when we praise Manning we really mean “Manning with his offensive line and his coaching staff throwing to Demaryius Thomas, Wes Welker, Eric Decker, and Julius Thomas.” And “Marino in 1984” means “Marino and Mark Clayton and Mark Duper and Dwight Stephenson and Ed Newman.”

This season, the Broncos have 51 touchdown passes. The other 31 teams (through 15 games) are averaging 22.8 passing touchdowns, which means Denver is 28.2 touchdowns above average. The standard deviation of the 32 teams in passing touchdowns is 7.4; as a result, we can say that the Broncos are 3.84 standard deviations above average, also known as their Z-score.

In 1984, the other 27 teams (through 16 games) averaged 21.0 touchdowns, while the Dolphins threw 49 scores (Jim Jenson, a college quarterback who played receiver for Miami, threw a 35-yard touchdown to Duper against the Patriots off a Marino lateral). The standard deviation that season in touchdown passes at the team level was 7.5, which gives Miami a Z-score of 3.72 in 1984.

So the Broncos this season have been more extraordinary, at least by this measure. One nice thing about using the Z-score is we don’t need to adjust for games played. I went ahead and calculated the Z-scores for every team since 1932. The current Broncos are #1, with the ’84 Dolphins in second place. The third place team isn’t the Tom Brady 2007 Patriots; that team is down at #7, because the standard deviation in passing touchdowns among the league’s 32 teams was 8.8 that season. Instead, the third slot goes to the 1986 Dolphins. Few remember that Marino threw 44 touchdowns that season; add in Don Strock’s two touchdowns, a lower league average and a smaller standard deviation, and those Dolphins get a Z-score of 3.70.

Let’s look at the top 100 teams using this metric. The 2004 Colts ranked fifth (if you click on the cell in the team column, the link takes you to that team’s PFR page) in Z-score. That year, Indianapolis threw 51 touchdowns, while the other 31 teams averaged 21.97 touchdown passes. That means Indianapolis was 29.03 touchdowns above average, the highest production above average to date. But that year, the standard deviation among the 32 teams in passing touchdowns was 8.53, giving the Colts a Z-score of “only” 3.41; that’s why they’re 5th, not first.
[continue reading…]

The Jets beat the Browns 24-13 today, bringing New York’s record up to 7-8. With Rex Ryan on the hot seat — more on this in a few hours — some have defended the controversial head coach by lauding his work this season. After all, if the Jets are one of the least talented teams in the NFL, isn’t it the product of great coaching that the Jets got to 7-8?

That would be true if the Jets were playing like a 7-8 team. But that’s not the case. The Jets have been outscored by 110 points this year, which makes them a bottom five team, a level of production more in line with the team’s talent. If Ryan is getting bottom five production out of a team that’s bottom five in talent, well, that’s not nearly as impressive.

But perhaps you want to argue that the Jets have overachieved in record (but not anywhere else) because of Ryan? Let’s investigate that claim. New York has just 4.45 Pythagorean wins, which means that they’ve won 2.55 more games than expected. The table below shows the 24 teams to exceed their Pythagorean record ^[1]Among teams in 16-game seasons by at least two wins while posting a negative points differential. [continue reading…]

Something just didn’t feel right. Here is what I wrote in last week’s column:

Through 12 weeks, the Cowboys had the strongest pass identity in the NFL. Then, against the Raiders in week 13, the Cowboys were pretty run-heavy. And against the Bears in week 14, Dallas produced its best game of the season on the ground. But Tony Romo attempted just 20 passes, and the Cowboys had their second lowest pass ratio of the season (behind a blowout win over the Rams). The weather played a factor against the Bears, and the running game was working, but in general, Dallas is at its best when Tony Romo, Dez Bryant, and Jason Witten are getting lots of touches. Against the Bears, a run-heavy game plan makes some sense; my guess is we’ll see a more pass-happy performance out of the Cowboys against Green Bay this weekend.

Well, at least I nailed one prediction this year. The Cowboys implosion against the Packers provided Nitroglycerin to the fire burning with second-guessers and Romo critics. First, some context: the Packers won with a Game Script of -9.7, the second lowest average by any winning team in 2013. And the Cowboys ran just 18 times, despite DeMarco Murray rushing for 134 yards on those 18 carries.

By my count, the most pass-happy games of the season have been:

• Atlanta calling 45 pass plays and just 16 runs in a win over the Rams where the Falcons held an average lead of 13.4 points.
• The Packers, when Aaron Rodgers was healthy but Eddie Lacy was not, calling 46 passes and 24 runs (including three kneel downs!) despite posting a Game Script of 17.9 against Washington.
• Dallas passing on 85.7% of all plays (54-9 ratio) despite holding an average lead of 1.4 points against the Vikings in week 9.
• Dallas, by recording a Game Script of 9.7 against Green Bay while passing 51 times and rushing just eighteen.

So when Tony Romo threw two late interceptions, the narrative had already been written: in addition to Romo being a choker, the burning question was why didn’t Dallas call more running plays? The Cowboys led 26-3 at halftime, yet called just seven runs in the second half? How is this even possible?

But as Bill Barnwell points out, this isn’t as much of a black and white issue as you might think. Dallas had five second half drives:

• Drive #1: Leading 26-10 (the Packers scored on the opening drive of the half), the Cowboys call five runs and five pass plays on a 10-play, 48-yard drive for a field goal. Dallas faced 1st-and-10 five times on this drive, and ran on four of those plays. A holding penalty on a negated running play ruined the drive, forcing Dallas to settle for a field goal.
• Drive #2: Leading 29-17, the Cowboys go three-and-out. Leading by 12 in the third quarter is hardly clock-killin’ time. A first down incompletion to Murray led to two more pass plays, but only with the benefit of hindsight can you really rip into Garrett for not calling yet another run here on 1st-and-10 (or for not running on 2nd-and-10, or 3rd-and-10). Had Dallas won the game, nobody would remember this series.
• Drive #3: Leading 29-24 with 12 minutes left in the first quarter, the Cowboys ran Murray on 1st-and-10, the 5th out of 7 opportunities to do so in the second half. After that, the Cowboys did in fact become very pass-happy, as Romo threw on eight of the next nine plays. The only problem with criticizing that approach is that it led to an 80-yard touchdown drive.
• Drive #4: Leading 36-31, the Cowboys took possession at their own 20-yard line with 4:17 remaining. The Packers had all their timeouts. At this point, a three-and-out gives Green Bay the ball back with 3:53 remaining. Even if the Cowboys get one first down, and get that on third down on the initial set of downs, the Packers will get the ball back with 1:54 remaining.That’s too much time for an offense that had scored four touchdowns on each of its four second half possessions. So on 1st down, the Cowboys called a pass play which was incomplete. On 2nd down, Romo was sacked. But on 3rd down, Romo hit Dez Bryant for the first down.
  
  You probably didn’t hear too much about that series, since it ended well. On the next 1st down, Dallas ran Murray for four yards. Two more runs wouldn’t have done much unless they gained six yards — the Packers could get the ball back with 1:54 and one timeout. Getting a first down is the priority in this situation, not running the clock.

  Of course, as we all know, Romo threw a pass on a run/pass option, and Sam Shields recorded the interception.

• Drive #5: Trailing 37-36, the Cowboys called two pass plays, and Romo’s pass for Cole Beasley was picked off when the receiver ran the wrong route.

It’s easy, and maybe a little bit fun, to rip Garrett and Romo and Jerry Jones. But I don’t think the pass-happy play-calling was the problem. Allowing 34 second-half points was the problem, and more runs up the middle wouldn’t have solved that problem, either. Unfortunately for the Cowboys, the problems on defense don’t seem to be getting any better.

Below are the Game Scripts data from each game in week 15; you can view the Game Scripts data from each game this season at the always up-to-date Game Scripts page here.
[continue reading…]

Dallas has been out-gained by 1,280 yards this season, the worst margin in the NFL. But with a 7-6 record, the Cowboys are hardly considered a bad team. So how can we reconcile these two facts?

In general, gaining yards and preventing opponents from gaining yards are correlated with success. The other teams in the bottom five in yards margin (the Jaguars, Vikings, Bucs, and Rams) are a combined 16-35-1, while the top three teams in yards margin are 32-8 (the Broncos, Saints, and Seahawks). On the other hand, as a statistic, “yards” is a flawed measure of team success. So let’s begin our investigation with a threshold question:

1) Are the Cowboys a bad team with a good record, or a good team with a bad yardage differential?
[continue reading…]

Last week, six teams won with a negative Game Script. During an unforgettable slate of 1PM games in week 14, four teams during that time slot won with a negative Game Script — and that doesn’t include the insane Ravens/Vikings game.  One of the teams to win with a negative Game Script was Miami, so had the Ben Roethlisberger/Antonio Brown miracle lateral play worked, it would have increased the craziness quotient but left us with just three negative Game Script victors.

The big comeback, of course, was in New England. The Patriots were shut out for the first 43 minutes, scored 14 points in the next 15 minutes, and then 13 points in the final two minutes. New England now has two of the biggest comebacks of the year, and joins Seattle as the only teams to win two games with Game Scripts of -6.0 points.

Big news out of Washington yesterday: Robert Griffin III has been benched for Kirk Cousins, in what is being described as collateral damage in the Dan Snyder/Mike Shanahan power struggle. The most interesting part of that sentence is Snyder’s hyperlinked name means yes, in fact, PFR now does have pages for executives. The quarterback change obscures the fact that the team has the worst special teams through thirteen weeks since at least 1989, and a pretty bad defense, too. More relevant for today’s post is that the beat down provided by Kansas City produced a Game Script of 23.8 points, the largest average lead in any game this year.

Below are the Game Scripts data from each game in week 14; you can view the Game Scripts from each game this season at the always up-to-date Game Scripts page here. [continue reading…]

For the second straight week, an NFC West team produced a monster game script. This week, it was Seattle dominating New Orleans and taking control of the NFC. The Seahawks can clinch homefield advantage throughout the playoffs by simply winning the team’s final two home games, and Seattle appears to be (again) getting hot just in time for the postseason. Among passers with at least nine starts, Russell Wilson has the second best ANY/A average behind Peyton Manning, and the team should have a healthy Percy Harvin for the playoffs. In other words, it’s going to take an incredible effort for a team to knock off the Seahawks, who also rank #1 in DVOA.

Six teams in week 13 won with negative Game Scripts, with Matt Ryan leading the biggest comeback of the week. In surprising twists, the Patriots and Cowboys trailed early before toppling the Texans and Raiders, while the Vikings came from behind late to defeat the Bears. The Jaguars won in the final minute in one of the more exciting games of the week, while the Giants won (in somewhat controversial fashion) after falling behind 14-0 early in Washington. Below are the Game Scripts data from each game in week 13: [continue reading…]

A couple of weeks ago, I presented another way to do team rankings. The method implicitly incorporates strength of schedule and margin of victory without having to do any hard math. For example, assume Team A hosts Team B and wins by 7 points. After adjusting for home field, Team A gets credit for winning by 4 points. The next step is to measure how Team B fared in its other 15 games. If Team B lost by more than 4 points in 4 other games, and won (or lost by less than 4) in its other 11 games, that would mean Team A had the 5th best result of the season against Team B. Therefore, we give Team A 5 points for this game. It’s that simple. You get credit for beating your opponent by more than other teams beat that opponent.

I don’t have a cool name for this sort of system, but I’m sure someone out there has been using this methodology for a long time and has already given it a name. So if you know it, post it in the comments. But I thought it would be fun to run through this method for every team since 1932. That’s what I’ve done in the table below. Keep in mind, though, that it’s only appropriate to compare teams who played the same number of games in a season. In a 9-game season, a team is obviously going to produce a much lower grade than a team in a 16-game season.

Here’s how to read the table below, which shows each team since 1932. It lists the top team in 2012, then the top team in 2011, then the top team in 2010, and so on, but you can use the search or sort functions to run whatever queries you like. In 2012, the Broncos ranked 1st in this system playing in the NFL (yes, that means I’ve got AFL and AAFC teams in here, too). The Broncos had an average score of 4.4 points. Denver had a win percentage of 0.831 that season, while playing 16 games (useful information when sorting), a 13-3 record. What’s the GR1 column? That means there was 1 Game where the Broncos Recorded a 1 — i.e., by delivering the biggest beatdown of the season (I also included games in this category if one other team delivered an equally-dominant performance against them). The Broncos ratings each week had a Standard Deviation of 3.1. I’m not quite sure what to do with the standard deviation column, but it was easy enough to include and might help you identify great teams that sat players in week 17.
[continue reading…]

The game of the week 12 was obviously Brady/Manning XIV, and the Patriots comeback victory resulted in the second lowest Game Script by a winning team this year. Due to the big lead, Denver rushed 55% of the time, and Knowshon Moreno set NFL season-highs with 37 carries for 224 rushing yards. In regulation, the Broncos held an average lead of 10.5 points, although that still trails the Andrew Luck-fueled comeback by Indianapolis against Houston in week nine. The other big comeback in week 12 was by Cam Newton and the Panthers.  Carolina trailed 16-3 with one minute left in the second quarter in the 2nd quarter, but scored the final seventeen points of the game to steal the win from Miami.

The biggest blowout of the week was by the Cardinals, who clobbered the Colts, 40-11.  Arizona led 34-3 entering the fourth quarter, and this was the second time this season Indianapolis has held an average deficit of 18+ points. That, in my expert opinion, is not good. Things are even worse for the team that selected after the Colts in the 2012 draft: for the second week in a row, Washington posted a Game Script of less than -9.0. I don’t have any desire to talk about the RG3 drama, but I will point you in the direction of this interesting article written by my former co-blogger.

Below are the Game Scripts data from week 12. I’ve highlighted the Vikings/Packers row in blue, since I know of no other way to shame both teams (you can move your cursor over that row to see it more clearly, not that I know why you would want to). [continue reading…]

Today’s insane statistic comes courtesy of RJ Bell: the difference between Denver and the #2 team in points per game is larger than the difference between the #2 and #31 teams. The Broncos are averaging 39.8 points per game this season, 11.6 points more than the (surprisingly second-ranked) Bears. And Chicago is averaging just 9.9 more points per game than the Jets, the #31 ranked scoring team.

That is, well, crazy. The record for points per game in a season is 38.8, set by the 1950 Rams. The 2007 Patriots are second at 36.8, and both of those teams scored slightly more points through ten games than the 2013 Broncos. So while Denver is on pace to break the scoring record, some regression to the mean over the final six games should be expected.

If the Broncos want to set the record for most points scored relative to the second highest scoring team in the league, Peyton Manning and company have some work to do. That mark is held by the ’41 Bears, who averaged 36.0 points per game, 12.5 more than the Packers that year. Second and third on that list are the ’07 Patriots (8.4) and ’50 Rams (8.3), so Denver has a realistic shot of setting the modern record.

I’ll be honest: as dominant as the Broncos offense has been, I’m a little surprised to see them so far ahead of the competition in points scored. After all, consider:

• The Eagles have just 19 fewer yards than the Broncos, and Nick Foles actually leads Manning in both passer rating and Adjusted Net Yards per Attempt;
• In PFR’s Expected Points Added, the Broncos offense is at 14.7 EPA-added per game, while the Saints offense is at 11.3. That’s a relatively small difference considering the fact that Denver has scored 12.1 more points per game than New Orleans.
• The Chargers have a higher completion percentage than the Broncos and six fewer turnovers, but have averaged 17 fewer points per game.
• The Packers are actually a hair ahead of Denver in yards per play (6.3531 to 6.3529), but have scored two fewer touchdowns per game.

So what’s going on? I’m perfectly fine with Denver being general run-of-the-mill dominant, but the team’s points scored numbers makes it seem like the Broncos might be the greatest offensive machine ever. I think I’ve identified the two reasons to explain the gap:

Red Zone success

Philadelphia has scored a touchdown just 46% of the time the Eagles made it into the red zone, which ranks 28th in the league. San Diego isn’t much better at 50% (22nd). The Saints are at 52.5% (20th), and the Packers are down at 30th at 43%. So some excellent offenses are really struggling in the red zone, which gives them disproportionately low points per game averages. Oh, and Denver? They’re at 79.1%, by far the highest rate in the league. It’s not unusual for a great offense to dominate in the red zone — the ’07 Pats were at 70% — but what is unusual is seeing the other top offenses struggle there.

I have red zone data going back to 1997, and the highest ever performance was set by Kansas City in 2003. The Trent Green–Priest Holmes–Tony Gonzalez Chiefs scored a touchdown on 77.8% of all red zone opportunities (42 out of 54), so the Broncos (34 out of 43) could break that record this year. More likely, though, is that the Broncos go from otherworldly in the red zone to just great, which would drop the team’s points per game average.

Number of Drives

The Broncos are averaging 2.85 points per drive, while the Saints are #2 at 2.46. That’s not a huge difference — the gap between #2 and #7 is slightly bigger. The difference, as you can deduce, is that the Broncos are averaging 13 drives per game while the Saints are at just 11.3 drives per game. Why is that? New Orleans’ average drive takes 2:56 minutes, the third-longest in the league (and San Diego is #1 at 3:13), while the Broncos are in the bottom five at 2:17 (the Eagles are last at 2:02). That Chip Kelly edge is erased, though, because Philadelphia’s opponents average 2:48 per drive, the third highest rate in the league. Denver’s opponents take just 2:18 per drive, the third lowest (just a second ahead of Detroit and eight seconds longer than Kansas City).

The Broncos defense is not great, but it does rank 6th in completion percentage allowed. Combine that with the fact that Denver ranks 4th in percentage of opponent plays that are passes, and incomplete passes occur on 25% of all plays run by Broncos opponents, the second-highest rate in the league behind Kansas City. That’s not surprising for a team with such a high Game Script, but it does stop the clock from running for long stretches, which gives Denver’s offense more possessions The Chargers are 28th in this statistic (18%), which is one reason why San Diego is dead last in offensive drives (10.2 per game).

But there’s another reason why Broncos’ opponents tend to have short drives: Denver leads the league in 20+ yard plays allowed at 54. As a result, teams don’t end up with many clock-chewing drives against Denver: opponents tend to gain big yards quickly or throw incomplete passes. That increases the number of drives for the Broncos, which (one could argue) inflates the success of the team’s offense. It’s all relative, of course — Denver is still #1 in points per drive by a wide margin — but it’s worth recognizing that Denver has scored 75% more points per game than an average of the other 31 teams, but “just” 62% more on a per-drive basis. That accounts for about 3 points per game. Add in the insane success in the red zone, and the lack of success there by the other top teams, and you have the reasons for the crazy stat at the top of today’s post.

Manning Record Watch Update

After six games, I analyzed how likely Manning was to break the single-season touchdown record. At the time, he had 22 touchdowns, and the formula projected him to throw 2.99 TDs/G the rest of the way to finish with 52 touchdowns, narrowly breaking Tom Brady’s record.

Now? Manning has 34 touchdowns, as his pace has only slightly declined. What does that mean? To calculate Manning’s odds using Bayes Theorem we need to know four things:

1) His Bayesian prior mean (i.e., his historical average): 2.38, as this number wouldn’t change from the original post.

2) His Bayesian prior variance (the variance surrounding his historical average): Again, no change here, so we use 0.0986.

3) His observed mean: Instead of 3.667, we will use 3.4.

4) His observed variance: This one involves just a little bit of work. What I suggested we do last time is calculate the number of passing touchdowns per game Manning averaged in the first six (now ten) games of each season since 2000, along with his average over the rest of the season (then, 8-10 games, now, 4-6 games). Then we take the difference of the variances of each column, as we did in step two.

Manning’s variance over the rest of the season is 0.3052 TDs/G, while his variance through ten games is 0.2214; the differential there is 0.0838, which is the variance of our current mean.

Once you have your number for these four variables, then you substitute those numbers into this equation:

Result_mean = [(prior_mean/prior_variance)+(observed_mean/observed_variance)]/[(1/prior_variance)+(1/observed_variance)]

Or, using our numbers:

[(2.38 /0.0986) + (3.4 / 0.0838)] / [(1/0.0986) + (1/0.0838)]

which becomes

[24.14 + 40.57] / (22.08) = 2.93

After averaging 3.667 TDs/G over 6 games, we projected Manning to average 2.99 TDs/G the rest of the year. Since he averaged “only” 3 touchdowns per game over his next four games, we downgrade him from 2.99 to 2.93. Of course, we already had a significant regression factored into his future projection — we dropped him by 0.67 TDs/game from his average, which is the point of using Bayes Theorem. So while he’s at “only” 3.4 TDs/G on the season after 10 games, since he’s played at that level for longer, he only loses about half a touchdown per game over his projection the rest of the way.

That gives Manning 17-18 touchdowns, which puts him at a season-ending projection of 51-52 touchdowns. He’s still more likely than not to break the record, although obviously this analysis ignores lots of elements like strength of schedule. And with a visit to Kansas City and a game against the Titans (who have allowed a league-low 7 touchdowns through the air), perhaps he’s actually an underdog to even tie Brady at 50.

Last week brought us the most lopsided game of the year. The games were more competitive this week, with the largest Game Script belonging to Tampa Bay (yes, Tampa Bay) at 14.0. The Philadelphia-Washington game provides a good example of the information conveyed — and not conveyed — by Game Scripts. Philadelphia won by 8 points, but that would be misleading if you thought it was a close game throughout: the Eagles held an average lead of 12.8 points. On the other hand, Game Scripts don’t necessarily tell you how lopsided the game was: Washington had the ball with a chance to tie, at the Eagles’ 27-yard line, with 54 seconds remaining. The Eagles came away with a very low Moral Margin of Victory (5.8) but a high Game Script, with neither bit of information being right or wrong. On one hand, Philadelphia’s Win Probability was over 85% for the final 2.5 quarters, but it was also a game where Washington was not really out of it until the final seconds. I prefer a toolbox with lots of different tools over trying to find one do-it-all device.

Here are the week 11 Game Scripts data:

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The Jets lost by 40 points to the Bengals in Cincinnati; adjust for home field, and that is still a 37-point adjusted margin of victory, the best single game for the Bengals this year.

New York lost by 23 in Buffalo this weekend; that 20-point adjusted MOV was the best single game for the Bills this year.

Back in week four, the Jets lost by 25 in Tennessee, and as you can probably guess, that is the best single game for the Titans this year.

And in week six, at home against the Steelers, New York lost by 13, and that 16-point adjusted MOV was the top performance for Pittsburgh this year.

That’s pretty bad, of course. Four different teams had their best games of the season against the Jets. The only team that’s been worse is the Jaguars, who have seen five different opponents (San Francisco, San Diego, Kansas City, Indianapolis, and Arizona) post their best games against Jacksonville. But the Jets were close to matching the Jaguars: Tampa Bay’s best game of the year came on Sunday against Atlanta, making the Bucs’ second best performance in 2013 the game against the Jets in week 1. ^[1]How can the 2-8 Bucs have had only one game better than their loss to the Jets? Because Tampa Bay lost in New York by 1, which is an adjusted MOV of +2, while their home win against Miami of 3 points … Continue reading
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Arizona is one of many teams in the NFL employing a running back by committee philosophy, but no team — now, or at any point in modern history — allocated time quite like the Cardinals. Through nine weeks, Rashard Mendenhall has 105 rushes for 323 yards, giving him a miniscule 3.1 yards per carry average. Mendenhall arrived in the desert this offseason, as part of a reunion with new Cardinals head coach Bruce Arians. The duo worked together for three years in Pittsburgh, where Mendenhall rushed for 3,309 yards and 29 touchdowns and averaged 4.2 yards per carry. But Mendenhall hasn’t been close to the best back added this offseason, as Clemson’s Andre Ellington — a sixth round of the 2013 Draft — has 388 yards this year on 54 carries. Thanks to his spectacular 7.2 yards per carry average, he has outgained Mendenhall despite seeing roughly half as many carries.

How crazy is it for one back in a committee to average more than four more yards per carry than the other back? I ran the following query for every team since 1970:

• First, I noted the two running backs who recorded the most carries for each team
• Next, I eliminated all running back pairs where the lead back had over 150 more carries than the backup.
• I also eliminated all pairings where the lead back was a lead back in name only due to injury to the starter (otherwise, years where Maurice Jones-Drew and Darren McFadden ranked second on their team in carries would be inappropriately included). To do that, I deleted sets where the “lead” back — defined as the back with the most carries — averaged fewer carries per game than the second running back.

After running through those criteria, the table below shows all situations where the backup averaged at least one more yard per rush than the lead back. As always, the table is fully searchable and sortable. It is currently sorted by the difference between the YPC average of the backup and the starter, but you can sort by year to bring the recent instances to the top.
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We have a new leader in the clubhouse for most lopsided game of the season. The St. Louis Rams pulled off one of the biggest blowouts by a heavy underdog in league history in week ten, defeating the Colts in Indianapolis, 38-8. In the process, the Rams also held an average lead of 23.2 points, the largest Game Script score of the season.

Indianapolis kept it close early, and the only first quarter score came via the St. Louis defense. On that play, Robert Quinn — who with 12 sacks through 10 games, is a legitimate Defensive Player of the Year candidate — stripsacked Andrew Luck, and Chris Long picked up the fumble and raced 45 yards for the touchdown. Incredibly, the Colts are lucky this game wasn’t even more one-sided. Late in the first quarter, Kellen Clemens and Zac Stacy botched the exchange on a handoff on the goal line with the Rams looking to go up 14-0, and Indianapolis recovered to end the scoring threat. That didn’t set back the Rams for long, however, as St. Louis scored 21 points in the third quarter to take a 28-0 lead into the locker room. Tavon Austin — who had a day for the ages — scored in the third quarter to give St. Louis a 35-0 lead early in the third quarter, effectively ending any hopes for another Luck comeback.

Three teams lost with positive Game Scripts in week 10, but unlike in week nine, there were no big comebacks, as all three games were back-and-forth affairs.  The Panthers won with the worst Game Script of the week, holding an average deficit of 2.6 points against the 49ers. San Francisco jumped out to a 9-0 early, but Carolina eventually won 10-9 on a late field goal. Since I wrote about how the 3-9 Panthers were about to turn things around, Carolina has gone 9-3. In an unrelated note, I recently injured my hand on my back.

The table below shows the Game Scripts data from week 10:
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The Tavon Austin breakout game is here. First, the #8 pick in the 2013 draft returned a punt off a bounce 98 yards for a second quarter touchdown. A few minutes later, Kellen Clemens hit him for a 57-yard touchdown pass. With St. Louis up 28-0 in the third quarter, Austin caught an 81-yard touchdown.

The third score made him just the 8th player in NFL history with three touchdowns of 50+ yards in the same game, joining Chris Johnson, Qadry Ismail, Randy Moss, Freddie Solomon, Gale Sayers, Billy Cannon, and Raymond Berry. That also means Austin has 236 yards of touchdowns today, the most of any player since 1970.

In fact, that’s the second most in NFL history. The table below shows all 78 players from 1940 to 2012 who recorded at least 160 yards worth of touchdowns in a single game.
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Week eight brough us the two biggest blowouts of the season; in week nine, we saw the third most dominant win of the year and the biggest comeback of the season.

The first game involved Chip Kelly’s blitzkrieg offense. Nick Foles threw for seven touchdowns against the Raiders in one of the most lopsided (and surprising) games of the season. The Eagles held a 28-13 lead at halftime and 49-13 at the end of the third quarter; over the course of the game, Philadelphia held an average lead of 21.3 points.

At the other end of the spectrum, we have yet another Andrew Luck comeback victory. The Texans led 14-0 after the first quarter and 21-3 at halftime; on average, Houston held an 11-point lead throughout the game, but a 15-0 edge in the fourth quarter gave Indianapolis the win. That’s the highest Game Script of any team to lose a game in 2012, replacing…. Houston’s victory over the Chargers on opening week, when the Texans had a Game Script of -7.7 points.

In addition to the Colts-Texans game, the crazy comeback in Seattle now gives each of the Seahawks and the Bucs two of the five biggest comebacks/giveaways of the year. In week four, Seattle won in overtime against Houston despite trailing by, on average, 7.7 points in regulation. That was probably an even more crazy game than the win against Tampa Bay, where Seattle came back from a 21-0 deficit but only outscored the Bucs by 10 points in the fourth quarter. As for Tampa Bay, this was the fourth game of the season where the team lost despite having a 95% win probability at some point in the game. This was also the second time the Bucs lost a game with a Game Script of over 6.0 points, joining the come-from-ahead loss to Arizona.

Without further ado, the table below shows the week 9 Game Scripts data:
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It’s Friday, so I thought it might be fun to play around with some stats.  Net Yards per Attempt is probably my favorite predictive statistic to measure quarterbacks, but there are some problems with even that metric.  One issue is that Net Yards per Attempt — which is simply yards per attempt but includes sacks data — is pretty sensitive to outliers.  A quarterback who consistently pieces together short passes with a high completion percentage can be pretty valuable, and may end up undervalued compared to some mad bombers.

There are a couple of ways to deal with this.  One is to use a different measure of central tendency than the average production per dropback; for example, we could look at the median yards gained per pass attempt (including sacks).  Another is to measure the standard deviation on all of a quarterback’s pass plays. I thought I’d compile the data on both and see what you guys found interesting.

No matter how you splice the data, Philip Rivers looks outstanding. After a couple of down years, Rivers is experiencing a career revival under new head coach Mike McCoy.  The Chargers no longer rely on a downfield passing attack (and with Vincent Jackson gone and Malcom Floyd on IR, that may be more out of necessity than design), but Rivers has found a Darren Sproles replacement in Danny Woodhead.  As a result, Rivers has completed an incredible 73.9% of his passes this season.

Rivers ranks 27th in average length of pass (or average depth of target), reflecting the shorter passing attack, but Tony Romo, Chad Henne, Matt Schaub, Sam Bradford, Matt Ryan, and Alex Smith have lower average distances and worse completion percentages (among other stats).  The Chargers star has also been great at avoiding sacks: he’s completing passes on 70.8% of his dropbacks this year, a stat I’m calling Adjusted completion percentage (A_Cmp% in the table). In the table below, I’ve listed each quarterback’s number of attempts and sacks, his Adjusted completion percentage, his Net Yards per Attempt, and his standard deviation on pass plays. Since standard deviation would be biased towards quarterbacks with higher averages, I’ve sorted the table by the Ratio of each quarterback’s standard deviation to his NY/A average. Finally, I’ve also displayed the median number of yards gained for each quarterback on each dropback. All data excludes last night’s Carolina-Tampa Bay game.
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After a week six in which no team won with a negative Game Script, only the Jets won with a negative Game Script in week seven, and that was in overtime. Only two quarterbacks led fourth-quarter comebacks this week: Robert Griffin III and Thaddeus Lewis. Three more quarterbacks — Andy Dalton, Ben Roethlisberger, and Geno Smith — led game-winning drives in tie games, but in general, it was a pretty uneventful week for comebacks. Overall, it’s been a quit few weeks in the NFL when it comes to fourth quarter craziness: the largest deficit entering the 4th quarter by a winning team in weeks 5, 6, and 7 was just five points.

Just like last week, Alex Smith and the Chiefs pulled out a late win in a game with a near-even Game Script. None of the 13 other games this week (excluding Jets/Patriots, Chiefs/Texans, and noting that the Saints and Raiders had byes) had a Game Script of fewer than 2 points.
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In week 1, the Seahawks beat the Panthers. In week two, the Panthers lost to the Bills.

In week 2, Seattle crushed San Francisco. In week three, the 49ers lost to Indianapolis.

In week 3, the Seahawks suffocated the Jaguars; in week four, Jacksonville lost to the Colts.

In week 4, Seattle won at Houston; in week five, Houston was embarrassed by the 49ers.

In week 5, the Seahawks played a tight game with Andrew Luck’s Colts, but lost 34-28. In week six, Indianapolis was upset in San Diego.

In week 6, Seattle defeated the Titans; in week seven, Tennessee lost against San Francisco.

As a result, NFL teams are 0-6 this year in games played the week after facing Seattle. Surely this is because of the physical style employed by the Seahawks, and not a quirky stat aided by the fact that four of those games came against the 49ers and Colts. Carolina nearly ended this streak before it began, but was too bruised up to prevent EJ Manuel from finding Steve Johnson alone in the end zone with six seconds left, giving the Bills a 24-23 win. And while at first glance, the Colts loss in San Diego is your classic let-down/look-ahead sandwich (after beating Seattle in week 5 and getting ready for the Peyton Manning return in week 7), the truth is, Indianapolis was just incapable of mustering the physical temerity necessary to beat the rugged Chargers.

Seattle beat the Cardinals in week seven, but the circumstances ensure that this streak will continue to be discussed. If Arizona loses to Atlanta in week eight, that would run the record to 0-7; if the Cardinals win, well, they had an extra three days of rest, so teams would still be winless in games on normal rest after playing the punishing Seahawks.

The “record” for worst record by teams after playing Team X the prior week is 1-13, with the ’97 Packers being Team X. The lone win came when the Vikings, after losing in Green Bay in week four, rebounded to overcome Ty Detmer and the Eagles in week five. You might think there’s something legitimate here — after all, the Packers had Brett Favre, Reggie White, and were the defending Super Bowl champions. Perhaps teams were so “up” to play Green Bay that they were very prone to let downs the following weeks. I’m not convinced.
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Peyton Manning is not a 51 touchdown per-season quarterback, but that doesn’t mean he won’t average the necessary 2.9 touchdowns per game over his final ten games this season to break Tom Brady’s touchdown record. Before the season, Footballguys.com projected Manning as a 2.38 passing touchdown per game player.  And while he has looked unstoppable thus far, with 22 touchdown throws in six games, Manning has been known to have great spurts before, too.  All quarterbacks have hot and cold streaks, Manning included.  From 2003 to 2012, after removing games where he sat late in the season, Manning averaged 2.17 passing touchdowns per game with a standard deviation of 1.31 touchdowns. ^[1]That was after removing week 17 of the ’04, ’05, ’07, ’08, and ’09 seasons, and week 16 of the ’05 and ’09 seasons, when Manning left early. Why did I pick the last ten years? I don’t … Continue reading  In the ’04 season, Manning threw at least 20 touchdowns in each of his trailing six game stretches from week 7 all the way through week 15, with a peak of 27 touchdowns in his prior six games in weeks 11 and 12.  Manning also threw 19 touchdowns in his last two full regular season games of 2010 and his first four games of 2011.  White-hot streaks happen, even to the best players, so we shouldn’t just assume that he’s now a 3.67 touchdown per game player.

On the other hand, it would be naive to assume that we should ignore the first six weeks of the season and continue to project Manning as a 2.38 touchdown per game player for the rest of the year.  The question becomes, how much do we base projection over the final 10 games on his preseason projection and how much do we base it on his 2013 results? In Part I, after four games, a regression model produced a projection of 2.56 touchdowns per game the rest of the year. But the problem with a regression analysis is that Manning is an extreme outlier among NFL quarterbacks; to project Manning, it would be best if we could limit ourselves to just quarterbacks named Manning Peyton Manning.

Before continuing, I want to give a special thanks to Danny Tuccitto, without whom this article wouldn’t be possible. Danny provided this great link and also spent a lot of time walking me through the process. To the extent I’ve mucked it up here, you should blame the student, not the teacher. But after walking through some models online, I realized that the best explanation about how to use Bayes Theorem for these purposes was on a sweet site called FootballPerspective.com. And the smartest person on that website had already laid out the blueprint.

In the comments to one of his great posts, Neil explained that we can calculate Manning’s odds using Bayes Theorem if we know four things:

His Bayesian prior mean (i.e., his historical average):

His Bayesian prior variance (the variance surrounding his historical average):

His observed mean:

His observed variance:

Let’s go through each of these:

1) Manning’s Bayesian prior mean: this is simply what we expected out of Manning before the season. I will use 2.38, since Footballguys is the gold standard of football projections in my admittedly biased opinion. But you can use any number you like, as I’ll provide the full formula at the end.
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Last night, the Seattle Seahawks defeated the Arizona Cardinals, 34-22. At the start of the fourth quarter, the Cardinals trailed 31-13, and faced 4th-and-goal from the Seattle four-yard line. Bruce Arians elected to kick a 22-yard field goal in that situation, which cut the lead from 18 to 15 points. On opening night, John Harbaugh made a similar decision trailing by 18 on 4th-and-4, although there was only 5:33 remaining (making it even less acceptable) and the ball was not at the four, but at the twelve-yard line (making it more acceptable).

Kicking a field goal down by 18 this late in the game is a poor decision unless it’s fourth and impossible. Since 1940, do you know how many teams have kicked a field goal, when trailing by 18 or more points in the second half, and went on to win the game? THREE. The “They Are Who We Thought They Were” game, when Chicago kicked a 23-yard field goal down 20-0 midway through the third quarter. After that field goal, Mike Brown, Charles Tillman, and Devin Hester scored touchdowns for the Bears, which doesn’t seem like the best model to follow in the future since none of those players played offense.

In 1998, the Rams kicked a field goal in Buffalo to make it 28-13 in the third quarter, ultimately winning 34-33 on a touchdown run in the final seconds. And in 1996, in Bill Parcells’ return to the Meadowlands to face the Giants, Adam Vinatieri kicked a third-quarter field goal down 22-0, and then Terry Glenn, Dave Meggett (on a punt return), and Ben Coates scored fourth quarter touchdowns.

You know what hasn’t happened? A team kicking a field goal, down by 18 or more points in the fourth quarter, and going on to win the game. Including the two teams this year, 117 teams since 1940 have kicked a fourth quarter field goal when trailing by more than 17 points, and none of them have ever won. I know, trailing by 18, it’s so comforting to kick a field goal and think “hey look, all we need to do is stop them, score a touchdown, stop them again, score a touchdown, convert a two-point conversion, and then win in overtime.” But that’s never, ever happened before.
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I’ve posted the Game Scripts data following every week this season, but week six was the first week that no team won with a negative Game Script. That includes New England: even though Tom Brady led a late comeback, finding Kenbrell Thompkins in the back of the end zone to pull out a last-second win, the Patriots posted a Game Script score of +3.6. New England led 17-7 at halftime and for most of the second half; in fact, the Saints only held the lead for about seven minutes of game time. The third closest Game Script in week six comes courtesy of the Kansas City-Oakland matchup, which might surprise any of you who just saw the final 24-7 score. Of course, quirky games like that one is one of the reasons I came up with concept of Game Scripts.

The first score of the game was Terrelle Pryor’s 39-yard touchdown pass to Denarius Moore, with 8:47 left in the second quarter. This means for the first 21.2 minutes, the game was tied. Kansas City answered with a Jamaal Charles touchdown run with 1:12 left in the half, so the Raiders held a 7-point lead for 7.6 minutes. The Chiefs didn’t take their first lead of the game until Charles scored again with 2:07 left in the third, which means the game was tied for another 14.1 minutes. That score held for nearly 15 full minutes: Ryan Succop hit a short field goal with 2:13 left in the game. Pryor then threw a pick six with 1:45 left and the team down by 10, providing the final points in the 24-7 Kansas City victory.

All told, however, the game was tied for 35.3 minutes and the Raiders had a 7-point lead for 7.6 minutes, while the Chiefs led by 7 for 14.9 minutes, by 10 for 0.5 minutes, and by 17 for 1.7 minutes. That’s why the Game Script was just +1.4 for Kansas City, which is a much better reflection of how the game unfolded than the 24-7 final score. The table below shows the Game Scripts data for each contest in week six:
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