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As a general rule, shorter and heavier guys tend to dominate the bench press. When I looked at this last year, the best-fit formula to predict the number of reps of 225 a prospect could achieve was:

Expected BP = 30.0 – 0.560 * Height + .1275 * Weight

What does that mean? All else being equal, if Prospect A is 7 inches shorter than Prospect B, we would expect Prospect B to produce about 4 more reps than Prospect A. And for every eight pounds of body weight a player has, we would expect one additional rep out of that prospect.

Which brings us to Clemson outside linebacker Vic Beasley. Standing 6’3 and “only” 246 pounds, Beasley doesn’t exactly fit the profile of a bench pressing machine. But in Indianapolis, he pumped out an incredible 35 reps, tied for the third most at the combine (no other player under 300 pounds had even 33 reps). Given his height and weight, the formula above would project Beasley for 19.4 reps, which means he exceeded expectations by a whopping 15.6 reps. No other player came close to exceeding expectations to such a significant degree.

The table below shows the results of all players who participated in the bench press at the combine.  All data comes courtesy of NFLSavant.com.

[click to continue…]

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One of the biggest headlines from the combine were the jumps from Byron Jones, a cornerback from Connecticut. Most impressive was his broad jump, which was not only 8 inches better than everyone else in Indianapolis, but also 8 inches better than anyone else in combine history. More on his broad jump in a future post, but Jones’ 44.5″ vertical too shabby, either: it was the best since 2009, when Ohio State and eventual Chiefs safety Donald Washington jumped 45 inches (a feat later matched by one other player at this year’s combine).

But Jones didn’t have the most impressive vertical at the combine, because at 199 pounds, there’s an expectation that he would do fairly well in that drill.  Given his weight, we would expect Jones to jump about 35.5 inches, based on the best-fit formula derived here, and defined below:

Expected VJ = 48.34 – 0.0646 * Weight

One way to think of that formula is that for every 15.5 pounds of player weight, the expectation on the vertical is one fewer inch.  So at 230 pounds, the expectation would be 33.5 inches.  Which brings us to Alvin “Bud” Dupree, whom we lauded yesterday for the top performance in the 40-yard dash.  At 269 pounds, he would be expected to jump roughly 31.0 inches.  Instead, the Kentucky edge rusher jumped a whopping 42.0 inches — or 11.0 inches over expectation — making it the best weight-adjusted performance of any player in Indianapolis.

Below are the results of the Vertical Jump for every player at the combine. All data comes courtesy of NFLSavant.com. [click to continue…]

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Forsett, praising his Excess Yards

Forsett, praising his Excess Yards

In the comments to the Greatest Running Back of All-Time Post — and reminder, entries are due by midnight Thursday — a debate broke out between sn0mm1s and Jay Beck, among others, about how to value running backs generally, and specifically, the value of long runs.

One idea I’ve had before is that the yards a player gains after picking up a first down are similar to the yards picked up by a returner. For example, when a punt returner gains 10 yards instead of 5, that’s obviously worth 5 additional yards of field position to his team. But it’s not as valuable as 5 yards on 3rd-and-5; the return yards were gained outside of the context of the down-and-distance/series-of-downs nature of the game.

Does this mean that all yards gained after a first down are exactly as valuable as return yards? I’ll leave up that to the reader to decide. But I do think one thing is noncontroversial: Lamar Miller ran for a 97-yard touchdown on 1st-and-10 against the Jets in week 17, the most valuable 10 yards during that run were the first ten. The last 87 yards were slightly less valuable (on a per-yard basis), or akin to the yards a player would gain on a return.

At this point, you might be thinking, “Who cares?” And that’s a very good question: after all, return yards are valuable. And the last 87 yards of Miller’s run were certainly more valuable to Miami than the first 10 yards, even if that may not be true on a per-yard basis.

But I thought it would be interesting to look at all running plays this season, and break them into two categories: yards that came after a first down had already been achieved, and all other rushing yards. So a 10-yard run on 3rd-and-5 has five yards in each bucket; if it was 3rd-and-1, 9 yards get assigned to the “excess yards” bucket, and 1 yard to the “going towards picking up a first down” bucket. [click to continue…]

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Quarterbacks and Passing Milestones

The first 3,000 yard passer came in 1960, when Johnny Unitas reached such feat in the NFL and Jack Kemp and Frank Tripucka did so in the AFL. Joe Namath became the first 4,000-yard passer seven years later, and Dan Marino in 1984 was the first to reach 5,000 yards.

The graph below shows the number of 3,000 yard passers in blue, 4,000-yard passers in red, and 5,000-yard passers in green in each season since 1960.  As you can see — and no doubt already knew — passing productivity is on the rise:

YardPassers [click to continue…]

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An Early Look at 2015 Vegas Win Totals

Like last year, CG Technology (formerly Cantor Gaming) is the first Las Vegas book to release win totals. For your convenience, I have produced them below, and sorted the list by the difference between 2015 Vegas wins and 2014 wins. [click to continue…]

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Last year, I wrote a post on the plays that had the biggest impact on the eventual Super Bowl champion. These were the plays that affected the Super Bowl win probability by the biggest amount among teams that did not win the title. At the time, the Buffalo Bills were on the short end of the most influential play in the Super Bowl era. When Frank Reich put the ball down for Scott Norwood, I estimated that the Bills had a 45% chance on winning the Super Bowl.1 After the kick went wide right, the Bills’ win probability fell to zero. The 45 percentage point fall was the biggest change for a non-champion of any play in the Super Bowl era. Over 48 years, a bunch of plays fell in that range, but no team could point to a single play as having lowered its championship chances by so large an amount.

A couple weeks ago, that long-held record got broken kind of like Michael Johnson broke the 200-meter record in the Atlanta Olympics. Malcolm Butler’s pick obliterated the old mark. My estimate has the Butler interception as increasing the Patriots’ chances of winning by 0.87. There is no doubt that what some have called the Immaculate Interception is on an island by itself as the most influential play in NFL history.

To get that change in win probability from Butler’s play, I am going to assume that the Seahawks would have run on third and fourth down. I am going to give a run from the one a 60% chance of working. That might seem high, but the Patriots were the worst team in football in stuffing the run in important short-yardage situations either on third or fourth down, or down by the goal line. And their limited success mostly came against terrible running teams. It is not a huge sample, but against teams outside the worst quarter of rushing teams by DVOA, the Patriots had allowed opponents to convert 16 of 17 times with two yards or less to go for a first down or touchdown. If we add the playoffs, they actually had three more stops against good running teams (Baltimore and Seattle), albeit in games where the opponent had a good amount of success on the ground.2 With Seattle being the best rushing team in football by a mile and the Patriots being at best not great in run defense in that situation, it seems hard to think that Seattle had anything less than a 0.60 chance of scoring on a run. [click to continue…]

  1. Recent research by Chase suggests something similar. []
  2. Note that the stop against Baltimore should not even count. In an otherwise great game for Gary Kubiak, he called for a reverse to Michael Campanaro on third-and-1 in the second quarter. The run was stopped for a loss. The Patriots basically could not stop Justin Forsett, making the reverse call very unnecessary. []
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2014 Fumble Recovery Data

There are few statistics more random in all of sports than fumble recoveries. When a football is on the ground, it’s not the case that better teams are more likely to fall on the ball than bad teams: in the NFL, recovering fumbles is nearly all luck and little skill. This is a fact widely accepted by all statisticians, and I also ran a study which confirmed such intuition just last year.

The 49ers fumbled 18 times in 2014; San Francisco also forced 18 fumbles. When the 49ers fumbled, they managed to recover (or have the ball go harmlessly out of bounds) just six fumbles; when they forced a fumble, they… also only recovered just six times! So of the 36 times the ball hit the ground, San Francisco recovered 12 times, and lost it 24 times. [click to continue…]

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Brown was number one in 2014

Brown was number one in 2014

On Monday, I noted that Pittsburgh wide receiver Antonio Brown led the NFL in True Receiving Yards for the second straight season. He also, by the slimmest of margins, your leader in Adjusted Catch Yards per Attempt, too.

On October 1st, I looked at the leaders in Adjusted Catch Yards per Team Pass Attempt; at the time, Jordy Nelson had a big lead on the rest of the NFL, although Brown was in second place. You can read the fine details of the system in that post, but the short version is:

  • Begin with each player’s number of receiving yards. Add 9 yards for every first down gained, other than first downs that resulted in touchdowns, to which we add 20 yards. For Brown, this gives him 2,624 Adjusted Catch Yards (1,698 receiving yards, 87 first downs, 13 touchdowns).
  • Divide that number by the number of team pass attempts, including sacks, by that player’s offense. Pittsburgh recorded 645 dropbacks in 2014, which means Brown averaged 4.07 ACY/TmAtt. Jordy Nelson (1519/71/13) had 2,301 Adjusted Catch Yards and the Packers had 566 team pass attempts. That translates to .. 4.07 ACY/TmAtt, too. But go to three decimal places, and Brown (4.068 to 4.065) becomes your winner.
  • I have also included a column for Adjusted Catch Yards per Estimated Team Dropback; here, we use the same formula, but multiply the numerator by 16, and the denominator by the number of games played by the receiver. Let’s use Odell Beckham as an example. The Giants wide receiver finished with 1,959 ACY (1305/58/12) and New York had 637 dropbacks, giving Beckham 3.08 ACY/TmAtt. But if we adjust for the fact that Beckham missed four games, he gets credited with 4.10 ACY/EstTmAtt, which is the highest rate in the NFL.

The table below shows the top 50 receivers in ACY/TmAtt: [click to continue…]

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Additional Thoughts on Turnover Rates

On Sunday, I looked at turnover rates for every year in the NFL since the merger. Today, I want to re-examine turnover data but in a different light. In 2014, the average team committed 23.7 turnovers. As you might suspect, there’s a strong relationship between turnovers and winning percentage, with a correlation coefficient of -0.56. This says nothing about causation, of course, and the causal arrow does in fact run in both directions (committed fewer turnovers leads to more wins, and winning in games leads to fewer turnovers).

Here’s another way to think about the relationship between winning percentage and turnovers. The Patriots were responsible for 4.7% of all wins this year and committed 13 turnovers; as a result, when calculating a weighted league average turnover total, I made New England’s 13 turnovers worth 4.7% of that total. Meanwhile, the Buccaneers and their 33 turnovers were only worth 0.8% of the weighted league average turnover total, since Tampa Bay was responsible for just 0.8% of all wins.

Using this methodology, the weighted league average turnover total in the NFL was 22.5 per team, or 95% of the unweighted league average. I used that same methodology to calculate the percentage of “weighted league average turnover total” to “unweighted league average turnover total” for each year since 1960. In the graph below, the blue line represents the NFL ratio, while the red line represents the AFL ratio. [click to continue…]

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Brown was number one in 2014

Brown was number one in 2014

You may recall that in 2013, Antonio Brown led the NFL in True Receiving Yards, which felt controversial at the time. Remember, Calvin Johnson and Josh Gordon were the runaway choices by the Associated Press as the top receivers in the NFL; in addition, A.J. Green also received more votes, and Demaryius Thomas finished with as many votes as Brown.

Well, Brown has done it again, but I doubt it will surprise many people this time around. Brown led the NFL in receptions and receiving yards, and received 49 of 50 first-team All-Pro votes. Regular readers are familiar with the concept of True Receiving Yards, but let’s walk through the system using Brown and Dez Bryant, who jumps from 8th in receiving yards to 4th in True Receiving Yards. [click to continue…]

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Bettis ran for only five yards on this play

Bettis ran for only five yards on this play

Congrats to the newest members of the Pro Football Hall of Fame. You can read my thoughts on the candidates here; while this class is not exactly the one I would have picked, Jerome Bettis, Tim Brown, Charles Haley, Junior Seau, Will Shields, and Mick Tingelhoff were all outstanding players. In addition, Bill Polian and Ron Wolf were the inaugural selections for the Contributors spots, so congratulations to them as well.

The Bettis candidacy is an interesting one. Many want to focus on his underwhelming 3.9 career yards per carry average. But as I have written many times, I am not keen on putting much weight on YPC as a statistic. Brian Burke has also written about how coaches don’t view running backs in terms of yards per carry, but rather by success rate (which correlates poorly with yards per carry). Danny Tuccitto calls yards per carry essentially “a bunkum stat.” [click to continue…]

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Rams/Raiders was the Least-Conforming Game of 2014

50/50 chance these guys show up

50/50 chance these guys show up

They’re baaack! In November 2013, the St. Louis Rams blew out the visiting Indianapolis Colts in what was the least-conforming game of the 2013 season.

In November 2014, the Rams blew out the visiting Oakland Raiders in what was the least-conforming game of 2014 (although for my money, the runner-up game between the Titans and Chiefs was probably still the strangest result of the year). The Rams finished the season with a -0.8 SRS rating, eight points better than the 2014 Raiders SRS rating of -8.8. Given that the game was in St. Louis, we would have expected the Rams to win by around 11 points.

In reality, the Rams shut out the Raiders, 52-0. That gave St. Louis a single-game SRS score of 40.2, meaning the Rams were 40.2 points better than average that day.1 Since St. Louis won by 52 when the Rams were expected to win by 11, they exceeded expectations by a whopping 41 points.

That 41-point total — the amount by which St. Louis exceeded expectations — was the highest of any game in 2014. The table below lists all relevant information from every regular season game this year, with the “diff” column showing the difference between the expected and actual margins of victory. I have also included a link to the boxscore of each game embedded in the “Wk” cell. Note that the table, by default, lists only the top 10 games, but you can view more using either the dropdown box, the search bar, or the previous/next buttons at the bottom of the table. [click to continue…]

  1. Technically, this bit of information is superfluous for the main point of this post, but I always err on the side of including interesting data. []
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Quite the clickbait title, I know. But given where this post is going, I thought precision was more important than anything else.

Over the last three seasons, Seattle has allowed 15.2 points per game. That’s really, really good. How good?

pa2012

There are flaws with using points allowed as a measure of defensive play, of course. Seattle is known for its long drives on offense, which limits the number of possessions an opponent might have. And the Seahawks offense generally puts the team’s defense in pretty good situations. Using points allowed per drive might be preferable, or using DVOA, or EPA per drive, or a host of other metrics. And adjusting these results for strength of schedule (or, at least, removing non-offensive scores) would make sense, too.

But hey, it’s Friday, and I wanted to keep things relatively simple.1 Points allowed is a number we can all understand. Given our era of inflating offenses, it’s quite possible that Seattle’s 15.2 points per game average doesn’t stand out as particularly impressive to you. After all, the ’76 Steelers once allowed 28 points over a nine-game stretch! But consider that since 2012, the NFL average has been 22.6 points per game, which means the Seahawks have allowed 7.4 fewer points per game than the average defense.

How good is that? [click to continue…]

  1. In about ten minutes, we can all have a good laugh at this line. []
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Guest Post: Marginal YAC, 2014 in Review

Adam Steele is back to discuss Marginal YAC, this time in the context of the 2014 season. You can view all of Adam’s posts here.



Manning is more of a downfield thrower than you think

Manning is more of a downfield thrower than you think

Back in September, I posted a three part series introducing Marginal Air Yards and Marginal YAC. Today, I’m going to update the numbers for 2014 and analyze some interesting tidbits from the just completed season.1

League-wide passing efficiency reached an all-time high in 2014 with a collective 6.13 Adjusted Net Yards per Attempt average. However, this past season was also the most conservative passing season in NFL history; 2014 saw the highest completion rate ever (62.6%), the lowest interception rate ever (2.5%), and also the lowest air yards per completion rate ever (5.91 Air/C). Passing yards were comprised of 51.4% yards through the air and 48.6% yards after the catch, the most YAC-oriented season in history.2 This trend shows no sign of reversing itself, so expect more of the same in 2015.

Here are the 2014 Marginal Air Yards (mAir) and Marginal YAC (mYAC) for quarterbacks with at least 100 pass attempts. The 2014 leader in Marginal Air Yards is…Peyton Manning? Yes, the noodle-armed, duck-throwing, over-the-hill Peyton Manning averaged 4.54 Air Yards per pass Attempt; given that the average passer on this list averaged 3.70 Air Yards per pass Attempt, this means Manning averaged 0.84 Air Yards per Attempt over average. Over the course of his 597 attempts, this means Manning gets credited with 500 marginal Air Yards, the most of any quarterback in the NFL. [click to continue…]

  1. A big thanks to Chad Langager at sportingcharts.com for helping me compile this data. []
  2. Even though YAC data only goes back to 1992, I feel safe in using the phrase “all-time” with regard to YAC dependency. The offensive schemes of yesteryear emphasized downfield passing, which generated far less YAC than the short passing games of today. []
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Probably was picked off

Probably was picked off

I still can’t quite comprehend what happened. Leading 19-7 with less than three minutes remaining, Green Bay somehow lost the NFC Championship Game. It was the most remarkable comeback in conference championship game history since at least 2006, when Peyton Manning and the Colts came back from the dead against the Patriots.

But this game had the added element of Russell Wilson looking like he had no idea what he was doing out there. With four minutes remaining, Wilson had one of the ugliest stat lines in playoff history: he was 8/22 for 75 yards with no touchdowns, four interceptions, and four sacks for 24 yards. He was averaging -4.96 Adjusted Net Yards per Attempt. It was worse than Ryan Lindley against Carolina, a performance that would rival Kerry Collins in the Super Bowl against the Ravens for worst playoff passing performance ever.

Wilson’s stat line was straight out of a 1976 boxscore featuring a rookie quarterback against the Steelers. Yet, somehow, minutes later, the game would be in overtime. Wilson ended regulation with a still miserable stat line of 11/26 for 129 yards, with 0 touchdowns (to be fair, he did run one in), 4 interceptions, and 4 sacks for -24 yards. That translates to an ANY/A average (which gives a 45-yard penalty for interceptions, and a 20-yard bonus for touchdowns, while penalizing for sacks) of -2.50.

If the Seahawks returned the overtime kickoff for a touchdown, the game would have easily gone down as the worst performance by a playoff-winning quarterback in history. But in overtime, Wilson did his best work: first, he found Doug Baldwin for ten yards. Then, after taking a one-yard sack, he hit Baldwin on 3rd-and-7 for 35 yards. The next play, Wilson hit Jermaine Kearse for a 35-yard touchdown, and Seattle was headed back to the Super Bowl.

Wilson finished 14/29 for 209 yards, with 1 touchdown, 4 interceptions, and five sacks for -25 yards. That translates to an anemic ANY/A average of +0.71. How does that compare historically? I thought it would be worthwhile to compare the ANY/A average of every winning quarterback in a playoff game to the league average ANY/A that season. So, in 2014, the NFL averaged 6.13 Adjusted Net Yards per Attempt per pass. This means Wilson finished 5.42 ANY/A below average. And given that Wilson had 34 dropbacks, it means that Wilson produced -184 Adjusted Net Yards over average. As it turns out, that’s only the … third worst ever by a winning quarterback. [click to continue…]

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Brady was happy to have the game put in his arm on Saturday

Brady was happy to have the game put in his arm on Saturday

Every week during the season, I compile Game Scripts data, which measures the average points margin during every second of every game. Since most people don’t have a chance to watch every game, it’s helpful to have this information.

During the playoffs, most of us are watching each game, so we know what’s going on. But after two weeks, I thought it was still worthwhile to check in on the numbers. There have been two big comebacks during the playoffs: the Cowboys against the Lions during the wildcard round, and the Patriots against the Ravens last weekend.

The Dallas comeback against Detroit would rank as the 4th biggest comeback of 2014, or the 4th worst Game Script produced by a winning team. Those with longer memories may recall that in 2011, the Lions beat the Cowboys despite having a Game Script of -9.4, and last year, the two teams scored 41 combined fourth quarter points. In other words, don’t turn off the game early when the Lions and Cowboys are playing.

The Patriots also pulled off a big comeback. New England trailed 14-0 and for most of the first half, and entered the locker room down seven. The Patriots are no strangers to these sorts of comebacks, though: since 2001, New England has the third best winning percentage when trailing at halftime by between 7 and 14 points.

Here are the full numbers from the first two rounds of the playoffs:

TeamH/ROppBoxscorePFPAMarginGame ScriptPassRunP/R RatioOp_POp_ROpp_P/R Ratio
INDCINBoxscore2610167.8452564.3%382164.4%
CARARIBoxscore2716115.8333945.8%321568.1%
SEACARBoxscore3117145.8242747.1%382956.7%
BAL@PITBoxscore3017134.6302554.5%531973.6%
IND@DENBoxscore2413113.8432860.6%482070.6%
GNBDALBoxscore26215-0.4362955.4%232845.1%
NWEBALBoxscore35314-4.8531380.3%452861.6%
DALDETBoxscore24204-8.1372163.8%452267.2%

[click to continue…]

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There are lots of bad things one could write about the NFC South. But for the most part, the Atlanta Falcons had been a competitive team this year, and not just by NFC South standards. Entering week 17, Atlanta had posted an average Game Script of +0.7; sure, that’s not very good, but it’s above average! The Falcons had not been embarrassing, and in fact, had outscored opponents by 40 points through three quarters.

Sure, Atlanta had issues maintaining leads in the fourth quarter, but they were rarely soundly beaten from start to finish. The Falcons had (prior to Sunday) four bad Game Scripts this year. Three of them came on the road: -12.8 in Baltimore, -10.6 in Green Bay, and -8.5 in Cincinnati, and all three of those teams are notable for being much stronger at home in recent years. The fourth was a -8 against the Steelers, but even then, Atlanta had the ball down by just seven with 6 minutes remaining.

Then, week 17 came. The Panthers led by 10-0 after the first quarter, the largest deficit Atlanta faced after one quarter all year. Carolina upped that margin to 21 points at halftime, the second largest halftime lead an opponent had against the Falcons this year (Green Bay was up by 24 points). The 31-point margin after three quarters was easily the largest margin, too. It was a start-to-finish beating by the Panthers, who posted a Game Script of 16.4 in the process.

That was the second largest Game Script for Carolina this year, and by quite a large margin. Other than another December blowout over a division rival (New Orleans), the Panthers didn’t have a Game Script of over +7. But are the Panthers peaking at the right time, or just beating up on NFC South opponents? Tune in next week: actually, never mind. The Cardinals are an NFC West team in name only; with Ryan Lindley under center, Arizona is actually the fifth member of the NFC South. [click to continue…]

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With their season on the line, the San Diego Chargers chose to dig deeper. Into a hole, that is.  On Saturday night, the 49ers jumped out to a 21-0 lead just 20 minutes into the game, and San Francisco took a 28-7 record into halftime. Even with six minutes left, San Diego still trailed by two touchdowns.

Down to their final drive, the Chargers needed to convert a 4th-and-8 (on a 17-yard pass to Eddie Royal) and a 4th-and-10 (to Dontrelle Inman), just to set up an 11-yard touchdown from Philip Rivers to Malcom Floyd with 32 seconds remaining.

Through 60 minutes, the Chargers had a Game Script of -11.3, which would tie the Lions/Falcons game for the most negative Game Script by a winning team all season. Because the game went to overtime, that Game Script number ended at -10.5, but that’s still easily the biggest comeback since the Detroit/Atlanta contest.

The other notable comeback of week 16 was in Miami, where the Vikings and Dolphins staged a crazy affair that resulted in a whopping 41 fourth quarter point. But Minnesota jumped out to an early lead and led 17-7 at the break, so the Vikings ended up with a Game Script of +4.3.

On the other end of the spectrum, there was only one large blowout: the Cowboys dominated the Colts by a score of 42-7, producing a Game Script of +23.9 in the process. The table below shows the week 16 Game Scripts data: [click to continue…]

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Rookie Receivers and the 2014 Season

Odell Beckham is ridiculous. Period.

Mike Evans, in just about any other year, would be considered the best rookie wide receiver in the NFL. Players like Kelvin Benjamin and Sammy Watkins would stand out in most years, too: both have over 25% of their team’s receiving yards.

Jordan Matthews has 767 receiving yards, which is only considered unimpressive against when compared against the above backdrop. Ditto Jarvis Landry and his 79 receptions. Martavis Bryant has seven touchdowns. The Jaguars have three rookie receivers playing well. And on and on we could go (just as I did in late October, and as Bill Barnwell did after week twelve).

Through 16 weeks of the 2014 season, rookies have been responsible for 12.6% of all receptions in the NFL, 12.7% of all receiving yards, and 13.7% of all touchdowns. As it turns out, that does make the 2014 class a very special one. The table below shows the percentage of all receptions, receiving yards, and receiving touchdowns by rookies in each year (other than 1987) since 1970: [click to continue…]

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Quarterback Passing Value and First Downs

Nine days ago, I looked at the leaders in passing value, measured as the difference between each quarterback’s ANY/A average and league average, multiplied by such passer’s number of dropbacks. This is the conventional method I have used to measure passing value, but that doesn’t make it the best.

Over the summer, Brian Burke of Advanced Football Analytics fame, helped me determine the value of first down. His research concluded that a first down was worth about 9 marginal yards. I was short on time, so I didn’t have the chance to incorporate that into my formula last week. But I will rectify that today.

In addition, I will provide -30 yards for each “net fumble” — defined as fumbles minus fumbles recovered. And since last week I calculated the numbers relative to average, this time around I will compare player production to replacement value, defined as 80% of league average.1

Let’s use Aaron Rodgers as an example. The Packers star has thrown 458 times for 3,837 yards, 35 touchdowns (+700), with 5 interceptions (-225), 9 fumbles, and 5 fumble recoveries (-120). He has also been sacked 27 times and lost 166 yards on those plays. Finally, Rodgers has picked up 188 first downs (+1692), which means he has a total of 5,718 adjusted net yards. Over his 485 dropbacks, that gives him an average of 11.79 “ANY/A”, while the league average is 8.91. That means Rodgers has produced 1,397 yards of value over average, and 2,261 yards of value over replacement. [click to continue…]

  1. Customarily, I use 75%, but I think with the first down bonus, 80% makes more sense here. []
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Records Against the Spread

The Titans lost to the Jaguars last night, dropping Tennessee’s record to a woeful 2-13. The 2014 season started off nicely for the Titans, who upset the Chiefs in Kansas City, 26-10, on opening day. Since then, not only has Tennessee gone just 1-13 (the sole win being a 2-point home victory against Jacksonville), but the team is a mind-bogglingly poor 2-11-1 against the spread.

Points spread data is not official, of course, and some sources of data are better than others. Using what is available at Pro-Football-Reference, I calculated the worst teams against the spread since 1978. If the Titans fail to cover next week against the Colts, they will end the year at 3-12-1 against the spread. That would make them one of just 13 teams since 1978 to post such a poor ATS record. On the other hand, it would only tie them with another AFC South team from the past two years:

TeamYearWLTwin%ATS WATS LATS TPerc
BAL200751100.31331300.188
NWE198121400.12531300.188
PIT19809700.56331300.188
CIN198741100.26731200.2
HOU201321400.12531210.219
STL201121400.12531210.219
NYG200341200.2531210.219
OAK200341200.2531210.219
DAL199761000.37531210.219
HOU199421400.12531210.219
BAL198121400.12531210.219
SFO197821400.12531210.219
HOU19821800.1112700.222
PHI201241200.2541200.25
TAM201141200.2541200.25
CAR201021400.12541200.25
JAX200851100.31341200.25
STL20027900.43841200.25
CIN200221400.12541200.25
ARI200031300.18841200.25
OAK199741200.2541200.25
CIN199131300.18841200.25
RAM199131300.18841200.25
NWE199011500.06341200.25
NYJ198941200.2541200.25
NOR198551100.31341200.25
ATL198441200.2541200.25
HOU198431300.18841200.25
DEN20088800.541110.281
PHI200561000.37541110.281
SFO200210600.62541110.281
NOR199931300.18841110.281
CIN199831300.18841110.281
NYJ199241200.2541110.281
DEN199051100.31341110.281
MIA198861000.37541110.281
DET197921400.12541110.281
CHI20138800.541020.313
WAS201331300.18851100.313
OAK201241200.2551100.313
KAN201221400.12551100.313
CLE201051100.31351100.313
ARI201051100.31351100.313
DEN201041200.2551100.313
JAX20097900.43851100.313
DET200921400.12541020.313
DEN20077900.43851100.313
STL200731300.18851100.313
DEN20069700.56351100.313
STL200561000.37551100.313
NOR200531300.18851100.313
SEA20049700.56351100.313
TEN200451100.31351100.313
CHI200241200.2551100.313
CLE200031300.18851100.313
MIN199910600.62541020.313
SFO199941200.2551100.313
DET199851100.31351100.313
STL199841200.2551100.313
DET199651100.31351100.313
DEN19947900.43841020.313
PHI19947900.43851100.313
RAM199351100.31351100.313
IND199341200.2551100.313
NYG199261000.37551100.313
CHI199251100.31351100.313
NYG19918800.551100.313
IND199111500.06351100.313
CHI198961000.37551100.313
WAS19887900.43851100.313
STL198551100.31351100.313
MIN198431300.18851100.313
GNB19838800.541020.313
SDG198361000.37551100.313
NYG198331210.21951100.313
NYJ198041200.2551100.313
DAL197911500.68851100.313

The 2007 Ravens went 5-11 overall and 3-13 against the spread, making them the worst team in recent history when it comes to covering the point spread. That year marked the end of the Brian Billick, Steve McNair, and Kyle Boller eras in Baltimore. And while first-year head coach Ken Whisenhunt is probably safe, Titans fans can rest easy knowing that the Jake Locker era is almost certainly over. As for Zach Mettenberger and Charlie Whitehurst? The door may be about to close on them as well. After losing to the Jets and Jaguars, Tennessee looks to be in great shape once the music stops to land Marcus Mariota or Jameis Winston.

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Week Fifteen Game Scripts: Bengals Dominate Manziel

Entering week 15, one of the biggest storylines was that Johnny Manziel was set to make his first start of the season. Manziel’s opening performance was a flop: his -0.56 Adjusted Yards per Attempt average was the second lowest by a quarterback this season, although not the lowest by a quarterback in a Browns/Bengals game. The Bengals won 30-0 in a game that was never in doubt for much of the second half; Cincinnati’s +16.6 Game Script was the highest of the week.

The Patriots, Chiefs, and Saints all posted double digit Game Script scores as well. In the process, New England clinched the AFC East, Kansas City kept their playoff hopes alive and avenged an uglier loss to Oakland, and the Saints? Well, New Orleans still controls its own destiny for the playoffs despite a 6-8 record.

The comebacks were light this week, as only Detroit (-3.3) and the Jets (-1.5) managed to win with a negative Game Script. The table below shows the Game Scripts data from week 15: [click to continue…]

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The Worst Matchups in NFL History

Johnson returns to Nashville

Johnson returns to Nashville

The Jets and the Titans play tomorrow, in a matchup of 2-11 teams that ranks as one of the worst in NFL history. If you’re watching this game, you’re either a diehard fan of both teams or are fascinated by the idea of a Chris Johnson revenge game (which is probably even sadder than being a fan of either team). It’s even worse than the Colts-Jaguars game of a few years ago, when the 2-13 Colts needed a loss in Jacksonville to the 4-11 Jags in order to secure the rights to Andrew Luck. Something similar could be on the line in Tennessee: with the Jets, Bucs, and Titans all 2-11 (not to mention the Jaguars and Raiders), there are three quarterback-needy teams in a draft with two marquee quarterbacks: Jameis Winston and Marcus Mariota. As a result, the loser of the New York/Tennessee game could ultimately be the long-term winner.

This will be the first matchup of 2-11 teams since a 2008 game between the Rams and Seahawks. That game turned out to be much less exciting for draftniks with the benefit of hindsight: St. Louis selected Jason Smith with the second overall pick, while the Seahawks drafted Aaron Curry fourth overall.

So what’s the worst matchup of teams in NFL history? You can’t use just winning percentage, and it’s hard to compare teams who have played a different number of games. One solution is to add 11 games of .500 ball to each team. For the Jets and Titans, that would make both teams 7.5-16.5, which translates to an adjusted winning percentage of 0.313. That would be tied for the 19th worst game in NFL history.

The worst? There’s a tie there, too, involving a pair of Colts teams a decade apart. In 1981, the 1-14 Colts defeated the 2-13 Patriots. Baltimore had an adjusted (after adding 11 games of .500 play) winning percentage of 0.250, while New England was at 0.288, for an average of 0.269. The win swung the first overall pick to the Patriots and dropped the Colts to second overall, although Kenneth Sims and Johnie Cooks didn’t change the fate of either franchise. Ten years later, the Colts were again 1-14 and were scheduled to play the 2-13 Bucs. The twist here: Tampa Bay had already traded the team’s first round pick in 1992 to Indianapolis in exchange for Chris Chandler in 1990. The Bucs defeated the Colts, and Indianapolis selected Steve Emtman and Quentin Coryatt with the first two picks. Spoiler alert: that didn’t change the fate of the franchise, either. [click to continue…]

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Over the prior two weeks, we were short on comebacks. Things took a big turn towards exciting in week 13:

  • The Bengals trailed the Bucs 3-0 for most of the first quarter, and then 10-0 for the majority of the second. Cincinnati would ultimately take the lead by the end of the third quarter, but the Bengals still finished with a -3.0 Game Script.
  • On Monday Night Football, the Jets also jumped out to a 10-0 first-half lead before ultimately falling to Miami, 16-13. But more to come on this game later in the post.
  • Another team that fell behind 10-0 early was San Diego. In fact, the Chargers didn’t take their first lead against the Ravens until the final minute, winning 34-33 despite posting a Game Script of -5.9.
  • But the biggest “comeback” of the week was in Jacksonville, where the Jaguars ruined Tom Coughlin’s homecoming. New York stormed out to a 21-0 lead, but imploded in the second half, allowing Jacksonville to steal the win, 25-24. Jacksonville won with a Game Script of -6.8, the fourth largest of the year and the worst Game Script by a victor since the Lions 21-point comeback in London against the Falcons.

Below are the Game Scripts data for each game in week 13: [click to continue…]

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The AFC North is 10-1-1 Against the NFC South

The NFC South has been miserable this season: the Falcons, Saints, Panthers, and Bucs are now 6-23-1 (0.217) in games outside of the division. If Atlanta, New Orleans, Carolina, and Tampa Bay combine to go 3-7 in their ten remaining non-division games, they would eclipse the 2008 NFC West and become the worst division in modern history (at least, by record).

The NFC South has been particularly bad against the AFC North, going 1-10-1 this year. The two non-losses were the shocking upset by Tampa Bay in Pittsburgh in week 4, and the tie between the Bengals and Panthers in week 6 (which ended, you may recall, with Cincinnati kicker Mike Nugent missing a 36-yarder as time expired).

The remaining games between these two divisions in 2014 are: Cincinnati at Tampa Bay, Cleveland at Carolina, Pittsburgh vs. New Orleans, and Pittsburgh at Atlanta.  If the AFC North can go 3-1, that would up its record to 13-2-1, which would set the post-2002 mark for the best record by one division against another in a single season.

The current record? A mark of 13-3, set five times in the current era.  It was most recently done by the NFC West last year, when the only losses it had against the AFC South came versus the Colts (in the case of the 49ers and Seahawks) or the Titans (Rams).

What if the AFC North went on a clean sweep the rest of the way, finishing 14-1-1? That would be the best mark since the merger, but not the best mark of all time.  That honor belongs to the 1965 NFL West: that year, the division went 13-1 against the NFL East.  That’s going to be a tough mark to ever eclipse, as it would require a 15-1 mark given the current format. How about the best mark of the post-merger era by one division against another?

The honor belongs to the AFC West, which went 31-9 outside of its division in 1984. The division really beat up on the NFC Central, going a collective 15-2 in such games. Not surprisingly, the two losses were against the Bears (by Denver and Los Angeles).

Measuring success by one division against another across eras is complicated due to differing number of games. One tweak we can make is to use True Winning Percentage, which adds 11 games of 0.500 ball to any record. If your record is 1-0, True Winning Percentage will strongly regress that 1.000 winning percentage to the mean; if your record is 90-10, not so much: we add 5.5 wins and 5.5 loss regardless of your record. Using that methodology here would translate the AFC North’s record against the NFC South in 2014 from 10-1-1 to 15-6-2 (or 16-7), equivalent to a 0.696 winning percentage. That would be the 5th best in NFL history, and the 3rd best since the merger:

RankYearDivDivRecordWin%True Win%
11965NFL WestNFL East13-10.9290.74
21984AFC WestNFC Central15-20.8820.732
31991NFC EastAFC Central14-20.8750.722
41936NFL WestNFL East18-40.8180.712
51935NFL WestNFL East16-40.80.694
61993AFC WestAFC East9-10.90.69
72013NFC WestAFC South13-30.8130.685
72010NFC SouthNFC West13-30.8130.685
72008NFC SouthNFC North13-30.8130.685
72007AFC SouthNFC South13-30.8130.685
72004AFC EastNFC West13-30.8130.685
71989NFC WestAFC East13-30.8130.685
71980AFC CentralNFC Central13-30.8130.685
71979AFC WestNFC West13-30.8130.685
151934NFL WestNFL East15-40.7890.683
161946AAFC WestAAFC East22-7-30.7340.674
171983NFC WestNFC Central10-20.8330.674
171976NFC EastNFC West10-20.8330.674
171975AFC CentralAFC West10-20.8330.674
171949AAFC WestAAFC9-1-20.8330.674
211950NFL AmericanNFL National11-30.7860.66
221969AFL WestAFL East20-7-30.7170.659
231999AFC EastAFC Central7-10.8750.658
231975NFC EastNFC West7-10.8750.658
251970NFC EastAFC Central5-010.656
261960NFL EastNFL West9-2-10.7920.652
272009AFC SouthNFC West12-40.750.648
272008NFC EastNFC West12-40.750.648
272008AFC EastAFC West12-40.750.648
272007NFC NorthAFC West12-40.750.648
272007AFC SouthAFC West12-40.750.648
272006AFC EastNFC North12-40.750.648
272005AFC NorthNFC North12-40.750.648
272004AFC NorthNFC East12-40.750.648
271988AFC CentralNFC East12-40.750.648
271969NFL CapitalNFL Century12-40.750.648
371968AFL WestAFL East21-90.70.646
381999NFC CentralNFC West8-20.80.643
381994AFC EastAFC West8-20.80.643
381991AFC WestAFC East8-20.80.643
381990NFC EastNFC Central8-20.80.643
381989AFC WestAFC East8-20.80.643
381987AFC CentralAFC West8-20.80.643
381979NFC EastNFC Central8-20.80.643
381978AFC EastAFC West8-20.80.643

The 1991 NFC East was what TV executives apparently think that division will always be. That year, teams from the NFC East went 14-2 against the NFC Central, with both losses coming by three points (Giants at Bengals), with one going to overtime (Dallas at Houston).

A 3-1 finish would give the AFC North a 13-2-1 record, good enough for a 0.704 true winning percentage.  One more loss would knock it behind the five 13-3 teams of the post-2002 era, but so far this year, the AFC North has been dominating the NFC South at a historic level.

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The Travis Kelce Post

Last year, Jeff Cumberland finished #2 in DVOA among all tight ends.  This really happened.  Of course, that required some digging, so I wrote the following about Cumberland in the 2014 Football Outsiders Almanac:

What’s going on here? How did Cumberland produce such strong numbers, and wind up second in DVOA among tight ends? Among the 52 tight ends with at least 20 targets, Cumberland ranked fifth in yards gained through the air (per reception) and seventh in yards gained after the catch (per reception). Incredibly, [Ladarius] Green ranked first in both of those metrics, but there’s generally an inverse relationship between those two statistics: you either catch passes downfield, or you gain a lot of yards after the catch, but rarely both. In fact, Green and Cumberland were the only two tight ends to rank in the top 15 in both categories, which underscores just how impressive Cumberland’s efficiency numbers were in 2013.

So is Cumberland coming off a sneaky strong season and about to break out? There’s no denying that his efficiency numbers were great, but sometimes, the best course of action is to take a step back and look at the bigger picture. In 2012, Cumberland finished second on the team with 53 targets. In the offseason, New York allowed Dustin Keller to head to Miami, but instead of handing the job to Cumberland, signed Kellen Winslow. As a result, Cumberland wound up seeing only 40 targets in 2013. If the Jets were as high on Cumberland as his numbers would suggest, he would have managed to pick up more than 2.5 targets per game in one of the league’s most anemic passing attacks. Then, New York drafted Jace Amaro in the second round of the 2014 draft. Efficiency numbers are fun to look at, but the revealed preference of the Jets organization would seem to trump those metrics. And it appears as though the organization views Cumberland as a role player and little more.

Cumberland split time with Winslow, and his low target numbers were a strong indicator that he was an average talent.1 Yards per target is not a good stat because it is not very sticky; yards per route run is quite a bit better.  After all, a route run is more the analog of a “pass attempt” than a target, so YPRR is really the receiver’s version of yards per attempt.

The next great tight end?

The next great tight end?

This year, as he did in 2013, and 2012, and nearly in 2011, Rob Gronkowski leads all tight ends in yards per route run. He is averaging 2.67 yards per route run on his 304 routes, and the only receivers with a higher yards per route run average on over 225 routes are Demaryius Thomas (2.77) and Jordy Nelson (2.84). In short, Gronkowski is the man.

But, assuming you read the title to this post, you know that today we want to focus not on Gronk, but on baby Gronk. Kansas City’s Travis Kelce is second among tight ends in yards per route run, with a 2.49 average over 218 snaps. Those are incredible numbers, and a reflection that Kelce is already one of the top playmaking tight ends in football.

The Chiefs star hase has 542 yards on 52 targets, and his 10.4 yards/target average is the best among all tight ends.  But remember, Y/T is not a good stat: Cumberland ranked 3rd last season in yards per target, with a 10.2 average. Cumberland’s issue was that he wasn’t targeted very much despite being on the field.  As a result, his yards/target average overstated his value. Let’s throw some math into this equation: in 2013, Gronkowski was targeted on 30% of his routes, the best rate among all tight ends; Cumberland was targeted on 16% of his routes, the 35th best rate among tight ends.

Kelce isn’t the best receiving tight end in football because he leads with a 10.4 yards/target average.  It’s not just about what you do per target, it’s how often you get targeted. As he did last year, Gronk leads all tight ends in targets per route run, as he has been targeted on 28% of his pass routes.  But Kelce ranks 4th — and just a hair behind Jimmy Graham for 3rd place2 — in targets per route run! He’s not having a fluky season at all, or at least, not in the way Cumberland did. The Chiefs are throwing to Kelce very often when he’s running routes, which is a very good sign that he’s the real deal.

So why is Kelce “only” 6th in receiving yards among tight ends? Because he’s just 28th in pass routes run by tight ends this year. And that’s the real conundrum: he simply isn’t getting much playing time. For the 2013 Jets, Cumberland was on the field for more offensive plays than any Jets player other than the quarterback and offensive linemen. Kelce simply doesn’t get the same level of playing time, as he ranks 4th among non-OL/QBs for Chiefs offensive players in snaps.

The other problem for him is that Kansas City ranks just 31st in pass attempts this year, which is going to depress his raw totals. But the good news is his playing time is on the rise — he was on the field for 63 of 67 snaps in week 12 and 50/52 in week 11. He can’t do anything about how often the Chiefs pass, but in his case — unlike Cumberland’s — the organization seems to be buying into the numbers. Kelce’s been dominant on a per-route basis this year, and now, Kansas City keeps giving him more playing time. The next big question is whether he can maintain his level of production as a full time starter, but the hunch here is that he can. And hey, maybe we just identified the first undervalued fantasy player of 2015.

  1. In his defense, Cumberland was tied for 11th in yards per route run, but that’s (1) still a far cry from #2 and (2) more a reflection of the weakness of the 2013 Jets supporting cast. []
  2. Jordan Reed is second. []
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Guest Post: Bryan Frye and Win Contribution Rating

Bryan Frye is back with another fun guest post.  Bryan, as you may recall, owns and operates his own great site at nflsgreatest.co.nf, where he focuses on NFL stats and history.  You can view all of Bryan’s guest posts at Football Perspective at this link.

Oh, and Happy Thanksgiving to all the loyal Football Perspective readers!


Win Contribution Rating

It’s Thanksgiving. I don’t have a ton of time to write; you don’t have a ton of time to read. Let’s make this snappy.

A few months ago, I began using a rating that I feel better describes a quarterback’s contributions to helping his team win. I am terrible at coming up with names for stuff like that, but Football Guy Adam Harstad swooped in like a guardian angel and suggested the name “Win Contribution Rating.” I liked it, and I began using it without delay.

I used three metrics that correlate highly with future wins: Brian Burke’s EPA/P, Football Outsiders’ DVOA, and my Adjusted Yards per Play (AYP).1  The correlation coefficients with future wins (i.e., Year N+1 wins) for the individual metrics are .273 for EPA/P, .265 for DVOA, and .256 for AYP.2 When I ran those in a multiple regression, I got the following best fit equation (rounded):

Win% = .5 + EPA/P *.39 + DVOA * .13 + AYP * .008

Because the basis of this regression is win percentage, the equation spits out small decimals that I find aren’t relatable to most of the casual fans I know. To transform this into a number that resembles the NFL passer rating that people already know, I simply multiply by 140 to find the Win Contribution Rating.3

The highest score since 1999 belongs to Peyton Manning in his virtuoso 2004 performance. Let’s take a look at his rating:

EPA/P: .38
DVOA: 58.9%
AYP: 9.1
WCR = (.5 + .38 * .39 + .589 * .13 + 9.1 * .008) * 140 = 111.7 [click to continue…]

  1. Please note that the difference between the 45 yard penalty Chase uses and the 50 yard penalty I use for interceptions is based on this article by Brian Burke. I chose 50 as a compromise between the traditional and the new research. For fumbles, I used the standard 50 yard penalty and divided it in half to account for the randomness of recovery. []
  2. This includes all quarterbacks for which data is available from both FO and AFA, from 1999-2012. I did not include 2013 because I didn’t know year N + 1 wins; I’m not in the fortune telling business. []
  3. This may seem strange, but keep in mind that the NFL multiples by 16.67 to achieve its final passer rating. []
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Last week, the Game Script winners went 14-0. In week 12, there were two moderately-sized comebacks:

  • In New York, the Giants jumped out to 14-3 and 21-10 leads, fueled in part by Odell Beckham being ridiculous. But then the Cowboys offensive line got ridiculous, and Dallas went on a 21-7 run to win the game despite posting a Game Script of -4.0.
  • In Denver, the the Dolphins controlled the game for most of the first three quarters. Miami led 21-10 with two minutes left in the first half, and later took a 28-17 lead into the fourth quarter. But the Broncos scored three straight touchdowns to put the game out of reach, despite finishing with a Game Script of -4.3.

Denver had posted a Game Script of at least +5.0 in 6 of the team’s first 7 games, after doing so in 10 of 16 games in 2013. But things have changed drastically in Denver over the last month: the Broncos have had Game Scripts of -11.5, -7.7, and now -4.3 in three of the team’s last four games.

On the positive Game Script side, the Eagles (+14.3) and Bills (+14.2) were the big producers this week, although New England’s +11.2 against Detroit might have been the most impressive when you consider strength of schedule. The table below shows the week 12 Game Scripts data: [click to continue…]

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Week Eleven Game Scripts: Game Script Winners go 14-0

Some weeks, the NFL is filled with comebacks. Some weeks there are teams that wind up winning with strongly negative Game Scripts. And then there was week 11. There were only three comebacks last weekend, and none of them were last-minute comebacks:

  • Seattle led 20-17 entering the 4th quarter, but the Chiefs scored the game’s final points — a Knile Davis touchdown — with over 13 minutes left in the quarter.
  • Pittsburgh technically trailed 24-13 entering the 4th quarter, before Le’Veon Bell scored on the first play of the final frame. The Steelers scored the go-ahead score with just over nine minutes left, and since Pittsburgh led for most of the first half, the Steelers finished with a Game Script of +0.3.
  • Carolina took a 1-point lead with just over 6 minutes left in the game against Atlanta, but the Falcons responded with a field goal on the ensuing drive to win the game. The kick came with just over 2 minutes remaining, but a 1-point 4th quarter deficit doesn’t move the Game Script needle.

The table below shows the Game Scripts for each game in week 11. As you can see, despite some shocking upsets, week 11 was as straightforward as it gets: all 14 teams with positive Game Scripts were victorious. For the second straight week, the Packers provided the biggest Game Script of the week, while the Bucs (!) were the only other team with a Game Script in double digits. [click to continue…]

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Week Ten Game Scripts: Primetime Blowouts

Don't bet against Rodgers in Movember

Don't bet against Rodgers in Movember.

It was another week of blowouts in the NFL, particularly in prime time. On Thursday night, the Browns shocked the Browns with a 24-3 blowout, as Andy Dalton produced a historically bad performance. Cleveland had a Game Script of +12.8, which turned out to be the third largest of the week.

On Monday Night Football, the Eagles dominated the Panthers. Darren Sproles scored two first quarter touchdowns, Jordan Matthews chipped in with two touchdown catches from Mark Sanchez later in the game, and Philadelphia won, 45-21. That score is a bit misleading, as Kelvin Benjamin caught two late touchdowns: the Eagles had a Game Script of +20.0, which is more in line with about a 40-point win.

But the biggest Game Script of the week came in the other primetime game, Bears at Packers on Sunday Night Football. Aaron Rodgers was an insane 18/24 for 315 yards and 6 touchdowns… in the first half! Green Bay produced a Game Script of +28.7. The Packers took a 42-0 halftime, lead, and finished with the second best Game Script of 2014.

The table below shows the Game Scripts from every game in week 10: [click to continue…]

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