≡ Menu

NYT Fifth Down: Post-week 12

My article for the New York Times this week takes a look at one interesting statistic for each of the eight division winners.
 

Atlanta Falcons – Record in Close Games
In 2010, Atlanta raced to a 10-2 record on the strength of an improbable 7-1 record in games decided by 7 or fewer points. How a team fares in close games has a heavy impact on a team’s final record, but statisticians agree that such a metric holds little predictive value. The Falcons earned the No. 1 seed in the N.F.C. thanks to their success in close games, but ranked only seventh in the Football Outsiders advanced statistical rankings and 21st in the Advanced NFL Stats efficiency ratings. Atlanta lost badly in its playoff opener, not surprising to those who felt the Falcons’ record was more mirage than reality.

This season, Atlanta has raced to a 10-1 record on the strength of an improbable 7-1 record in games decided by 7 or fewer points. Football Outsiders ranks the Falcons 12th, and according to its founder, Aaron Schatz, the Falcons have by far the worst efficiency rating of any of the 18 teams that have started 10-1 since 1991. Advanced NFL Stats is slightly more generous, placing the Falcons fifth, although the gap between the fifth and 12th teams in its rating is miniscule. The takeaway: Don’t get caught up in the Falcons’ record. It will give Atlanta a bye, but no other guarantees come with it.

San Francisco – Top Pass Defense in the N.F.L.

Last season, the 49ers’ reputation for having an elite defense was built on their superb run defense, which ranked first in rushing yards allowed, rushing yards per carry allowed and rushing touchdowns allowed. But the 49ers were not dominant against the pass, ranking ninth in net yards per pass attempt allowed. This season, the San Francisco defense is without weakness.

The 49ers (8-2-1) actually lead the N.F.L. in net yards per pass attempt allowed. In the process, the 49ers lead the N.F.L. in points allowed, and their defense ranks in the top three in both first downs allowed and Pro-Football-Reference’s Expected Points Added statistic. The run defense remains stout, ranking in the top four in yards, yards per carry and touchdowns allowed, but the improvement in the pass defense makes this an even better defense than the 2011 version. As long as San Francisco continues to shut down opposing passers, it won’t matter very much whether Coach Jim Harbaugh picks Alex Smith or Colin Kaepernick at quarterback.

Chicago – 11th in Points Scored Without an Offense

As a technical matter, the Bears (8-3) rank 11th in points scored. Just don’t let anyone tell you that in the context of a story about how Chicago’s offense is underrated. The Bears have scored eight non-offensive touchdowns this season — seven on interception returns, one on a blocked punt — and their great defense and special teams consistently set up the offense for success even when those units aren’t scoring touchdowns. Chicago is in the bottom five in Net Yards per Attempt, Adjusted Net Yards per Attempt, total yards and sacks allowed. The Bears’ running game benefits from a high number of carries, but ranks below average in both yards per carry and PFR’s Expected Points Added statistic.

The defense is excellent, but a poor offensive line and mediocre wide receiver talent behind Brandon Marshall leave the Bears with one of the worst offenses in the N.F.L. — regardless of how many points they’ve scored. Advanced NFL Stats ranks the Bears’ offense as the second worst in the league.

You can read the full article here.

{ 5 comments }

Game Scripts, Part II: Analyzing team seasons

Yesterday, I rolled out Game Scripts, a way to measure the flow of every game since 1940. The sum of each team’s Game Script in each game can be used to give us an average Game Script score on the season. You might think that this number would be a good proxy for how dominant a team was, and that’s largely true: the teams with the highest game script scores tend to have been the most dominant teams. However, there are some reasons to be cautious with this approach: game scripts are not adjusted for strength of schedule and in any given game, the losing team can end up with a better score than the winning team. That said, here are the teams with the highest Game Scripts since 1940:

RkYearTeamLeagueW-L-TPFPASCRIPT
11942CHINFL11-0-03768413.5
21948CHINFL10-2-037515111.3
31941CHINFL10-1-039614710.4
41948SFOAAFC12-2-049524810.4
52007NWENFL16-0-058927410.3
61968BALNFL13-1-040214410.1
71948PHINFL9-2-137615610.1
81947CLEAAFC12-1-141018510
91946CLEAAFC12-2-042313710
101949PHINFL11-1-03641349.5
111969MINNFL12-2-03791339.4
121954CLENFL9-3-03361629.2
131999STLNFL13-3-05262429.1
141973MIANFL12-2-03431509.1
152001STLNFL14-2-05032738.9
161961HOUAFL10-3-15132428.8
171951CLENFL11-1-03311528.8
181972MIANFL14-0-03851718.7
191998MINNFL15-1-05562968.6
201973RAMNFL12-2-03881788.5
211983WASNFL14-2-05413328.4
221984SFONFL15-1-04752278.4
231948CLEAAFC14-0-03891908.3
241949SFOAAFC9-3-04162278.2
251998DENNFL14-2-05013098.1
261968DALNFL12-2-04311868
271966KANAFL11-2-14482767.9
281995SFONFL11-5-04572587.7
291962GNBNFL13-1-04151487.7
301953CLENFL11-1-03481627.6
311971DALNFL11-3-04062227.6
321944PHINFL7-1-22671317.6
331948CRDNFL11-1-03952267.6
341960CLENFL8-3-13622177.5
351980RAMNFL11-5-04242897.4
362010NWENFL14-2-05183137.4
372011GNBNFL15-1-05603597.4
381976BALNFL11-3-04172467.4
391975MINNFL12-2-03771807.3
401975PITNFL12-2-03731627.3
411992DALNFL13-3-04092437.3
421969KANAFL11-3-03591777.3
431964BALNFL12-2-04282257.2
441997DENNFL12-4-04722877.2
451968OAKAFL12-2-04532337.2
461945RAMNFL9-1-02441367
471943CHINFL8-1-13031577
481967OAKAFL13-1-04682337
491963NYGNFL11-3-04482807
501994SFONFL13-3-05052966.9

The teams with the highest game scripts last year? Green Bay (7.4), New Orleans (5.6) and Houston (5.4), while the Rams (-6.4), Colts (-7.2), and Bucs (-8.7) were at the bottom of the league. But let’s get to the real point of using Game Scripts — to help put passing and rushing ratios in context.

Last year, the Buccaneers had the second highest effective pass/run ratio in the league (defined as total pass attempts divided by rushes plus total pass attempts, but with all kneels and spikes excluded). But that’s misleading, because Tampa Bay had the worst Game Script in the league. Conversely, were Houston and San Francisco really the second and third most run-heavy teams in the NFL last year? The table below lists each team from highest to lowest pass/run ratio:
[continue reading…]

{ 7 comments }

Introducing Game Scripts – Part I

Emmitt knows which way the arrow points.

From 1991 to 1995, Emmitt Smith and the Cowboys were one of the dominant teams in the NFL. As you probably heard, the Cowboys were pretty successful whenever Smith had a lot of carries. Over that five-year span, the Cowboys went 49-7 whenever Smith had 20+ carries in a regular season game, and an additional 8-1 in playoff games.

Despite the implication, we know that the causation arrow looks less like this…

Give Smith 20+ carries ——>>>>>>> Win football game

And more like this…

Have lead late in football game ——>>>>>> call running plays for Smith

How a game unfolds is what my friend Sigmund Bloom likes to call the game script. Sometimes, the game script goes away from one player and to another, and the final boxscore doesn’t truly reflect coaching preference. Rather, the boxscore simply reflects the way the game unfolds. And one of the weirdest boxscores you’ll ever see is a game between the Bills and the Dolphins in December 2005, which Miami won.

Knowing that the Dolphins won, you’d probably be surprised to know that they called 68 passing plays (65 attempts, 3 sacks) against just 22 rushing plays, while the Bills ran more than they passed. But the game script was very unique. In the first quarter, Lee Evans caught three touchdown passes from J.P. Losman. In the second, Miami settled for a 23-yard field goal to cut the lead to 18. The Bills got a safety in the third quarter, to go up 23-3. But Ricky Williams, Ronnie Brown, Chris Chambers and Sage Rosenfels led a spirited comeback, and scored three fourth-quarter touchdowns to win the game. The way the game unfolded was for the Bills to play conservative and the Dolphins to go to the air.

Since 1940, there have been nearly 13,000 professional football games played between the NFL, AFL and AAFC. And each game has had its own game script, unfolding in its own, unique way. My goal was to come up with a single number to explain how the game went down. In this post, I’ll do my best to explain it.
[continue reading…]

{ 8 comments }

Checkdowns: College Team Efficiency Ratings

Note: There are many things theoretical issues with this post. That said, if I had to write down and explain all the drawbacks of the data I’m about to present, I wouldn’t have the time to make quick posts like this. So….

I thought it would be cool to take a slightly (emphasis on slightly) more nuanced look at team rushing and passing stats so far in 2012.

The first table shows how many “rushing yards over average” each team has this year. First, I calculated each team’s “Adjusted Yards per Carry” which is simply Yards per Carry with a 20-yard bonus for each touchdown. On average, teams are averaging 5.31 AYPC in 2012; to calculate rushing yards over average, I multiplied the number of carries for each team by the difference between their AYPC average and 5.31. As expected, Oregon ranks first.
[continue reading…]

{ 0 comments }

NYT Fifth Down: Post-week 10

This week at the New York Times I looked at several interesting statistical developments in both the 2012 season and in week 10.

Even in today’s pass-happy N.F.L., it pays to have one of the best running backs. In one of the bigger surprises of the season, the best of the best is Minnesota’s Adrian Peterson.

He’s a four-time Pro Bowler and a two-time first-team All-Pro selection, but few expected a big year out of Peterson. That’s because last year, on Christmas Eve, Peterson tore the anterior cruciate ligament and medial collateral ligament in his left knee against the Redskins. Such a brutal injury often permanently robs a player of his elite ability; the rule of thumb tells us that it’s not until the second full season after the injury that the player regains his old form, if he ever does.

An injury so late in the 2011 season had most people figuring his 2012 season would be a lost year. Instead, Peterson leads the league in rushing with 1,128 yards and is on pace for a remarkable 1,804. Peterson is the first player since 2009 to rush for 1,100 yards in his team’s first 10 games, and he’s showing no signs of slowing. He has rushed for 629 yards in his last four games, including an impressive 171 rushing yards in a victory over the Lions on Sunday.

Peterson is also averaging 5.75 yards per rush the season, the most among players with at least 100 carries. He joins Jim Brown, Walter Payton, Barry Sanders and Chris Johnson as players with 1,100 or more rushing yards and such a high yards-per-carry average after his team’s first ten games.

Minnesota’s passing game ranks 26th in Adjusted Net Yards per Attempt and last in the league in yards per completion, a sign of an offense that doesn’t stretch the field through the air. But despite a passing attack that doesn’t scare any defense, thanks to Peterson, Minnesota is 6-4 and a potential playoff team.

The Return of Megatron

For most of the season, N.F.L. fans wondered what was wrong with Calvin Johnson. It wasn’t until the final minutes of Detroit’s loss to the Vikings on Sunday that Matthew Stafford and Johnson connected on a touchdown pass this season (Johnson did catch a touchdown pass from Shaun Hill earlier this year). Well, after a 207-yard game against Minnesota, Johnson is again leading the league in receiving yards. With 974 yards in nine games, Johnson is actually ahead of last year’s pace, when he gained a league-high 1,681 yards. The big difference: in 2011, he caught 16 touchdown passes, but he has only two in 2012.

Continued Dominance in New England

When it comes to the Patriots, mind-boggling offensive numbers are the norm. That means we occasionally ignore just how impressively the New England machine is operating. The Patriots lead the league in points scored, yards gained and first downs. Since 1990, only the 1993 49ers, the 1997 Broncos, the 2001 Rams and the 2007 Patriots have finished first in each metric.

The Patriots are averaging 33.2 points per game, 3.1 points more than the second-place Broncos. At 430.3 yards per game, the Patriots far outpace the rest of the league; Detroit (406.1) is the only other team averaging more than 400 yards per game.

But where New England really stands out is the 259 first downs it has gained. Last year, New Orleans set the N.F.L. record for first downs in a season with 416; the 2011 Patriots also broke the old record (held by the 2003 Kansas City Chiefs) with 399. This year’s team is on pace for an incredible 460 first downs. And the Patriots are on pace to crush the record in a surprising way: New England leads the N.F.L. in rushing first downs with 92, and Stevan Ridley leads all running backs with 54 rushing first downs.

You can read the full article here.

{ 1 comment }

Quarterback performance on third and fourth downs

So far this season, teams have converted on 37.2% of all pass plays on third or fourth downs. Looking at success rates on these downs helps to identify which quarterbacks are keeping drives alive for their teams and coming through in the most important situations. For example, Peyton Manning leads the league with an impressive 52.6% rate. How impressive is that?

The table below lists the conversion rates for quarterbacks on passing plays (excluding scrambles) on third and fourth downs; the table is sorted by the far right column, which shows how many third downs over average each quarterback converted. This is calculated by subtracting from the number of actual conversions the number of expected conversions (which is 37.2% multiplied by the number of third down plays):

RankQuarterbackPlaysConvRate3DCovOvAvg
1Peyton Manning784152.6%12
2Ben Roethlisberger974647.4%9.9
3Matt Ryan793949.4%9.6
4Drew Brees1044745.2%8.3
5Andrew Luck994444.4%7.1
6Matthew Stafford1024544.1%7
7Tom Brady863844.2%6
8Matt Schaub723244.4%5.2
9Matt Hasselbeck783443.6%5
10Matt Cassel713143.7%4.6
11Ryan Fitzpatrick803442.5%4.2
12Tony Romo913841.8%4.1
13Michael Vick953941.1%3.6
14Aaron Rodgers943739.4%2
15Jay Cutler843339.3%1.7
16Christian Ponder943537.2%0
17Philip Rivers843035.7%-1.3
18Ryan Tannehill782734.6%-2
19Eli Manning893134.8%-2.1
20Alex Smith612032.8%-2.7
21Josh Freeman802733.8%-2.8
22Kevin Kolb682232.4%-3.3
23Sam Bradford872933.3%-3.4
24Mark Sanchez963233.3%-3.7
25Russell Wilson832732.5%-3.9
26Brandon Weeden1113733.3%-4.3
27John Skelton591627.1%-6
28Robert Griffin III782329.5%-6
29Carson Palmer922830.4%-6.3
30Cam Newton681927.9%-6.3
31Joe Flacco782126.9%-8
32Blaine Gabbert842226.2%-9.3
33Andy Dalton771620.8%-12.7

[continue reading…]

{ 3 comments }

Sunday morning, I noted that the Falcons had a 2.4% chance of going undefeated and that the team most likely give them their first loss was the Dallas Cowboys. After Atlanta’s victory on Sunday night, they halfway to perfection. This is the first time in franchise history the Falcons have started off 8-0, although star tight end Tony Gonzalez once played on a 9-0 team and Matt Ryan went 8-0 in 2007 at Boston College. After the victory over the Cowboys, what is the current probability that Atlanta goes 16-0?

First, we need to calculate SRS standings. Neil gave us his Weighted SRS Ratings earlier today, but the table below shows the vanilla SRS ratings:

RkTeamGMOVSOSSRS
1San Francisco 49ers810.81.111.8
2Chicago Bears814.5-2.811.7
3New England Patriots812.3-1.510.7
4Houston Texans811.8-2.19.6
5Denver Broncos87.51.69.1
6New York Giants97.318.4
7Atlanta Falcons89.6-2.37.4
8Green Bay Packers95.41.67
9Seattle Seahawks92.13.25.3
10Tampa Bay Buccaneers85.1-1.83.3
11Dallas Cowboys8-3.15.72.6
12Pittsburgh Steelers84.1-2.12.1
13Miami Dolphins83.4-2.31
14Detroit Lions81.3-0.50.7
15Baltimore Ravens82.9-2.20.7
16Carolina Panthers8-3.94.50.6
17Washington Redskins9-2.11.2-0.9
18Minnesota Vikings90.4-1.3-0.9
19Arizona Cardinals9-3.62.1-1.5
20New Orleans Saints8-1.4-0.3-1.7
21San Diego Chargers83.5-5.3-1.8
22New York Jets8-4.82.6-2.1
23St. Louis Rams8-6.94.3-2.6
24Philadelphia Eagles8-6.31.1-5.2
25Cincinnati Bengals8-3.6-1.9-5.5
26Indianapolis Colts8-4.8-1.9-6.6
27Cleveland Browns9-5-2.6-7.6
28Oakland Raiders8-7.3-0.7-7.9
29Buffalo Bills8-7.8-0.8-8.5
30Jacksonville Jaguars8-12.81-11.7
31Tennessee Titans9-14.31.9-12.4
32Kansas City Chiefs8-13.4-1.3-14.7

[continue reading…]

{ 3 comments }

For years, I was an unabashed Philip Rivers supporter. I had no preexisting affinity for the Chargers or Rivers, but in all the metrics I care about, Rivers was always one of the best. In 2008, 2009, and 2010, Philip Rivers led the league in yards per attempt. He finished first in ANY/A in ’08 and second in ’09 and ’10; he finished second in NY/A in ’08 and then first in NY/A in 2009 and 2010. Simply put, going into the 2011 season, no quarterback had been better over the last three years.

Rank Player Tm Gms Cmp Att Cmp% Yds TD Int Rate Sk Y/A SkYds AY/A ANY/A Y/G
1 Philip Rivers SDG 48 986 1505 65.5% 12973 92 33 103.8 88 8.62 545 8.86 8.02 270.3
2 Tom Brady NWE 33 702 1068 65.7% 8374 64 17 102.9 41 7.84 261 8.32 7.78 253.8
3 Drew Brees NOR 47 1224 1807 67.7% 14077 101 50 98.1 58 7.79 412 7.66 7.20 299.5
4 Aaron Rodgers GNB 47 1003 1552 64.6% 12394 86 31 99.4 115 7.99 730 8.20 7.19 263.7
5 Tony Romo DAL 35 771 1213 63.6% 9536 63 30 94.8 61 7.86 360 7.79 7.13 272.5
6 Matt Schaub HTX 43 1012 1537 65.8% 12183 68 37 94.7 80 7.93 524 7.73 7.02 283.3
7 Peyton Manning CLT 48 1214 1805 67.3% 13202 93 45 95.4 40 7.31 251 7.22 6.93 275.0
8 Kurt Warner CRD 31 740 1111 66.6% 8336 56 28 95.2 50 7.50 354 7.38 6.75 268.9
9 Ben Roethlisberger PIT 43 858 1364 62.9% 10829 60 32 92.5 128 7.94 852 7.76 6.53 251.8
10 Eli Manning NYG 48 945 1527 61.9% 11261 79 49 88.3 73 7.37 507 6.97 6.33 234.6
11 Donovan McNabb TOT 43 887 1486 59.7% 10846 59 36 85.4 95 7.30 684 7.00 6.15 252.2
12 Matt Ryan ATL 46 885 1456 60.8% 10061 66 34 86.9 59 6.91 354 6.77 6.27 218.7
13 Kyle Orton TOT 43 901 1504 59.9% 10427 59 33 84.8 90 6.93 562 6.73 6.00 237.0
14 Joe Flacco RAV 48 878 1416 62.0% 10206 60 34 87.9 108 7.21 788 6.97 5.96 212.6
15 Brett Favre TOT 45 923 1411 65.4% 10183 66 48 88.1 86 7.22 599 6.62 5.84 226.3
16 Jay Cutler TOT 47 981 1603 61.2% 11466 75 60 82.9 98 7.15 625 6.40 5.67 244.0
17 Matt Cassel TOT 45 860 1459 58.9% 9733 64 34 83.9 115 6.67 644 6.50 5.62 211.6
18 David Garrard JAX 46 885 1417 62.5% 9951 53 38 84.7 117 7.02 777 6.56 5.56 216.3
19 Jason Campbell TOT 44 836 1342 62.3% 9250 46 29 85.1 114 6.89 759 6.61 5.57 205.6
20 Carson Palmer CIN 36 719 1181 60.9% 7795 50 37 81.4 63 6.60 481 6.04 5.34 216.5
21 Ryan Fitzpatrick TOT 33 603 1040 58.0% 6327 40 34 74.9 83 6.08 465 5.38 4.57 175.8
22 Matt Hasselbeck SEA 35 668 1141 58.5% 7246 34 44 71.2 80 6.35 503 5.21 4.46 207.0

[continue reading…]

{ 14 comments }

Wins with quarterbacks drafted by that team

Good stat today by ESPN’s Adam Schefter, who notes that Kansas City has gone 25 years without winning a game with a quarterback drafted by the Chiefs. This Todd Blackledge-led victory over the Chargers in 1987 was the last time a quarterback drafted by the Chiefs won a game in red and gold.

That’s remarkable, but as always, we need context. The table below looks at all team wins from 1988 to 2012 and shows how many games were won by a quarterback drafted by that team. Note: For purposes of this post, I’m considering John Elway, Jim Everett, Kelly Stouffer, Eli Manning, and Philip Rivers as having been drafted by the Broncos, Rams, Seahawks, Giants, and Chargers, respectively. Additionally, quarterbacks drafted before 1988 count, but only their wins starting in 1988 count for purposes of the table below. The last two columns show, for each, the quarterback with the most wins among those quarterbacks drafted and not drafted by that team.

TmWinsWbDQBPercMWbDQBMWbnDQB
NWE22521294.2%Tom Brady (128)Doug Flutie (7)
IND21318185.0%Peyton Manning (141)Jim Harbaugh (20)
PIT23719883.5%Ben Roethlisberger (83)Tommy Maddox (15)
NYG21717781.6%Eli Manning (74)Kerry Collins (35)
PHI22515970.7%Donovan McNabb (92)Michael Vick (18)
DEN22115570.1%John Elway (102)Jake Plummer (39)
TAM17311767.6%Trent Dilfer (38)Brad Johnson (26)
CIN15610466.7%Carson Palmer (46)Jeff Blake (25)
BUF20312662.1%Jim Kelly (91)Drew Bledsoe (23)
SDG19111861.8%Philip Rivers (66)Stan Humphries (47)
ATL18411361.4%Matt Ryan (49)Chris Chandler (34)
MIA20411857.8%Dan Marino (99)Jay Fiedler (36)
WAS18010457.8%Mark Rypien (45)Brad Johnson (17)
DAL20411556.4%Troy Aikman (94)Tony Romo (50)
NYJ1799955.3%Chad Pennington (32)Vinny Testaverde (35)
TEN21611955.1%Steve McNair (76)Warren Moon (51)
CLE1276853.5%Bernie Kosar (29)Derek Anderson (16)
DET1508053.3%Rodney Peete (21)Scott Mitchell (27)
MIN21410850.5%Daunte Culpepper (38)Warren Moon (21)
JAX1397050.4%David Garrard (39)Mark Brunell (63)
BAL1457350.3%Joe Flacco (49)Steve McNair (15)
ARI1497147.7%Jake Plummer (30)Kurt Warner (27)
CHI1968844.9%Jim Harbaugh (35)Jay Cutler (29)
STL1626540.1%Jim Everett (38)Marc Bulger (41)
SFO2298336.2%Alex Smith (37)Steve Young (89)
HOU712433.8%David Carr (22)Matt Schaub (38)
GNB2316929.9%Aaron Rodgers (45)Brett Favre (160)
CAR1263225.4%Kerry Collins (22)Jake Delhomme (53)
SEA1863619.4%Rick Mirer (20)Matt Hasselbeck (69)
OAK1772011.3%Steve Beuerlein (8)Rich Gannon (45)
NOR19921.00%Danny Wuerffel (2)Drew Brees (64)
KAN20200.00%--Trent Green (48)

As bad as the Chiefs record has been, the Saints record isn’t any better. In fact, since Archie Manning’s last game for the Saints, New Orleans has only drafted two quarterbacks – Dave Wilson and Danny Wuerffel – who have started and won a game for the team. JaMarcus Russell couldn’t even break the Raiders list, ending his career with seven wins. Two other interesting notes. Tony Romo is the only undrafted quarterback in the league currently starting. And of the 32 starting quarterbacks, three of them — Michael Vick, Matt Schaub, and Matt Ryan — were drafted by the Falcons.

{ 7 comments }

Cam Newton is having an interesting year

I don’t care about any of the nonsense with Cam Newton. Instead, take a look at his 2011 and 2012 stat lines:

                                                                                            
Year   GS  QBrec Cmp Att Cmp%  Yds TD TD% Int Int% Y/A  AY/A Y/C  Yd/G Sk Yds NY/A ANY/A Sk% Rsh Yds TD  YPC Y/G  C/G
2011   16 6-10-0 310 517 60.0 4051 21 4.1  17  3.3 7.8  7.2 13.1 253.2 35 260  6.9   6.2 6.3 126 706 14  5.6 44.1 7.9
2012    6  1-5-0 101 173 58.4 1387  5 2.9   6  3.5 8.0  7.0 13.7 231.2 15 102  6.8   5.9 8.0  46 273  3  5.9 45.5 7.7

His Y/A is actually higher this year (although his sack rate is a little worse), and his rushing yards per game and yards per carry are both slightly up. Obviously the biggest change is that Newton simply isn’t scoring very much — he’s on pace for just 21 touchdowns after scoring 35 last year. But touchdowns are more volatile than metrics like yards per attempt, and tend to rebound quickly when paired with a strong yards per attempt average. Compared to league average, Newton’s only slightly worse in NY/A and ANY/A than he was last year, and he’s still above-average in both statistics. Statistically, he looks fine.

But the eye test certainly says Newton is struggling. And some stats back that up, too. Newton ranks 25th in Total QBR, although he only ranked 17th in that metric a year ago. Perhaps more importantly, the Carolina offense has plummeted to 29th in points per drive so far in 2012 (while ranking 17th and 19th in drive success rate), after ranking 6th in points per drive (and 6th in yards and 5th in DSR) in 2011. So the offense has been quite a bit worse, and significantly worse when it comes to scoring. That sort of matches what the “eye test” tells me.

But as Aaron Schatz pointed out to me, there are some odd splits going on with Newton. Take a look at how Newton’s performed on pass attempts on 1st downs this year:
[continue reading…]

{ 2 comments }

Have you taken a look at a passing leaderboard lately? Here’s the PFR passing leaderboard sorted by ANY/A; as always, all columns are sortable.

RkQBTmGCmpAttCmp%YdsTDTD%IntInt%Y/AAY/AY/CSkYdsNY/AANY/ASk%
1Peyton ManningDEN615422767.81808146.241.888.411.710637.47.84.2
2Josh FreemanTAM610418755.61538115.952.78.28.214.89657.57.54.6
3Eli ManningNYG716926563.82109124.572.687.712.55407.77.41.9
4Robert Griffin IIIWAS713318970.4160173.731.68.58.512151067.37.47.4
5Drew BreesNOR616627360.82097186.672.67.77.812.612867.17.24.2
6Ben RoethlisbergerPIT6155235661765114.731.37.57.911.413726.87.25.2
7Tom BradyNWE718628565.32104124.231.17.47.811.314966.77.14.7
8Aaron RodgersGNB718326269.81979197.341.57.68.310.8261426.47.19
9Matt SchaubHOU714022263.11650104.541.87.47.511.88596.973.5
10Jake LockerTEN46710663.278143.821.97.47.311.731676.92.8
11Matt RyanATL616023667.81756145.962.57.47.511131076.66.75.2
12Carson PalmerOAK614824161.4173272.941.77.2711.712936.56.34.7
13Alex SmithSFO712719066.8142794.752.67.57.311.2181006.46.28.7
14Joe FlaccoBAL715025259.5183793.662.47.36.912.2181106.46.16.7
15Andy DaltonCIN715624364.21831135.3104.17.56.811.7171026.75.96.5
16Cam NewtonCAR610117358.4138752.963.58713.7151026.85.98
17Tony RomoDAL615022167.9163683.694.17.46.310.99596.95.83.9
18Ryan FitzpatrickBUF7133218611435156.994.16.66.110.88446.25.73.5
19Christian PonderMIN71522276714929462.66.66.29.816685.95.56.6
20Sam BradfordSTL713121959.8159273.262.77.36.712.2211316.15.58.8
21Ryan TannehillMIA611819859.6145442637.36.412.3121096.45.55.7
22Matthew StaffordDET616426462.1175451.962.36.6610.7128665.44.3
23Michael VickPHI613623158.9163283.583.57.16.21217906.25.46.9
24Andrew LuckIND613425053.6167472.872.86.7612.516995.95.36
25Mark SanchezNYJ711621853.2145394.173.26.7612.514775.95.36
26Jay CutlerCHI610618756.7135984.373.77.36.412.81912165.39.2
27Russell WilsonSEA710417559.4123084.67476.111.8149765.27.4
28Brandon WeedenCLE715427256.6178393.3103.76.65.611.611696.15.13.9
29Philip RiversSDG613920966.51492104.894.37.16.210.7181186.15.17.9
30Kevin KolbARI610918359.6116984.431.66.46.510.7271594.84.912.9
31Matt HasselbeckTEN59615661.593153.242.665.59.710745.24.76
32Blaine GabbertJAX68815855.790663.831.95.75.610.3151054.64.58.7
33Matt CasselKAN510317658.5115052.895.16.54.811.213745.74.16.9

[continue reading…]

{ 1 comment }

More work on POPIP and predicting INT rates

A couple of weeks ago, I wrote about interceptions per incompletion, or POPIP. In that article I showed how a player’s completion percentage is a better predictor of his future interception rate than his actual interception rate. And in this article by Brian Burke, one comment stuck with me:

Griffin has thrown deep, defined as attempts of greater than 15 yards through the air, on only 13% of his attempts, 30th among league quarterbacks. This is also likely the largest factor in his very low interception rate.

That makes sense — quarterbacks throwing short, safe passes should throw fewer interceptions. But this statement is a more important one than you might originally think, thanks to some great research by Mike Clay.

Clay came up with a metric he calls ‘aDOT’ — average depth of target — which measures exactly what you think it does. For each targeted or aimed pass, Pro Football Focus tracks how far from the line of scrimmage the intended target is. What’s makes this stat particularly appealing to me is that it’s very predictable as far as football statistics go. That’s not all that surprising because aDOT is based on a large sample of plays and basically frames how an offense operates.

Clay posted the 10 passers with the largest and smallest aDOT in 2011, which I’ve reproduced below. Note that there are some passes — spikes, throwaways, passes tipped at the line (these are grouped together as ‘other’) — with no target, and therefore are excluded when calculating aDOT. In the far right column, I’ve shown how the player’s aDOT compares to the league average rate of 8.8.

PasserYrAttAimOtheraDOTlgAVG
Tim Tebow20113182863213.3151%
Vince Young2011114111311.6131%
Jason Campbell20111651511410.5119%
Matt Moore20113473281910.4118%
Carson Palmer20113283121610.3117%
Eli Manning20117526985410.1114%
Cam Newton20115174942310113%
Joe Flacco2011605568379.8111%
Ben Roethlisberger2011553529249.8110%
Chad Henne2011112102109.7110%
T.J. Yates2011189171189.6109%
Matt Hasselbeck2011518490288.394%
Drew Brees2011763730338.293%
Blaine Gabbert2011413381328.192%
Alex Smith2011513463508.191%
Tony Romo2011522497258.191%
Ryan Fitzpatrick201156954425890%
Donovan McNabb2011156145117.989%
Colt McCoy2011463434297.888%
Tyler Palko201113512787.484%
Josh Freeman2011551519327.483%

[continue reading…]

{ 2 comments }

Attempting to measure fatigue in the NFL

Fatigue in the NFL is definitely real, and a team that’s tired is not a team that’s likely to excel. But I don’t know if it’s even possible to accurately measure the effect of fatigue in the NFL, and if it is, I certainly don’t know how to do it. Fatigue is a useful descriptive term but one hard to define. Is playing 3 games in 11 days likely to lead to a fatigued team? What about traveling west to east for a 1:00 game? How does that compare to being on the field for 10 minutes? And how does that compare to playing opposite a defense that’s gone 3 and out on three straight drives?

I don’t know. What I can do is look at the data we have from the last 12 years and see what general trends we can discern. So, are defenses worse off if they’ve been on the field for awhile?

There have been nearly 15,000 instances of teams having 1st and 10 near mid-field, defined as between the two 47 yard lines. On average, when teams gain possession in that area, they scored 2.2 points per drive. And, on average, those teams over the course of the season, averaged 1.75 points per drive over all drives.

So what happens if the “1st and 10 from the 47, 48, 49, 50, 49, 48, or 47” is the second play of the drive? Or the third? Or the 9th?

The 2.2 points per drive average when the situation occurs on the first play of the drive is the lowest in the group, although I don’t think that’s due to fatigue. Take a look:

Play #Pts/DrvAvg PPD
12.201.75
22.291.76
32.391.75
42.461.76
52.401.78
62.491.76
72.261.75
82.371.81
9+2.341.75

The middle column shows how many points, on average, teams scored in that situation, while the far right column shows the quality of the offenses in general (not that it really matters in this case). If fatigue had an impact in this situation, you would see the teams that start at their own 20, take 6 or 7 plays, and then have 1st and 10 at midfield be very successful. But that’s not the case.
[continue reading…]

{ 8 comments }

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

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

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

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

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

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

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

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

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

QBYearTmAttNY/ANY/A LgAvRd.Ovrl
Dan Marino1984MIA5648.6146%1.27
Ben Roethlisberger2005PIT2687.8131%1.11
Daunte Culpepper2000MIN4747.4127%1.11
Peyton Manning1999IND5337.3126%1.1
Eric Hipple1981DET2797117%4.85
Boomer Esiason1985CIN4316.8117%2.38
Jay Cutler2007DEN4676.8112%1.11
Matt Robinson1978NYJ2666109%9.227
Bernie Kosar1986CLE5316.3107%1.1
David Carr2003HOU2956.2106%1.1
Josh Freeman2010TAM4746.5105%1.17
Kerry Collins1996CAR3646.1105%1.5
Trent Edwards2008BUF3746.4105%3.92
Brett Favre1992GNB4716104%2.33
Eli Manning2005NYG5576.1104%1.1
Drew Bledsoe1994NWE6916.2104%1.1
Doug Williams1979TAM3975.9103%1.17
Joe Flacco2009BAL4996.3103%1.18
Jim Everett1987RAM3026103%1.3
John Elway1984DEN3806103%1.1
Gus Frerotte1995WAS3966.1103%7.197
Michael Vick2002ATL4216102%1.1
Brian Griese1999DEN4526102%3.91
Rodney Peete1990DET2716101%6.141
Vinny Testaverde1988TAM4665.9100%1.1
Charlie Batch1999DET2705.899%2.60
Joe Montana1980SFO2735.999%3.82
Byron Leftwich2004JAX4416.199%1.7
Tom Brady2001NWE4135.899%6.199
Craig Erickson1993TAM4575.799%4.86
Jake Plummer1998ARI5475.898%2.42
Timm Rosenbach1990PHO4375.998%1.2
Tony Eason1984NWE4315.898%1.15
Matt Ryan2009ATL451697%1.3
Tarvaris Jackson2007MIN2945.997%2.64
Tony Banks1997STL4875.597%2.42
Chuck Long1987DET4165.797%1.12
David Woodley1981MIA3665.897%8.214
Vince Young2007TEN3825.997%1.3
Carson Palmer2004CIN4325.997%1.1
Drew Brees2002SDG5265.696%2.32
Jim McMahon1983CHI2955.795%1.5
Mark Sanchez2010NYJ5075.895%1.5
Billy Joe Tolliver1990SDG4105.795%2.51
Alex Smith2006SFO4425.694%1.1
Mike Pagel1983BAL3285.694%4.84
Steve Walsh1990NOR3365.694%1.1
Shaun King2000TAM4285.493%2.50
Neil O'Donnell1991PIT2865.593%3.70
Chad Henne2009MIA4515.792%2.57
Patrick Ramsey2003WAS3375.392%1.32
David Whitehurst1978GNB3285.192%8.206
JaMarcus Russell2008OAK3685.590%1.1
Don Majkowski1988GNB3365.389%10.255
Tyler Thigpen2008KAN4205.589%7.217
Trent Dilfer1995TAM4155.389%1.6
Danny Kanell1997NYG294588%4.130
Troy Aikman1990DAL3995.288%1.1
Marc Wilson1981OAK3665.287%1.15
Todd Blackledge1984KAN2945.187%1.7
John Friesz1991SDG4875.287%6.138
Chris Miller1988ATL3515.187%1.13
Donovan McNabb2000PHI5695.187%1.2
Steve Fuller1980KAN3205.286%1.23
Browning Nagle1992NYJ387586%2.34
Joey Harrington2003DET554586%1.3
Charlie Frye2006CLE392584%3.67
Kellen Clemens2007NYJ250583%2.49
Cade McNown2000CHI2804.882%1.12
Rick Mirer1994SEA3814.982%1.2
Colt McCoy2011CLE4635.282%3.85
Steve DeBerg1978SFO3024.480%10.275
Phil Simms1980NYG4024.880%1.7
Tim Tebow2011DEN2714.978%1.25
David Klingler1993CIN3434.578%1.6
Sam Bradford2011STL3574.977%1.1
Kyle Boller2004BAL4644.675%1.19
Jeff George1991IND4854.575%1.1
Andrew Walter2006OAK2764.474%3.69
Akili Smith2000CIN2673.560%1.3

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

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

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

References

References
1 Of course, there is the obvious “chicken or the egg” question involved there. The other four teams on that list? The 2004 Ravens (Kyle Boller), 2003 Lions (Joey Harrington), 1997 Buccaneers (Trent Dilfer) and 1992 Bengals (Boomer Esiason/David Klingler) featured four first round quarterbacks who ended up being busts.
2 Note that for purposes of this post, I am considering Eli Manning, Philip Rivers, Jim Everett, and John Elway as being drafted by the Giants, Chargers, Rams and Broncos.
{ 17 comments }

I’m always interested in creative ways to maximize your team’s chances of winning. A few weeks ago, I wrote that when trailing by 14 or 15 points, teams should go for two if they score a touchdown. A different scenario came up during the Ravens-Cowboys game, as Baltimore was up 24-23 with just under five minutes to go when Ray Rice went in for a one-yard score. At that point, some clamored that Baltimore should have gone for two and essentially put the game away. A conversion would have given the Ravens a 9-point lead, while a miss would still leave Baltimore a touchdown. On the surface, it might sound like a risk-free proposition, where even if the gamble fails, you’re still in good shape.

But I don’t think I’d advocate for the bold decision in that situation. In essence, you’re deciding whether your offense is more likely to convert when going for two than your defense is likely to prevent your opponent’s attempt. The decision depends on the likelihood of success: If the league-average rate was 75%, then you’d want to go for two, but if the average rate was 25%, you’d rather force your opponent to have to convert.

In reality, the conversion rate hovers around 50% in the NFL. From 2007 to 2011, teams went for two on 269 plays and converted on 130 of them (48.3%). We can break that down further:

  • On 21 quarterback runs, the conversion was successful 13 times (62%). That is made consists of a 6-for-11 rate on runs up the middle and a 7-for-10 rate on other quarterback runs (which may include some scrambles on designed pass plays).
  • On 50 running back runs, teams converted 33 times (66%). That includes being 21-of-32 on runs to the outside and a 12-for-18 rate on runs up the middle (which includes two Danny Woodhead runs from shotgun).
  • There were also three trick plays with wide receivers throwing passes (Cedrick Wilson, Josh Cribbs and Anquan Boldin) with two of them being successful.
  • On the other 195 pass plays, four times the quarterback was sacked (2%), twice the pass was complete but short of the end zone (1%), and 107 times the pass was incomplete (55%). That leaves 82 successful passes (42%) on two-point conversion pass attempts.

It’s tempting to say that teams should simply run the ball more frequently in these situations, but I think we need to be careful and not let the data speak too loudly. The fact that teams passed on 74% of these plays is itself an indication that passing is the higher-percentage play. When a backup running back has a higher yards per carry average than the starter, it doesn’t mean that the starter is the worse player. I think running is a nice surprise move in these situations, but if teams ran more frequently, the success rate would surely drop (of course, the success rate on passes would then increase, which might make it wise just as a matter of course for teams to try to run more frequently in these admittedly rare situations).

In any event, I don’t think teams should get overconfident about their ability to convert when going for two. However, it’s worth noting that usually it is losing teams — and perhaps that means bad teams — that are going for two. Indeed, on only 108 of the 269 conversion attempts (40%) was the team winning before attempting to go for two. In those 108 cases, teams converted 60 times (56%). For what it’s worth, 31 of the 71 rushing plays (44%) came in these situations, and teams were 41-of-77 (53%) when passing with the lead.

So there does seem to be something to the idea that “bad” teams are dragging down the league average rate, although we’re dealing with small sample sizes. It’s the easy way out, but my gut tells me the actual rate really is right around 50/50.

In that case, does going for 2 up by 1 (before the touchdown) make sense? I don’t think so, unless your offense is much better than your defense (or your opponent’s offense is much better than its defense). In some ways, we should be indifferent about whether we go for two or if our opponent is forced to; it’s like caring about whether you get to call the coin toss or your opponent does.

Say you are up by 1 point and score a touchdown with two minutes to go. Let’s stipulate that the opponent has a 28% chance (to use what will be round numbers in a minute) of going down and scoring to tie it up. If you kick the extra point to go up 8, you have a 93% chance of winning — a 72% chance you stop your opponent plus a 14% chance that even if they score, you stop them on the 2-point conversion, plus another 7% chance that even if they force overtime, you win.

Now, if you go for two and convert, let’s say you have a 100% chance of winning. So converting gives you an extra 7% chance of winning. If you go for two and miss, you still have an 86% chance of winning — the 72% chance your opponent does not score plus the 14% chance you win in overtime. So choosing to go for two and missing only lowers your odds 7% — again, teams should be relatively indifferent about whether it is them or their opponent who ultimately goes for two, assuming the roughly 50% success rate.

But the above analysis is ignoring something, which to me, makes the decision easy. With a minute to go, and a good offense and bad defense, maybe you go for two. But that’s not the situation Baltimore was in — the clock read 4:41 when Rice scored his touchdown. Here is the part that is counter-intuitive but true assuming a 50% conversion rate: the difference between being up by 7 or being up by 8 is *larger* than the difference between being up by 8 or being up by 9.

That’s because going for 2 and converting doesn’t end the game; your win probability doesn’t shoot up to 100%. Down 9, the opponent will play more aggressively, knowing they need two scores. Assume you go for 1 and extend the lead to 8. If your opponent faces a 4th and short on their next drive, they may still punt, because it’s (in their minds) a one-possession game. They won’t punt if down by 9. They are more likely to take their time trying to score (which is beneficial to you, the leading team), which means the odds are very low that they win in regulation. Trailing by 9, they know they need two scores, and will play more aggressively to win the game. To me, I don’t see any reason to incentive bold moves by my opponent, and the more time remaining, the worse the decision to try to “ice the game” by going up 9 looks.

The Ravens game provides a good example. Suppose Baltimore had gone for two and missed. Well, the Cowboys went down and scored, and would have kicked off to the Ravens. Instead — in this case, it is irrelevant that Baltimore kicked the XP and Dallas missed the 2-point conversion, we can assume Baltimore went for two and made it — the Cowboys went for the onside kick and got the ball back. That’s not a move you make in a tie game, but one an aggressive team trailing has to do. Going for 2 early doesn’t bring your win probabiliy up to 100%, and this effect is magnified the more time remaining in the game.

{ 9 comments }

Rating offenses and defenses since 1970

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

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

Part I – Team Rankings

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

{ 2 comments }

Extreme Outliers: Rookie Edition

Griffining: Playing for a coach that tries to help you.

Both Andrew Luck and Robert Griffin III have been very successful this year. Griffin ranks 2nd in Y/A, 2nd in AY/A, 4th in NY/A, and 4th in ANY/A, an incredible performance nearly across the board (he’s 23rd in sack rate) by the Redskins rookie. He also is leading the league with a 69.1% completion rate and ranks 5th in passer rating. Luck ranks only 23rd in Y/A, 22nd in AY/A, 21st in NY/A, and 18th in ANY/A, respectable numbers for a rookie but on the surface, little more than that. He does rank 7th in sack rate, which is an excellent sign, but he ranks last in the NFL in completion percentage (in the non-Mark Sanchez division) and only 25th in passer rating.

But there are some other stats out there that paint a different picture. ESPN’s Total QBR ranks Griffin 11th overall — slightly below most of his other metrics — but ranks Luck as the fourth most effective quarterback so far this season. Also, despite Griffin’s edge in most metrics, the Colts and Redskins are essentially tied in three key drive metrics — points per drive, yards per drive and drive success rate — and I don’t think that’s because Donald Brown is so awesome. As Nate Dunlevy pointed out to me, one reason for this is that Luck has accumulated a large number of rushing first downs: Luck is tied for the league lead with Arian Foster on third down rushes that resulted in a first down. And once you account for strength of schedule, Luck vaults to #1 on the QBR list.

But let’s put aside effectiveness for right now. Some advanced metrics show you that they’ve been skinning cats in very different ways:

  • According to Advanced NFL Stats, Luck has thrown a pass 15 yards past the line of scrimmage on 24.3% of his throws, the 5th highest rate in the league. Griffin ranks 32nd with a deep rate of just 12.2%, ahead of only Matt Hasselbeck.
  • If you completely removed Yards After the Catch from the equation, Luck would rank in the top 10 at 4.5 yards per attempt while Griffin would rank 25th with just 3.5 yards per attempt.
  • Griffin ranks third behind just Christian Ponder and Philip Rivers when it comes to percentage of passing yards that are due to YAC, at 58.7%; Luck ranks 32nd, ahead of only John Skelton and Mark Sanchez, with only 33.4% of his yards coming on yards after the catch by his receivers.
  • According to Footballguys.com’s subscriber content, the Colts have targeted their wide receivers on 72.1% of their passes, the second highest rate in the league behind the Rams. The Colts are also last in the league with only 6.4% of their passes aimed at running backs (this also jives with the numbers from Mike Clay of Pro Football Focus.). The Redskins are more middle of the road in these metrics, but Andrew Luck is being forced to rely on his wide receivers with no real receiving threat in the backfield to help him out. As a result, it’s probably not too surprising that his completion percentage is so low.

Luck has also excelled in the two-minute drill and no-huddle situations early this year, although Griffin has been no slouch in those departments, either. But it’s clear that the Colts — rightly or wrongly — aren’t treating Luck with kid gloves. In fact, one could argue that they’re treating him no differently than they did Peyton Manning. Luck is averaging 44.3 pass attempts per game so far this season, second behind only Drew Brees. With a mediocre defense and a bad running game, the Colts are basically putting each game in the hands of Luck to win. Griffin is averaging only 27.8 pass attempts per game right now, and the Redskins have done a fantastic job molding the offense to to suit Griffin’s strengths.

Griffin’s numbers are better right now — ESPN excluded, of course — but that may be a reflection that the Shanaclan is more nurturing than Bruce Arians. Griffin’s success is outstanding, but Luck has been doing just as well under much more challenging conditions.

Update: Jeff Bennett, one of the creators behind ESPN’s Total QB, e-mailed me some notes this morning:

We break rushing out into two categories, scrambles and designed rushes. The quarterback receives more credit for scrambles then designed rushes – the reason being designed rushes are, well, designed to help the quarterback get more yards on the rush. Scrambles are not. Whatever positive or negative that comes from those is mostly on the quarterback.

So back to Luck. He has nine first down rushes this season on scrambles, most in the NFL. Seven of the nine have come on 3rd down, which generally is more important since the alternative to not picking up a 1st down is likely a punt instead of 2nd or 3rd down. No one else in the league has more than three 1st down rushes on scrambles.

Luck’s average pass is traveling 9.8 yards downfield this season. That is the third longest average pass distance in the league (behind Joe Flacco and Jay Cutler). Griffin averages 7.2 yards, a full yard below league average.

The average quarterback this season is getting 56% of their passing yards via “air yards” (meaning 44% of yards are coming after the catch). Griffin has 43% of his yards through the air. Luck has 68%.

{ 3 comments }

www.notacompiler.com.

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

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

The #50YG column shows how many games the player had over 50 rushing yards, and the next column shows what percentage of games the running back gained over 50 yards. For players like Jim Brown, this study only includes his seasons starting in 1960, and for active players, 2012 data is *not* included:
[continue reading…]

{ 11 comments }

We don’t know anything and we never will

Five weeks in, you start to hear NFL experts trade their preseason overconfidence for regular season overconfidence. It’s tempting to fall into the trap thinking that with over a quarter of the season in the books, now we have an idea of how the rest of the regular season will unfold.

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

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

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

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

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

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

wk#gmsSpreadLineOffw/i 10pts
13624.710.760%
23615.210.558%
33405.610.456%
43225.510.956%
53195.21064%
63155.41060%
73155.610.957%
83155.510.659%
93265.310.559%
103395.610.157%
113585.69.461%
123625.71062%
133635.410.159%
14360610.859%
15363610.957%
163635.710.957%
173355.710.461%

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

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

GameYrFavUnderlineFavPtsUnPtsLineOff
2003 rav -3 vs. sea2003ravsea344410
2003 tam -6 vs. nyg2003tamnyg619130
2003 clt -3 vs. buf2003cltbuf317140
2003 min -10.5 vs. det2003mindet10.524140.5
2003 oti -6.5 vs. atl2003otiatl6.538310.5
2003 nyj -4 vs. jax2003nyjjax413101
2003 dal -3 vs. car2003dalcar324201
2003 nwe -5 vs. htx2003nwehtx523202
2003 cin -3 vs. sdg2003cinsdg334274
2003 ram -7 vs. crd2003ramcrd730274
2003 mia -7 vs. was2003miawas724236
2003 gnb -3.5 vs. sfo2003gnbsfo3.520106.5
2003 phi -5.5 vs. nor2003phinor5.533207.5
2003 kan -11 vs. rai2003kanrai1127248
2003 cle -3 vs. pit2003clepit361310
2003 den -10.5 vs. chi2003denchi10.5101919.5

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

GameYrFavUnderlineFaPtsUnPtsLineOff
1993 nwe -6 vs. clt1993nweclt638032
1993 cle -2 vs. ram1993cleram2421426
1993 gnb -3 vs. rai1993gnbrai328025
1993 sdg -1 vs. mia1993sdgmia1452024
1993 kan -3 vs. min1993kanmin3103023
1993 den -13.5 vs. tam1993dentam13.5101720.5
1993 dal -17 vs. was1993dalwas1738318
1993 nyg -3 vs. crd1993nygcrd361714
1993 pit -3 vs. sea1993pitsea361613
1993 sfo -7.5 vs. oti1993sfooti7.571010.5
1993 chi -3 vs. det1993chidet314209
1993 phi -3 vs. nor1993phinor337268
1993 atl -3.5 vs. cin1993atlcin3.517217.5
1993 buf -7 vs. nyj1993bufnyj716145
{ 8 comments }

How predictive is 4th quarter play?

Last week, Neil had a fascinating post on how each team’s win probability has varied by quarter over the last 35 years. The 2004 Pittsburgh Steelers were the poster child for wins added during the 4th quarter and overtime. Pittsburgh went 15-1, which means they exceeded the league average by 7 wins (the average team, of course, goes 8-8). So how did Pittsburgh go about getting those extra 7 wins?

The table below lists all 16 regular season games for the Steelers. The fifth column shows the point spread before the game, and the sixth column assumes that the home team has a 57.9% chance of winning every game. Of course, that’s going to be modified by the actual point spread, so the next column shows the win probability added based on the Vegas line. This is neutral of the home field WP, and the “wpa bg” column shows the total win probability of the team before the game. So when the Steelers hosted the Raiders in week 1, they were a 3.5-point home favorite, which meant they had a 60% chance of winning. The next four columns show how much win probability was added by the end of each quarter.

WkOppPFPALinewpa_locwpa_vegwpa bgwpa_1stwpa_2ndwpa_3rdwpa_4thwpa_tot
1rai2421-3.57.9%2.1%60.0%18.9%2.5%14.3%4.4%100.0%
2rav13304-7.9%-3.5%38.7%-18.4%-13.9%-6.2%-0.1%0.0%
3mia1332.5-7.9%0.7%42.8%10.9%3.3%21.0%22.0%100.0%
4cin2817-47.9%3.5%61.3%-1.5%13.1%-34.3%61.3%100.0%
5cle3423-4.57.9%4.9%62.7%10.3%22.1%4.6%0.3%100.0%
6dal24203-7.9%-0.7%41.4%1.1%1.4%-37.8%93.9%100.0%
8nwe342037.9%-16.4%41.4%49.1%-0.6%10.0%0.2%100.0%
9phi2731.57.9%-12.2%45.7%40.1%10.3%3.8%0.1%100.0%
10cle2410-3.5-7.9%17.8%60.0%11.0%19.4%5.3%4.4%100.0%
11cin1914-4-7.9%19.2%61.3%-14.7%-4.8%29.8%28.2%100.0%
12was167-107.9%18.6%76.5%4.4%15.8%-7.7%11.1%100.0%
13jax1716-3-7.9%16.4%58.6%-1.1%23.3%-20.7%40.1%100.0%
14nyj176-4.57.9%4.9%62.7%7.5%0.2%-13.9%43.6%100.0%
15nyg3330-10-7.9%34.3%76.5%-15.0%25.4%-28.3%41.5%100.0%
16rav207-57.9%6.2%64.1%-1.8%9.0%23.5%5.3%100.0%
17buf29249.5-7.9%-17.5%24.7%11.9%18.5%-23.7%68.6%100.0%
Total0.00.88.81.11.4-0.64.215.0

For a 15-1 team, the Steelers were rarely heavy favorites; in fact, based on the point-spread in each game, Vegas would have expected Pittsburgh to win only 8.8 games. And while the Steelers played well in the first half, the main reason they achieved their lofty record was their 4th quarter performance. In fact, over half of their wins over average could be attributed to their great 4th quarter play. To put it another way, if you turned off every Pittsburgh game in 2004 right at the end of the 4th quarter, you would have guessed that the Steelers would win only 11.8 games.

That may not mean much in the abstract, but let’s compare the Steelers to the other teams with 15+ wins in NFL history:
[continue reading…]

{ 1 comment }

.

Buffalo's defense probably would have been better yesterday if Bruce Smith played. Even at age 49.

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

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

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

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

TmYrwkOppDatePFPAATTPYDRSHRYDMin
SFO20125BUF10/7/20124532531038311310
PHI201010WAS11/15/201059282833238260260
NYG20085SEA10/5/20084462626936254254
DEN20058PHI10/30/200549213530936255255
CIN200412CLE11/28/200458482925132253251
KAN20047ATL10/24/200456102826949271269
JAX199919MIA1/15/20006272026346257257
PHI19956WAS10/8/199537344525238272252
GNB199415CHI12/11/19944033425946257257
KAN19906DET10/14/199043242625643310256
RAM19886ATL10/9/19883302624945252252
NYJ19885KAN10/2/198817174827046272270
WAS198719DEN1/31/198842103032240280280
CIN198614NWE12/7/19863173128442300284
NOR198614MIA12/7/198627313426936257257
CIN198514DAL12/8/198550243329642274274
SEA198313KAN11/27/198351483225147280251
PIT19829CLE1/2/198337212426049261260
CHI198014GNB12/7/19806172432748267267
PIT197913CLE11/25/197933304435145255255
ATL19791NOR9/2/197940343829547257257
DAL19781BAL9/4/19783802730545278278
CHI197613SEA12/5/19763472825048259250
DEN19762NYJ9/19/19764632829244251251
OAK19758NOR11/9/197548102726353260260
DAL19733STL9/30/197345102532842250250
SDG196316BOS1/5/196451102629232318292
GNB19629PHI11/11/19624903133455294294
HOU196113NYT12/10/196148213830735266266
SFO19614RAM10/8/19613502626240259259
CRD19591WAS9/27/195949212831926250250
SFO19589GNB11/23/195833123528342256256
RAM19587SFO11/9/19585672725339324253
CRD19582WAS10/4/195837103227039261261
RAM195711GNB12/8/195742173129742302297
RAM195612GNB12/16/195649212729749314297
BAL19568RAM11/25/195656212631639258258
SFO195312BAL12/13/195345144434533252252
RAM195010NYY11/19/195043354937038266266
CRD19502BAL10/2/195055133028151272272
PHI19498WAS11/13/194944212829455256256
CRD19498NYY11/13/194965203625347319253
PHI194812DET12/12/194845212425850262258
SFO19485BUF9/26/19483828252680268268
CRD19484NYG10/17/194863352028034299280
LAD194612BUF12/1/19466214193370288288
{ 1 comment }

The shutout effect

This picture is unrelated to the content of this post.

Do teams play better after getting shut out? There is a certain added level of embarrassment when a team fails to score in a game. If you lose 34-3, you spend the week hearing about how terribly you played. If you lose 34-0, you spend the week hear about how terribly you played and how you couldn’t even score!

This made me wonder: do teams perform better in the week after a shutout than after a similar blowout? Perhaps the added embarrassment encourages a team to go back to the drawing board and really focus on what went wrong. That was just a theory, though. So I looked at all games since 1960 where a team (a) lost by at least 20 points and (b) had scored 0 points entering the 4th quarter. There were 593 such games, and the team was shutout in 379 times.

Sixty-five of those games of those games occurred in a team’s last game of the season, so I eliminated those games. Of the remaining 528 games, how did the teams that failed to score fare the next week relative to the non-shutout teams? The table below lists the points scored and allowed by each group of teams, along with how many points they scored and allowed in their next game (and their winning percentage in that game):

Category#GmsPFPAN+1 PFN+1 PAN+1 WIN%
Shutout336030.217.921.40.375
Non-SO1927.83518.222.70.365

As you can see, there seems to be nothing to the shutout effect. Teams score and win at roughly the same rates regardless of whether they get a meaningless late score or not. So why am I making this a post? Because a “no effect” answer is often just as meaningful as any other type of answer.

{ 3 comments }

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

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

DateTmOppQBWPA_locWPA_vegasWPA_1stWPA_2ndWPA_3rdWPA_4thWPA_otWPA_tot
10/1/2012Chi@DalJay Cutler-0.079-0.021+0.013+0.137+0.420+0.029+0.000+0.500

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

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

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

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

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

[continue reading…]

{ 13 comments }

The definitive analysis of offensive fumbles

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

— Interceptions
— Offensive fumbles
— Special teams/defensive fumbles

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

The six types of offensive fumbles

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

— Quarterback/center exchange fumbles

— Quarterback sack fumbles

— Quarterback fumbles on running plays

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

— Fumbles on running plays (non-QB)

— Fumbles following completed passes

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

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

CategoryNumber%ofOffFumOut of BoundsRec by FumblerRec by OffRec by DefDef rec of FIP
Aborted Snaps136718.2%2.6%43.2%29.8%24.4%44.9%
QB Sacks244032.5%2.3%13.8%33.2%50.8%60.5%
QB Running plays2623.5%12.6%27.1%14.9%45.4%75.3%
QB Negative runs1081.4%6.5%43.5%16.7%33.3%66.7%
Non-QB Runs174123.2%5.6%13.3%18.9%62.1%76.7%
Fumbles on Receptions158821.2%17.3%9.9%12.8%60.0%82.4%
Total7506100.0%6.7%19.1%24.1%50.1%67.6%

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

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

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

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

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

References

References
1 Technically, it could go harmfully go out of bounds, too. There were a few examples where the ball went out of bounds for a safety, or a team driving for a touchdown fumbled out of the other team’s end zone for a touchback.
{ 16 comments }

Interceptions per Incompletion (or POPIP)

The closest I'm willing to get with a baseball photo.

I leave the baseball analysis to my brothers at baseball-reference.com, but I know enough to be dangerous. There’s a stat called BABIP, which stands for Batting Average on Balls In Play. A “ball in play” is simply any at bat that doesn’t end in a home run or a strikeout. The thinking goes that luck and randomness is mostly responsible for the variance in BABIP allowed by pitchers to opposing batters. Pitchers can control the number of strikeouts they throw and control whether they allow home runs or not, but they can’t really control their BABIP.

Therefore, if a pitcher has a high BABIP, sort of like an NFL team with a lot of turnovers, he’s probably been unlucky. And good things may be coming around the corner. A high BABIP means a pitcher probably has an ERA higher than he “should” and that his ERA will go down in the future. In fact, you can easily recalculate a pitcher’s ERA by replacing the actual BABIP he has allowed with the league average BABIP. And that ERA will be a better predictor of future ERA than the actual ERA. At least, I think. Forgive me if my baseball analysis is not perfect.

Are you still awake? It’s Monday, and I’ve brought not only baseball into the equation, but obscure baseball statistics. Let’s get to the point of the post by starting with a hypothesis:

Assume that it is within a quarterback’s control as to whether he throws a completed pass on any given pass attempt. However, if he throws an incomplete pass, then he has no control over whether or not that pass is intercepted.
[continue reading…]

{ 14 comments }

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

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

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

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

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

{ 0 comments }

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

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

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

The Comeback ranks as the 2nd most impressive comeback after two quarters, but only 20th overall.

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

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

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

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

Stdev = 13.86 / sqrt(60 / n)

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

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

Team1st2nd3rd4thTotal
Atlanta10107027
Denver0701421

To this:

TeamPregameAfter 1stAt HalfAfter 3rdFinal
Atlanta58.6%84.6%93.0%99.9%100.0%
Denver41.4%15.4%7.0%0.1%0.0%

[continue reading…]

{ 7 comments }

How much should the week 1 results impact your projections for team wins in 2012? That’s what this post will attempt to answer.

Let’s start with the basics. Before the season, if you knew nothing about a team other than how many games it won the prior year, how many wins should you project for such team this year? This is a relatively simple question to answer giving enough historical data and a program to perform a regression analysis. After doing just that, I can tell you that you should project each team to win 5.28 games plus 0.34 times the number of games they won in the previous season. So a 4-win team projects to 6.6 wins, a 6-win team projects to 7.3 wins, and an 11-win team should drop down to 9.0 wins. There is a significant regression to the mean force at play here, unsurprisingly. Even a 15-win team projects to “only” 10.4 wins.

Of course, this is far from perfect. The R^2 of this model is just 0.11, an indication that there are significantly more factors at play in determining a team’s record than their amount of wins the prior year. Well, duh. However, we can improve on that 0.11 number. If we use SRS ratings as inputs instead of wins, that R^2 goes to 0.15. This is not surprising, and this is exactly what I mean when I say that the SRS is more predictive of future performance than wins. What’s the best-fit formula?

Each team should win 8.05 games plus or minus 0.196 wins for every point a team had in the SRS in the prior season. This means that a team that was 5 points better than average should be projected to win 9.0 games the next season, while a team that was 11 points below average in 2010 projects as a 5.9-win team in 2011.

At this point, you might think: okay, great, now let’s combine them both! Let’s use both SRS ratings and team wins as inputs and Year N+1 wins as outputs. Well, doing that adds nothing to the predictive power of the model. This is another reason not to use actual records for predictive purposes. For illustrative purposes, I performed such a regression, and the model tells us that the “record” variable has a p-value of 0.61, making it nowhere near statistically significant (and the weight on the variable was -0.04, making it practically insignificant as well). In layman’s terms, what this means is that if we already know a team’s SRS ratings, also knowing their won-loss record is not helpful to predicting their future performance.

Now have a simple way to project each team’s number of wins in a given season: 8.05 + 0.196*each team’s SRS rating from the prior year. You might wonder why that number is at 8.05 and not 8.00; that’s because I didn’t simply use the standard, regular season SRS ratings, but rather I calculated each team’s SRS score based on all of their games, postseason included. Therefore, the average is slightly higher than 8.00 since the best teams played the most games. There’s no good reason to ignore the postseason when projecting future performance (other than laziness, in which case I approve). I didn’t put special weight on games from the 2011 playoffs, but simply counted them as additional games. Anyway, the table below shows the SRS ratings from each team in 2011 and their projected 2012 wins based on the above formula:
[continue reading…]

{ 10 comments }

The first Monday night of the regular season gives us two football games to enjoy. At 7:00, the Bengals travel to Baltimore giving Cincinnati an immediate chance to prove that last year’s playoff berth was no fluke. At 10:15, the Chargers travel to Oakland and look to show that missing out on the last two postseasons was nothing more than a fluke.

Let’s start with the Bengals. In 2011, Cincinnati lost every game they played against playoff teams and won every game against non-playoff teams. The nine wins came against Cleveland (twice), Buffalo, Jacksonville, Indianapolis, Seattle, Tennessee, St. Louis and Arizona. On the other hand, the Bengals lost twice each to Pittsburgh, Baltimore, and Houston, and lost in Denver and San Francisco early in the year.

That is more an interesting bit of trivia than anything else. No team since the merger had ever done that before, and only two pre-merger teams managed to pull of that feat. [1]In the early ’50s, the playoffs consisted of just a championship game between the two division winners. In 1953, the 49ers lost both games division rival Detroit and to Eastern division champ … Continue reading For the Bengals, the odd split is more an embarrassing blemish that rival fans can point to than anything else. It’s not as if the Bengals can’t beat playoff teams, it’s simply that they didn’t. In 1969, the Cowboys went 0-3 against playoff teams and 11-0-1 against non-playoff teams; the next season, Dallas made the Super Bowl and in 1971 the Cowboys won it. Lombardi’s Packers pulled off the same feat in the middle of their great run: in ’63, Green Bay was 0-2 against playoff teams and 11-0-1 against non-playoff teams a year after having one of the most dominant seasons in football history. The Bengals weren’t a great team last year, but had they gone 7-2 against non-playoff teams and 2-5 in the regular season against playoff teams, would they — or rather, should they — be viewed as any better? Swapping a win against Pittsburgh and Baltimore for losses against say, Cleveland and Seattle?
[continue reading…]

References

References
1 In the early ’50s, the playoffs consisted of just a championship game between the two division winners. In 1953, the 49ers lost both games division rival Detroit and to Eastern division champ Cleveland; the 49ers went 9-0 against the rest of the league. The year before, the Rams pulled off the same feat: they lost week 1 in Cleveland, week 2 against Detroit, and week 4 in Detroit, while winning every other game. That gave them a 9-3 record, the same as the Lions, which at the time mandated a play-in game. Detroit beat Los Angeles in that one, too. If you limit the study to just regular season results, you end up with two more teams. In 1999, the Jacksonville Jaguars went 14-0 against non-playoff teams but lost both games to division rival Tennessee; the Titans would also defeat the Jaguars in the AFC Championship Game, proving that Tom Coughlin was incapable of winning the big one. And in 1950, the Cleveland Browns were swept by division rival New York but won every other game that season; they didn’t face Western Division champ Los Angeles in the regular season. Cleveland ended the season 10-2, just like the Giants. The Browns avoided losing a third straight game to New York, winning 8-3 in the play-in game, and then captured the NFL championship by defeating Los Angeles the next week.
{ 6 comments }

The third and fourth most popular quarterbacks in New York this week.

There are few nights as precious as tonight, the official start of the 2012 regular season. Even after tonight, 255 regular season games remain for us to enjoy. As usual, the defending Super Bowl champion hosts the opening game, and it didn’t take the NFL schedule makers long to decide on an opponent. This will be the 6th time in 8 meetings that the Giants and Cowboys will meet on primetime television. And as usual, the media will turn this game into another referendum on Tony Romo and Eli Manning.

Public perception says that Manning is the better quarterback, based largely exclusively on his post-season success and reputation as a clutch quarterback. And there’s a good reason he has such a reputation: Manning has won 8 of his last 9 playoff games and tied NFL single-season records with seven 4th-quarter comebacks and eight game-winning drives in 2011. Romo has a reputation as the chokiest of chokers, is 1-3 in playoff games, and has been less stellar than Manning late in games. While Manning has 21 career 4th quarter comebacks and is 21-22 in games where he had an opportunity for a 4th quarter comeback, Romo is just 13-20 in 4th quarter comeback opportunities. But let’s leave that to the side for now.

Because based on their regular season statistics, Romo absolutely crushes Manning, at least statistically. The gap shrunk significantly in 2011, but Romo’s track record of production and efficiently is considerably more impressive. Manning entered the league in 2004 but struggled his first three years; Romo first started in 2006 and was above average immediately. But let’s just focus on the past five seasons. The table below displays the statistics each quarterback produced from 2007 to 2011. Note that since Romo has missed time due to injury, I have added a third row, which pro-rates Romo’s numbers to 80 starts:
[continue reading…]

{ 38 comments }
Next Posts Previous Posts