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Tag Archives: TB Buccaneers

Player performance grades from Pro Football Focus; salary information from Spotrac.com; contract quality is the number of standard deviations a player’s performance is above/below the average, minus the number of standard deviations his average annual salary is above/below the average; all rankings are positional; Michael Johnson is a 4-3 defensive end.

Numbers

Age: 27 (28 on February 7th, 2015)
Old Team: Cincinnati Bengals
Old Contract: 1 year/$11.175 million, $11.175 million average (5th highest paid of 62)
2013 PFF Grade: 25.9 (4th)
2013 Contract Quality: -0.53 (39th)
New Team: Tampa Bay Buccaneers
New Contract: 5 years/$43.75 million, $8.75 million average (projected 7th highest paid)

* indicates a franchise tag contract

Notes

Yesterday I wrote how the Carolina Panthers will likely regret using the franchise tag on Greg Hardy (who is also a 4-3 defensive end) this season. Michael Johnson shows exactly why. Unable to lock Johnson down long-term, the Bengals seemingly overpaid him by $2 million last year to keep him for one season.1 Cincinnati could not workout a long-term deal again this year, and unable to franchise Johnson again he took his talent to Tampa Bay. Ta-da!

But hey, last season the salary cap was $123 million. What could an additional $2 million (or $11 million if they had let Johnson walk) have bought the Bengals anyway? In 2013, average NFL tight ends, fullbacks, and guards (who played 75% or more of their teams’ snaps) earned average annual salaries of less than $2.5 million. The average starting NFL offensive line last season cost a team $14.691 million. True, good, even average players are not necessarily available for the signing, in which case seemingly overspending to keep a player that is attainable is less harmful. Nonetheless, the Bengals likely could have put the money spent on Johnson last year to better use.

But that is all in the past. How do things look from the perspective of Johnson’s new team? Tampa Bay fans should like this signing. Last season Johnson’s approximate worth was $9.206 million; the Bucs will pay him a little less than that for five years, most of which will come before Johnson turns 30. There may be an adjustment period with a new team, but he seems well in his prime.

Though Johnson will no longer have Geno Atkins to assist him along the line, the equally freakish Gerald McCoy will be with him in Tampa through 2015. The Bucs defensive front looks set. If they get a deal or two of Johnson’s quality on the offensive side, just maybe they can challenge in the NFC South.


  1. Johnson did earn the fourth-highest PFF grade while making the fifth-most money at his position, which seems like a steal. But based on the performances and salaries of all 4-3 defensive ends last year, only Robert Quinn’s outlying expertise is worthy of $10 million-plus annually; Johnson was not the only player overpaid last season. And while there are factors to consider besides on-field performance, Johnson likely would not win an NFL fan popularity contest. 
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I embarked on a pretty sweet mini-project today, if I do say so myself. It starts with a couple of… “problems” that had been nagging me, regarding the lack of use of football’s Pythagorean formula. Pythagorean wins (or winning percentage) have been showing up in NFL analysis for, I dunno, at least a few years now. I learned of them in a Bill Barnwell preseason piece before the 2012 NFL Season (on Grantland.com). A team’s Pythagorean winning percentage (PW%) is as follows:
PW% = (Points Scored ^ 2.37) / {(Points Scored ^ 2.37) + (Points Allowed ^ 2.37)}1
Say the Bengals and Browns are both 8-8. The Bengals blew out their opponents in their eight wins, and lost narrowly in their eight losses, while the Browns won narrowly in their wins and lost big in their losses. You probably agree that even with the same record, the Bengals are likely better than the Browns. PW% is a measure of how much.

What’s bothered me is that Pythagorean analysis usually stops there: with a team’s points scored and points allowed. But one could apply the same analysis to a group of teams, say the 49ers’ opponents in the 2013 season, and determine that group’s PW%. Then one would know how tough the 49ers’ competition had been this year, beyond simple wins and losses. And, instead of using this year’s record as a strength of schedule statistic for next year’s season, one could use it for this very season itself, adding context to those final standings. We don’t have to just assume that all ten-win teams are equally skilled (or that they aren’t); we can quantify other useful metrics and see if there’s any evidence for our assumptions. And that’s exactly what I’ve done. For all 32 teams, and all 13 of their opponents, through 15 games.2 Let’s take a look!

First off, we have our sin-context base, nothing but the ‘W’s:

Rank Team W W%
1 SEA 12 80.00%
1 DEN 12 80.00%
3 SF 11 78.57%
4 CAR 11 73.33%
4 KC 11 73.33%
4 NE 11 73.33%
7 CIN 10 66.67%
7 NO 10 66.67%
7 ARI 10 66.67%
7 IND 10 66.67%
11 PHI 9 60.00%
12 SD 8 53.33%
12 DAL 8 53.33%
12 MIA 8 53.33%
12 BAL 8 53.33%
12 CHI 8 53.33%
17 GB 7.5 50.00%
18 DET 7 46.67%
18 STL 7 46.67%
18 PIT 7 46.67%
18 NYJ 7 46.67%
22 TEN 6 40.00%
22 BUF 6 40.00%
22 NYG 6 40.00%
25 MIN 4.5 30.00%
26 ATL 4 28.57%
27 TB 4 26.67%
27 CLE 4 26.67%
27 OAK 4 26.67%
27 JAC 4 26.67%
31 WAS 3 20.00%
32 HOU 2 13.33%

Alright. Mostly, all that’s good for is figuring out who gets the first pick in the draft. Let’s add some context. Here are the same figures, for Pythagorean wins:

Rank Team Pythagorean Wins PW%
1 SEA 11.9 79.17%
2 CAR 11.1 74.18%
3 SF 10.9 72.95%
4 DEN 10.8 71.88%
5 KC 10.7 71.05%
6 CIN 10.2 68.02%
7 NO 9.7 64.90%
8 NE 9.7 64.62%
9 ARI 9.0 60.29%
10 PHI 8.8 58.76%
11 SD 8.6 57.65%
12 IND 8.4 56.01%
13 DET 8.0 53.18%
14 DAL 7.7 51.29%
15 STL 7.6 50.35%
16 PIT 7.4 49.34%
17 MIA 7.4 49.05%
18 GB 7.1 47.58%
19 BAL 7.1 47.14%
20 CHI 6.9 46.16%
21 TEN 6.9 45.88%
22 BUF 6.6 43.86%
23 MIN 5.6 37.58%
24 ATL 5.4 36.32%
25 TB 5.4 35.76%
26 CLE 5.4 35.68%
27 OAK 4.9 32.53%
28 NYG 4.8 31.94%
29 WAS 4.7 31.19%
30 NYJ 4.6 30.79%
31 HOU 3.9 26.17%
32 JAC 3.1 20.58%

Now Carolina and San Francisco appear a little bit better than Denver; Jacksonville still has a firm grip on last place, in the Pythagorean world. Curious how these little differences do add up and do affect rankings. You can get an idea of how teams landed where they did by checking out their point totals, presented here, in order of most net points through the 15 games so far:

Rank Team Net Points PF PF Rank PA PA Rank
1 DEN 187 572 1 385 22
2 SEA 168 390 8 222 2
3 SF 131 383 10 252 3
4 KC 128 406 6 278 4
5 CAR 124 345 19 221 1
6 CIN 108 396 7 288 6
7 NE 92 410 5 318 9
8 NO 85 372 13 287 5
9 PHI 58 418 2 360 16
9 ARI 58 359 16 301 7
11 SD 45 369 14 324 11
12 IND 35 361 15 326 12
13 DET 20 382 11 362 17
14 DAL 9 417 3 408 25
15 STL 2 339 20 337 13
16 PIT -4 359 16 363 18
17 MIA -5 310 24 315 8
18 BAL -15 303 26 318 9
19 GB -16 384 9 400 24
20 TEN -25 346 18 371 19
21 CHI -28 417 3 445 30
22 BUF -35 319 23 354 15
23 TB -76 271 29 347 14
24 CLE -85 301 27 386 23
25 ATL -89 333 21 422 29
26 MIN -90 377 12 467 32
27 NYG -103 274 28 377 20
28 NYJ -110 270 30 380 21
29 OAK -111 308 25 419 27
30 WAS -130 328 22 458 31
31 HOU -146 266 31 412 26
32 JAC -182 237 32 419 27

Those are the inputs. And the outputs? Subtracting actual wins from Pythagorean wins, we reveal how many “lucky” wins (or losses) each team has:

Rank Team W – PW W PW
1 NYJ 2.4 7 4.6
2 IND 1.6 10 8.4
3 NE 1.3 11 9.7
4 DEN 1.2 12 10.8
5 NYG 1.2 6 4.8
6 CHI 1.1 8 6.9
7 ARI 1.0 10 9.0
8 BAL 0.9 8 7.1
9 JAC 0.9 4 3.1
10 MIA 0.6 8 7.4
11 GB 0.4 7.5 7.1
12 KC 0.3 11 10.7
13 DAL 0.3 8 7.7
14 NO 0.3 10 9.7
15 PHI 0.2 9 8.8
16 SEA 0.1 12 11.9
17 SF 0.1 11 10.9
18 CAR -0.1 11 11.1
19 CIN -0.2 10 10.2
20 PIT -0.4 7 7.4
21 STL -0.6 7 7.6
22 BUF -0.6 6 6.6
23 SD -0.6 8 8.6
24 OAK -0.9 4 4.9
24 TEN -0.9 6 6.9
26 DET -1.0 7 8.0
27 MIN -1.1 4.5 5.6
28 CLE -1.4 4 5.4
29 TB -1.4 4 5.4
30 ATL -1.4 4 5.4
31 WAS -1.7 3 4.7
32 HOU -1.9 2 3.9

The Jets have outperformed by more than two wins! And Rex Ryan still might get fired. Also, Jacksonville’s good luck has ruined formerly promising chances of getting the first pick in the draft, as likely they’ll instead see it go to Houston. It’s really Houston that has performed better, losing by significantly fewer points, albeit more often. Well, perhaps Houston’s competition was much easier? Or perhaps not? You don’t have to wonder, let’s see! Here are the teams ranked by the average net points of their opponents, adjusted by removing totals from games against the team in question.3

Tm Rk O Nt Pts /Gm O PF /Gm Rk O PA /Gm Rk
DET 1 -338 -1.64 5,043 24.48 23 5,381 26.12 3
GB 2 -304 -1.48 5,047 24.50 24 5,351 25.98 4
PHI 3 -282 -1.37 5,106 24.79 27 5,388 26.16 2
KC 4 -279 -1.35 5,118 24.84 28 5,397 26.20 1
CHI 5 -259 -1.26 4,964 24.10 19 5,223 25.35 10
BAL 6 -216 -1.05 5,105 24.78 26 5,321 25.83 6
PIT 7 -211 -1.02 4,859 23.59 11 5,070 24.61 14
BUF 8 -179 -0.87 4,539 22.03 1 4,718 22.90 23
DAL 10 -177 -0.86 5,140 24.95 29 5,317 25.81 7
CIN 9 -177 -0.86 4,934 23.95 16 5,111 24.81 12
OAK 11 -176 -0.85 4,979 24.17 22 5,155 25.02 11
CLE 12 -129 -0.63 4,883 23.70 13 5,012 24.33 15
NYJ 13 -89 -0.43 4,722 22.92 4 4,811 23.35 19
NE 14 -84 -0.41 4,679 22.71 2 4,763 23.12 20
SD 15 -56 -0.27 5,195 25.22 30 5,251 25.49 8
MIN 16 -28 -0.14 5,048 24.50 25 5,076 24.64 13
JAC 17 -1 0.00 4,912 23.84 15 4,913 23.85 16
DEN 18 15 0.07 4,833 23.46 9 4,818 23.39 17
TEN 19 29 0.14 4,846 23.52 10 4,817 23.38 18
WAS 20 78 0.38 5,417 26.30 31 5,339 25.92 5
SEA 21 81 0.39 4,783 23.22 5 4,702 22.83 24
SF 22 109 0.53 4,808 23.34 6 4,699 22.81 25
MIA 23 127 0.62 4,823 23.41 7 4,696 22.80 26
CAR 24 161 0.78 4,829 23.44 8 4,668 22.66 27
ATL 25 208 1.01 4,701 22.82 3 4,493 21.81 32
HOU 26 220 1.07 4,969 24.12 21 4,749 23.05 21
IND 27 222 1.08 4,967 24.11 20 4,745 23.03 22
STL 28 255 1.24 4,902 23.80 14 4,647 22.56 28
NYG 29 328 1.59 5,571 27.04 32 5,243 25.45 9
ARI 30 333 1.62 4,871 23.65 12 4,538 22.03 30
NO 31 361 1.75 4,954 24.05 17 4,593 22.30 29
TB 32 458 2.22 4,961 24.08 18 4,503 21.86 31

You see, there’s really quite a difference. Buffalo’s opponents, in games not against Buffalo, scored an average of 22.03 a game; five points a game fewer than the unfortunate New York Giants, who went up against all four top offenses in the league, two of them (Philadelphia and Dallas) twice! Notice Washington is down there too; teams in the same division tend to clump together, as 75% of their opponents are in common. Kansas City played the worst defenses overall (through 15 games), while Atlanta faced the toughest. All in all, Detroit’s opponents, in games not against Detroit, lost by 1.64 points on average, while Tampa Bay’s opponents won by 2.22 points, nearly a four-point swing between extremes.

Putting it all together, these are the Pythagorean winning percentages of the opponents of all thirty-two teams, along with the PW% of the team itself. The difference, which I quite originally dub “Relative Performance” (actual PW% minus expected PW% given those opponents), indicates how well a team fared against its competition, relative to other teams against the same opponents.

Team Rank Relative Performance Opp. PW% Rank Expected PW% Actual PW%
SEA 1 30.19% 51.01% 21 48.99% 79.17%
CAR 2 26.19% 52.01% 24 47.99% 74.18%
SF 3 24.31% 51.36% 22 48.64% 72.95%
DEN 4 22.06% 50.18% 18 49.82% 71.88%
NO 5 19.37% 54.47% 31 45.53% 64.90%
KC 6 17.90% 46.86% 4 53.14% 71.05%
CIN 7 15.93% 47.91% 9 52.09% 68.02%
ARI 8 14.48% 54.19% 30 45.81% 60.29%
NE 9 13.56% 48.95% 14 51.05% 64.62%
IND 10 8.72% 52.71% 27 47.29% 56.01%
SD 11 7.01% 49.36% 15 50.64% 57.65%
PHI 12 5.58% 46.82% 3 53.18% 58.76%
STL 13 3.51% 53.16% 28 46.84% 50.35%
MIA 14 0.63% 51.58% 23 48.42% 49.05%
DET 15 -0.65% 46.16% 1 53.84% 53.18%
DAL 16 -0.71% 48.00% 11 52.00% 51.29%
PIT 17 -3.17% 47.48% 6 52.52% 49.34%
TEN 18 -3.77% 50.36% 19 49.64% 45.88%
BAL 19 -5.31% 47.55% 7 52.45% 47.14%
GB 20 -5.88% 46.54% 2 53.46% 47.58%
CHI 21 -6.85% 46.99% 5 53.01% 46.16%
BUF 22 -8.43% 47.71% 8 52.29% 43.86%
TB 23 -8.53% 55.71% 32 44.29% 35.76%
ATL 24 -11.00% 52.68% 25 47.32% 36.32%
MIN 25 -12.75% 49.67% 16 50.33% 37.58%
NYG 26 -14.47% 53.59% 29 46.41% 31.94%
CLE 27 -15.87% 48.46% 12 51.54% 35.68%
WAS 28 -17.95% 50.86% 20 49.14% 31.19%
OAK 29 -19.52% 47.94% 10 52.06% 32.53%
NYJ 30 -20.32% 48.89% 13 51.11% 30.79%
HOU 31 -21.15% 52.68% 26 47.32% 26.17%
JAC 32 -29.43% 49.99% 17 50.01% 20.58%

So take my 49ers. Their average opponent should expect to win 51.36% of their games not against the 49ers, but only 27.05% of their games against the 49ers.4 That difference, 24.31%, is the third largest in the league. GO NINERS! Only Carolina and Seattle have dominated more thoroughly, giving their opponents quite a whooping, much more so than their opponents receive from other teams. Kansas City, meanwhile, boasts a healthy 71.05 PW%; but against its crummy competition, other teams have been averaging a 53.14 PW% anyway, so it’s a little less impressive, knocking their relative performance to sixth in the league.

Oh, and check out the Jets! Further evidence that I was right when I declared that their 2013 campaign was quite impressive earlier this week. Other teams facing the Jets’ competition have a respectable 51.11 PW%; they outperform them over half the time. The Jets, meanwhile, only manage 30.79%, getting badly outperformed by mediocre teams. Ick. I should point out that by these measures, Tampa Bay had the toughest schedule, while Detroit had the easiest– and still missed the playoffs. Ouch.

Lastly, we’ll return to the “real” numbers, straight-up wins, side-by-side with their Pythagorean expectations. This post has been about context. Wins and losses mean different things in different contexts; a context of narrow defeats and blowout wins suggests a team is merely having some bad breaks, and inspires optimism; a context of blowout defeats and narrow wins indicates the opposite, and the tempering of future expectations. But context is only that: context. The real content, the wins and losses themselves, is what we care about. Here they are, side by side:

Team Rank W Expected PW Actual PW PW Over/Under Expected W Over/Under PW
SEA 1 12 7.3 11.9 4.5 0.1
DEN 1 12 7.5 10.8 3.3 1.2
CAR 3 11 7.2 11.1 3.9 -0.1
SF 3 11 7.3 10.9 3.6 0.1
KC 3 11 8.0 10.7 2.7 0.3
NE 3 11 7.7 9.7 2.0 1.3
NO 7 10 6.8 9.7 2.9 0.3
CIN 7 10 7.8 10.2 2.4 -0.2
ARI 7 10 6.9 9.0 2.2 1.0
IND 7 10 7.1 8.4 1.3 1.6
PHI 11 9 8.0 8.8 0.8 0.2
SD 12 8 7.6 8.6 1.1 -0.6
MIA 12 8 7.3 7.4 0.1 0.6
DAL 12 8 7.8 7.7 -0.1 0.3
BAL 12 8 7.9 7.1 -0.8 0.9
CHI 12 8 8.0 6.9 -1.0 1.1
GB 17 7.5 8.0 7.1 -0.9 0.4
STL 18 7 7.0 7.6 0.5 -0.6
DET 18 7 8.1 8.0 -0.1 -1.0
PIT 18 7 7.9 7.4 -0.5 -0.4
NYJ 18 7 7.7 4.6 -3.0 2.4
TEN 22 6 7.4 6.9 -0.6 -0.9
BUF 22 6 7.8 6.6 -1.3 -0.6
NYG 22 6 7.0 4.8 -2.2 1.2
MIN 25 4.5 7.5 5.6 -1.9 -1.1
TB 26 4 6.6 5.4 -1.3 -1.4
ATL 26 4 7.1 5.4 -1.6 -1.4
CLE 26 4 7.7 5.4 -2.4 -1.4
OAK 26 4 7.8 4.9 -2.9 -0.9
JAC 26 4 7.5 3.1 -4.4 0.9
WAS 31 3 7.4 4.7 -2.7 -1.7
HOU 32 2 7.1 3.9 -3.2 -1.9

  1. Multiply the % by the number of games played to obtain Pythagorean wins. You may then compare the number of Pythagorean wins to actual wins; if actual wins are greater, the team has been lucky, while if Pythagorean wins are greater, they’ve been unlucky. The two figures even out in the long run but may differ over short stretches. (Even a full sixteen game season. Sixteen games isn’t that many. You know they play 162 in baseball?) 
  2. Remember, teams play 16 games against 13 opponents because they play each team in their division twice; the last game of the season is always an intra-division match-up, so at the moment each team has played 15 games against 13 teams. 
  3. Sorry this chart’s headers are a little lacking; it was the only way I could get it to fit onto one page. It was either that or splitting it into three separate charts, which I thought worse. 
  4. 100% – San Francisco’s actual PW% of 72.95% = 27.05%. 

It’s been a couple of weeks since I checked my notes on silly things announcers say during games, and I thought I’d get back to it. Let’s go!

At home in Week 13, the Texans force a Patriots’ punt and get the ball back just before halftime.

CBS play-by-play veteran Greg Gumbel:

And with 28 seconds on the clock, the Texans will have the ball at their own 20 yard line, and unless something really, really strange happens they’re going to go the locker room with the lead.

I guess this is the equivalent of whatever an honorable mention would be in this series. John Madden said things like this all the time. When you’re public speaking for three hours, you’ll probably end up saying something “really, really” obvious somewhere in there. I’m mostly fine with announcers saying a few things here and there just to fill in the broadcast, but I still thought this was funny.

At home in Week 13, the Panthers gain two yards on 3rd&G from the Bucs’ three yard line with 30 seconds left in the second quarter.

Fox’s play-by-play man Chris Myers:

Now let’s see if he’s going to go for it or not, remember he said he plays percentages, he’s going to let the clock run, of course you can always go for it, if you miss it you pin Tampa Bay back there with your time outs.

Color commentator Tim Ryan, former third round pick of the Chicago Bears in the 1990 NFL Draft:

I’m never chasing points early in games, Riverboat Ron or not, check the analytics, take the points.

Ugh. Tim, I’m taking your advice, and actually checking the analytics. (Although it’s really obvious going for it is the better strategy.) HEY, the analytics say that going for it provides the Panthers a 79% chance of winning and kicking the field goal results in a 74% chance of winning. Tim, if I agreed to give you $3 every day (100% of days) over four weeks, or if I agreed to give you $7 on 19 days within four weeks (68% of days), which would you prefer? The 100% chance of $3 ($3 on average each day, $84 total), or the 68% chance of $7 ($4.76 on average each day, $133 total)? Yes Tim, as the NFL average of converting 4th&1 is 68%1, the deal I’m offering is pretty much analogous to this situation. This is what checking the analytics means, Tim. What do you think?

Tim Ryan:

I think two missed opportunities to give Cam Newton the ball there on second and third down, and it looks like they’re going to be out there and they’re gonna go for it here on fourth down. I would just take the points and go up by a touchdown.

Chris Myers:

Ron Rivera chooses otherwise, if you were going to do that, maybe leave a little more time in case you stop ’em, but let’s see.

Before the play, the Bucs call timeout. Chris Myers:

So how about this call?

Tim Ryan:

I don’t ever want to chase points, especially in the first half of games, you’ve got an opportunity to kick a field goal, Ron knows way more about it than I do, he’s got obviously great trust in his football team, I would not give an opportunity for Tampa to change the momentum, if they can get a stop here.

The Panthers go for it, and Cam Newton dives over the line for a touchdown.

Eventually Ryan says:

I guess if I had that guy and I was Ron Rivera I’d be going for it too. … I don’t care what your cards say, you’re always holding a royal flush when Cam’s out there.

Way to go Tim! Way to go. Next time, maybe have an intern check the analytics for you, and you won’t have to use poker vernacular to distract your audience that you just used the phrase “chasing points” several times like it actually means something, but really you don’t know what you’re talking about.

At home against the Bears in Week 13, the Vikings get a first down at the Bears 21 with 9:03 left in overtime.

Thom Brennaman:

Well they’re going to continue to run plays here, for the time being anyway, after the penalty the ball all the way down to the 21. …

On first down, Peterson loses three yards, setting up 2nd&13 from the Bear 24.

Thom Brennaman:

Right now it would be a 43 yard field goal attempt, maybe 42 yards, and we mentioned earlier Walsh, has been lights out in his career, short albeit it. But a Pro Bowler as a rookie a season ago, and only two misses all of this year.

Brian Billick:

Can Blair Walsh make it from here? Then center it up and kick the ball. There are too many things that can go wrong.

The “Can Blair Walsh make it from here?” question is, well, disturbing coming from a former Super Bowl winning head coach, who presumably took the same logic in his own decisions. As we saw in Week 14, Matt Prater can hit a 64 yard field goal in Denver. Should the Broncos kick every time they get to their opponents’ 47 yard line? Probably not, right? You’ll notice the Broncos only kicked that field goal because there was no time left in the first half. If there was, they would have kept running plays to get closer. And that’s the thing about field goals: closer is always better. Always. Brian Burke’s research suggests that every yard closer increases field goal percentage by 1.6% (between the 10 and 35 yard lines). But anyway, Peterson gained three yards, setting up 3rd&10 from the 21. The Vikings put out their field goal unit.

Brian Billick:

And this is a good call, why do it on fourth down, do it on third down, than god forbid if there’s a bad snap, something happens, then you can fall on the ball and re- and take another kick, so this is a good move by Minnesota, by doing this on third down.

How likely is a bad snap, or a “something happens”, that lets the Vikings get another shot? (Note: a missed field goal ends the offense’s position, even if it’s not on fourth down.) Burke guesses it’s around 0.5%, or one in every two hundred. That seems fair given that of the last 500 extra point attempts, where the process for snapping and holding is exactly the same, only seven have been missed. If all seven are the result of bad snaps or holds (which they probably aren’t), that’s a bad snap/hold rate of 1.4%. But even if you really go crazy and think it’s 2%, the Vikings can increase their chances of winning by 3.2% just by gaining two yards! Adrian Peterson averaged 6 yards per carry in that game, and is around five yards per carry in his career.

As it turned out, Walsh hit the field goal, but a 15 yard penalty on the Vikings set up 3rd&25 from the Bear 36. The Vikings put their offense back out on the field.

Brian Billick:

They feel like they need to grind out a couple more yards, rather than- rather than give Blair Walsh the shot from here.

Vikings head coach Leslie Frazier decided to make the field goal easier here… only for Peterson to actually lose three yards, and see Walsh miss the ensuing 57 yarder. The Bears got the ball and got to a 2nd&7 from the Vikings 29, and sent their field goal unit out to attempt a 47 yard field goal.

Brian Billick:

You know same mentality, why risk the turnover, you’ve got a great deal of faith in your field goal kicker. You know I had a great one in Baltimore Thom in Matt Stover, and by quarter, Matt would tell me exactly where I needed to be in order to attempt these field goals. … There’s no question it’s within his range. Once you cross that 30 as a I said, you set that mark, once you get past it then that’s when you make your decision as a coach. … This is clearly his range.

Again, being incredibly generous to this thinking, we’re looking at a 2% chance of bumbling the snap/hold process, and a 1.6% improvement of making the kick for every yard the Bears continue to advance down the field. A kicker’s “range” is not static: every bit closer the odds go up, every bit farther away the odds go down. Plus, it was only second down! Even if you want to go on third down in the very unlikely event your field goal unit botches it, at least use second down! Yeah, the Bears could turn the ball over, but have the odds of that changed in the last couple plays? If you’re worried about a turnover why not just punt as soon as you get the ball? Anyway, Gould missed wide right; the Vikings eventually won on the next possession.

In Tennessee down 10-7, the Cardinals kick a field goal on 4th&2 from the Titan 7 with 7:25 left in the second quarter.

FOX color commentator Charles Davis:

I think it’s the right call this early in the game, Arizona plenty more opportunities on offense, and moving and clicking pretty well now, you don’t turn down points here, not anywhere close to a desperation move. Munchak, we saw him, head coach of the Titans, happy with his defense coming up with that third down stop and forcing a field goal attempt.

Blegh. Forget the hyperbole of momentum, turning down points, etc. Going for it gave the Cardinals a 50% chance of winning; kicking the field goal, 48%. Oh yeah, and also the Cardinals ended up with a big lead before a Titans comeback led to an eventual Cardinals’ win in overtime. Arizona could have avoided that by actually putting them away and taking the most points, instead of just taking (some of) the points.

A.J. Hawk breaks up a Tony Romo pass on 1st&10 from the Cowboy 23 with 13:23 to go in the second quarter.

Fox color guy Troy Aikman:

Hawk makes a nice play on that ball, and, and A.J. Hawk, I think he’s one of the more under appreciated guys around the league, and, I think a lot of expectations when he came into the league from Ohio State because where he was drafted, but you know he’s probably been their most consistent player defensively, he shows up every week, he used to be a first and second down guy and now he even stays in nickel situations.

Also be wary when “experts”, including former players like Hall of Fame quarterback Troy Aikman, make praising statements for being underrated and showing up. Pro Football Focus has A.J. Hawk as the sixth worst inside linebacker on the season. In my mid-season evaluation of inside linebacker contracts, I found his contract quality to be the third worst in the league. He’s been overrated, not underrated, Troy.

While no means a comprehensive list, that’s sans-49ers announcer material I had for the last three weeks. I’ll probably next return to announcers when their playoff assignments are locked down. In fact, after New Year’s Eve I may even pursue a fan suggestion for “The Search for the Best (& Worst!) Contract in Sports Television: NFL Announcers”. Stay tuned.


  1. Gerald McCoy and Lavonte David probably make the Bucs an above-average short-yardage defense, but Cam Newton and DeAngelo Williams probably make the Panthers an above-average short-yardage offense, so 68% is probably pretty close to the Panthers true 4th&1 success rate against the Bucs. 

The football was the most amazing football last Sunday. I’m still processing it, and probably won’t be ready to talk about it until at least Friday. But I must go on with my continuing Economics and Sports Management recurring feature, The Search for the Best (& Worst!) Contract in Football. The end is near!1 We’re finally in the defensive backfield, as I look at cornerback pay and performance. And we have a serious challenger for guard Davin Joseph’s former stranglehold on the worst contract in the league.

First, some usual disclaimers: other things go into a player’s market value besides on-field performance. Measuring those things, how popular a player is, if he makes his teammates better, if he’s a good guy to have around, works well with the coaches, etc, is really, really hard. Certainly performance is a huge component of pay though. Tim Tebow, even Brett Favre, hell even Mike Tyson would still probably sell some tickets, but you don’t see them getting NFL contracts. Also, while certain players may rake in the ticket and jersey sales, that is at least partially controlled for by doing the analysis by position. The backs and receivers, even the tight ends may bring a lot of money in without their play, but take Davin Joseph. Earlier this season I estimated he was overpaid by $10+ million dollars.2 You can’t make a case that he’s helping the Buccaneers recoup that in other ways, certainly not all $10 million. Similarly, with a few exceptions, I don’t think fans go to watch other offensive linemen, or really any defensive players.3

Secondly, the Pro Football Focus grades I use for this analysis are super awesome, but not 100% perfect. I think their main weakness is not controlling for the quality of the opposition, down to the individual level. If a cornerback blankets Calvin Johnson and holds him without a catch on 10 targets with three passes defensed and no penalties, it counts the same as another corner who does exactly the same thing to Greg Little.4 Still, over the course of a season, things should even out a good deal, if not completely. Doing the analysis after one game would be almost meaningless. But after thirteen games of players getting graded on every play, it’s much more compelling.

Cornerbacks! 111 have played 25% or more of their teams’ snaps through Week 14. The Buffalo Bills released Justin Rogers earlier this season, so I dropped him from the sample. (He lost an opportunity to perform, and they stopped paying him, so…) Here are the Top 10 performing cornerbacks on the field this season (PFF grade in parentheses):

  • 1. Darrelle Revis, TB (18.1)
  • 2. Tyrann Mathieu, ARI (15.5)
  • 3. Patrick Peterson, ARI (13.1)
  • 4. Brent Grimes, MIA (12.5)
  • 5. William Gay, PIT (11.1)
  • 6. Jason McCourty, TEN (10.9)
  • 7. Dominique Rodgers-Cromartie, DEN (10.8)
  • 8. Tramaine Brock, SF (10.7)
  • 9. Vontae Davis, IND (10.5)
  • 10. Leon Hall, CIN (8.7)

That Derrelle Revis guy, still pretty good it turns out, even after age and injuries have had their say. Poor rookie sensation Tyrann Mathieu tore his ACL and LCL this past Sunday, ending his season. It’s truly a shame, as Arizona had a good, and entertaining, duo going on with Mathieu and his former LSU teammate Patrick Peterson reunited. And while some of San Francisco’s Tramaine Brock’s grade was as the third corner usually covering the opponent’s third wide receiver, the last few weeks he’s been starting for an injured Tarell Brown, performing very well. On to the Bottom 10:

  • 101. Dee Milliner, NYJ (-9.1)
  • 102. Leonard Johnson, TB (-9.2)
  • 103. David Amerson, WAS (-9.3)
  • 104. Brandon Flowers, KC (-9.7)
  • 105. Antonio Cromartie, NYJ (-10.5)
  • 106. Ike Taylor, PIT (-11.2)
  • 107. Derek Cox, SD (-11.8)
  • 108. Shareece Wright, SD (-12.4)
  • 109. Brice McCain, HOU (-12.7)
  • 110. Cortland Finnegan, STL (-19.7)

Revis left the Jets for Tampa Bay, and his first round draft pick replacement Dee Milliner hasn’t quite fit the bill just yet. (Though note that another thing PFF grades don’t measure is potential.) Antonio Cromartie has played well in the past though, not sure what’s up with him. Down at the bottom, solidly entrenched by his terrible play, is Cortland Finnegan of the Rams. Again, the worst corner so far this season is Cortland Finnegan, by a sound margin. The average grade is a 0.18, with a standard deviation of 6.69. Eeesh, as usual, tremendous variation in player performance.

Here are the Top 10 paid cornerbacks who’ve played 25% or more of their teams’ snaps (average annual salaries in millions of dollars, reported by Spotrac.com, in parentheses):

  • 1. Darrelle Revis, TB ($16 million)
  • 2. Brandon Carr, DAL ($10.02m)
  • 3. Cortland Finnegan, STL ($10m)
  • 4. Johnathan Joseph, HOU ($9.75m)
  • 5. Joe Haden, CLE ($8.547m)
  • 6. Leon Hall, CIN ($8.475m)
  • 7. Lardarius Webb, BAL ($8.333m)
  • 8. Brandon Flowers, KC ($8.225m)
  • 9. Antonio Cromartie, NYJ ($8m)
  • 10. Tramon Williams, GB ($7.615m)

Hey, it’s Cortland Finnegan! He is the third most expensive corner in the league and on average makes $10 million a year. Alright! Also Darrelle Revis’ contract is more than two standard deviations above the next most paid player. Remember, while his play was tops as well, it was less than one standard deviation above the next best player. Not looking like a good contract for the Buccaneers. These are the Bottom 10 paid cornerbacks:

  • 101. Alfonzo Dennard, NE ($0.539m)
  • 102. Byron Maxwell, SEA ($0.538m)
  • 103. Jimmy Wilson, MIA ($0.521m)
  • 104. Robert McClain, ATL ($0.51m)
  • 105. Nolan Carroll, MIA ($0.497m)
  • 106. Nickell Robey, BUF & Melvin White, CAR ($0.495m)
  • 108. Leonard Johnson, TB ($0.483m)
  • 109. Chris Harris Jr, DEN ($0.466m)
  • 110. Isaiah Frey, CHI ($0.45m)

The average annual salary is $2.722 million, with a standard deviation of $2.873 million. As with a couple other positions that unusually had a standard deviation greater than the average, this indicates a few players (or in this case, a Derrelle Revis) who are just paid boatloads of money more than their peers. Are they worth it? What do you think?

The Top 10 cornerback contracts so far this season (contract quality5 in parentheses):

  • 1. Tyrann Mathieu, ARI (2.99)
  • 2. Tramaine Brock, SF & William Gay, PIT (2.06)
  • 4. Chris Harris Jr, DEN & Richard Sherman, SEA (1.85)
  • 6. Will Blackmon, JAC (1.84)
  • 7. Alterraun Verner, TEN (1.81)
  • 8. Vontae Davis, IND (1.78)
  • 9. Alan Ball, JAC (1.77)
  • 10. Corey White, NO (1.75)

And it’s Honey Badger in front! Congratulations to Arizona Cardinals General Manager Steve Keim! And apologies to the Cardinals for their bad luck that Mathieu went out for the season two days ago. That just sucks. But hey, at least he’s really good and you’re not paying him very much money and he’s only a rookie! It could be worse…

… and the Worst 10 contracts (so far):

  • 101. Cary Williams, PHI (-1.93)
  • 102. Darrelle Revis, TB (-1.94)
  • 103. Chris Houston, DET (-2.03)
  • 104. Charles Tillman, CHI (-2.11)
  • 105. Derek Cox, SD (-2.58)
  • 106. Brandon Carr, DAL (-2.81)
  • 107. Ike Taylor, PIT (-3.19)
  • 108. Brandon Flowers, KC (-3.39)
  • 109. Antonio Cromartie, NYJ (-3.43)
  • 110. Cortland Finnegan, STL (-5.5)

Ladies and gentlemen, Cortland Finnegan! A -5.5! AAAUUUGGGHHH!!! That is so, so, so bad. A few players had -3 or so (they may have since improved, or worsened ). Guard Davin Joseph had a -4.78. A -5.5 through thirteen games… There are a couple more things I want to point out (like Darrelle Revis!), but I just… I’m done. There are no words. -5.5.


  1. Well, not really. I’ll be doing this all again, bigger and better, with even MOAR analysis, at the end of the season. 
  2. He only makes $7.5 million a year. He’s so bad is just doesn’t even make sense. He broke the analysis. I’m still working on it. 
  3. Yeah, there are some exceptions. I said that! But when you look at all the starting defensive players in the league, that’s 11 * 32 = 352. How many can you name off the top of your head? How many of those don’t play for your team? 20? 30? The vast majority of them lack “star power”. I may not be able to measure it, but I know it when I see it. Most guys don’t have it. If most guys did have it, we’d have to call it something else, or move to Lake Wobegon. 
  4. Currently PFF’s worst graded receiver with a -13.9 through Week 14. 
  5. Reminder: contract quality is determined by how a player’s on-field performance, relative to the average using standard deviations, relates to his salary, relative to the average using standard deviations. CQ = performance SDs above/below the average – salary SDs above/below the average 

On this morning of December 4th, the ESPN ScoreCenter iPad application announced that the NFL had fined Mike Tomlin $100 thousand1 for his little sojourn onto the field and into the path of Baltimore’s Jacoby Jones’ kickoff return on Sunday Night Football. What a perfect lead for looking at player fines!

The good people at Spotrac.com2 indicate that Mike Tomlin makes $5.75 million a year. It is troublesome when people manipulate numbers like that ($100 followed by $5.75) so to be clear: the NFL fined him $0.1 million, and he makes $5.75 million. It is a fraction, though not a very pretty one, and works out to 1.74% of his annual salary. $100 thousand is the biggest in-game fine in league history, with only Bill Belichick’s $500 thousand for Spygate outstripping it among all fines.3 The language in the NFL’s press release officially stated that Tomlin’s actions were “like, a pretty not-so-good thing to do”. But exactly how bad? Well…

Most NFL fines target players. Through Week 12, there have been 163 fines (including 16 in the preseason), ranging from uniform violations to physically engaging an official. The fines total $2.37 million, averaging $0.015 million ($14,543). The most common fines have been for roughing the passer– 34 fines, $0.534 million total, $0.016 million average ($15,691)– and hitting a defenseless player– 21 fines, $0.421 million total, $0.02 million average ($20,068). Tomlin was hit with a 1.74% salary reduction this season. How does that stack up with the players?

The average NFL salary is $2.016 million ($2,015,942), with a median of $0.753 million ($753,229). The average fine ($14,543) is 0.72% of the average salary, and 1.93% of the median salary.4 For half of all players, the average fine is a harsher punishment than Tomlin’s 1.74% loss. But are those the guys getting fined? Most fines occur on the defensive side, specifically at defensive end and safety (the guys hitting those quarterbacks and defenseless receivers). Defensive ends average $2.577 million a year, with a median of $0.933 million, and safeties average $1.634 million a year, with a median of $0.715 million.

The average roughing the passer fine costs the average defensive end 0.61% of his yearly take, and the median defensive end 1.68%. The average hitting a defenseless player fine costs the average safety 1.23% of his salary, and the median safety 2.81%. Starters make more than backups, are on the field more, and presumably receive most of the fines, but not all of them. Another thing to consider is that fines escalate for repeated offenses. Still, while in terms of absolute value, Tomlin’s fine is the biggest in the league this season, in relative value it is certainly not. For example, though released a few weeks ago, Ahmad Black, safety of the Buccaneers, was earning an average of $556,685 and was fined $21,000 in Week 2 for hitting a defenseless player, a 3.77% pay cut. To my knowledge, the ESPN ScoreCenter iPad application has never informed anyone where Ahmad Black’s losses rank in league history.

Lastly, the NFL does not announce their fines, nor disclose them without being asked (making Spotrac.com’s work even more impressive). At the beginning of each season they issue guidelines concerning fines, and there are minimums for certain offenses, but basically an NFL fine is like a box of chocolates: you never know what you’re going to get. Lots of economists have studied lots of things about risk analysis, decision-making, etc, but in terms of policy it basically comes down to two things: you can increase enforcement, or increase the fine, and your action depends on whether you want the behavior to stop, or whether you want to collect money.

Given the relative secrecy, and that we keep hearing more and more about how officials are cracking down on dangerous hits, and that all suspect hits are reviewed after the game anyway to bring enforcement to 100%, and that the league has not significantly altered fine values, and that 134 of the 163 (82.2%) player fines this season have been for illegal hitting or tackling, and that we all have no trouble believing that the league does not give a damn about player safety anyway, I think it is clear what policy the NFL has chosen. Making sure every illegal hit receives a fine brings in money; making fines so expensive that the players actually change their behavior does not. If the NFL really wanted these hits to stop, they would raise the price.


  1. Apparently the NFL may also take a draft pick or two away from the Steelers, but has not made a decision yet. 
  2. Unless otherwise noted, all information concerning fines and salaries in this article comes from the good people at Sportrac.com. They are pretty cool. 
  3. Apparently head coaches Wade Wilson and Mike Tice also received fines of $100 thousand. So we have either got alliterative initials or guys named Mike T. COINCIDENCE? YOU DECIDE
  4. How dies that compare to us mortals? Per the U.S. Census Bureau, the average household income is $72,555, the median $52,762. That is an average and median of $84,422 and $64,293 among family households, respectively, with a non-family household averaging $45,893 with a median of $31,749. A speeding ticket varies, but a seemingly reasonable estimate is $150. That is 0.28% of the median household income. A first-time DUI offense similarly varies, but a seemingly reasonable estimate is $10,000, accounting for all fines, necessary classes, legal fees, increasing auto insurance rates, etc. That is 18.95% of the median household income. 
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