Week 3 NFL Quarterback Ratings

Quarterback game scores from Week 3 in the NFL, with season averages in parentheses. An average game score for a starting quarterback is 52. Scores are based on the weights I used for the 2016 season. Those will change, but very little from year-to-year.

Kirk Cousins 96 (59)
Case Keenum 94 (62)
Jared Goff 91 (76)
Dak Prescott 85 (56)
Tom Brady 83 (73)
Jacoby Brissett 82 (54)
Drew Brees 78 (68)
Josh McCown 78 (57)
Tyrod Taylor 78 (60)
Andy Dalton 77 (45)
Alex Smith 72 (82)
Blake Bortles 69 (49)
Eli Manning 65 (59)
Brian Hoyer 62 (43)
Aaron Rodgers 60 (55)
Matt Ryan 59 (71)
Marcus Mariota 58 (50)
Ryan Mallett 56 (56)
Deshaun Watson 55 (39)
Carson Wentz 54 (50)
Russell Wilson 54 (40)
Jameis Winston 52 (52)
Carson Palmer 51 (40)
Matthew Stafford 39 (54)
Ben Roethlisberger 38 (52)
Mike Glennon 37 (43)
Trevor Siemian 34 (49)
Cam Newton 32 (42)
Jay Cutler 32 (50)
Derek Carr 29 (61)
Joe Flacco 19 (41)
Philip Rivers 18 (50)
DeShone Kizer 16 (29)

Week 2 NFL Quarterback Ratings

Updating with the game scores from Week 2 in the NFL. For each quarterback, their average game score for this season is in parentheses.

As an aside, I’ve actually received a handful of questions about why I stopped doing the college ratings. The simple answer is just that I’m busy and it’s a lot of work to set them up for a new season. It means compiling the season-opening ratings and then entering the schedules for 130 FBS schools. I ran them for 20 years. I don’t think I’ve received a single comment about them in a decade.

For a while I had hoped (because I’ve been part of Kenneth Massey’s comparison since the beginning and mine are decently accurate) I might become part of the media that takes part in the national computer rankings. But, somewhat like Lana Turner and the Top Hat Malt Shop and the legions of wannabe actresses who put away endless sodas hoping to follow her footsteps, no one discovered them. So I’m seeing what it feels like not to spend every early Sunday morning during the fall entering lots of data.

The NFL ratings are a lot less work and they tie more into the continual research I’m doing on pro football, so I’m continuing with them for now.

As another aside, I’ve noticed someone out there has made a rather determined effort to hack into this blog through a Ukrainian proxy. Just in case Robert Muller is one of my readers, no, I had nothing to do with the 2016 election. And I hope I’m practicing safe blogging by keeping WordPress updated and greatly limiting login attempts. Not sure why someone would want access to this blog – but if you ever see anything completely weird on my web site, like pictures of unclothed Ukranians, it wasn’t me.

Anyway…

Quarterback, Week 2 (Season Average)
Tom Brady 95 (68)
Derek Carr 81 (77)
Philip Rivers 81 (66)
Alex Smith 80 (88)
Matt Ryan 74 (78)
Jay Cutler 67 (67)
Ben Roethlisberger 61 (59)
Joe Flacco 60 (52)
Josh McCown 58 (47)
Kevin Hogan 58 (58)
Trevor Siemian 58 (56)
Matthew Stafford 56 (61)
Drew Brees 55 (64)
Cam Newton 54 (47)
Eli Manning 54 (56)
Jameis Winston 52 (52)
Jared Goff 52 (68)
Kirk Cousins 52 (41)
Aaron Rodgers 49 (53)
Tyrod Taylor 49 (51)
Marcus Mariota 48 (47)
Mike Glennon 46 (46)
Carson Palmer 45 (35)
Andy Dalton 43 (29)
Deshaun Watson 43 (32)
Carson Wentz 38 (48)
Russell Wilson 35 (34)
Blake Bortles 31 (39)
Dak Prescott 31 (42)
Case Keenum 29 (29)
Brian Hoyer 25 (33)
Jacoby Brissett 25 (25)
DeShone Kizer 15 (35)

Week 1 NFL Quarterback Ratings

I don’t know how often I’ll do this, but I thought I’d put out my quarterback scores for week 1 of the NFL season. The average game score is about 52 for a starter. These scores are based on 2016 normalizations. I’ll redo the normalizations and factor in a couple of other minor things at the end of the year, but these scores won’t change much. In parentheses is a quarterback’s average game score from 2016, if he had six or more qualified games.

Alex Smith, KC 95 (55)
Sam Bradford, MIN 94 (62)
Jared Goff, LAR 84 (30)
Matt Ryan, ATL 81 (74)
Derek Carr, OAK 72 (55)
Drew Brees, NO 72 (63)
Matthew Stafford, DET 66 (56)
Carson Wentz, PHI 58 (44)
Ben Roethlisberger, PIT 57 (55)
Eli Manning, NYG 57 (48)
Aaron Rodgers, GB 56 (60)
DeShone Kizer, CLE 55
Trevord Siemian, DEN 54 (48)
Tyrod Taylor, BUF 52 (52)
Dak Prescott, DAL 52 (64)
Philip Rivers, LAC 51 (51)
Blake Bortles, JAC 46 (41)
Mike Glennon, CHI 45
Marcus Mariota, TEN 45 (54)
Joe Flacco, BAL 43 (48)
Brian Hoyer, SF 41 (59)
Tom Brady, NE 40 (66)
Cam Newton, CAR 39 (42)
Josh McCown, NYJ 35 (34)
Tom Savage, HOU 33
Russell Wilson, SEA 32 (58)
Kirk Cousins, WAS 30 (60)
Scott Tolzien, IND 30
Carson Palmer, ARI 24 (48)
Deshaun Watson, HOU 20
Andy Dalton, CIN 14 (57)

Two-Point Conversions

From time to time, I like to take the opportunity to challenge my own perceptions about football. One strategy that’s often debated is whether teams should attempt a two-point conversion after a touchdown.

Decades ago, legendary coach Dick Vermeil, then a coordinator at UCLA, created a chart that’s still in popular use today. It’s not a chart I used when developing Front Office Football, but it’s not that different. Some of it is obvious; for instance if you trail by two after scoring a touchdown, you should try and tie the game. But even then, when in the game should you start consulting the chart?

Some of it is complex, or even controversial. You’re supposed to go for two when you lead by four after scoring. The idea is that if you make it, a subsequent field goal will give you a two-score lead. But if you miss, a subsequent field goal will leave your opponent the opportunity to tie on a touchdown without going for two.

Since the two-point conversion rate in the NFL is about 45% and the extra-point rate with the new distance rule is about 97%, I’d hesitate to start using any chart until well after halftime. Just take the point.

A few months ago, when working on a percentage win calculator that I’ve yet to put into any product, I analyzed a few seasons’ worth of play-by-play data and compiled a chart that could be useful for making these decisions. As an aside, I do this kind of thing a lot. Most of what I discover on these odysseys never amounts to meaningful work within my products. This might well fall into that category. But it could also be useful in compiling a more fine-tuned chart – one that even incorporates time remaining.

Today, I watched a good part of the Lions’ opener against Arizona. With 3:07 left in the third quarter, the Lions scored to cut the Cardinal lead to two. Some might say it was too early to start using a chart, but no one would question the wisdom of the decision in the closing moments. The Lions tried the conversion and failed. How did that change their win chances?

I have my play-by-play data broken into 100-second increments. To take a broader brush to include as much as is reasonable, I used the categories from the end of the third quarter to 6:40 remaining in the third quarter. There have been 2,797 plays undertaken with the score tied and a team with the ball, 1,031 plays with a one-point lead and 397 with a two-point lead. Not an overwhelming amount of data, especially since you can’t assume a reasonably uniform distribution of field position within that small a data set with the two-point lead. But in 1,629 of the tied scenarios (58%), the team with the ball won (possession matters – Arizona would have possession after the ensuing kick). In 574 of the one-point lead scenarios (56%), the team with the ball won and in 280 of the cases with a two-point lead (71%), the team with the ball won.

This is a great example of where sample size lets you down. The reason I’m writing about this is to give you some insight into my process when examining a particular question. When do you make a conclusion and when do you accept that you just don’t have enough information? This is several seasons’ worth of complete play-by-play data (350,000+ plays broken down by lead and time to go blocks), and the raw data set still isn’t good enough to make solid conclusions.

What the above numbers suggest is that the value of the extra point is immaterial, but there’s a 10-15% game-win cost in going for two and failing. That just doesn’t feel like a reasonable conclusion. When I was doing the initial work with this particular data set, I ran a whole series of rolling averages to come up with a win-percentage chart that supported the data with less precision, but a consistent set of percentages that required the least amount of intervention on my part (making “decisions” about interpretation and then using those decisions to influence how the rolling averages were applied). I feel more confident in presenting that chart as reasonable, at least in the sense that it could be used to help with this kind of decision. If this work results in use in any product, I would use that more processed chart.

Going back to the analysis of the first Lions decision: I come up with a 55.8% Arizona win percentage when tied during that time block, 61.1% with a one-point lead, 64.3% with a two-point lead. So, -3.2% for Detroit with a failure, +5.3% with success. Assuming 45% success on two-point tries and 97% success on extra points, the decision to go for it, in itself, raises Detroit’s win percentage by about a half a percentage point. I think it’s reasonable to conclude that it was a good decision. In general, my data supports this case up to about midway through the third quarter. Earlier than that, I would advise against ever going for a two point conversion except when desperate and in need of multiple positive results (let’s say you score and trail by 18).

The second Lions decision came with 9:27 remaining in the game. This time, they led by four. Vermeil’s chart says “go for it.” I don’t know if Jim Caldwell uses this particular chart, but he went for it. The Lions failed. The value of success and penalty for failure is explained at the start of this article. Now to break down it down using the data…

If the Cardinals gain possession with a six-point deficit (Detroit makes the conversion), they have a 29.2% chance of winning. With a five-point deficit, that’s 36.0% and a four-point deficit, that’s 41.0% percent. Factoring in analysis of success and failure, that amounts to, again, about a half-percentage-point increase in the Lions’ win chances. This surprised me a little, but, as it turns out, a six-point lead is quite a bit better than a five-point lead, even relatively early in the game.

Vermeil’s chart may be old, but it holds up even in the modern game.

NFL Quarterbacks for 2017

Now that the preseason is over and around 1,000 players were released this weekend, we have a good picture of the quarterback situation for 2017. I’ve put together a chart that I use as a quick reference.

Teams usually activate two quarterbacks for a game. Many keep a third quarterback on the 53-man roster and leave him inactive most weeks. This is a good place for a draft pick that isn’t expected to contribute his rookie year. Teams that don’t have three quarterbacks on the 53-man roster often have a quarterback on their practice squad. This is often a young player, but since any team can sign someone else’s practice-squad player by offering a roster spot, it’s not a place to stash a draft pick with a high upside.

There are some exceptions. A handful of teams won’t have a third quarterback, figuring on holding tryouts the next Monday in case of an injury. After all, 37 quarterbacks were released this week. Even though that group has a combined record of 26-78, there’s some talent in there, presumably in playing shape. And there are a few injured quarterbacks who need to be protected because they could be activated later in the season.

Practice squads will be formed this week. These will include at least a few of the quarterbacks released this week. A team will sometimes sign a quarterback to its practice squad that it just released.

A quarterback’s age and NFL record is in parenthesis. For rookies, their draft position is included rather than a record. For a quarterback’s record, I calculate wins and losses much like baseball does. Playoffs are included, though. Age is as of opening day next week.

*A – Indicates player is on Injured Reserve and won’t play this season. *B – Indicates player is on 53-man roster, but is likely to be placed on Injured Reserve with the possibility of returning later in the season. *C – Indicates player is on 53-man roster, is injured, but is likely to be healthy early enough to be worth keeping off of the PUP list. *D – Indicates player is on the non-football injury list and could be reinstated later in the season. *E – Indicates player is on the PUP list and will be eligible to return to the active roster after six weeks.











































AFC East
BuffaloMiamiNew EnglandNew York Jets
Tyrod Taylor (28, 15-15)Jay Cutler (34, 70-70)Tom Brady (40, 207-60)Josh McCown (38, 17-41)
Nathan Peterman (23, 171st)Matt Moore (33, 16-15)Jimmy Garoppolo (25, 2-0)Bryce Petty (26, 1-3)
T.J. Yates (30, 5-3)Ryan Tannehill *A (29, 36-40)
Christian Hackenberg (22, 0-0)
AFC North
BaltimoreCincinnatiClevelandPittsburgh
Joe Flacco (32, 93-59)Andy Dalton (29, 54-39)DeShone Kizer (21, 52nd)Ben Roethlisberger (35, 135-65)
Ryan Mallett (29, 3-5)A.J. McCarron (26, 2-2)Cody Kessler (24, 0-7)Landry Jones (28, 3-2)

Jeff Driskel *B (24, 0-0)Kevin Hogan (24, 0-1)Joshua Dobbs (22, 135th)
AFC South
HoustonIndianapolisJacksonvilleTennessee
Tom Savage (27, 2-2)Andrew Luck *C (27, 46-30)Blake Bortles (25, 11-34)Marcus Mariota (23, 11-16)
Deshaun Watson (21, 12th)Scott Tolzien (30, 0-3)Chad Henne (32, 19-37)Matt Cassel (35, 37-46)

Jacoby Brissett (24, 1-1)
Alex Tanney *A (29, 0-0)
AFC West
DenverKansas CityLos Angeles ChargersOakland
Trevor Siemian (25, 8-6)Alex Smith (33, 80-58)Philip Rivers (35, 98-85)Derek Carr (26, 22-25)
Paxton Lynch *C (23, 1-1)Patrick Mahomes (21, 10th)Cardale Jones (24, 0-0)E.J. Manuel (27, 6-10)
Brock Osweiler (26, 12-9)Tyler Bray (25, 0-0)
Connor Cook (24, 0-1)
Chad Kelly *D (23, 253rd)


NFC East
DallasNew York GiantsPhiladelphiaWashington
Dak Prescott (24, 13-3)Eli Manning (36, 116-94)Carson Wentz (24, 7-9)Kirk Cousins (29, 20-23)
Cooper Rush (23, undrafted)Geno Smith (26, 11-19)Nick Foles (28, 21-18)Colt McCoy (31, 8-17)

Davis Webb (22, 87th)

NFC North
ChicagoDetroitGreen BayMinnesota
Mike Glennon (27, 4-14)Matthew Stafford (29, 52-57)Aaron Rodgers (33, 99-50)Sam Bradford (29, 32-44)
Mitchell Trubisky (23, 2nd)Jake Rudock (24, 0-0)Brett Hundley (24, 0-0)Case Keenum (29, 9-15)
Mark Sanchez (30, 40-39)

Teddy Bridgewater *E (24, 16-12)
NFC South
AtlantaCarolinaNew OrleansTampa Bay
Matt Ryan (32, 87-62)Cam Newton (28, 54-44)Drew Brees (38, 137-105)Jameis Winston (23, 15-17)
Matt Schaub (36, 48-46)Derek Anderson (34, 21-27)Chase Daniel (30, 1-1)Ryan Fitzpatrick (34, 49-67)



Ryan Griffin *B (27, 0-0)
NFC West
ArizonaLos Angeles RamsSan FranciscoSeattle
Carson Palmer (37, 89-85)Jared Goff (22, 0-7)Brian Hoyer (31, 14-16)Russell Wilson (28, 64-27)
Drew Stanton (33, 9-7)Sean Mannion (25, 0-0)C.J. Beathard (23, 104th)Austin Davis (28, 3-8)
Blaine Gabbert (28, 9-29)


Preseason Prodigies

There’s some buzz in Cleveland because the Browns won all four of their preseason games. This coming off a 1-15 season and just 38 wins in the nine seasons since they last posted ten wins.

Is this buzz rational?

It’s easy to dismiss the preseason. Established starters see about 4-5 quarters’ worth of action in four weeks. Playbooks remain vanilla. Youngsters are fighting for jobs and a third of the players won’t play a single down during the regular season. Wins and losses aren’t that important.

I’ll also point out that the 2008 Detroit Lions, the only 0-16 team in NFL history, were 4-0 during that preseason.

Studying the issue going back to the beginning of the eight-division format, 30 teams have gone undefeated in the preseason and 32 teams have gone winless. How have they done?

The undefeated teams are more-or-less average during the regular season, with 7.97 wins per team.

How did they fare the previous season? These teams averaged 8.29 wins. So there was a slight decline – nothing too exciting given the small sample size.

What about teams that won six or less games the previous season? They averaged 1.8 wins more, on average. The Lions even made the playoffs in 2011 after an undefeated preseason following a 6-10 mark in 2010.

I’m not convinced Browns fans should be ecstatic about their 4-0 preseason, but it’s certainly not a negative.

The flip side of this argument is more interesting. Of the 0-4 preseason teams, their average record was only 7.34 wins that season. That’s a bit concerning. It gets even more concerning when you consider that the average wins for these teams the previous season was 8.77. While I’m not certain this is significant with the sample size, either, that’s potentially a study.

The average decline, season-to-season, of teams with ten or more wins the previous season that went winless in the preseason, is 3.9.

Atlanta and Oakland fans, maybe there is something there to worry about this year.