Can inexperienced quarterbacks succeed in the playoffs? The Houston Texans and the T.J. Yates experiment

The Houston Texans are currently having the finest season in their nine-year existence. With an 8-3 record, Houston is almost certainly going to make the playoffs. But after losing quarterbacks Matt Schaub and Matt Leinart in consecutive games, the Texans are down to their third string quarterback.

Doing a lot of this

That man is T.J. Yates, a rookie quarterback out of North Carolina. Yates did manage to torch LSU for over 400 yards and 3 touchdowns last season, one of three 400-yard performances by Yates in his senior season. But you can’t fault Texans fans if they’re a little concerned.

Houston signed Jake Delhomme this week, but he’s expected to serve as the primary backup and mentor. If the Texans go with Yates for the final five games of the season, will he be the most inexperienced quarterback to ever start a game in the playoffs?

Hardly. There have been 13 quarterbacks to start a playoff game with five or fewer career regular season starts. In fact, he’d only be the third rookie quarterback with to be inserted into his team’s lineup for the last five games of the season and then start in the playoffs. Perhaps more surprisingly, there have been five times since 1960 when a quarterback made only one regular season start in his entire career before being called on to start a playoff game. Going chronologically:

Tom Matte, 1965 vs. the Green Bay Packers

In 1965, the NFL was a 14-team league with two divisions. The playoffs were simple: the two division winners would play in the last championship game before the start of the Super Bowl era. Under Johnny Unitas, the Colts raced out to 7-1 record, with the only loss coming at Lambeau Field by a score of 20-17 in week two. Unitas missed the Colts’ ninth game with a back injury, but backup Gary Cuozzo (more on his reputation as the best backup quarterback in football here) led the Colts to victory and threw for five touchdowns in his absence. Unitas returned the next week and helped the Colts pick up another victory and one tie. By then, the 9-1-1 Colts held a 1.5 game lead on the 8-3 Packers with only three games left to play. But against the Bears, Stan Jones and Earl Leggett tore Unitas’ knee in a classic high-low hit that ended his season. The Colts offense was helpless against Chicago, losing the game 13-0.


Simple Rating System for 11/28/2011 – Finish line in sight, the final picture sharpens

Last week, the Simple Rating System did not have Arkansas in the top three; the SRS was unimpressed by the Razorbacks, which explains why they didn’t drop too much. Last week, the SRS had Arkansas at #13 with a score of 53.4; this week they’re 13th at 52.1. LSU is now alone at the top, with a 1.2 point edge over Alabama. That jives with the early reports that the Tigers will be a 1.5 to 2 point favorite over the Crimson Tide in the potential rematch. Oklahoma State actually drops by over a point because Tulsa dropped by 2.5 points after getting blown out by Houston, and Texas Tech, Texas A&M and Missouri all dropped by a couple of points after unimpressive conference games (and their opponents did not improve by as much). But the Cowboys are still third, and the SRS puts them within a couple of points of Alabama.

Houston had by far its best game of the season this weekend — a 72.5 score after beating Tulsa (43.0) by 32 on the road (adjusted MOV of 29.5). According to the SRS, Houston’s worst five games all came in September. Since then, Houston has an average SRS score of 60.2 in its last seven games with not a single sub-50 performance. I was (appropriately) skeptical of the Cougars based on an easy schedule for most of the season, but they’ve been playing at an elite level for the last two months. The full SRS results:

Rk.  Team                 Conf  G    MOV      SOS      SRS      W-L
1.   LSU                  SEC   12   24.1     41.1     65.2     12-0
2.   Alabama              SEC   12   23.4     40.7     64.0     11-1
3.   Oklahoma St          B12   11   20.0     42.6     62.6     10-1
4.   Oklahoma             B12   11   17.7     43.3     61.1      9-2
5.   Oregon               P12   12   19.0     41.6     60.6     10-2
6.   Stanford             P12   12   19.3     39.9     59.1     11-1
7.   Wisconsin            B10   12   23.0     35.9     59.0     10-2
8.   Boise St             MWC   11   19.8     36.4     56.2     10-1
9.   Michigan             B10   12   15.3     40.1     55.4     10-2
10.  Houston              CUS   12   26.3     27.7     53.9     12-0
11.  Southern Cal         P12   12   11.0     42.4     53.5     10-2
12.  Texas A&M            B12   12    8.0     44.9     52.9      6-6
13.  Arkansas             SEC   12   12.3     39.8     52.1     10-2
14.  Texas                B12   11    7.1     44.0     51.1      7-4
15.  Georgia              SEC   12   13.4     37.6     51.0     10-2
16.  Michigan St          B10   12   12.7     38.1     50.8     10-2
17.  Notre Dame           IND   12    8.0     41.8     49.8      8-4
18.  Missouri             B12   12    6.3     43.3     49.7      7-5
19.  South Carolina       SEC   12   10.1     39.6     49.7     10-2
20.  Baylor               B12   11    5.7     43.9     49.6      8-3
21.  TCU                  MWC   11   15.6     33.6     49.2      9-2
22.  Kansas St            B12   11    4.7     44.0     48.6      9-2
23.  Nebraska             B10   12    7.0     41.5     48.5      9-3
24.  Virginia Tech        ACC   12   12.8     35.4     48.2     11-1
25.  Florida St           ACC   12   13.2     34.2     47.4      8-4

Simple Rating System: Where the SEC is not 1-2-3 – 11/21/2011

Last week, Oklahoma State, Oklahoma and Oregon all ranked in the top five of the SRS. And while all three lost this weekend as big favorites — and each of their SRS ratings dropped significantly — they had built up such a large lead over the rest of the pack that they remain in the top five. Let’s break it down:

Oklahoma State was at 67.3 but lost to Iowa State (SRS of 38.1 before the game) in Ames, Iowa by six points. How shocking was this? The SRS pegged the Cowboys last week as 26.2 point favorite while the actual point spread was 26.5. Oklahoma State played their worst game of the season, producing an SRS score of 33.7 (a -7 adjusted margin of victory against an opponent that — thanks in part to beating OSU — has an SRS score of 40.7). Oklahoma State is now at just 63.7 for the year.

Oklahoma fared a little better, as Baylor (current SRS of 49.4) is a better opponent. Losing by 7 in Waco (SRS grade for OU of 42.4) was still a better performance for the Sooners than the inexplicable 3-point home loss against Texas Tech (32.4), but the two bad performances are somewhat canceled out by the monster blowouts against Kansas State and Texas. Oklahoma dropped from 66.0 to 62.7 with the loss.

Oregon lost at home, but to a much better opponent. USC is now the 13th best team in the SRS, and not just because they took Stanford to triple overtime. The Trojans beat Notre Dame by 14 in South Bend and blew out California, Washington and Colorado. The Trojans were ugly against the good version of Arizona State, but that wasn’t even their worst performance of the season (in week 1, USC won by only two at home against Minnesota). But USC is one of the hottest teams in colege football, scoring an SRS grade of over 60 in four of their last six games. In fact, for SRS purposes, the Stanford loss was their worst game in that stretch.

Oregon put up a -7 adjusted MOV against USC, but USC’s 52.4 rating makes it a somewhat forgivable loss. The Ducks drop from 64.2 to 61.2.

Prior to the game, Oregon was at 64.2 and USC at 52.5, which suggests a point spread of 14.7 points for a game in Eugene. The actual spread was 15.5. Oklahoma was at 66.0 and Baylor at 47.8, which would put the point spread for a game in Waco at 15.2; the actual line was 17. The SRS standings below, along with the projected line for LSU/Arkansas:

Rk   Team                 Conf   G   MOV      SOS      SRS      Rec
1.   LSU                  SEC   11   24.4     40.5     64.8     11-0
2.   Alabama              SEC   11   23.0     41.4     64.4     10-1
3.   Oklahoma St          B12   11   20.0     43.8     63.7     10-1
4.   Oklahoma             B12   10   17.8     44.9     62.7      8-2
5.   Oregon               P12   11   18.5     42.7     61.2      9-2
6.   Stanford             P12   11   20.0     39.5     59.5     10-1
7.   Wisconsin            B10   11   22.5     35.9     58.3      9-2
8.   Boise St             MWC   10   19.9     37.8     57.7      9-1
9.   Michigan             B10   11   16.0     40.6     56.6      9-2
10.  Texas A&M            B12   11    9.4     45.5     54.9      6-5
11.  Arkansas             SEC   11   15.3     38.1     53.4     10-1
12.  Houston              CUS   11   26.0     27.1     53.0     11-0
13.  Southern Cal         P12   11    8.8     43.6     52.4      9-2
14.  Missouri             B12   11    5.6     46.0     51.6      6-5
15.  Texas                B12   10    7.2     44.4     51.5      6-4

A couple of good pieces on Oklahoma State receiver Justin Blackmon

One is a profile (or anti-profile), while the other includes excerpts of interviews with Big 12 cornerbacks who’ve had to face him. The latter touches on the points that make Blackmon maybe the best college wide receiver I’ve seen in some time: (1) that he is focused and goes hard on every play and seems to never get tired because he’s in fantastic condition and (2) that he is not as big as he plays — he’s listed at 6’1″, but is extremely physical and plays large.

When Holgorsen got to Oklahoma State, he was an underclassmen receiver who had just a few catches to his name. Indeed, they said that, coming out of spring practice, they thought Blackmon was probably only their third-best receiver. But the light went on for him and it’s been fireworks ever since. This season his average yards per catch is down because he’s such a marked player, but that’s helped open things up for his teammates as the receivers around him have been playing at a higher level (in their second year in the offense with Blackmon as fantastic role model). And of course, he and Weeden have the best connection in football, and when they throw that fade route it’s unstoppable — and gorgeous.

Analyzing NFL running games through 10 weeks

NFL teams are passing more frequently and more effectively than ever before. Given enough opportunities, most teams will eventually connect on big plays through the air. But while running backs have taken a backseat in most offenses, a successful rushing attack is still a significant component in most effective offenses.


As teams — and by extension, their opponents — become more prolific at passing, the opportunity cost of not passing increases. That makes an unsuccessful run particularly damaging. A run on third and short that forces a punt, or a run on 1st or 2nd down that makes it harder for his team to move the chains, hurts a team more significantly than ever before. In the ’70s, the running game was supposed to win games for teams, as running was a more effective optionthan passing. In some ways, the goal of the running game now is to not mess things up for the passing game, by forcing a punt or an unfavorable third down situation.

About 25 years ago, Bob Carroll, Pete Palmer and John Thorn wrote the Hidden Game of Football, a fascinating book on football theory and win probability. They went through and graded each play as a success or failure based on how many yards were gained as a percentage of how many yards were needed to pick up a first down or touchdown.

When I wrote a series on the most dominant running backs of all-time, I noted that yards per carry was a misleading statistic for running backs. Rushing is more about consistent success than passing, and rushing has a positive feedback loop in place that might lower yards per carry averages. Yards per carry is highly sensitive to large runs, decreasing the correlation it would have with the overall strength of a running game. I had a discussion with Brian Burke about this a couple of years ago, and he now uses rush success rate in his team efficiency models.

So to analyze NFL running games so far this season, I decided to use my own version of rush success rate. Here’s exactly what I did:

What coach said this about facing what team and quarterback?

Quiz time:

“Well. We have another big one ahead of us. This next one, I guess you’d say that every game is really really big, but I think this one will pose a real challenge to our defense because they’re like three offenses in one. They’re a power attack . . . . They go from that to being able to be an option attack with the quarterback. . . . You see where their offense is. It makes the defense have to be sound in all phases. You can’t load up and play the power because you may be getting optioned. You can’t go in there with an idea of being a finesse or assignment totally or you’re going the power run right at you. This is going to be a big test. And he can throw it. He’s put some yardage on people. The last thing they do that challenges your defense is they have a fast pace, so they do that to try to get your defense so they’re not in great alignments. Just to be a little sloppy because they hurry up and if you’re not a real disciplined defense, you don’t get set correctly, and you know as well as I do that we’re not good enough to not be perfect in our assignments and our alignments.”

The answer is after the jump.


Can Tebow’s non-passing offense work in the NFL?

I particpated in this week’s Slate/Deadspin roundtable, and my topic was — wait for it — Tim Tebow:

In the last two weeks, in victories over Oakland and Kansas City, the Broncos ran for 299 yards and 244 yards. Meanwhile, the top rushing team in the NFL (the Philadelphia Eagles) averages merely 172 yards per game on the ground. Denver’s grind-it-out performance against the Chiefs on Sunday was especially surprising given that the Broncos’ top two running backs, Willis McGahee and Knowshon Moreno, had to leave with injuries, and third-stringer Lance Ball gained only 96 yards. So how did the Broncos succeed? By mixing in traditional runs and college-style read plays, sometimes even using receiver Eddie Royal as a third option as a pitch man after he’d gone in motion.

Television football pundits often say this stuff can’t work for long in the NFL because pro defenses are too fast, and that they will just “load the box” and play “assignment” football against the reads and options. While there’s truth in this cliché, stopping Denver’s Tebow-ized offense is much more complicated than that. Football is governed as much by arithmetic as it is by physics. Though each side gets 11 guys, the defense “gains” a defender when Tom Brady or Aaron Rodgers hands the ball off and does nothing but watch the running back. The Patriots and Packers can get away with this because they are a threat at any moment to fake a handoff and throw downfield. That’s why the base defense for most NFL teams includes two deep defenders, safeties who are a lot more useful at defending passes than they are at stopping the run.

Read the whole thing at Slate and Deadspin. Thanks to the guys at both spots for thinking of me for participating.

LaMichael James, unbalanced sets, and Chip Kelly’s gashing of Stanford

It’s up over at the Grantland Blog:

That Oregon coach Chip Kelly has a plethora of spread and read concepts in his offense is by now well-known. And Saturday evening against Stanford was no different. Kelly has often remarked that it sometimes takes him a couple of series to tease out how the opponent wants to defend him. At that point, his up-tempo offense usually explodes.

Against Stanford, Kelly repeatedly went to his basic zone-read run game but with three receivers to one side and a tight end to that same side — an unbalanced set. Because Kelly forces the defense to cover his three receivers with three defenders, or else his quarterback is instructed to throw a bubble screen to one receiver while the other two block, he forces the defense to make decisions in how it will defend the inside runs.


Read the whole thing. Highlight of the play after the jump.


Simple Rating System: The Oklahoma State Cowboys rise to top the rankings, with a caveat

By now, you understand how the Simple Rating System works. Last week, Stanford and Boise State were top six teams with BCS aspirations. Following home losses, both teams can still take pride in how far they’ve come: a second straight 12-1 season will be viewed as a disappointment.

As always, thanks to Dr. Peter Wolfe for providing the game scores. Here are the SRS results through week 10. The SRS places equal weight on each game and cares more about margin of victory than records (which is why it’s a predictive system). As a result, Stanford (5th last week) and Boise State (7th last week) are still top 10 teams, as is 5-5 Texas A&M. All three of those teams are 16+ favorites this week.

Rk   Team                 Conf   G   MOV      SOS      SRS      Rec
1.   Oklahoma St          B12   10   22.7     44.7     67.3    10-0
2.   Alabama              SEC   10   23.2     43.0     66.2     9-1
3.   LSU                  SEC   10   23.0     43.0     66.0    10-0
4.   Oklahoma             B12    9   20.6     45.5     66.0     8-1
5.   Oregon               P12   10   21.1     43.2     64.2     9-1
6.   Stanford             P12   10   22.0     40.0     62.0     9-1
7.   Wisconsin            B10   10   23.3     36.6     59.9     8-2
8.   Boise St             MWC    9   19.9     38.9     58.8     8-1
9.   Michigan             B10   10   15.2     41.0     56.2     8-2
10.  Texas A&M            B12   10    6.6     47.9     54.5     5-5
11.  Notre Dame           IND   10   10.7     43.0     53.7     7-3
12.  Texas                B12    9    8.7     44.7     53.4     6-3
13.  Georgia              SEC   10   13.7     39.7     53.3     8-2
14.  Missouri             B12   10    5.5     47.5     53.0     5-5
15.  Houston              CUS   10   26.0     26.8     52.8    10-0
16.  Arkansas             SEC   10   14.3     38.4     52.7     9-1
17.  Southern Cal         P12   10    9.0     43.5     52.5     8-2
18.  TCU                  MWC   10   15.1     36.7     51.8     8-2
19.  Nebraska             B10   10    9.9     41.8     51.7     8-2
20.  Arizona St           P12   10    9.8     41.8     51.6     6-4
21.  Michigan St          B10   10    9.9     41.5     51.4     8-2
22.  South Carolina       SEC   10    8.5     42.3     50.8     8-2
23.  Florida St           ACC   10   14.9     35.6     50.5     7-3
24.  Kansas St            B12   10    4.5     45.9     50.3     8-2
25.  Virginia Tech        ACC   10   12.1     36.9     49.0     9-1

Grantland: How and why Jim Harbaugh eliminated sight-adjustments in the 49ers passing game to make it go

It’s up over at Grantland:

A key reason for this is that Harbaugh has made the passing game easier for Smith, particularly when it comes to beating the blitz. Of course, coaches often say they are “simplifying the playbook,” but Harbaugh has been able to do it coherently and in a way that actually aids his quarterback’s ability to succeed rather than simply removes options.

One reason for this is that many NFL plays simply duplicate each other; you only need so many ways to throw the same pass to the flat or run off tackle. You might as well perfect the plays you have rather than keep adding new ones every week. But Harbaugh has also changed the entire theory behind how Smith and his offense approach the blitz, and this is where Smith’s greatest improvement has come. That’s because Harbaugh eliminated “sight adjustments” from the 49ers playbook. Indeed, this change has been so successful that, according to Pro Football Focus, Smith’s completion percentage, quarterback rating, average yards per attempt, and touchdown-to-interception ratio against blitzes have all been much better than Smith’s historical averages, but also better than his performance on all other downs.

Read the whole thing. Video diagrams after the jump (and in the article).