Houston Texans

The first round of the playoffs were full of upsets. The Titans upset the Patriots and the Vikings upset the Saints. Both upsets were driven by ground games that both scored and consumed the clock.

The methodology of how we pick is given here.

Second Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
San Francisco 49ers Minnesota Vikings 0.372 0.59 2.8
Green Bay Packers Seattle Seahawks -0.194 0.45 -1.4
Baltimore Ravens Tennessee Titans 0.985 0.73 7.3
Kansas City Chiefs Houston Texans 0.429 0.61 3.2

The methodology of this work is described here.

This year, the formulas favor the Baltimore Ravens and the Seattle Seahawks. Baltimore has the advantage in any possible encounter in the AFC. Seattle has the advantage over any team not named the New Orleans Saints. As the Saints lose their HFA against Green Bay, they are not favored against Green Bay. The odds of a Seattle-New Orleans matchup are small.

2019 NFL Playoff Teams, C&F Worksheet.
Rank Name Home Field Adv Playoff Experience SOS Total Score
1 San Francisco 49ers 0.660 0 0.125 0.785
2 Green Bay Packers 0.660 0.747 -0.225 1.182
3 New Orleans Saints 0.660 0.747 0.015 1.422
4 Philadelphia Eagles 0.660 0.747 -0.511 0.896
5 Seattle Seahawks 0.0 0.747 0.690 1.376
6 Minnesota Vikings 0.0 0.747 -0.334 0.413
1 Baltimore Ravens 0.660 0.747 0.015 1.422
2 Kansas City Chiefs 0.660 0.747 0.061 1.468
3 New England Patriots 0.660 0.747 -0.535 0.872
4 Houston Texans 0.660 0.747 0.292 1.699
5 Buffalo Bills 0.0 0.747 -0.380 0.367
6 Tennessee Titans 0.0 0.747 -0.310 0.437


The total score of a particular team is used as a base. Subtract the score of the opponent and the result is the logit of the win probability for that game. You can use the inverse logit (see Wolfram Alpha to do this easily) to get the probability, and you can multiply the logit of the win probability by 7.4 to get the estimated point spread.

Because the worksheet above can be hard to decipher, for the first week of the 2019 playoffs, I’ve done all this for you, in the table below. Odds are presented from the home team’s point of view:

First Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
New Orleans Saints Minnesota Vikings 1.009 0.733 7.5
Philadelphia Eagles Seattle Seahawks -0.541 0.368 -4.0
New England Patriots Tennessee Titans 0.435 0.607 3.2
Houston Texans Buffalo Bills 1.332 0.791 9.9

It was news when it happened. Laremy Tunsil and Kenny Stills were traded for Johnson Bademosi and Julia’n Davenport and a bundle of picks. We have done extensive analysis of draft trades using approximate value calculations. So how does the Laremy Tunsil trade rack up in the AV department?

Miami received 2 first round draft picks, one in 2020 and one in 2021. They also received a 2021 second round draft pick. As these have not been consumed, we can’t be precise about their value, but the Texans tend to be a second tier playoff team. There are 16 playoff teams, so valuing the first rounds at around 22 and the second rounder as 54 would put us into the right ball park.

Miami is likely to be dreadful for the next couple years so the 2020 fourth we can value around 130 and the sixth rounder about slot 200.


Laremy Tunsil Trade
Houston Texans Miami Dolphins Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
130 7 22 27
200 7 22 27
54 21
Total 14 Total 75
62 5.36


Because the chart does not include the people involved in the trade, the risk ratio is actually meaningless in this context. The real issue are the 62 AV given up for Laremy and Stills and whether they would be able to recover that value.

Laremy has averaged 6 or 7 AV so far in his first three seasons, so that’s not really all that helpful. We will look instead at the career of Andrew Whitworth of the LA Rams. In his first 11 years he totalled 104 AV, so a good left tackle can rack up, say, 10 AV a season (this would be about a 30% increase in productivity for Laremy). Laremy is going to have to do it for 6 seasons for the trade to break even.

Looking at Kenny Stills, he was used a lot in certain years with New Orleans and Miami, but he’s not young and he’s not being used much. If he could average 5 AV for 5 more season, he would be a substantial boost to the value of the trade, but I suspect he’s not going to be with Houston for long.

Update: overvalued the Texans picks in the first iteration of this article. That has been corrected.

This would have been done earlier, but Pro Football Reference dropped its very handy chart of draft position versus AV. I started missing it more and more, and using the Wayback Machine I found it here.

The three major QB trades of 2017 were the trade for Mitch Trubisky, Patrick Mahomes, and Deshaun Watson. We will analyze them in sequence.

Mitchell Trubisky Trade
Chicago Bears 49ers Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
2 46 3 45
67 19
111 12
(71) 21
Total 46 Total 97
51 2.11


The Bears have a trade risk comparable to a typical trade for a #1 draft choice and a quarterback at that. The trade has less fundamental risk than Goff or Wentz. The comparable that comes to mind is Eli Manning. By contrast, the delta AV of the other two trades are substantially less.

Patrick Mahomes Trade
Chiefs Bills Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
10 41 27 25
91 17
(25) 24
Total 41 Total 66
25 1.61

Mahomes merely has to give six seven good years, and the trade ends up warranted. The issue in the case of Deshaun Watson is keeping him upright. A fistful of whole years almost as good as his freshman year in the NFL and he would end up bordering on Hall of Fame numbers.

Deshaun Watson Trade
Texans Browns Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
12 35 25 24
4 44
Total 35 Total 68
33 1.94


So here is wishing Deshaun Watson a healthy career from now on.

I suspect  to a first approximation almost no one other than Baltimore fans, such as Brian Burke, and this blog really believed that Baltimore had much of a chance(+). Well, I should mention Aaron Freeman of Falc Fans, who was rooting for Baltimore but still felt Denver would win. Looking, his article is no longer on the Falcfans site. Pity..

WP graph of Baltimore versus Denver. I tweeted that this graph was going to resemble a seismic chart of an earthquake. Not my work, just a screen shot off the excellent site Advanced NFL Stats.

WP graph of Baltimore versus Denver. I tweeted that this graph was going to resemble a seismic chart of an earthquake. Not my work, just a screen shot off the excellent site Advanced NFL Stats.

After a double overtime victory by 3 points, it’s awfully tempting to say, “I predicted this”, and if you look at the teams I’ve  favored, to this point* the streak of picks is 6-0. Let me point out though, that you can make a limiting assumption and from that assumption figure out how accurate I should have been. The limiting assumption is to assume the playoff model is 100% accurate** and see how well it predicted play. If the model is 100% accurate, the real results and the predicted results should merge.

I can tell you without adding up anything that only one of my favored picks had more than a 70% chance, and at least two were around 52-53%. So 6 times 70 percent is 4.2, and my model, in a perfect world, should have picked no more than 4 winners and 2 losers. A perfect model in a probabilistic world, where teams rarely have 65% chances to win, much less 100%, should be wrong sometimes. Instead, so far it’s on a 6-0 run. That means that luck is driving my success so far.

Is it possible, as I have argued, that strength of schedule is an under appreciated playoff stat, a playoff “Moneyball” stat, that teams that go through tough times are better than their offense and defensive stats suggest? It’s possible at this point. It’s also without question that I’ve been lucky in both the 2012 playoffs and the 2013 playoffs so far.

Potential Championship Scenarios:


Conference Championship Possibilities
Home Team Visiting Team Home Win Pct Est. Point Spread
NE BAL 0.523 0.7
HOU BAL 0.383 -3.5
ATL SF 0.306 -6.1
SF SEA 0.745 7.9


My model likes Seattle, which has the second best strength of schedule metric of all the playoff teams, but it absolutely loves San Francisco. It also likes Baltimore,  but not enough to say it has a free run throughout the playoffs. Like many modelers, I’m predicting that Atlanta and Seattle will be a close game.


+ I should also mention  that Bryan  Broaddus tweeted about a colleague of his who predicted a BAL victory.

* Sunday, January 13, 2013, about 10:00am.

** Such a limiting assumption is similar to assuming the NFL draft is rational; that the customers (NFL teams) have all the information they should, that they understand everything about the product they consume  (draft picks), and that their estimates of draft value thus form a normal distribution around the real value of draft picks, and that irrational exuberance, or trends, or GMs falling in love with players play no role in picking players. This, it turns out, makes model simulations much easier.

The wins by Houston and New Orleans ensure that the #3 NFC and AFC seeds will be playing the #2 seeds, and that the #1 seeds will be playing the winner of the #4-#5 game. For now we’ll simply ask: if a team has playoff experience, but a rookie quarterback, does the rookie negate that experience advantage? Houston certainly looked good in their game.


In San Francisco-New Orleans, the Saints have the advantage of playoff experience, but San Francisco has home field and a tough schedule. My code suggests the odds in this game are 50-50. In Baltimore-Houston, Baltimore has all three advantages, and is favored to the tune of a 81% chance to win.

This playoff game is one of the more unique battles, as Houston is lacking playoff experience, as well as Matt Shaub. But with this post we’re going to introduce mods to the code we presented in our previous article, to allow us to set relative bounds on strength of schedule. The SOS effect has a large relative error, about 80%, so what happens to the odds in this matchup when we do exactly that?

Bengals: Playoff Exp 2 yrs ago, Away, SOS = -0.85, Pythagorean = 54.1%

Texans: No Playoffs ever, Home, SOS = -1.90, Pythagorean = 69.5%

Plugging these numbers into my formula, you get CIN favored by 66%. On the low end of the SOS relative, you get CIN favored by 60% and at the high end, CIN favored by 71%.  Given that 68% is what you get from playoff experience proper, the effect of Houston’s better record (and thus HFA) is roughly cancelled out by CIN’s better SOS.

So why isn’t Houston  favored  more, given their powerful offense? As stated previously, offensive metrics aren’t predictive to p = 0.05, more like p = 0.15 or so. Further, Houston had the easiest schedule in all of football. Cinncinnati also had an easy one, but not the easiest one.

Both Rex and Rob Ryan are known to use the Bear front, otherwise known as the double eagle, and in its 1985 incarnation, the 46, and  in preseason week 1 year 2011, both brothers flashed some double eagle with 8 man line.

The image above is the most famous Bear of the night, as Jon Gruden mentioned it, but  the very next play featured a Bear with a flexed nose tackle.

Rob’s double eagle had 5 down linemen instead of 6, but the 6 players along the line, and two players at linebacker depth and over the tackle leads me to designate this the first Bear the Cowboys have run under Rob Ryan.