Tennessee Titans


Conventional wisdom went 3-1 and my picks went 2-2; perhaps I would have done better if the Seahawks hadn’t suffered so many injuries at the end of the season. The Titans continue their upset ways while Green Bay, Kansas City, and San Francisco won as favorites.

The methodology of how we pick is given here.

Conference (NFC/AFC) Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
San Francisco 49ers Green Bay Packers 0.263 0.56 2.0
Kansas City Chiefs Tennessee Titans 1.031 0.74 7.6

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.
NFC
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
AFC
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

Bill traded up to pick 7 to get QB Josh Allen.

Josh Allen Trade
Buffalo Bills Buccaneers Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
7 32 12 35
53 22
56 19
Total 32 Total 76
44 2.38

~~~
The Cards moved up to pick 10 to draft Josh Rosen
~~~

Josh Rosen Trade
Cardinals Raiders Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
10 41 15 28
79 18
152 9
Total 41 Total 55
14 1.34

~~~
Saints move up to get Marcus Davenport, DE
~~~

Marcus Davenport Trade
Saints Packers Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
14 29 27 25
147 8
(25) 24
Total 29 Total 57
28 1.97

~~~
Bills go up to get Tremaine Edmunds
~~~

Tremaine Edmunds Trade
Bills Ravens Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
16 32 22 27
154 12 65 21
Total 44 Total 48
4 1.09

~~~
Packers trade again to get Jaire Alexander
~~~

Jaire Alexander Trade
Packers Seahawks Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
18 29 27 25
248 5 76 17
188 5
Total 34 Total 47
13 1.38

~~~
Titans trade up for Rashaan Evans
~~~

Jaire Alexander Trade
Packers Seahawks Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
18 29 27 25
248 5 76 17
188 5
Total 34 Total 47
13 1.38

~~~
Ravens trade 2019 assets to get their QB
~~~

Lamar Jackson Trade
Ravens Eagles Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
32 23 52 22
132 11 125 15
(48) 25
Total 34 Total 62
28 1.82

The first round is over and in terms of predicting winners, not my best (by my count, 1-2-1, as we had Jax and Bills in a de facto tie). I was pleased that the model got Rams and Atlanta correct, and the Sunday games all came down to the wire. One or two plays and my formal results would have been impressive. Still, back to the predictions for this week.

To add some spice, we will predict results for New Orleans normally, and also as if Drew Brees is elite. Values in parentheses are the elite numbers. With elite status or no, Minnesota is still favored in this data set.

The only home team not favored is Philadelphia. We discussed this in part in this article.

Second Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Philadelphia Eagles Atlanta Falcons -0.878 0.294 -6.5
Minnesota Vikings New Orleans Saints 1.231 (0.484) 0.774 (0.619) 9.1 (3.6)
New England Patriots Tennessee Titans 1.674 0.842 12.4
Pittsburgh Steelers Jacksonville Jaguars 1.915 0.872 14

Determining how to assess draft trades in the NFL is not hard (see here, here, and here). Ever since Pro Football Reference went through the trouble of determining what average AV can be assigned to a draft slot, it’s merely a matter of counting. The technique has some variance, as the draft slot of a future pick is not known. Even so, with a bit of conservative extrapolation, you can still get a feel for the overall cost of a trade.

 

First, the numbers:

 

The AV costs of the 2016 Rams Titans trade.
Rams Titans Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
1 51 15 28
113 14 43 24
177 5 45 25
76 17
(20) 29
(84) 13
Total 70 Total 136
66 1.94

 

In the data above, we assume that the Rams will improve 5 slots in draft placement, so that the first and third they sent to the Titans would be picks 20 and 84. If the Titans end up 18th or 23rd, it’s notable that the difference in value at this point is less than the point-to-point deviation, so that kind of change won’t affect the calculation much. Pro Football Reference’s raw data are moderately noisy.

The Rams total investment is 136 AV, roughly equal to the career value of John Elway. That’s not entirely accurate, as the Rams actually received three picks in return, and if the other two return 19, then the player they pick at #1, to return the value of the investment, only has to yield 117 AV.Now, 117 points is about mid in between Phillip Rivers and Aaron Rogers in value.

Update: Johnny Unitas, at 114, is a closer comparable.

In terms of risk, the trade is riskier than the Eli Manning trade, and less risky than the RG III trade or the Earl Campbell trade. For 9 more AV than the RG III trade, they received 24 more AV in return.

Best of luck to the Rams. I hope their picks work out well for them.

The recent success of DeMarco Murray has energized the Dallas fan base. Felix Jones is being spoken of as if he’s some kind of leftover (I know, a 5.1 YPC over a career is such a drag), and people are taking Murray’s 6.7 YPA for granted. That wasn’t the thing that got me in the fan circles. It’s that Julius Jones was becoming a whipping boy again, the source of every running back sin there is, and so I wanted to build some tools to help analyze Julius’s career, and at the same time, look at Marion Barber III’s numbers, since these two are historically linked.

We’ll start with this database, and a bit of sql, something to let us find running plays. The sql is:

select down, togo, description from nfl_pbp where season = 2007 and gameid LIKE "%DAL%" and description like "%J.Jones%" and not description LIKE '%pass%' and not description LIKE '%PENALTY on DAL%' and not description like '%kick%' and not description LIKE '%sacked%'

It’s not perfect. I’m not picking up plays where a QB is sacked and the RB recovers the ball. A better bit of SQL might help, but that’s a place to start. We bury this SQL into a program that then parses the description string for the statement “for X yards”, or alternatively, “for no gain”, and adds them all up. From this, we could calculate yards per carry, but more importantly, we’ll calculate run success and we’ll also calculate something I’m going to call a failure rate.

For our purposes, a failure rate is the number of plays that gained 2 yards or less, divided by the total number of running attempts, multiplied by 100. The purpose of the failure rate is to investigate whether Julius, in 2007, became the master of the 1 and 2 yard run. One common fan conception of his style of play in his last year in Dallas is that “he had plenty of long runs but had so many 1 and 2 yards runs as to be useless.” I wish to investigate that.

(more…)

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