New Orleans Saints


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

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The Cards moved up to pick 10 to draft Josh Rosen
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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

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Saints move up to get Marcus Davenport, DE
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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

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Bills go up to get Tremaine Edmunds
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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

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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

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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

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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
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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

Back in the day there was a board game named “NFL Strategy”, created by Tudor Games. The game was available in 1970, and my brother and I played it hard core. Because it had a spring based probability generator, we pushed the edges of creative spring twinking as much as possible. But the game had a lot more depth  than most games of the period, in terms of play calling.

To bring back a blast from the past,  I present Pass 24 B Fly.

Pass 24 B Fly.  A great way to get your fast running back out of the backfield

Pass 24 B Fly. A great way to get your fast running back out of the backfield

In far too many ways this play reminds me of the game ender in the 4th week contest, Cowboys and Saints. So, did Sean Payton dig into  the playbook  of a 45 year old game to spring a surprise on the ‘Boys? I guess we’ll never really know.

There were, of course, two substantial trades of Ricky Williams. The first netted the Washington Redskins the whole of the Saints 1999 draft, plus the Saint’s first and third round picks of 2000. Three years later, Ricky was traded to the Miami Dolphins for a pair of first rounders, plus change. The first was obviously not paid off. How did the Miami Dolphins fare in their trade, using our new risk metrics?

Risk Ratio no longer makes sense as a term when you’re talking about someone already drafted. The important term becomes the net risk term, 52 AV. That’s 1 more AV than the typical #1 draft choice, and that’s the amount of AV Ricky had to generate in order for this trade to break even. And note, these calculations are derived from weighted career AV, not raw AV. So any raw AV we apply to these numbers is a rough approximation (A typical career summing to, say, 95 AV, might end up around 76 or so WCAV).

That said, Ricky Williams had a great first season with the Dolphins, generating 19 AV in that season alone. His total ended up somewhere around 57 AV. I’d suggest the second trade approximately broke even.

End notes: I’ve seen a lot of discussion around  this set of data, discussing the quality of draft picks on a per pick basis, posted in of all places, a Cav’s board. If this board isn’t the original source of these graphs, please let me know. An excellent resource for high quality NFL draft trade information is here. And finally, a reader named Frank Dupont writes:

I wrote a book about decision making in the NFL.  It’s sort of a pop science book because it seeks to make what happens in the NFL understandable via some work that people like David Romer, Richard Thaler, and Daniel Kahneman have done.  But because all pop science books make their point through narrative, I spend a lot of time looking at why football coaches are so old, but other game players like chess players and poker players are so young (Tom Coughlin is 65 and yet the #1 ranked chess player in the world is 21, the world’s best poker players are 25-ish).

The link for the book is here, if this topic sounds interesting to you. I’ll only note in passing  that while physics prodigies are common, biologists seem to hit their stride in their 60s.  Some areas of knowledge do not easily lend themselves to the teen aged super genius.

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.

Odds:

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.

Much as in the previous series, we’re going to analyze the playoff prospects of New Orleans and Detroit. We’re also going to post the code (very hacky) that I’ve been using to study playoff teams. The code (2 pics required) is as follows:

Now one thing about this code, because it’s using Getopts::Long, numbers have to be positive or else this code will think that the number is an option. The simple fix is to find  the value of the most negative SOS and add a positive number equal in magnitude to both SOSs. As the only important  value is the difference, this is a valid form of data entry.

Ok, the significant factors, plus Pythagoreans:

Detroit: No playoff exp, Away, SOS = 0.63, Pythagorean 62.9%

New Orleans: Won Super Bowl 2 years ago, Home, SOS = -1.60, Pythagorean 77.7%

Because NO’s SOS is negative, just let it equal zero and add 1.60 to the SOS of Detroit, yielding 2.23. That’s the info you would pump into the calculator above. And it gives you the  following results:

New Orlean’s advantage due to playoff experience alone give NO a 68% chance of winning.

Adding in home field advantage give New Orleans a 76% chance of winning.

Adding in strength of schedule reduces New Orleans chances to 69%. New Orleans is heavily  favored.

By comparison, after all is said and done, had Atlanta been slotted into this game, the playoff calculator gives Atlanta a 51% chance of winning. Atlanta has a slightly better SOS than Detroit, and it also has recent playoff experience.

Given how powerful the New Orleans offense is, should Atlanta have sought out a team with a weaker offense, such as New York? That’s one of the counterintuitive points of my previous playoff analysis. Offensive metrics tend to yield a p of 0.15, not 0.05. They’re suggestive, not etched in stone advantages. New Orleans’ powerful offense may come into  play, but then again, it may not.