2019 NFL Playoff Season. Playoff Worksheet. First round playoff odds.

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

 

2018 NFL Playoffs, Conference Round (third round).

Not much to say, other than my system went 4-0 predicting winners. It did predict a bigger margin of victory in the Kansas City game, and closer games than most would have expected in the Saints game and Rams game. I don’t think in all honesty, that my odds were that much different from Vegas odds.

Once again, my data favor the home team, and by more than HFA. In both cases the home teams faced tougher competition throughout the year than the challenger.

Conference (NFC/AFC) Playoff Odds
Home Team	Visiting Team	Score Diff	Win Prob	Est. Point Spread
New Orleans Saints	LA Rams	0.986	0.73	7.3
Kansas City Chiefs	NE Patriots	1.162	0.76	8.6

 
In this instance the old and new formulas are close in terms of their predictions. That is because the strength of schedule adjustments between the teams are a little larger in the old formula.

Conference (NFC/AFC) Playoff Odds Old Formula
Home Team	Visiting Team	Score Diff	Win Prob	Est. Point Spread
New Orleans Saints	LA Rams	0.89	0.71	6.6
Kansas City Chiefs	NE Patriots	1.092	0.75	8.1

 

2018 NFL Playoffs. Second (Divisional) Round Odds.

In terms of picking winners, the system went 2-2, unable really to deal with tough underdogs such as the Chargers and Colts. It picked Philadelphia, which by traditional means was the most in favor of the home team, though that game was one foot from being a Chicago win. So it went 2-0 in the NFC and 0-2 in the AFC.

In this round the home teams are favored in all four contests, but by varying amounts compared to the spread.

The methodology of how we pick is given here. The 2018 worksheet is given here. And as an aside, Doug Farrar’s new football book is very very good and I recommend that hard core fans buy it.

In the worksheet below, the factor 0.66 is the logit of home field advantage as calculated by the logistic regression. That’s equivalent to a HFA of 4.9 points. The playoff HFA of 62.7% is equivalent to 3.8 points. So, if you prefer 3.8 or even 3, just subtract 1.1 points or 1.9 points from the points margin respectively. Just for yucks we calculated the Rams and Cowboys odds both with the 0.66 factor of the fitted formula and the 0.518 factor of actual results, the latter in parentheses.

Whether I stick with this new formula is up in the air. I have an older formula that is much the same but not inclined to generate 15 point advantages, a bit tamer, if you will. We’ll see. I don’t do this for a living, just for fun, and the methodology link above gives the old formula.

That said, the second round worksheets.

Second Round Playoff Odds
Home Team	Visiting Team	Score Diff	Win Prob	Est. Point Spread
New Orleans Saints	Philadelphia Eagles	0.685	0.66	5.1
LA Rams	Dallas Cowboys	0.48 (0.34)	0.62 (0.58)	3.6 (2.5)
Kansas City Chiefs	Indianapolis Colts	2.067	0.89	15
New England Patriots	LA Chargers	0.942	0.72	7.0

 
Update: decided to add the old formula predictions, and also use the measured HFA factor.

 

Second Round Playoff Odds Old Formula
Home Team	Visiting Team	Score Diff	Win Prob	Est. Point Spread
New Orleans Saints	Philadelphia Eagles	0.546	0.63	4.0
LA Rams	Dallas Cowboys	0.313	0.58	2.3
Kansas City Chiefs	Indianapolis Colts	1.707	0.85	12.6
New England Patriots	LA Chargers	0.42	0.60	3.1

 

Trades on day 1 of the 2018 Draft. AV analysis.

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

 

2017 Playoff Odds. Second Round.

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

 

When real life reminds you of a 45 year old board game (Cowboys Saints 2015)

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

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.

 

Assessing the break even point of the two Ricky Williams trades

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.

 

NFL Playoffs 2012: 3 Seeds at 2 seeds

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.

 

Playoffs 2011: New Orleans versus Detroit – code for playoff analysis.

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.