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
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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

It’s a new playoff season, and another time to try our new playoff formulas. Methodology of this work is described in depth here.

The playoff formulas like New Orleans and Kansas City. They like Baltimore, but Baltimore, which will lose home field after the first round, is unlikely to be favored after that point. The formulas place a substantial penalty on the lack of playoff experience, and so does not favor Chicago, the Chargers, or the Colts. Update: Baltimore has not been in the playoff since 2014, and so the results have been amended.

2017 NFL Playoff Teams, C&F Worksheet.
NFC
Rank Name Home Field Adv Playoff Experience SOS Total Score
1 New Orleans Saints 0.660 0.747 0.192 1.599
2 LA Rams 0.660 0.747 -0.134 1.273
3 Chicago Bears 0.660 0.0 -0.711 -0.051
4 Dallas Cowboys 0.660 0.747 0.046 1.453
5 Seattle Seahawks 0.0 0.747 -0.170 0.577
6 Philadelphia Eagles 0.0 0.747 0.167 0.914
AFC
1 Kansas City Chiefs 0.660 0.747 -0.033 1.374
2 New England Patriots 0.660 0.747 -0.535 0.872
3 Houston Texans 0.660 0.747 -0.465 0.942
4 Baltimore Ravens 0.660 0 0.195 0.855
5 LA Chargers 0.0 0.0 -0.070 -0.070
6 Indianapolis Colts 0.0 0.0 -0.693 -0.693

 
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 2018 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
Chicago Bears Philadelphia Eagles -0.965 0.276 -7.1
Dallas Cowboys Seattle Seahawks 0.876 0.706 6.5
Houston Texans Indianapolis Colts 1.635 0.836 12.1
Baltimore Ravens LA Chargers 0.925 0.716 6.8

 

But to summarize, the formulas used here were found by logistic regressions and each element in the formula has a playoff significance of 95%. I promise if the more common offense metrics could say that, they would. I’ll also note that in vogue stats like FPI don’t really give answers markedly different from other common offensive metrics, such as Pythagorean expectation.

That said, offensive metrics like Pythagorean Expectation favor Seattle over Dallas by about half a point, or 52% win probability for Seattle. Offensive stats still favor Baltimore, but not as much. Simple Ranking stats favor Chicago by around 8 points, circa 75% WP. Houston-Indianapolis have approximately even offensive stats, so the difference between the teams is about 3 points. HFA is worth a bit more in the playoffs, circa 63%.

I’ll continue posting my odds, though this has not been the best season for them. Jacksonville continued to be best modeled by their median point spread, as opposed to their playoff formula. Philadelphia outperformed any reasonable prediction of their play once Wentz went down.

My system gives an edge to New England. Philadelphia played a tougher schedule but lacks playoff experience by my system. There is no home field in the Superbowl.

Super Bowl Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
New England Patriots Philadelphia Eagles 0.586 0.642 4.3

Outside of the New England game, all the games were good and exciting, from the final goal line stand by the Eagles, to the win with ten seconds left by the Vikings. The Jacksonville Jaguars are just not well managed by this system. It was easy to see that through the year that they were a boom or bust team. They could win big or lose big, and in the game with the Steelers, they were enough in “win big” mode that the Steelers could not keep up.

Philadelphia won because of their stout defense, a Nick Foles that gave them a AYA of 8.2 for the game, much akin to Carson Wentz’s average.

To remind people, the 2017 worksheet is here, and the methodology is here. The odds for the next round are below.

Conference (NFC/AFC) Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Philadelphia Eagles Minnesota Vikings -0.604 0.353 -4.5
New England Patriots Jacksonville Jaguars 1.872 0.867 13.9

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

Summary: Replacing Wentz with Foles removes about 6.5 points of offense from the Philadelphia Eagles, turning a high flying offense into something very average.

Last night the Atlanta Falcons defeated the LA Rams. Now we’re faced with the prospect of the Falcons playing the Eagles. I have an idiosyncratic playoff model, one I treat as a hobby. It is based on static factors, the three being home field advantage, strength of schedule, and previous playoff experience. And since it values the Eagles as 0.444 and the Falcons as 1.322, the difference is -0.878 (win probability in logits). The inverse logit of -0.878 is 0.294, which is the probability of the Eagles winning, and an estimated point spread would be a 6.5 point advantage for the Falcons.

Another question that a Falcons or Eagles fan might have is how much is Carson Wentz worth as a QB, in points scored? We can use the adjusted yards per attempt stat of Pro Football Reference to estimate this, and also to estimate how much Carson Wentz is better than Foles. We have made these kinds of analyses before for Matt Ryan and Peyton Manning.

Pro Football Reference says that Carson Wentz has a AYA of 8.3 yards per attempt. Nick Foles has a AYA of 5.4. Now lets calculate the overall AYA for every pass thrown in the NFL. Stats are from Pro Football Reference.

(114870 yards + 20*741 TDs – 45*430 Ints) / 17488 Attempts
(114870 yards + 14820 TD “yards” – 19350 Int “yards”) / 117488 Attempts
110340 net yards / 17488 yards
6.31 yards per attempt to three significant digits

So about 6.3 yards per attempt. Carson Wentz is 2 yards per attempt better than the average. Nick Foles is 0.9 yards less than the average. The magic number is 2.25 which converts yards per attempt to points scored per thirty passes. So Carson, compared to Foles, is worth 2.9 * 2.25 = 6.5 points per game more than Foles, and 4.5 points more than the average NFL quarterback.

This doesn’t completely encompass Carson Wentz’s value, as according to ESPN
‘s QBR stat
, he account for 10 expected points on the ground in 13 games, so he nets about 0.8 points a game on the ground as well.

Now, back to some traditional stats. The offensive SRS assigned to Philadelphia by PFR is 7.0 with a defensive SRS of 2.5. If Carson Wentz is worth between 6.5 and 7.3 points per game, then it reduces Philadelphia’s offense to something very average, about 0.5 to -0.3. That high flying offense is almost completely transformed by the loss of their quarterback into an average offense.

Note: logits are to probabilities as logarithms are to multiplication. Rather than multiplying probabilities and using transitive rules, you just add the logits and convert back. Logarithms allow you to add logarithms of numbers rather than multiplying them.