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
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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.

One of the ESPN folks posted FPI odds today, retweeted by Ben Alamar. The numbers are very different from my playoff formulas. The nature of those odds made me suspect that FPI is intrinsically an offensive stat, with the advantages and disadvantages of such a stat.

One of the issues I’ve has with offensive stats is that the confidence interval of any I’ve looked at, in terms of predicting playoff performance, is that those confidence intervals are on the order of 85%. Whatever flaws of my formulas, they fit to confidence intervals of 95%. The effects they touch on are real.

But still, the purpose of this is to compare FPI odds to the odds generated by some common offensive stats. We’re using Pythagorean expectation, as generated by my Perl code, SRS as generated by my Perl code, and median point spread, also calculated by my code.

Results are below.

FPI Odds versus Other Offensive Stats
Game FPI Pythag Simple Ranking Median Pt Spread
Kansas City – Tennessee 0.75 0.75 0.79 0.73
Jacksonville – Buffalo 0.82 0.89 0.86 0.73
Los Angeles – Atlanta 0.62 0.75 0.74 0.68
New Orleans – Carolina 0.70 0.73 0.74 0.78

 

The numbers correlate too well for FPI not to have a large offensive component in its character. In fact, Pythagorean odds correlate so well with FPI I’m hard pressed to know what advantages FPI gives to the generic fan.

Note: the SRS link above points out that PFR has added a home field advantage component to their SRS calcs. I’ll note that our SRS was calibrated against PFR’s pre 2015 formula.

 

As I’m really away from home and short of time, I’ll mention my methodology is described in depth here.

The first table is the worksheet. Values will not change throughout the playoffs.

2017 NFL Playoff Teams, C&F Worksheet.
NFC
Rank Name Home Field Adv Playoff Experience SOS Total Score
1 Philadelphia Eagles 0.660 0 -0.216 0.444
2 Minnesota Vikings 0.660 0.747 0.301 1.708
3 Los Angeles Rams 0.660 0.0 -0.046 0.614
4 New Orleans Saints 0.660 0 0.471 1.131
5 Carolina Panthers 0.0 0.747 0.629 1.376
6 Atlanta Falcons 0.0 0.747 0.575 1.322
AFC
1 New England Patriots 0.660 0.747 -0.377 1.030
2 Pittsburgh Steelers 0.660 0.747 -0.334 1.073
3 Jacksonville Jaguars 0.660 0 -0.842 -0.182
4 Kansas City Chiefs 0.660 0.747 -0.404 1.003
5 Tennessee Titans 0.0 0.0 -0.644 -0.644
6 Buffalo Bills 0.0 0.0 -0.146 -0.146

 

LA is not favored by this model and neither are AFC teams. The NFC South’s toughness shows through in the SOS marks for this data set. Minnesota and/or NFC South Teams largely have advantages over almost any matchup the AFC can offer. Finally, an open Q is, is Drew Brees elite enough that his team should be granted the PPX bonus? For now I’d consider this a question to be answered later.

This second table shows odds for the first round, calculated for you. The only home team it favors is Kansas City.

First Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Los Angeles Rams Atlanta Falcons -0.708 0.330 -5.2
New Orleans Saints Carolina Panthers -0.245 0.439 -1.8
Jacksonville Jaguars Buffalo Bills -0.036 0.491 -0.3
Kansas City Chiefs Tennessee Titans 1.647 0.838 12.1

This question came up when I was looking up the last year in the playoffs for seven probable NFC playoff teams. Both New Orleans and Philadelphia last played in the playoffs four years ago, in 2013. And then the thought came up in my head, “But Drew Brees is a veteran QB.” This seems intuitive, but wanting to actually create such a definition and then later to test this using a logistic regression, there is the rub.

There are any number of QBs a fan can point to and see that the QB mattered. Roger Staubach seemed a veteran in this context back in the 1970s, Joe Montana in the 1980s, Ben Roethlisberger in the 21st century, Eli Manning in 2011, and Aaron Rogers last year. But plenty of questions abound. If a veteran QB is an independent variable whose presence or absence changes the odds of winning a playoff game, what tools do we use to define such a person? What tools would we use to eliminate entanglement, in this case between the team’s overall offensive strength and the QB himself?

The difference between a good metric and a bad metric can be seen when looking at the effect of the running game on winning. The correlation between rushing yards per carry and winning is pretty small. The correlation between run success rate and winning are larger. In short, being able to reliably make it on 3rd and 1 contributes more to success than running 5 yards a carry as opposed to 4.

At this point I’m just discussing the idea. With a definition in mind, we can do one independent variable logistic regression tests. Then with a big enough data set – 15 years of playoff data should be enough, we can start testing three independent variable logistic models (QB + SOS + PPX).