New England Patriots

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

First things first. You cannot hurt yourselves much by buying Doug Farrar’s new book “The Genius of Desperation”. I have only one complaint about it. It does mangle the history of the one gap 4-3 when it discusses the Miami 4-3 that Jimmy Johnson helped introduce into the pros. From the beginning there were one gap 4-3s. Just, the 4-3s of Tom Landry were about gap control, not hard core pursuit. Otherwise it’s a very good book. Oh yes, the first edition has some issues in the diagrams, but if he gets a second edition, perhaps those will be fixed.

Dr Z’s classic now has a Kindle edition. If you have Kindle Unlimited, you can get the book for free (for now).

Also, for a limited time, Coach Paul Alexander has a video of the back and forth of Super Bowl LIII, of the 5 UP defense the Patriots used, the tricks the Rams used, and how both teams adapted to defeat the respective defenses. Just, its now unlisted
(can’t be searched for) and it may disappear in time. Don’t say I didn’t warn you.

Well, the system went 0-2, and probably would have gone 1-1 had the refs been able to call pass interference in the last two minutes of the Saints-Rams game. The story as I gather it, is the lack of experience in the current crop of referees and the lack of good positioning during the infraction. Count me among the folks who think “the booth” should be able to overrule this kind of blatant miss on the field.

The other factors that don’t seem to change is that New England outplays its strength of schedule and that Kansas City underperforms its playoff predictions. All that said, the formula predicts a close game with the Rams emerging with a victory.

Super Bowl Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Los Angeles Rams New England Patriots 0.380 0.59 2.8

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

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

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.

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

I suspect  to a first approximation almost no one other than Baltimore fans, such as Brian Burke, and this blog really believed that Baltimore had much of a chance(+). Well, I should mention Aaron Freeman of Falc Fans, who was rooting for Baltimore but still felt Denver would win. Looking, his article is no longer on the Falcfans site. Pity..

WP graph of Baltimore versus Denver. I tweeted that this graph was going to resemble a seismic chart of an earthquake. Not my work, just a screen shot off the excellent site Advanced NFL Stats.

After a double overtime victory by 3 points, it’s awfully tempting to say, “I predicted this”, and if you look at the teams I’ve  favored, to this point* the streak of picks is 6-0. Let me point out though, that you can make a limiting assumption and from that assumption figure out how accurate I should have been. The limiting assumption is to assume the playoff model is 100% accurate** and see how well it predicted play. If the model is 100% accurate, the real results and the predicted results should merge.

I can tell you without adding up anything that only one of my favored picks had more than a 70% chance, and at least two were around 52-53%. So 6 times 70 percent is 4.2, and my model, in a perfect world, should have picked no more than 4 winners and 2 losers. A perfect model in a probabilistic world, where teams rarely have 65% chances to win, much less 100%, should be wrong sometimes. Instead, so far it’s on a 6-0 run. That means that luck is driving my success so far.

Is it possible, as I have argued, that strength of schedule is an under appreciated playoff stat, a playoff “Moneyball” stat, that teams that go through tough times are better than their offense and defensive stats suggest? It’s possible at this point. It’s also without question that I’ve been lucky in both the 2012 playoffs and the 2013 playoffs so far.

Potential Championship Scenarios:

Conference Championship Possibilities
Home Team Visiting Team Home Win Pct Est. Point Spread
NE BAL 0.523 0.7
HOU BAL 0.383 -3.5
ATL SF 0.306 -6.1
SF SEA 0.745 7.9

My model likes Seattle, which has the second best strength of schedule metric of all the playoff teams, but it absolutely loves San Francisco. It also likes Baltimore,  but not enough to say it has a free run throughout the playoffs. Like many modelers, I’m predicting that Atlanta and Seattle will be a close game.

~~~

+ I should also mention  that Bryan  Broaddus tweeted about a colleague of his who predicted a BAL victory.

* Sunday, January 13, 2013, about 10:00am.

** Such a limiting assumption is similar to assuming the NFL draft is rational; that the customers (NFL teams) have all the information they should, that they understand everything about the product they consume  (draft picks), and that their estimates of draft value thus form a normal distribution around the real value of draft picks, and that irrational exuberance, or trends, or GMs falling in love with players play no role in picking players. This, it turns out, makes model simulations much easier.

There were eight trades in the first day involving the first round of the 2012 NFL draft. Most of them involved small shifts in the primary pick, with third day picks added as additional compensation. The one outlying trade was that of the St Louis Rams and the Dallas Cowboys, which involved a substantial shift in  the #1 pick (from 6 to 14) and the secondary compensation was substantial. This high secondary compensation has led to criticism of the trade, most notably by Dan Graziano, whose argument, boiled to its essence, is that Dallas paid a 2 pick price for Morris Claiborne.

Counting  picks is a lousy method to judge trades. After all, Dallas paid a 4 pick price for Tony Dorsett. Was that trade twice as bad a trade as the Morris Claiborne trade?  The Fletcher Cox trade saw Philadelphia give up 3 picks for Fletcher Cox. Was that trade 50% worse than the Morris Claiborne trade?

In order to deal with the issues raised above, I will introduce a new analytic metric for analyzing trade risk, the risk ratio, which is the sum of the AV values of  the picks given, divided by the sum of the AV values of the picks received. For trades with a ratio of 1.0 or less, there is no risk at all. For trades with ratios approaching 2 or so, there is substantial risk. We are now aided in this kind of analysis by Pro Football Reference’s new average AV per draft pick chart. This is a superior tool to their old logarithmic fit, because while the data may be noisy, they avoid systematically overestimating the value of first round picks.

The eight first round trades of 2012, interpreted in terms of AV risk ratios.

The first thing to note about the 8  trades is that the risk ratio of 6 of them is approximately the same. There really is no difference, practically speaking, in the relative risk of the Trent Richardson  trade, or the Morris Claiborne trade,  or the Fletcher Cox trade. Of the two remaining trades, the Justin Blackmon trade was relatively risk free. Jacksonville assumed an extra value burden of 10% for moving up to draft the wide receiver. The other outlier, Harrison Smith, can be explained largely by the noisy data set and an unexpectedly high value of AV for draft pick 98. If you compensate by using 13 instead of 23 for pick #98, you get a risk ratio of approximately 1.48, more in line with the rest of the data sets.

Armed with this information, and picking on Morris Claiborne, how good does he  have to be for this trade to be break even? Well, if his career nets 54 AV, then the trade breaks even. If he has a HOF career (AV > 100), then Dallas wins big. The same applies to Trent Richardson. For the trade to break even, Trent has to net at least 64 AV throughout his career. Figuring out how much AV Doug Martin has to average is a little more complicated, since there were multiple picks on both sides, but Doug would carry his own weight if he gets 21*1.34 ≈ 28 AV.

Four historic trades and their associated risk ratios.

By historic measures, none of the 2012 first round trades were particularly risky. Looking at some trades that have played out in  the past, and one  that is still playing out, the diagram above shows the picks traded for Julio Jones, for Michael Vick, for Tony Dorsett, and also for Earl Campbell.

The Julio Jones trade has yet to play out, but Atlanta, more or less, assumed as much risk (93 AV) as they did for Michael Vick (94 AV), except for a #4 pick and a wide receiver. And although Michael is over 90 AV now, counting AV earned in Atlanta and Philadelphia, he didn’t earn the 90+ AV necessary to balance out the trade while in Atlanta.

Tony Dorsett, with his HOF career, paid off the 96 AV burden created by trading a 1st and three 2nd round choices for the #2 pick. Once again, the risk was high, the burden was considerable, but it gave value to Dallas in the end.

Perhaps the most interesting comparison is the assessment of the Earl Campbell trade. Just by the numbers, it was a bust. Jimmie Giles, the tight end that was part of the trade,  had a long and respectable career with Tampa Bay. That, along with the draft picks, set a bar so high that only the Ray Lewis’s of the world could possibly reach. And while Campbell was a top performer, his period of peak performance was short, perhaps 4   years. That said, I still wonder if Houston would still make the trade, if somehow someone could go back in to the past, with the understanding of what would happen into the relative future. Campbell’s peak was pretty phenomenal, and not entirely encompassed by a mere AV score.

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