Kansas City Chiefs


Conventional wisdom went 3-1 and my picks went 2-2; perhaps I would have done better if the Seahawks hadn’t suffered so many injuries at the end of the season. The Titans continue their upset ways while Green Bay, Kansas City, and San Francisco won as favorites.

The methodology of how we pick is given here.

Conference (NFC/AFC) Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
San Francisco 49ers Green Bay Packers 0.263 0.56 2.0
Kansas City Chiefs Tennessee Titans 1.031 0.74 7.6

The first round of the playoffs were full of upsets. The Titans upset the Patriots and the Vikings upset the Saints. Both upsets were driven by ground games that both scored and consumed the clock.

The methodology of how we pick is given here.

Second Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
San Francisco 49ers Minnesota Vikings 0.372 0.59 2.8
Green Bay Packers Seattle Seahawks -0.194 0.45 -1.4
Baltimore Ravens Tennessee Titans 0.985 0.73 7.3
Kansas City Chiefs Houston Texans 0.429 0.61 3.2

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

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

This would have been done earlier, but Pro Football Reference dropped its very handy chart of draft position versus AV. I started missing it more and more, and using the Wayback Machine I found it here.

The three major QB trades of 2017 were the trade for Mitch Trubisky, Patrick Mahomes, and Deshaun Watson. We will analyze them in sequence.

Mitchell Trubisky Trade
Chicago Bears 49ers Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
2 46 3 45
67 19
111 12
(71) 21
Total 46 Total 97
51 2.11

 

The Bears have a trade risk comparable to a typical trade for a #1 draft choice and a quarterback at that. The trade has less fundamental risk than Goff or Wentz. The comparable that comes to mind is Eli Manning. By contrast, the delta AV of the other two trades are substantially less.

Patrick Mahomes Trade
Chiefs Bills Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
10 41 27 25
91 17
(25) 24
Total 41 Total 66
25 1.61

 
Mahomes merely has to give six seven good years, and the trade ends up warranted. The issue in the case of Deshaun Watson is keeping him upright. A fistful of whole years almost as good as his freshman year in the NFL and he would end up bordering on Hall of Fame numbers.

Deshaun Watson Trade
Texans Browns Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
12 35 25 24
4 44
Total 35 Total 68
33 1.94

 

So here is wishing Deshaun Watson a healthy career from now on.

Jason Lisk has tweeted that it has been 30 years since Joe Delaney perished trying to save three boys from drowning. And yes, the tragedy of that day strikes me. What strikes me as much, though, is that I was in the same high school as he was, one class ahead of Joe, and if, on my last day of high school, you had mentioned his name, I would have said, “Joe who?”

See, he wasn’t branded as a star in our school. The running back for the team, usually the star, was Sonny Lewis. People were still talking about the team in 1972, the one that went to the state semifinals and lost to Denham Springs. Players from those years, guys like quarterback Gene Couvillion, whose family lived up the road from us, or Arry Moody, a running back and routinely described as the most talented player anyone in those parts had ever seen, were the ones folks talked about. Lets make this clear. In high school get togethers, people still talk about Arry Moody. I was in band in those years. I saw every high school game. Gene had a strong arm and could have played college ball.

By the time of Joe’s freshman and sophomore years, he must have been playing wide receiver. We didn’t have good quarterbacks. There wouldn’t have been much stardom for a skinny fast kid. I’m saying skinny because I’ve seen Joe’s photos once he was a Chief, and nobody was built like that in high school. I say fast because I recall a quote in Sports Illustrated by Houston’s Bill Yeoman, to the effect that everyone knew about a kid running a 100 yard dash in well under 10 seconds, but no one could get near him.

I can vouch for the hard core influence of Northwest Louisiana University in our area. The local golf course was owned by a retired Northwest Louisiana coach. Looking around the Internet for that Yeoman quote, I find a short blurb from the October 31, 1978 Tuscaloosa News instead, which goes:

Joe Delaney, Northwestern Louisiana’s sophomore running back, made only his second start Saturday and rushed for 299 yards…

So, given the physique I see in his post high school years, the amazing performances in his college years, I can only assume he was a product of his college system. He went to college and paid the price required to star there.

Gene Couvillion? He went to Louisiana Tech and balked at being redshirted. Arry Moody? You would hear occasional mentions of his presence at Tech, or later on, with the minor league Shreveport Steamer, but nothing approaching the hero worship of his high school years.

If I look on Facebook, I find a guy called Arry Moody, with over a 1,000 friends, Haughton area. 5 of my friends are also his friends. Seems to be doing fine, and I wish him luck.

But what drove Joe that didn’t drive the others? What made him able to endure the college grind and even prosper? I know that people almost eternally ponder the tragedy of it all, but for me, the mystery of his success is equally as ponderable.

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