Saturday, January 14, 2012

NFL Team Rankings - Divisional Playoff Round

Here are the NFL Team Rankings for the Divisional Round of the Playoffs. These rankings were already posted at the Advanced NFL Stats Community site earlier this week.
The general methodology is the same as that used in the NBA Team Rankings.  I had to tweak the weighting approach somewhat to account for the playoffs (additional details can be found at the Advanced NFL Stats link above).

Here is a glossary of terms:

LSTWK - The betting market rank as of the prior week (the Wildcard Round)
GPF - Stands for Generic Points Favored. It’s what you would expect a team to be favored by against a league average opponent at a neutral site.
oGPF – Offensive Generic Points Favored. The component of a team’s total GPF attributable to its ability to score points.
dGPF – Defensive Generic Points Favored. The component of a team’s total GPF attributable to its ability to prevent the other team from scoring points

GWP - Stands for Generic Win Probability. I converted the GPF into a generic win probability using the following formula: GWP = 1/(1+exp(-GPF/7)).
oRANK – The team’s oGPF ranking.
dRANK – The team’s dGPF ranking.

The Rankings (only playoff teams included):

1 NO110.
2 GB28.
3 NE38.
5 SF44.
6 PIT64.
7 BAL74.
8 NYG93.
9 DET103.04.0-1.00.60424
11 ATL82.
16 HOU160.0-
19 CIN17-1.0-
24 DEN26-3.0-2.5-0.50.392522

Superbowl Futures

My post at Advanced NFL Stats covered in detail the point spread and over/under predictions from the model.  For this post, I thought I'd take an updated look at Superbowl Futures.  For the original analysis, Chris Cox at was nice enough to run a one-off of his fantastic NFL Forecast tool using the team strengths from my rankings.  However, with only 8 teams left, and no complicated tie-breaker scenarios to figure out, even an excel-monkey such as myself can run a proper simulation.  Using the GPF's from the rankings table above, I simulated the remainder of the playoffs 5000 times in Excel, and summed each team's Superbowl wins.  I then compared those implied probabilities against a few sources:

BTM: Superbowl win probabilities according to my simulation
ANS: Superbowl win probabilities according to the Advanced NFL Stats Team Efficiency Rankings
FBLocks: Implied Superbowl win probabilities according to Superbowl futures odds posted at Football Locks.  The probabilities are calculated after removing the "vig", which was 17%, in case you were interested.
JustBet Implied Superbowl win probabilities according to Superbowl futures odds posted at JustBet.  The vig in this case was 25%.


What really puzzles me is New England.  Both my simulation and the ANS simulation have New England as the most likely Superbowl winner (with a plurality, not a majority).  And the ANS model doesn't even think that highly of New England (at season's end it was ranked 7th, behind Houston, New Orleans, Green Bay, and  the New York Giants).  But New England has the easiest path to the Superbowl.  It has home field advantage, and the average strength of the teams it would have to beat to get to the Superbowl is far less than what the NFC teams face.  Green Bay and New Orleans are, in effect, "splitting the vote" in the NFC.

New England is actually a positive expected value bet, if you can get the odds on Football Locks.  Fair odds according to my simulation would put the Pats at about 2 to 1, whereas last time I checked the odds offered were 5 to 2.  New Orleans is also a slightly positive bet at 4 to 1.  But it's possible my simulation overstates the Saints' chances as it doesn't account for the cold weather effect on dome teams.


  1. Hey man, it's really interesting what you're doing. I'm not really a math guy but I appreciate numbers & believe you can break everything down into them.

    Will you be doing weekly analysis of teams/matchups for the 2012 season?

  2. Thanks. That's definitely the plan.