The playoffs are here, and it’s time to work on the Code and Football playoff formula. The one used the past five years has been this one, and it has served the author well.
(PX within 1 year) Logit P = 0.668 + 0.348(SOS) + 0.434(PPX)
Where P is probability of winning, SOS is Strength of Schedule, as measured by the Simple Ranking System, PX is playoff experience, and PPX is previous playoff experience. But with the advantage of time, and also the question of whether Dallas’s playoff experience in 2014 is significant, we added five more years of data and calculated the following three formula, for which all coefficients were statistically significant to better than 95%.
(PX within 1 year) Logit P = 0.594 + 0.288(SOS) + 0.621(PPX)
(PX within 2 years) Logit P = 0.660 + 0.304(SOS) + 0.747(PPX)
(PX within 3 years) Logit P = 0.699 + 0.323(SOS) + 0.895(PPX)
With the original 11 year data set (2001-2011), the 2 year and 3 year formulas were not significant, but with the larger data set (2001-2015), they are. You can also derive significant formulas for playoff experience within 1 year, 2 years, or 3 years using just the last 11 years of data (2005-2015). So the outlier appears to be that original 11 years of NFL data.
Since the only teams where the difference might be significant would be Dallas-Seattle, Dallas-GB, or Dallas-AFC matchups (and we can deal with those on a case-by-case basis), we will now work our spreadsheet out using the 2 year formulas this year.
|2016 NFL Playoff Teams, C&F Worksheet.|
|Rank||Name||Home Field Adv||Playoff Experience||SOS||Total Score|
|4||Green Bay Packers||0.660||0.747||0.024||1.431|
|5||New York Giants||0.0||0.0||0.152||0.152|
|1||New England Patriots||0.660||0.747||-0.806||0.601|
|2||Kansas City Chiefs||0.660||0.747||0.219||1.626|
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.
All that said, a couple more notes about the playoff formulas. Note that the PPX coefficient grows larger as the PPX gap grows larger. I’m not so sure that a team with playoff experience three years ago is all that much advantaged, as the sum of all teams that lack playoff experience over three years or more are disadvantaged. Teams that come to mind are Oakland and Miami and Detroit this year, who haven’t been in the playoffs in many years. The Giants also, have not been in the playoffs in a fairly long time, and that factors into the survivability of their team in the playoffs.
The home field advantage these formulas give is larger than the historic playoff gap. Playoff teams over the 15 year period win games by 62.7%, corresponding to a logit coefficient of 0.518. Note this is still a bigger advantage than regular season home field advantage (57-58%) but not as much as predicted by the raw formulas.
The team this system favors best is the Kansas City Chiefs. But if Oakland had not lost Carr in the 16th week of the season, would they have had home field advantage and a bye? I’m not sure about that.
Because the sheet above can be hard to decipher, for the first week of the 2016 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|
|Seattle Seahawks||Detroit Lions||0.349||0.586||2.4|
|Green Bay Packers||New York Giants||1.279||0.782||9.5|
|Pittsburgh Steelers||Miami Dolphins||1.887||0.868||14.0|
|Houston Texans||Oakland Raiders||1.104||0.751||8.2|
None of the games are judged as close. The dominant factor is the lack of playoff experience on the part of the visiting teams.
Update: forgot that Detroit was in the playoffs in 2014, which changes odds in Seattle-Detroit significantly.