NFL: what can you predict from a team's first two games?

In 2006, the Chicago Bears won their first two games by wide margins: 26-0 on the road, then 34-7 at home. What should this have meant for predicting the rest of the season? Do teams who destroy the opposition in their first two games go on to dominate the league thereafter? Or does their initial dominance turn out to be temporary?

In another excellent study on his blog, Doug Drinen takes on this question.

First, he created a version of Bill James' similarity scores to find the 20 other seasons with their first two game scores most similar to the Bears' 2006. (Most similar season: the 1996 Packers, who won their first two games 34-3 and 39-13.)

In those 20 seasons, the teams wound up winning an average of 9.8 games.

So the answer to the original question is that the two blowouts don't mark the Bears as one of the best ever -- they predict the Bears are simply a bit better than average. Teams that started like they did won only 7.8 of their next 14 games (.557). The study shows that the Bears are probably a 9-7 team that got a bit lucky.

It's fun to look at the Bears' twenty comparables. The best was the 1984 Dolphins (14-2). But two of those teams won only three games after their two blowouts -- the 1987 Raiders (5-10) and the 1992 Buccaneers (5-11).

Drinen ran the algorithm for all 30 teams, and gives his method's predictions for the rest of 2006. They seem pretty good to me -- but if you asked a bunch of experts for their own predictions two games into the season, you'd probably get something similar.

The top five were Indianapolis (10.1 wins), Atlanta (9.9), Chicago (9.8), Cincinnati (9.8), and New Orleans (9.8). Bottom five were Oakland (5.3 wins), Tampa Bay (5.3), Cleveland (5.4), Green Bay (5.6), and Carolina (5.6).

Another interesting finding:

I don’t know if this means anything or not, but it’s intriguing that the Colts, who have scored a lot of points and also given up a lot, project better than the Chargers, Ravens, and Bears, who have scored a lot and given up almost none.

And I especially like this comment:

In some sense, this exercise is just a whole lot of work to get … the same results you’d get by running a simple regression … But I like this method better, because it’s not a black box.

You say the Bears should expect to win X games this year. Your friend calls BS: haven’t you seen how dominant they’ve looked? If regression is what you’ve got, it’s tough to give a decent counterargument unless he understands regression. But this method lays the reasoning right out there in a crystal clear way … [it's] the same kind of information that your regression was taking into account, but it’s just so much more transparent here.

I agree with Doug 100%. No matter how much you study a regression, even if you've seen hundreds of them in your lifetime, there's always the nagging doubt that the results don't mean what they seem to mean. This study lays it all out in a way that's comprehensible to anyone.

Great stuff.