Leading Indicators 2


In the last post I compared the results of two regression models. The first model estimated current year wins based on current year stats. The second model predicted next year’s wins based on last year’s stats. The comparison of the regression results revealed how well various team stats persist from year to year as predictors of team wins.

I found that the stats that persisted from season to season as a predictor of wins were offensive running efficiency (36%), team penalties (52%), and defensive forced fumble rates (45%). I also found two stats that could be considered anti-predictors. Offensive and defensive interception rates both reverse their direction of prediction across seasons. In other words, a low offensive pass interception rate for a team in one year foretells fewer wins the following year, all other things being equal.

This is a confusing result, to say the least. One possible explanation is random coincidence--the stats may show a connection only by chance. However, the significance of the offensive interception coefficient was 0.07 and the defensive interception coefficient was 0.09. As I wrote earlier, the FDA might not approve heart medication based on trials with marginal significance levels, but it is still highly unlikely that both coefficients suffer from statistical Type I errors. After all, we’re not splitting the atom here. We’re just talking about football.

In the last post I wrote, “My theory is that we are witnessing regression to the mean. For many teams, interception rates have a lot of variance due to luck. So a team that is unlucky with interceptions one year is not likely to be as unlucky the next year, [and vice versa]. That could partially explain the reversed signs. Another possibility is that teams systematically swing from high to low interception rates from one season to the next, something I strongly doubt.”

A friend at work suggested I actually look at teams, their players, and what happened that might cause such a result. (What? There’s more to football than statistics?) I couldn’t bring myself to qualitatively analyze what might be going on, but he did inspire me to dig a little deeper.

Below is a list teams that demonstrated the trend of impressive interception rates one year, followed by a severe drop-off in wins the next. The average for both offensive and defensive interception rates is 0.32.

YearTeamWinsNext WinsO Int RateD Int Rate

These teams all exhibited a notably better than average interception rate on either offense or defense, only to suffer a dramatically worse record the next year.

Below is the list of teams that exhibit the opposite trend, no matter how slight. They exhibited better than average interception rates, then improved their record the following year.

YearTeamWinsNext WinsO Int RateD Int Rate

Whatever the reason for the phenomenon, it appears real. It might be summed up by "Live by the interception, die by the interception." If a team wins a lot of games based on superior offensive (low) or defensive (high) interception rates, it tends to be extremely difficult to repeat, and that team will very probably not win as many games the following year.

In the next post, I'll apply the leading indicators to the team stats from 2006. I'll list how each team can be expected to benefit (or suffer) in 2007 from the leading indicators of NFL wins.

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1 Responses to “Leading Indicators 2”

  1. Doug Walters says:

    Wow Brian, that is fascinating. I'm very interested to see what 2006 looked like. I've got some teams in mind that I'm thinking will regress, so I'm hopeful that your list matches.

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