Leading Indicators 2

"Anti-Predictors"

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
2002TB1270.0180.061
2002OAK1140.0160.037
2003TEN1250.0180.038
2003KC1370.0220.044
2004PHI1360.0200.031
2005TB1140.0290.036
2004NYJ1040.0250.038
2005JAX1280.0120.039
2003SF720.0290.045
2005CIN1180.0260.060
2004HOU720.0300.042
2002ATL950.0250.047
2003MIA1040.0420.042
2005DEN1390.0150.033
2005WAS1050.0230.030
2004BUF950.0370.049
2004SD1290.0180.038
2003STL1280.0380.047
2004NE14100.0290.037
2004BAL960.0240.042
2005SEA1390.0210.028
2004NO830.0300.024
2004GB1040.0320.015


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
2005TEN480.0240.019
2005NYJ4100.0320.045
2004NYG6110.0270.030
2002KC8130.0270.029


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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