Friday, January 16, 2009

Small Observation on College Football Bowls

As a part of a paper I am working on, I found this interesting: Looking at bowl games played in the 2002-2007 season (n=96)...
  1. The team with the higher AP ranking going into the bowl game won 59.5% of the time.
  2. The team with the higher USA Today ranking going into the bowl game won 62.5% of the time.
  3. The correlation coefficient (Excel function CORREL) between these two polls giving the higher ranking to the team that ultimately won the bowl game is 0.542
The observation that the team with a higher ranking in the poll going into the game will win most of the time implies rationality in the polling. However, I expected the correlation between them to be much closer to one (i.e. they would error on the same games), and I am surprised at how close it is to .5. The inference of being less than one (but still positive) is that they agree most of the time on the ordinal ranking but that upsets occur for different games.

Data here.

Should I have expected this magnitude of disagreement? What would you have expected?


Anonymous said...

Maybe look at only observations where there is a clear favoring (i.e. not just ranked higher, but ranked 5 spots higher at a minimum or something like that). I am guessing that some of those bowls are between 10 thru 20 ranked teams and often at that level the ordering is not the same for the 2 polls (I suspect). This may apply to even lower ranked ones too, I would imagine. How low do your rankings go? Can you try further back? How about with the BCS and Harris polls as well?

Justin M Ross said...

All very good suggestions, and my eyeballs suggests you are right.

As per your all the bowl games at least 1 team was ranked by both polls in the top 25. So teams without a ranking I cannot judge if they were seen as #26 or #50 by the pollsters.

I'm still working on going further back and getting more polls. What I have now is just what is relatively easy to obtain from the internet. Would love it if anyone has other sources of data to suggest!

Anonymous said...

Try this for AP.

Anonymous said...

It is unclear why you use the coefficient of correlation here, which is designed to find the correlation between two numerical variables.

Why not, much more simply, look at the % of the time that they match or do not match? They do not match slightly less than 22% of the time (21/96 do not match). Is this significantly high? It would imply that if you knew one, that you would know the other more than 78% of the time (it is basically equivalent to an r-squared of .78 in an ols regression.)

Perhaps if you were looking at the difference in ranks, it would be appropriate to use the coefficient of correlation.

Matt E. Ryan said...

If this is heading down the probit path...there are plenty of things to control for, as well as one that I think is why you're looking at this in the first place. ;)

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