Determining College Football Rankings With Clustering

Where do we start? Look for statistics on the web –This keeps data up to date – smoother updates. Determine good statistic set –Don’t want too many so that data is redundant –Don’t want too few – Not enough data for good approximation Download Data

I Don’t Think Were In Kansas Anymore Now that we’ve got data what should we do? Parse it! –Create PERL scripts to transform meaningless.html into nice numbers Put these numbers in files so that MATLAB can use it

And Now Cluster Away! Cluster each data set. Award weights to closest points next to each. Use 10 clusters for each data set.

Reorganize the Data Group the Data according to its team Make it a nice file so MATLAB can perform a function on it.

Make a function from the data Use one year’s stats and complete rankings as a training set. Use the next year as the test set. Note: Real function is linear, not squared.

Finally! Sort the data, according to the output of the function. And your winner is……

You, because you are finished! Here’s the real data for all you nay-sayers (2004): #1. Southern California #2. Auburn #3. Oklahoma #4. Texas #5. Miami (Fla.) #6. Virginia Tech #7. California #8. Florida St #9. Utah #10. Louisville #11. Iowa #12. Boise St #13. Georgia #14. Bowling Green #15. Purdue #16. Virginia #17. Arizona St #18. Texas A&M #19. Wisconsin #20. Navy #21. Fla. Atlantic #22. Ohio St #23. Tennessee #24. UTEP #25. Texas Tech Matched 20 of 25 top 25 teams Exactly matched top 4 teams