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 - 167.3486• #2. Auburn - 161.4341

• #3. Oklahoma - 116.2092• #4. Texas - 112.4908

• #5. Miami (Fla.) - 112.4448• #6. Virginia Tech - 111.4653

• #7. California - 108.7134• #8. Florida St. - 108.4097

• #9. Utah - 107.8165• #10. Louisville - 107.5966

• #11. Iowa - 107.5766• #12. Boise St. - 104.7642• #13. Georgia - 100.8888

• #14. Bowling Green - 95.4476• #15. Purdue - 91.1031• #16. Virginia - 88.6308

• #17. Arizona St. - 88.2669• #18. Texas A&M - 86.3354• #19. Wisconsin - 83.4357

• #20. Navy - 83.3217• #21. Fla. Atlantic - 83.0395

• #22. Ohio St. - 80.6131• #23. Tennessee - 79.4607

• #24. UTEP - 79.2717• #25. Texas Tech - 79.0969

•Matched 20 of 25 top 25 teams

•Exactly matched top 4 teams