|Tries to get best fit to current season's data||Tries to produce ratings useful for predicting future games|
|Determines which teams had the most impressive season||Determines which teams are strongest|
|Does not publish predictions||Might publish predications|
|Does not use data from previous season||Uses data from previous season early in year to avoid bad predictions from insufficient data|
|Early season ratings don't mean much because of lack of data (some systems don't publish early in the season for this reason)||Early season ratings are meaningful|
|Has no preseason ratings because there's no data||Might have preseason ratings|
|Might be based on win/loss record only||Systems serious about predictions usually use the margin of victory and maybe home field advantage|
|Judged by how well past results are fit. For example, what % of games were upsets based on the current ratings.||Judged by how well the predictions work. For example, what % of games were upsets based on the ratings from the day before the game.|
Predictions are for entertainment purposes only.
Kenneth Massey's College Football Ranking Comparison now includes a final row giving the games per thousand that were upsets based on the current ratings. This "Ranking Violation %" is one way to compare retrodictive systems. See also On Ranking Violation Percentage by Jeff Bihl.
Eugene Potemkin has a better way of comparing retrodictive systems. (Be patient; this page comes from Russia!) This measure is also now included on Kenneth Massey's page as "Weighted Mistakes".
sum(games such that Rl > Rw) (Rl-Rw)*(2n-Rw-Rl) ----------------------------------------------- 1000
David L. Wilson / email@example.com