So I’m working on my own computer poll for the College Football season. I’ve developed a simulated annealing algorithm that works well from a simple premise: maximize the correlation between the rank distance of teams and the relative margin of the game.
- Let be the rank of team t.
- Let be a game in which team i beat team j.
- Let be the rank distance of game .
- Let be the margin of victory for , where and are the scores of teams i and j from the game.
- Find the rankings that maximize the correlation between and .
I am using a simple simulated annealing algorithm to maximize the correlation. Even with a very simple algorithm, it shows a lot of promise in sorting out teams with harder schedules from teams with weaker schedules. The dataset I am using includes teams from Div I-A to Div III, and the simple algorithm does a good job at sorting them as we’d expect. I am looking to improve the algorithm using adaptive simulated annealing which should provide me with a better search.
I’ll probably publish my results for this season, towards the middle of October after enough games have been played.