This is a simple resume-based algorithm I developed for ranking college football teams. It just uses wins and losses.
- Preaseason rankings can bias a poll's effectiveness. To overcome this, this algorithm uses randomly generated preaseason rankings.
- In most polls a recent win or loss tends to be counted more strongly than earlier wins. This algorithm randomly shuffles the records of all games in order to alleviate this problem.
- Only wins and losses are counted in the algorithm (see below). Strength of schedule is built in, because winning against a team ranked higher in an iteration moves your team up.
See docs/about.md for more information on how to
compile the program and how the algorithm works.
After 11 weeks of play in the 2017-2018 season:
| Rank | Team | Record | Score | |
|---|---|---|---|---|
| 1 |  |
Alabama | 10-0 | 0.631117 |
| 2 |  |
Wisconsin | 10-0 | 0.628449 |
| 3 |  |
Miami (FL) | 9-0 | 0.609664 |
| 4 |  |
Clemson | 9-1 | 0.608251 |
| 5 |  |
UCF | 9-0 | 0.608147 |
| 6 |  |
Oklahoma | 9-1 | 0.606280 |
| 7 |  |
Georgia | 9-1 | 0.598680 |
| 8 |  |
USC | 9-2 | 0.596910 |
| 9 |  |
Notre Dame | 8-2 | 0.588513 |
| 10 |  |
Penn State | 8-2 | 0.587236 |
| 11 |  |
Ohio State | 8-2 | 0.586727 |
| 12 |  |
Memphis | 8-1 | 0.585124 |
| 13 |  |
Boise State | 8-2 | 0.583462 |
| 14 |  |
Michigan | 8-2 | 0.582554 |
| 15 |  |
Oklahoma State | 8-2 | 0.582454 |
| 16 |  |
Washington State | 9-2 | 0.582083 |
| 17 |  |
South Florida | 8-1 | 0.577032 |
| 18 |  |
TCU | 8-2 | 0.574353 |
| 19 |  |
Auburn | 8-2 | 0.574293 |
| 20 |  |
Washington | 8-2 | 0.571059 |
| 21 |  |
San Diego State | 8-2 | 0.570475 |
| 22 |  |
Toledo | 8-2 | 0.569525 |
| 23 |  |
Troy | 8-2 | 0.566354 |
| 24 |  |
Army | 8-2 | 0.566037 |
| 25 |  |
Michigan State | 7-3 | 0.564476 |