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:

Full rankings

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