College Football Computer Rankings: Week 9 Rankings

Monday, October 29, 2007

Week 9 Rankings

1 OHIO STATE 17.861
2 OREGON 17.409
3 ARIZONA ST 15.781
4 LSU 15.286
5 BOSTON COLLEGE 14.563
6 WEST VIRGINIA 14.507
7 KANSAS 13.577
8 MICHIGAN 11.330
9 MISSOURI 11.227
10 OKLAHOMA 10.443
11 SOUTH FLORIDA 9.893
12 KANSAS ST 9.616
13 AUBURN 9.316
14 GEORGIA 8.558
15 CONNECTICUT 8.235
16 FLORIDA 8.119
17 ALABAMA 8.031
18 VIRGINIA TECH 7.687
19 PURDUE 7.375
20 CLEMSON 7.308
21 SOUTHERN CAL 7.140
22 PENN STATE 7.081
23 CALIFORNIA 6.656
24 CINCINNATI 6.639
25 ARKANSAS 5.687
26 BOISE ST 5.509
27 WAKE FOREST 5.494
28 ILLINOIS 5.470
29 KENTUCKY 5.279
30 SOUTH CAROLINA 5.242
31 WISCONSIN 4.755
32 FLORIDA ST 4.750
33 TENNESSEE 4.733
34 TEXAS 4.730
35 HAWAI`I 4.554
36 GEORGIA TECH 4.423
37 RUTGERS 4.178
38 TEXAS TECH 3.931
39 VANDERBILT 3.523
40 OKLAHOMA ST 3.432
41 BRIGHAM YOUNG 3.235
42 OREGON ST 3.182
43 TROY 3.146
44 MIAMI FL 2.947
45 COLORADO 2.831
46 MICHIGAN ST 2.741
47 UCLA 2.621
48 TEXAS A&M 2.474
49 LOUISVILLE 2.276
50 MISSISSIPPI ST 2.038
51 VIRGINIA 1.878
52 UTAH 1.642
53 MARYLAND 1.606
54 CENTRAL FLORIDA 1.537
55 CENTRAL MICHIGAN 1.327
56 AIR FORCE 0.933
57 HOUSTON 0.812
58 FRESNO ST 0.809
59 NEW MEXICO 0.654
60 INDIANA 0.503
61 WASHINGTON 0.490
62 ARIZONA 0.006
63 WASHINGTON ST -0.068
64 BALL ST -0.226
65 EAST CAROLINA -0.403
66 WYOMING -0.486
67 TCU -0.573
68 NORTHWESTERN -0.587
69 MISSISSIPPI -0.605
70 NEBRASKA -0.667
71 IOWA -0.822
72 PITTSBURGH -1.018
73 NORTH CAROLINA -1.027
74 NAVY -1.038
75 STANFORD -1.298
76 TULSA -1.503
77 LOUISIANA TECH -1.904
78 MIDDLE TENNESSEE ST -1.996
79 NORTH CAROLINA ST -1.998
80 UTEP -2.924
81 FLORIDA ATLANTIC -3.008
82 NEVADA -3.415
83 MIAMI OH -3.669
84 BUFFALO -3.672
85 BOWLING GREEN -3.701
86 SOUTHERN MISS -4.026
87 NOTRE DAME -4.359
88 DUKE -4.987
89 NEW MEXICO ST -5.589
90 UNLV -5.696
91 MINNESOTA -5.947
92 COLORADO ST -5.995
93 AKRON -6.129
94 OHIO U. -6.210
95 ARKANSAS ST -6.264
96 BAYLOR -6.573
97 LOUISIANA-MONROE -6.641
98 ARMY -6.803
99 TOLEDO -6.824
100 WESTERN MICHIGAN -6.911
101 KENT ST -6.929
102 SYRACUSE -6.933
103 MEMPHIS -7.027
104 SAN JOSé ST -7.577
105 SAN DIEGO ST -7.809
106 TEMPLE -8.000
107 IOWA ST -8.414
108 EASTERN MICHIGAN -8.687
109 MARSHALL -9.361
110 WESTERN KENTUCKY -10.763
111 TULANE -11.439
112 ALABAMA-BIRMINGHAM -11.454
113 UTAH ST -12.193
114 LOUISIANA-LAFAYETTE -12.616
115 RICE -13.438
116 SMU -13.641
117 FLORIDA INT'L -14.149
118 NORTH TEXAS -14.181
119 NORTHERN ILLINOIS -14.377
120 IDAHO -17.800

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Description of Ratings System

My goal was to devise a system that relied exclusively on simple mathematical calculations without any built-in biases. This ranking system is based solely on the resulting score of the games played to date of the current season and the teams win-loss record. I will briefly cover some of my thoughts and philosophies while developing the system in the following.
After analyzing game statistics I concluded that the final score of a game is an all encompassing summary of the statistics. For example, if a team outgained an opponent by 200 yards of offense but still lost the game, the loss was usually due to turnovers or penalties. I started by developing a system that would record and determine the average margin of victory (AMV) for a team. A system based on this alone has obvious flaws due to teams winning by large margins over lesser opponents. After crunching data with various limitations on margin of victories, I decided to limit the margin of victory to a maximum of 21 points. However, this still allowed teams that play weak opponents to remain high in the rankings. To penalize teams that play weak opponents and give credit to teams that play strong opponents, I then added the team’s opponents’ average margin of victory (OAMV) and their opponent’s opponents’ average margin of victory (OOAMV) to their AMV. This process is carried out 5 times (OOOOOAMV) which mathematically relates every team to each other by the end of the season. This effectively lowered the teams rankings that had a high AMV due to playing weak opponents. However, this allowed teams with high AMVs and multiple losses to be ranked higher than no-loss or one-loss teams at the end of the season. I factored in the win-loss record by lowering the final result of a team with a positive ranking value by the ratio of the team’s losses over the total games played and raising the final result of a team with negative ranking value by the ratio of the team’s wins over the total games played.
To illustrate this more clearly, I will use a couple of examples.
As of week 6 of the 2000 season, TCU had the highest margin of victory of the 115 Division 1-A teams at 26.750. However, their opponents AMV was -12.400 indicating that TCU has played weak teams but have outscored these teams by a larger margin than the other opponents of these teams. Summing the AMV and the OAMV reduces their ranking value to 14.350. Carrying this process out 5 times (OOOOOAMV) results in a ranking value of 21.623. TCU is currently undefeated (4-0) so 21.623 is their final ranking value. This places TCU 4th out of 115 teams.
As of week 6 of the 2000 season, Texas A&M’s AMV is 15.000 and their opponents’ AMV was 0.438. Summing these two numbers increases their ranking value to 15.438. Carrying this process out 5 times (OOOOOAMV) results in a ranking value of 16.284. Texas A&M has played four 1-A opponents and their win-loss record is 3-1. Since Texas A&M has a positive ranking value they are penalized for the game they lost by reducing their result by the total number of games they have lost divided by the games played, or 25%. This result is 12.213 which places them 13th out of 115 teams. If Texas A&M wins their game in week 7 their record will then be 4-1 and their final result will be reduced by only 20%. This will enable them to possibly move up in the rankings by continuing to win games, similar to what would be seen in the AP or Coaches poll.
Additional comments:
The results from previous years have no effect on this years rankings. I feel that the two years are independent from each other. This causes some anomalies early in the season but everything settles out after week 6 or 7. I believe this is also why the BCS does not publish their rankings until late October although some ranking systems carry over from the previous year.
This system only uses the 120 Division 1-A teams. Any team that plays a team from another division receives no credit for the win. There can be a slight penalty for playing a team outside of Division 1-A. For instance, a team that has a victory over a 1-AA opponent does not receive credit for that victory in the win column. If their actual record is 7-2 this would be recorded as 6-2, therefore the penalty for the 2 losses would be 25% instead of 22.2 %. Likewise, losing to a 1-AA opponent would not be recorded in the loss column but this rarely happens.
This system is simple compared to some of the other systems being used but the results similar. I would welcome any comments or questions from anyone. I can be contacted at smaurer@kc.rr.com
Scott

College Football Computer Rankings

Monday, October 29, 2007

Week 9 Rankings

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Description of Ratings System

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