College Football Computer Rankings: December 2009

Monday, December 7, 2009

Week 14 Rankings

1 ALABAMA 20.414
2 TEXAS 17.796
3 FLORIDA 15.464
4 TCU 14.891
5 CINCINNATI 13.240
6 BOISE ST 11.986
7 VIRGINIA TECH 11.125
8 OREGON 10.827
9 PENN STATE 10.340
10 OHIO STATE 10.128
11 GEORGIA TECH 9.976
12 MIAMI FL 8.343
13 PITTSBURGH 7.815
14 LSU 7.599
15 IOWA 7.485
16 OKLAHOMA 6.964
17 NEBRASKA 6.653
18 ARKANSAS 6.598
19 TEXAS TECH 6.561
20 STANFORD 6.396
21 CLEMSON 6.262
22 OKLAHOMA ST 5.650
23 OREGON ST 5.593
24 WEST VIRGINIA 5.528
25 MISSISSIPPI 4.910
26 SOUTHERN CAL 4.793
27 TENNESSEE 4.771
28 ARIZONA 4.729
29 AUBURN 4.520
30 BRIGHAM YOUNG 4.439
31 WISCONSIN 4.394
32 NORTH CAROLINA 4.174
33 UTAH 3.979
34 CONNECTICUT 3.504
35 CENTRAL MICHIGAN 3.225
36 MISSOURI 3.010
37 HOUSTON 2.790
38 GEORGIA 2.723
39 SOUTH CAROLINA 2.635
40 NOTRE DAME 2.552
41 BOSTON COLLEGE 2.420
42 TEXAS A&M 2.326
43 RUTGERS 2.319
44 KENTUCKY 2.167
45 CALIFORNIA 2.129
46 NEVADA 1.899
47 EAST CAROLINA 1.856
48 AIR FORCE 1.815
49 TROY 1.718
50 FLORIDA ST 1.498
51 MISSISSIPPI ST 1.459
52 NAVY 1.279
53 MICHIGAN ST 1.142
54 WASHINGTON 0.823
55 UCLA 0.698
56 WAKE FOREST 0.697
57 SOUTH FLORIDA 0.554
58 PURDUE 0.520
59 MINNESOTA 0.397
60 FRESNO ST 0.281
61 CENTRAL FLORIDA 0.134
62 KANSAS 0.058
63 TEMPLE -0.130
64 MIDDLE TENNESSEE ST -0.152
65 ARIZONA ST -0.215
66 SOUTHERN MISS -0.343
67 VIRGINIA -0.465
68 OHIO U. -0.484
69 IOWA ST -0.524
70 BOWLING GREEN -0.596
71 NORTHWESTERN -0.882
72 KANSAS ST -0.937
73 DUKE -1.171
74 NORTHERN ILLINOIS -1.260
75 MICHIGAN -1.365
76 MARSHALL -1.556
77 BAYLOR -2.084
78 LOUISIANA-MONROE -2.393
79 SMU -2.410
80 NORTH CAROLINA ST -2.532
81 IDAHO -2.584
82 ALABAMA-BIRMINGHAM -3.157
83 TULSA -3.260
84 INDIANA -3.326
85 COLORADO -3.486
86 ILLINOIS -3.626
87 SYRACUSE -3.646
88 BUFFALO -3.915
89 WYOMING -4.009
90 UNLV -4.247
91 HAWAI`I -4.335
92 UTEP -4.521
93 LOUISVILLE -4.522
94 FLORIDA ATLANTIC -5.288
95 LOUISIANA-LAFAYETTE -5.476
96 MARYLAND -5.533
97 UTAH ST -5.570
98 WESTERN MICHIGAN -5.907
99 TOLEDO -5.974
100 KENT ST -5.993
101 LOUISIANA TECH -6.392
102 VANDERBILT -6.417
103 SAN DIEGO ST -6.594
104 COLORADO ST -7.228
105 ARKANSAS ST -7.520
106 ARMY -7.630
107 FLORIDA INT'L -8.585
108 NORTH TEXAS -10.212
109 BALL ST -10.549
110 MEMPHIS -11.851
111 AKRON -11.863
112 RICE -13.226
113 SAN JOSé ST -13.318
114 WASHINGTON ST -13.366
115 TULANE -13.419
116 MIAMI OH -13.584
117 NEW MEXICO -13.826
118 NEW MEXICO ST -14.778
119 WESTERN KENTUCKY -18.701
120 EASTERN MICHIGAN -20.115

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, December 7, 2009

Week 14 Rankings

Description of Ratings System

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