College Football Computer Rankings: December 2011

Sunday, December 4, 2011

Week 14 Computer Rankings

1 LSU 23.538
2 ALABAMA 19.220
3 OKLAHOMA ST 17.205
4 OREGON 14.625
5 BOISE ST 13.944
6 STANFORD 13.739
7 WISCONSIN 12.243
8 MICHIGAN 12.021
9 OKLAHOMA 11.662
10 SOUTHERN CAL 9.870
11 SOUTH CAROLINA 9.099
12 ARKANSAS 9.019
13 GEORGIA 7.625
14 HOUSTON 7.557
15 KANSAS ST 7.450
16 TCU 7.230
17 BAYLOR 7.215
18 NEBRASKA 7.110
19 MICHIGAN ST 6.591
20 NOTRE DAME 6.368
21 SOUTHERN MISS 6.158
22 FLORIDA ST 6.023
23 VIRGINIA TECH 5.997
24 CLEMSON 5.931
25 TEXAS A&M 5.091
26 TEXAS 4.244
27 PENN STATE 4.110
28 MISSOURI 3.892
29 WEST VIRGINIA 3.647
30 TOLEDO 3.619
31 CINCINNATI 3.558
32 RUTGERS 2.681
33 VANDERBILT 2.663
34 TULSA 2.632
35 ARIZONA ST 2.521
36 IOWA 2.431
37 OHIO STATE 2.207
38 LOUISIANA TECH 2.088
39 FLORIDA 2.057
40 CALIFORNIA 2.049
41 MIAMI FL 2.014
42 BRIGHAM YOUNG 2.007
43 PITTSBURGH 1.976
44 GEORGIA TECH 1.974
45 NORTH CAROLINA 1.869
46 MISSISSIPPI ST 1.775
47 TEMPLE 1.649
48 ARKANSAS ST 1.507
49 LOUISVILLE 1.329
50 NORTHERN ILLINOIS 1.289
51 UTAH 1.130
52 WASHINGTON 1.124
53 TENNESSEE 1.122
54 AUBURN 1.110
55 SOUTH FLORIDA 1.011
56 NORTH CAROLINA ST 0.605
57 NEVADA 0.434
58 ILLINOIS 0.373
59 NORTHWESTERN 0.324
60 IOWA ST -0.051
61 NAVY -0.103
62 OHIO U. -0.105
63 SAN DIEGO ST -0.123
64 WESTERN MICHIGAN -0.161
65 VIRGINIA -0.444
66 FLORIDA INT'L -0.741
67 TEXAS TECH -0.791
68 WESTERN KENTUCKY -0.849
69 CONNECTICUT -0.885
70 WAKE FOREST -0.947
71 AIR FORCE -0.957
72 SMU -1.000
73 UCLA -1.048
74 PURDUE -1.213
75 LOUISIANA-LAFAYETTE -1.399
76 UTAH ST -1.421
77 WYOMING -1.433
78 KENTUCKY -1.493
79 CENTRAL FLORIDA -1.683
80 OREGON ST -2.299
81 ARIZONA -2.449
82 SYRACUSE -2.659
83 MINNESOTA -2.786
84 BALL ST -3.278
85 SAN JOSé ST -3.379
86 EAST CAROLINA -3.430
87 MARSHALL -3.523
88 BOWLING GREEN -3.590
89 BOSTON COLLEGE -3.629
90 MIAMI OH -3.881
91 HAWAI`I -4.031
92 DUKE -4.137
93 WASHINGTON ST -4.495
94 EASTERN MICHIGAN -4.500
95 KENT ST -4.572
96 FRESNO ST -4.918
97 LOUISIANA-MONROE -5.031
98 UTEP -5.255
99 NORTH TEXAS -5.272
100 RICE -6.105
101 KANSAS -6.216
102 COLORADO -6.230
103 MISSISSIPPI -6.277
104 NEW MEXICO ST -7.589
105 ARMY -8.727
106 MARYLAND -8.866
107 TROY -10.170
108 CENTRAL MICHIGAN -10.205
109 COLORADO ST -10.265
110 BUFFALO -10.310
111 ALABAMA-BIRMINGHAM -10.847
112 INDIANA -11.430
113 UNLV -11.546
114 IDAHO -12.391
115 MIDDLE TENNESSEE ST -13.029
116 NEW MEXICO -15.421
117 MEMPHIS -16.745
118 TULANE -18.334
119 FLORIDA ATLANTIC -18.488
120 AKRON -19.742

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

Sunday, December 4, 2011

Week 14 Computer Rankings

Description of Ratings System

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