College Football Computer Rankings: Week 7 Computer Rankings

Sunday, October 14, 2012

Week 7 Computer Rankings

1 ALABAMA 22.137 2 OREGON 20.298 3 KANSAS ST 17.394 4 OKLAHOMA 17.184 5 FLORIDA 15.814 6 NOTRE DAME 15.440 7 TEXAS TECH 14.979 8 SOUTH CAROLINA 13.228 9 OREGON ST 13.003 10 CINCINNATI 12.591 11 TEXAS A&M 11.611 12 OHIO STATE 10.303 13 FLORIDA ST 9.375 14 LSU 9.331 15 MISSISSIPPI ST 8.864 16 TEXAS 8.789 17 GEORGIA 8.569 18 IOWA ST 8.203 19 SOUTHERN CAL 8.127 20 STANFORD 7.350 21 WEST VIRGINIA 7.169 22 ARIZONA ST 7.158 23 RUTGERS 6.542 24 TCU 6.504 25 LOUISIANA TECH 6.295 26 CLEMSON 5.782 27 BOISE ST 5.673 28 WISCONSIN 5.213 29 MICHIGAN 4.867 30 UTAH ST 4.745 31 LOUISVILLE 4.713 32 NORTH CAROLINA 4.631 33 UCLA 4.156 34 ARIZONA 3.894 35 LOUISIANA-MONROE 3.181 36 NEBRASKA 2.986 37 MISSISSIPPI 2.942 38 WESTERN KENTUCKY 2.805 39 PENN STATE 2.790 40 MICHIGAN ST 2.570 41 OKLAHOMA ST 2.539 42 NORTHWESTERN 2.534 43 IOWA 2.511 44 FRESNO ST 2.348 45 NEVADA 2.197 46 NORTHERN ILLINOIS 2.151 47 LOUISIANA-LAFAYETTE 2.114 48 VIRGINIA TECH 2.011 49 TULSA 1.982 50 BRIGHAM YOUNG 1.968 51 TENNESSEE 1.854 52 BAYLOR 1.839 53 CALIFORNIA 1.638 54 ARKANSAS 1.338 55 SAN JOSé ST 1.336 56 WASHINGTON 1.258 57 TOLEDO 1.163 58 CENTRAL FLORIDA 0.481 59 MIDDLE TENNESSEE ST 0.469 60 PITTSBURGH 0.354 61 MIAMI FL 0.319 62 HOUSTON 0.179 63 SAN DIEGO ST 0.126 64 MISSOURI 0.112 65 VANDERBILT 0.030 66 OHIO U. 0.000 67 DUKE -0.053 68 NORTH CAROLINA ST -0.233 69 MARYLAND -0.249 70 UTAH -0.267 71 TROY -0.299 72 NEW MEXICO -0.351 73 KENT ST -0.727 74 TEXAS ST-SAN MARCOS -0.833 75 PURDUE -0.896 76 WAKE FOREST -1.434 77 INDIANA -1.458 78 SYRACUSE -1.490 79 TEMPLE -1.582 80 BOWLING GREEN -1.856 81 MINNESOTA -2.071 82 BALL ST -2.092 83 AUBURN -2.490 84 KENTUCKY -2.528 85 AIR FORCE -2.765 86 NAVY -2.861 87 CONNECTICUT -3.012 88 SOUTH FLORIDA -3.036 89 EAST CAROLINA -3.114 90 GEORGIA TECH -3.636 91 ARKANSAS ST -3.947 92 WYOMING -4.128 93 KANSAS -4.870 94 TEXAS-SAN ANTONIO -5.504 95 SMU -5.657 96 WASHINGTON ST -6.349 97 NORTH TEXAS -6.370 98 MARSHALL -6.603 99 UNLV -6.940 100 WESTERN MICHIGAN -7.030 101 ILLINOIS -7.668 102 VIRGINIA -7.688 103 RICE -7.974 104 MIAMI OH -8.352 105 ARMY -8.432 106 ALABAMA-BIRMINGHAM -8.631 107 BOSTON COLLEGE -8.951 108 UTEP -9.570 109 CENTRAL MICHIGAN -10.197 110 FLORIDA ATLANTIC -10.290 111 TULANE -10.489 112 FLORIDA INT'L -10.669 113 COLORADO -11.016 114 SOUTHERN MISS -11.332 115 IDAHO -11.809 116 COLORADO ST -12.948 117 MEMPHIS -13.367 118 BUFFALO -13.543 119 HAWAI`I -14.950 120 EASTERN MICHIGAN -15.008 121 SOUTH ALABAMA -16.718 122 AKRON -16.756 123 MASSACHUSETTS -20.636 124 NEW MEXICO ST -24.014

1 comment:

Unknown said...: Thanks for your grateful informations, this blogs will be really help for Exam results.; October 15, 2012 at 5:28 AM

Post a Comment

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, October 14, 2012

Week 7 Computer Rankings

1 comment:

Description of Ratings System

Blog Archive

About Me