College Football Computer Rankings: Week 8 Computer Rankings

Monday, October 22, 2012

Week 8 Computer Rankings

1	OREGON	22.846
2	ALABAMA	22.168
3	FLORIDA	18.953
4	KANSAS ST	18.639
5	OKLAHOMA	17.042
6	NOTRE DAME	14.990
7	OREGON ST	13.696
8	TEXAS TECH	13.015
9	MISSISSIPPI ST	11.294
10	LSU	10.589
11	FLORIDA ST	9.967
12	SOUTH CAROLINA	9.595
13	TEXAS A&M	9.585
14	STANFORD	9.213
15	OHIO STATE	9.072
16	TEXAS	8.856
17	RUTGERS	8.537
18	CLEMSON	8.460
19	SOUTHERN CAL	8.258
20	GEORGIA	7.178
21	ARIZONA	6.874
22	WISCONSIN	6.516
23	TCU	6.313
24	OKLAHOMA ST	6.210
25	LOUISIANA TECH	6.034
26	CINCINNATI	5.992
27	BOISE ST	5.962
28	MICHIGAN	5.201
29	PENN STATE	4.762
30	ARIZONA ST	4.735
31	WEST VIRGINIA	4.562
32	UTAH ST	4.557
33	IOWA ST	4.089
34	UCLA	3.686
35	TOLEDO	3.643
36	FRESNO ST	3.533
37	LOUISIANA-MONROE	3.414
38	LOUISVILLE	3.290
39	MISSISSIPPI	3.118
40	NEBRASKA	2.910
41	NORTH CAROLINA	2.774
42	SAN JOSé ST	2.334
43	BRIGHAM YOUNG	2.251
44	NORTHWESTERN	2.237
45	WESTERN KENTUCKY	2.235
46	NORTHERN ILLINOIS	2.183
47	MICHIGAN ST	2.155
48	BAYLOR	1.797
49	ARKANSAS	1.557
50	TENNESSEE	1.402
51	NEVADA	1.261
52	TULSA	0.959
53	DUKE	0.801
54	CALIFORNIA	0.686
55	WASHINGTON	0.679
56	SAN DIEGO ST	0.661
57	VIRGINIA TECH	0.622
58	LOUISIANA-LAFAYETTE	0.410
59	PURDUE	0.160
60	SYRACUSE	0.149
61	CENTRAL FLORIDA	0.059
62	MIAMI FL	0.051
63	OHIO U.	0.000
64	IOWA	-0.025
65	PITTSBURGH	-0.154
66	NORTH CAROLINA ST	-0.175
67	KENT ST	-0.209
68	MIDDLE TENNESSEE ST	-0.250
69	VANDERBILT	-0.292
70	MISSOURI	-0.708
71	TROY	-0.895
72	UTAH	-0.912
73	MARYLAND	-1.041
74	NEW MEXICO	-1.240
75	BALL ST	-1.364
76	BOWLING GREEN	-1.380
77	SMU	-1.563
78	WAKE FOREST	-1.573
79	GEORGIA TECH	-1.618
80	AIR FORCE	-1.623
81	NAVY	-1.698
82	KENTUCKY	-1.713
83	HOUSTON	-1.950
84	AUBURN	-2.066
85	INDIANA	-2.115
86	TEXAS ST-SAN MARCOS	-2.261
87	SOUTH FLORIDA	-2.474
88	KANSAS	-2.949
89	EAST CAROLINA	-2.990
90	TEMPLE	-2.990
91	MINNESOTA	-3.478
92	ARKANSAS ST	-3.662
93	NORTH TEXAS	-3.988
94	MARSHALL	-3.999
95	CONNECTICUT	-4.971
96	WYOMING	-5.638743
97	WASHINGTON ST	-6.301
98	VIRGINIA	-7.389
99	RICE	-7.553
100	ILLINOIS	-7.762
101	TEXAS-SAN ANTONIO	-7.845
102	UTEP	-7.889
103	UNLV	-8.259
104	WESTERN MICHIGAN	-8.403
105	MIAMI OH	-9.034
106	EASTERN MICHIGAN	-9.935
107	FLORIDA INT'L	-10.427
108	ARMY	-10.523
109	BOSTON COLLEGE	-10.891
110	TULANE	-10.935
111	COLORADO	-11.034
112	CENTRAL MICHIGAN	-11.099
113	ALABAMA-BIRMINGHAM	-11.388
114	IDAHO	-12.025
115	FLORIDA ATLANTIC	-12.073
116	COLORADO ST	-12.364
117	SOUTH ALABAMA	-12.815
118	BUFFALO	-13.968
119	MEMPHIS	-13.995
120	SOUTHERN MISS	-14.014
121	HAWAI`I	-15.731
122	AKRON	-16.638
123	MASSACHUSETTS	-21.689
124	NEW MEXICO ST	-22.451

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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 22, 2012

Week 8 Computer Rankings

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Description of Ratings System

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