Sunday, December 4, 2022

4 Dec 2022 Ranking - Before Bowl Games

 

Rank
Team Q Losses
1
Georgia 157 0
2
Michigan 149 0
3
TCU 144 1
4
Ohio St 142 1
5
Tennessee 139 2
6
Alabama 138 2
7
USC 135 2
8
Clemson 135 2
9
Tulane 134 2
10
Penn St 134 2
11
Kansas St 134 3
12
LSU 128 4
13
Troy 128 2
14
Oregon 127 3
15
Florida St 127 3
16
Utah 127 3
17
Texas 126 4
18
Washington 125 2
19
Mississippi St 124 4
20
Oregon St 124 3
21
UT San Antonio 124 2
22
Notre Dame 121 4
23
UC Los Angeles 121 3
24
Mississippi 120 4
25
South Carolina 120 4
26
South Alabama 119 2
27
NC State 118 4
28
Coastal Carolina 117 3
29
Kentucky 115 5
30
North Carolina 115 4
31
Cincinnati 115 3
32
Cent Florida 114 4
33
Texas Tech 114 5
34
Fresno St 112 4
35
Boise St 112 4
36
Syracuse 112 5
37
Maryland 112 5
38
Louisville 111 5
39
Marshall 111 4
40
Purdue 110 5
41
Pittsburgh 110 4
42
Wake Forest 109 5
43
Florida 109 6
44
Illinois 109 4
45
SMU 108 5
46
Oklahoma St 107 5
47
Air Force 107 3
48
Arkansas 107 6
49
Washington St 107 5
50
Missouri 106 6
51
Minnesota 105 4
52
Houston 104 5
53
Auburn 104 7
54
Oklahoma 104 6
55
Kansas 104 6
56
BYU 104 5
57
Baylor 103 6
58
James Madison 102 4
59
Iowa 102 5
60
East Carolina 101 5
61
Duke 100 4
62
West Kentucky 100 5
63
Appalachian St 100 5
64
Vanderbilt 100 7
65
Wyoming 99 5
66
San Jose St 98 4
67
Liberty 98 4
68
San Diego St 98 5
69
Toledo 98 5
70
Ohio 98 5
71
Michigan St 98 7
72
Georgia Tech 97 7
73
Wisconsin 96 6
74
Memphis 95 6
75
West Virginia 95 7
76
North Texas 95 6
77
Arizona 94 7
78
Southern Miss 94 6
79
UL Lafayette 94 6
80
Ga Southern 93 6
81
Mid Tenn St 93 5
82
East Michigan 93 4
83
Texas A&M 93 7
84
Navy 93 6
85
Miami OH 93 5
86
Bowling Green 92 6
87
Connecticut 92 6
88
UA Birmingham 91 6
89
Indiana 91 8
90
Utah St 91 6
91
Ball St 90 6
92
Iowa St 90 8
93
Kent 89 7
94
Miami FL 88 7
95
Army 86 6
96
Buffalo 86 6
97
California 85 8
98
Rice 85 7
99
Stanford 84 9
100
Rutgers 83 8
101
Tulsa 82 7
102
FL Atlantic 82 7
103
UL Monroe 82 8
104
Georgia St 81 8
105
UN Las Vegas 80 7
106
UT El Paso 79 7
107
Nebraska 78 8
108
Boston College 77 9
109
Virginia 76 7
110
Arizona St 76 9
111
Cent Michigan 74 8
112
Colorado 73 11
113
New Mexico St 73 6
114
Texas St 73 8
115
West Michigan 73 8
116
Louisiana Tech 69 9
117
Old Dominion 69 9
118
Florida Intl 68 8
119
Colorado St 68 9
120
Virginia Tech 67 8
121
Arkansas St 67 9
122
Hawaii 66 10
123
Northwestern 62 11
124
Charlotte 62 9
125
New Mexico 62 10
126
Temple 60 9
127
North Illinois 59 9
128
South Florida 58 11
129
Akron 58 10
130
Nevada 55 10
131
Massachusetts 44 11

A Further Explanation of the Calculation

This is how the calculation started in my mind. We are all familiar with a win loss record where an undefeated team has a record of 1.0 and a team that has won half its games has a record of 0.5.

So first step is to give a value for each game a team plays based on a 1 for victory and 0 for a loss, but the to those numbers add the opponent's win-loss record. For example, if you win a game against a team that has a record of 0.5 then the value of that game is 1.5, but if you lose it would be 0.5. By this method, the value of a loss to an undefeated team is the same as a victory over a team that has not won a game; both are 1.0. Now as the year goes on, the value of each game will update based on whether the opponent does well or not. In the example, if that opponent's record later in the season was 0.7, then the value of that game would be 1.7, or if they lost games it would go the other way.

But I added a correction into the calculation that removes the outcome of each game from the opponent's win-loss record, so it doesn't reward a team for losing and doesn't lower the value of a victory. For example, if a team defeats a team that otherwise won all of their games, then the score is 1.0 for the victory plus still 1.0 for the opponent's win-loss record even though they just lost.

So as the year goes on you add up the value for each game played and calculate the average value per game played. I then chose to multiply the average by 100 just to make it easier for people to think about, rather than having that decimal in there. So 100 is the "average" score for a team rather than 1.0. They did something like that for IQ measurements.

At the beginning of the year, every team starts exactly equal and have no score at all. Even after their first game, they are still exactly equal with all having a theoretical score of 1.0. It doesn't matter what conference they are in or estimated rankings at the beginning of the year. Only whether teams win and keep winning or lose and keep losing.

That is how I started it out. My idea was that the non-conference games would mix enough so that it would equalize the scores and pull those who played harder schedules up. When I started in the 1990s the conferences were smaller and everyone was playing more non-conference games. But as the years went on they added more and more FBS teams, conferences expanded, teams played fewer non-conference games, and most big teams routinely began adding FCS teams to the schedule.

So I then added up the collective win-loss record of the each teams opponents to be a surrogate for conference play. With each team playing playing 12 games against other teams that played 12 games, the variation in the win-loss record is low and by the end of the year is almost always between 0.45-0.55 with average being 0.5. I then used that as a factor to increase or decrease the value of the opponent's win-loss record by a small amount based on the win-loss record of all of that opponent's opponents. Here I made a judgement and just added 0.5 to that number to make the average 1.0 and then multiply that number against the the opponent's win-loss record, making it go up and down but by the end of the year accounting for less than 10% of the score. This factor does not include FCS at all, only performance against other FBS teams.

Again to make the value of each game more accurate, I remove the win-loss record of the team from the opponent's opponents win-loss record for each game so that it doesn't alter that game's value, and does not overvalue or undervalue the opponent's score for the for the team's own performance.

I analyzed it and the SEC and Big 10 conferences did on the average have higher scores than than the Sun Belt or Mid-America conferences. But some "power" conference teams were actually not very powerful at all. I actually compared several ways to get to this number. Another technique multiplied the 0.45-0.55 score by 2 which gave greater weight to the opponent's opponents records. The year I tested this was when Utah was undefeated and beat Alabama in the bowl game. That year, the way I ended up doing it better predicted the outcomes of bowl games, where teams from different conferences played each other.

I actually tried to take it to a third level measuring the win-loss records of the opponent's opponent's opponents, but change in score was insignificant, so I stopped it as it is.

Then what to do with FCS teams? At first I excluded them completely since they didn't really mix with FBS enough. But the year Appalachian St beat Michigan, I got annoyed because I felt like Michigan should have accounted for the loss. So I inserted the FCS teams into the calculation, but only with a 0.0 for a loss and 1.0 for a victory. It does not consider the record or power of the FCS team. It was purely an assumption that essentially penalizes teams for playing FCS teams. But now days everyone except Notre Dame plays one FCS team so it kind of balances out... maybe.