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.