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It's entirely valid and rational to say the team with the #2 SOS in the nation that is undefeated should not be behind a team with a loss that they beat on the road who has a lower rated SOS.

Not that it's a huge difference, but I found 7 measures of SOS, with a range of 2 to 22, a mean of about 14, and a median of 13. Regardless, I completely agree with this specific statement. I too would rank LSU ahead of Alabama, because I believe the results on the field matter, especially when two teams are comparably good.

My whole disagreement is that you have argued that any model that is trying to provide a quantifiable estimates (with error around each estimate) of each team based on some specific measurement scale (usually points or win probabilities) for ALL FBS teams that doesn't have the team ranked the same way should be disregarded. And you're entire rationale is based on the very specific criteria used purely for the relative ranking of two teams, and despite all of those metrics being only one rank off of your own.

What is frustarting is that I've spent years reading and often actively agreeing with your arguments when people ignore the process for the outcome, argue that a single=-game or event somewhat changes an entire season even career (e.g., Ray Allen hitting hitting the 3 didn't make Lebron and his career better or worse), and using ignoring the objective data and adding subjective criteria when the objective data do not fit its argument.

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Albeit, with their flaws.

But since every model ever created for any purpose has its flaws, especially when getting to granular level, would you argue that one should disregard them all? If not, then why would you do it here?

OR can we just agree that the models provide a pretty good estimate, WITH MARGIN OF ERRORS, and when the relative rankings of two teams doesn't seem right but still falls within that margin and is only one place off, then that's not enough to outright disregard the entire model altogether, especially before the complete data set if available?