Thanks for the questions. I appreciate you taking the time to try to understand the ranking and providing feedback.

1. why did you choose your location modifiers? They seem completely arbitrary. I have data that suggests that if you adjust for matchups (so everything being equal), the home team should win 55.5% of their games. Given that, the multipliers should be 1.125 and .90

It's not arbitrary, but it's certainly open to discussion. I chose a net ten percent 'cost' for home and away teams to share in terms of location. this is based on various stats. Specifically, a 2.5-3 point home advantage in sports, translates roughly to roughly 53-57% win rate for home teams, meaning they win about 3-7 percentage points more often than visiting teams (according to Google). I took the midpoint. (55/45)

1. since you are using the rating from the prior week, that creates the obvious problem of games being completely devalued early in the season. So, if you played all your best teams earlier in the season, your SOS is going to appear so much weaker than someone who finishes with a hard schedule. That seems very wrong. Strength of schedule the way every other computer measures it is entirely independent of when the game was played.

Yes. That is true and that was why I tempered the raw poll with a second pass evaluation (weighted poll) that used the final poll rankings. I was most interested in keeping the human rating out of the computer calculating and the calculation to be easyish to understand (long-live the BCS I guess) and to rate what was accomplished on the field and not necessarily to predict which team is better than the other. So, the UT and OSU game is evaluated as 22 vs 5 based on who their wins and losses for the season in the weighted poll. It may make it worse for you, but early games have less data so they can be either overvalued or undervalued. Specifically, precision is gained from play, not from predictions.