As the calendar reaches July and the college football preseason magazines begin to hit the shelves, I have an annual tradition of doing a simulation-assisted deep dive into the coming college football season. Over the years, I have developed an algorithm and tracking spreadsheet that I use to generate in-season power rankings and projected point spreads. In the preseason, I have found that I can use the same algorithm to project the outcome of the full season, if I use preseason rankings to seed the math. Basically, I ask the question: if the preseason rankings are correct, what is the most likely outcome of the coming season , considering each team’s actual schedule and a historically reasonable probability of upsets, specifically when good teams face slightly weaker teams on the road? Now, I could (and have in the past) spend several paragraphs going into the details of my methodology, but the key points are as follows. This year, I used the average rankings from 5 separate...