A difficult problem in analysis is when a small amount of data, such as how a team performs in Week 1, differs significantly from a projection based on a larger sample size, such as last season plus adjustments due to regressions and offseason changes. The problem is compounded when there exists some reason to believe the difference has good reasoning behind it, i.e., the adjustments may not be accurately reflecting the team.
The goal for a bettor in Week 2 is to solve this problem. In practice, most who aren't used to an approach involving modeling lean too much toward overestimating the significance of Week 1 outcomes. To properly examine this Week 2 slate, however, we want to compare the lines before Week 1 to the lines after Week 1 to see how they have adjusted.