GovTrack has a cool prognosis feature that predicts, based on a number of factors, whether a bill will be passed:

For the data wonks out there, the new prognosis is based on a logistic regression model. The model predicts a bill’s success based on the following binary factors:

• the title of the bill (such as if it is a bill to name a post office)
• whether the sponsor is a member of the majority party (in the House or Senate as appropriate)
• whether the sponsor is the chair, ranking member, or a member (if majority party) of a committee that the bill has been referred to
• if any cosponsor is the chair or ranking member (most senior minority party member) of a committee the bill has been referred to
• if there are 3-5 cosponsors of the bill serving on a committee the bill has been referred to
• if the bill has a cosponsor from both parties
• if the bill’s sponsor is in the majority party and at least 1/3rd of the cosponsors are from the minority party
• if the bill was a reintroduction of a bill from the previous Congress (same sponsor and title, ignoring any year found in the title) (suggested by Tom Lee shortly after this post was first published)

 

I wonder if I could reverse-engineer this to focus on the factors that make a law most likely to be passed, and try to focus on (and oppose) those bills.