Want a statistical model that tells you which Congressman to lobby on immigration reform? Look no further than a political scientist at UC San Diego:

In the mold of Silver, who is famous for his election predictions, Wong bridges the gap between equations and shoe-leather politics, said David Damore, a political science professor at the University of Nevada, Las Vegas and a senior analyst for Latino Decisions, a political opinion research group.

Activists already have an idea of which lawmakers to target, but Wong gives them an extra edge. He can generate a custom analysis for, say, who might be receptive to an argument based on religious faith. With the House likely to consider separate measures rather than a comprehensive bill, Wong covers every permutation.

“In the House, everybody’s in their own unique geopolitical context,” Damore said. “What he’s doing is very, very useful.”

The equations Wong uses are familiar to many political scientists. So are his raw materials: each lawmaker’s past votes and the ethnic composition of his or her district. But no one else appears to be applying those tools to immigration in quite the way Wong does.

So is there something extra in the models that others don’t have or is Wong extra good at interpreting the results? The article suggests there are some common factors all political scientists would consider but then it also hints there are some more hidden factors like religiosity or district-specific happenings.

A fear I have for Nate Silver as well: what happens when the models are wrong? Those who work with statistics know they are just predictions and statistical models always have error but this isn’t necessarily how the public sees things.