However, I remember the one graph that showed the clearest evidence was one of Google searches for each of the candidate’s names in days and weeks preceding the election. Remembering that and thinking it an oddly much more reliable, objective measure of the potential interest in Scott Brown vs Martha Coakley, I mined similar data in the upcoming Massachusetts Senate race.

The graph below shows the volume of search queries for “Scott Brown” and “Martha Coakley” originating in Massachusetts for the past 30 days, which, for the large part, filters out all non-Bay Staters.

Brown in red [A]; Coakley in blue [B]:

Google Trends for Massachusetts Senate Race

To be clear, the ratio of queries for Scott Brown to queries for Martha Coakley is 3.10:1.00. That is absolutely remarkable.

If you add in the other trending query, “Massachusetts Senate Race (MSR),” the ratio for Brown:Coakley:MSR is still 3.10:1.00:0.10, which I take to mean that the level of general interest among Democrats remains low – that is, the Democrats’ recent media blitz is not doing a good job of bringing in uninterested, unmotivated voters. In fact, the MSR query failed to register until a few days ago.

At the risk of sounding confident, I think this is going to be an historic election and completely remake the landscape for 2010.

Tommy Jardon is the former Executive Director of the College Republican National Committee and a recent graduate of the University Florida Law School.

Google, which captures and records search queries like “muscle aches” and “flu symptoms,” suggests their service can accurately predict an outbreak 10 days before the Center for Disease Control.

Some public health experts argue Google’s accelerated prediction can, if correct, work to lower the 36,000 flu-related US deaths a year. While others say that their predictions are unproven and often unnecessary as local health boards daily gather data from emergency rooms to keep tabs on disease trends in their own communities. “We don’t have any evidence that this is more timely than our emergency room data,” said Farzad Mostashari, assistant commissioner of the New York City Department of Health and Mental Hygiene. But Larry Brilliant, executive director of Google.org, argues otherwise, “We know it matches very, very well in the way flu developed in the last year.” Reports of flu are expected to increase as we approach the winter months, so we’ll know shortly who bet on the right horse.

“From a technological perspective, it is the beginning,” said Google’s CEO Eric Schmidt. Indeed, it is just the “beginning,” as these interesting, predictive results could theoretically be applied to any number of search queries, like politics and the stock market.

Could Google predict voter turnout and intentions? It’s a powerful question – one which I wish we had the answer to 3 months ago. The collective intelligence gained by recording this data is particularly powerful if we can first assume the keywords and phrases entered by individuals into a service like Google represent their immediate intentions. For example, a potential voter in (a nondescript target state) enters the query “Barack Obama,” and www.BarackObama.com is returned in the search results. This voter, who we can now assume is at least an undecided, in turn visits Obama’s campaign site and stays an approximated 15 minutes. While we’re still in the realm of speculation, we can now assume this voter is at least a soft, if not hard, Obama leaner.

The full extent to which this data—and the general free flow of information—can be employed is unknown. What is known, however, is that we’re just scratching the surface of what is possible through “collective intelligence.”