Most people who don’t follow politics closely still know one thing about polling: it’s often wrong, sometimes spectacularly so. So when a forecast lands within four-tenths of a percentage point of the actual result, it’s worth pausing on how that happened.
As covered by the Arizona Herald, Andy Biggs won Arizona’s Republican gubernatorial primary with 73.3% of the vote. Weeks before anyone cast a ballot, a Miami-based research firm called G Ratings had already put the number at 73.7% — a difference small enough to fall within typical rounding error.
That wasn’t a lucky guess on a single race. Second-place finisher David Schweikert came in at 14.7%, matched by a pre-election projection of 14.3% — the same 0.4-point gap. Further down the ballot, results got messier: Scott Neely and Ken Miceli finished at 7.1% and 4.7%, compared to earlier projections of 3.5% and 2.6%. Even with that wider miss, the average error across all four candidates worked out to 1.45%, and the model showed no consistent lean toward inflating or deflating any one candidate’s numbers.
Why This Particular State Is a Hard One to Call
Arizona has a reputation among pollsters for being difficult to read, and for good reason. Rural counties and suburban Maricopa County often move in opposite directions, border communities show up to vote on their own schedule, and the state leans heavily on mail-in ballots — meaning a poll taken during early voting can look completely disconnected from the electorate that actually decides the race by Election Day.
The tool behind G Ratings’ projections, an AI platform called Odysseus, was built with that volatility in mind rather than treating it as something to average away. It draws in real-time sentiment from local news and online discussion, checks that against historical turnout data and daily ballot-return counts by county, and uses demographic and economic clustering to separate voters who are locked in from those still on the fence.
That granularity showed up in what the model got right about voter priorities — inflation, job growth, and tax policy topped the list statewide, with healthcare, border security, and confidence in election administration close behind. But those concerns don’t weigh the same everywhere; what matters most to a voter in Yuma isn’t necessarily what matters most in Scottsdale, and the model was designed to account for both instead of flattening them into one statewide number.
A spokesperson for G Ratings summed up the underlying philosophy simply: the goal isn’t recreating what someone said in a phone survey weeks earlier, it’s figuring out who actually shows up to vote — and in a state like Arizona, that’s the number that decides everything.
Looking Ahead to November
With Biggs now set as the Republican nominee and Governor Katie Hobbs running unopposed on the Democratic side, Arizona heads into a general election expected to draw national attention. The primary offered a real-world test of whether combining live data with AI modeling can cut through a state’s structural polling problems — rural-versus-urban splits, unpredictable border-region turnout, and a mail-in system that moves the goalposts late.
Whether that same precision holds up in November, against a much larger and more varied electorate, is still an open question. But the primary has at least set a benchmark for what’s possible.
International Journal of Research (IJR) 