How an AI Platform Modeled Phoenix’s Voter Behavior to Within Half a Percentage Point

Forecasting elections in fast-growing metro areas requires accounting for variables that traditional polling methods often struggle to capture, population turnover, uneven ballot-return timing, and sharp differences between neighboring precincts. A recent primary in Arizona offers a working example of how an AI-based approach handled that complexity.

This case was documented by the Phoenix Herald, which reported that G Ratings, a Florida-based political research firm, applied its forecasting platform Odysseus to Arizona’s 2026 Republican gubernatorial primary. The platform projected winning candidate Andy Biggs at 73.7% of the vote; the certified result came in at 73.3%, a variance of 0.4 percentage points.

What the Data Showed

The accuracy held consistently across the ballot. Runner-up David Schweikert was projected at 14.3% against an official 14.7%, again a 0.4-point gap. Candidates further down the ballot, Scott Neely and Ken Miceli, were projected at 3.5% and 2.6% respectively, with actual results coming in higher at 7.1% and 4.7%. Averaged across all four contested candidates, the model’s margin of error was 1.45%.

Why Metro Phoenix Is a Difficult Region to Forecast

The Phoenix metro area presents specific structural challenges for any single polling model:

• Rapid population growth has added hundreds of thousands of residents in under a decade, with new subdivisions in areas like Buckeye and Queen Creek bringing in voters without an established voting history
• Older, more established precincts such as Scottsdale tend to vote more predictably and consistently
• Maricopa County processes a high enough volume of early ballots to shift a statewide result within the final ten days before an election

How the Model Is Built to Handle This

Rather than treating the metro area as one uniform voting bloc, Odysseus is designed to model it as a set of distinct, overlapping micro-electorates. According to the report, the platform works by:

1. Tracking local digital sentiment and neighborhood-level discourse to estimate how strongly voters support a given candidate
2. Cross-referencing that data against county-level early-ballot return rates as they’re reported day by day
3. Incorporating hyper-local economic indicators, distinguishing between, for example, a ZIP code facing rising housing costs and one where fuel and grocery prices are the more pressing concern

A G Ratings analyst involved in the project explained the reasoning behind this approach: conventional phone surveys often measure sentiment that has already shifted by the time the survey is complete, whereas the platform is intended to track those shifts in near real time rather than rely on a fixed snapshot.

What Comes Next

Maricopa County is expected to again play a significant role in the general election between Biggs and incumbent Governor Katie Hobbs. The primary results give researchers and campaign strategists a concrete reference point, a model that tracked a fragmented electorate to within roughly half a percentage point during the primary phase. Whether that same accuracy holds at the scale of a full general election, with a larger and more demographically varied electorate, remains to be tested in the months ahead.