Election Day, Nov 3 6 daysSenate D 85%House D 97%Governors D 86%Forecast updated Oct 28
2026 forecast · California 23rd

Jay Obernolte is very likely to win California's 23rd District.

Tessa Lynn Hodge vs Jay Obernolte. The model has Obernolte ahead by 11.6 points, winning 96 percent of 2,000 simulated elections.

Likely RModel R +11.6Updated October 28, 2026
Tessa Lynn Hodge
Democrat
44.2%
108,937 projected votes
Jay Obernolte
Republican
55.8%
137,407 projected votes
96%Obernolte
4%Hodge
Obernolte all but certainEach square is one simulated election night, Obernolte wins about 96 of 100
RatingLikely R96% chance for Obernolte
Model marginR +11.6No polls in the race
80 percent rangeR +3.0 to R +20.22,000 simulations
Projected votes246,344Obernolte 55.8%

The district map

Every district in the state, shaded by its rating

California districts

52 districts
Safe DLikely DLean DToss upLean RLikely RSafe R

Vote share

Each candidate's projected share and the range of 80 percent of simulations

Tessa Lynn HodgeDemocrat · 4% to win
44.2%
Jay ObernolteRepublican · 96% to win
55.8%

Scale runs from 25 to 75 percent. The dot is the forecast, the band is the 80 percent range and the brighter tick marks 50 percent.

Projected district vote

246,344 votes
CandidateVotesShare
Jay Obernolte R137,40755.8%
Tessa Lynn Hodge D108,93744.2%
Total246,344100%

Each candidate's expected vote in the On Point simulation, at the forecast's projected turnout.

The trend

Each side's chance of winning over the forecast's recent updates

0%25%50%75%100%4% Hodge96% Obernolte15 updates agoToday

Polling

Every poll in the average, and how the model reads it

The California governor polling average
25%30%35%40%45%50%55%60%65%JunJulAugSepOct55.7Becerra35.6Hilton
The line is the On Point average: a robust LOWESS fit through the polls, weighted by recency, sample, voter screen and pollster grade, refit three times so outliers count for lessDot size is each poll's weight todayHover or use the arrow keys to read any day
What moves the average today
Manhattan InstituteOct 1 · 1,000 LV · NR19%Becerra +19.0
McLaughlin & Associates (R)Oct 20 · 800 LV · B18%Becerra +16.0
UC Berkeley Citrin Center/TrueDot/PoliticoOct 14 · 909 LV · B17%Becerra +20.0
Emerson CollegeOct 19 · 1,000 LV · Gold11%Becerra +27.0
SurveyUSAOct 24 · 799 LV · C+8.4%Becerra +22.0
PPICSep 10 · 1,103 LV · NR6.4%Becerra +22.0
Every other poll together: 21%

This is the same On Point polling average as the full average page. The forecast reads the polls through its own Election Day projection, shown as the On Point average in the table below, and blends it with partisan lean and fundamentals.

Polls of the race

Full average
PollsterSampleAgeMargin
On Point averageWeighted averageNo polls

Each margin is the poll as the pollster published it. The forecast counts every poll after its house effect and the undecided split, which is what the trail above shows; hover a margin for the counted value. Lean R and Lean D mark partisan leaning firms.

Model margin
Obernolte +11.6
Chance of winning
Tessa Lynn Hodge4%
Jay Obernolte96%
Simulated vote
Tessa Lynn Hodge44.2%
Jay Obernolte55.8%
How the model gets there
Partisan leanD +19.0
FundamentalsR +11.3
With pollsR +11.6

How we got here

From the state's partisan lean to the forecast, one stage at a time

D +23R +23Partisan leanthe state's baselineD +19.0Fundamentalsapproval, economy, candidatesR +11.3With pollsno pollsR +11.6
On Point on YouTubeThe California House race, explained on YouTubeForecast breakdowns, the polls and election night coverage on the On Point channel.Subscribe
Methodology
Every county is rebuilt by simulating voters going to the polls, 2,000 times, from On Point Polling survey data, past results, and the polling average. How the model works.