Wisconsin 1st

Likely R

1 in 5

Chance the Democrat wins (20.1%)

4 in 5

Chance the Republican wins (79.9%)

us_map2See the national overview
Forecasted turnout
Partisan lean

# What goes into the
icon_classic_2
classic forecast in the Wisconsin 1st

The Classic version of our model projects a race’s outcome by taking a weighted average of polls of a district (if available), polls of similar districts (CANTOR) and non-polling factors (fundamentals). It is then reverted toward a mean based on long-term trends in midterms and presidential approval ratings.

  • R+5.0

    Adjusted polls

  • D+0.7

    CANTOR

  • R+6.6

    Fundamentals

  • R+5.0

    Experts

  • R+3.2

    icon_lite_2

    Lite

  • R+5.4

    icon_classic_2

    Classic

  • R+5.3

    icon_deluxe_2

    Deluxe

  • <0.1

  • <0.1

Historical adjustment

Key

More weight

Less

#Latest polls

We've collected two polls for the Wisconsin 1st. We’re adjusting poll results in three ways: Polls of registered voters or all adults are adjusted to a likely-voter basis; older polls are adjusted based on shifts in the generic congressional ballot since the poll was conducted; and polls are adjusted for house effects (the tendency for a firm’s polls to lean toward Democrats or Republicans). Polls with larger sample sizes and those conducted by higher-quality polling agencies are given more weight, as are more recent polls.

Adjustments
datespollstersampleweight
Bryce
Steil
marginlikely voterTime-lineHouse effects Adjusted margin
Sep. 11-13
9/11-13
Siena College500LV
1.12
44%50%R+6.3 <0.1 0.1 R+6.4
Jul. 11-15
7/11-15
Global Strategy Group
D
401LV
0.27
41%40%D+1.0 0.5 1.9 R+0.2
Weighted averageR+5.0

Key

A = adults

RV = Registered voters

V = voters

LV = likely voters

R
D

= partisan poll

Our latest coverage

#Similar districts and CANTOR

Our district similarity scores are based on demographic, geographic and political characteristics; if two districts have a score of 100, it means they are perfectly identical. These scores inform a system we use — CANTOR, or Congressional Algorithm using Neighboring Typologies to Optimize Regression — to infer what polling would say in unpolled or lightly polled districts, given what it says in similar districts.

Districts most similar to the Wisconsin 1st
Sim. score Polling avg.
MI-874.0R+5.3
OH-1672.0
IL-1670.6
WI-869.2
WI-568.9
MI-368.7
WI-668.7
PA-1068.6R+4.7
MI-268.2
OH-1467.6

#The “fundamentals”

The Classic and Deluxe versions of our model use several non-polling factors to forecast the vote share margin in each district.

FactorImpactExplanation
District partisanship
9.2
WI-1 is 11.5 percentage points more Republican-leaning than the country overall, based on how it has voted in recent presidential and state legislative elections. It voted for Trump in 2016 and Romney in 2012.
Previous incumbent's margin in last election
2.1
A Republican won by 34.7 percentage points in this district in 2016. Previous district results are not strongly predictive in races without incumbents.
Generic ballot
8.0
Democrats lead by an average of 8.3 percentage points in polls of the generic congressional ballot.
Fundraising
6.1
As of July 25, Randy Bryce had raised $6,001,000 in individual contributions (91% of all such contributions to the major-party candidates); Bryan Steil had raised $599,000 (9%).
Candidate experience
0.0
Neither Bryce nor Steil has held elected office before.
Scandals
9.4
Bryce is involved in a scandal that developed since the last election for this seat.
Total
R+6.6

#Expert ratings

The Deluxe version of our model calculates an implied margin for each race based on expert race ratings from The Cook Political Report, Inside Elections and Sabato's Crystal Ball; it then adjusts that margin toward its estimate of the national political environment.

Equivalent Margin
ExpertRatingRaw Adjusted
Cook Political Report
Lean R
R+6.7R+5.3
Inside Elections
Lean R
R+6.7R+4.3
Sabato's Crystal Ball
Leans R
R+6.7R+5.5
AverageR+6.7R+5.0

How this forecast works

Nate Silver explains the methodology behind our 2018 midterms forecast. Read more …

Comments