Monopoly Politics Algorithm
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This page describes the variables used in the model, the
algorithm used for four different types of races, and how we measure the
accuracy of a projected party and winning percentage. This
information is common to the methods used for 1996-2000 as well as for
First, we adjusted incumbents' prior winning percentages
according to the change in partisanship of the 2000 district compared to the
2002 district. For example, if a candidate received 58% in a
district that became 5% stronger for the incumbent party, we would treat
the prior race as a 63%. If the district became 5% less favorable
to the incumbent, we would treat it as a 53%.
Second, in districts that lacked 2002 partisanship data
based on the Gore's percentage in the new district, we cautiously
estimated a partisanship by subtracting 3% from the partisanship of
corresponding 2000 district.
Third, because our knowledge of the 2002 districts is
limited, we increased the parameter, "Decrease for long term overachievers," from 42% for 1996-2000 to 60%. This means we
make slightly more cautious projections for 2002.
In all other respects, the model treats races from
1996-2000 identically to 2002 races.
This section describes each variable used in the model,
along with the default value and the category of seats it applies to.
The variables typically refer to an incumbent’s weakest
performance out of the last two elections.
Variables related to projections
Buffer for open seats (11%).
This variable is amount by which partisanship is reduced to
make an open seat projection.
For example, a 70% district is reduced to a 59% projection.
for underachievers (33%). If
a freshman runs behind her partisanship in her first election, her
projection is raised 33% of the way up from her election result to
the partisanship. For
example, a freshman elected with 55% in 61% district is projected to
win with 57% in her second election.
for overachievers (67%). If
a freshman or a two-term member runs ahead of their district
partisanship, we reduce their projection 67% of the way back to the
example, a candidate who wins with 61% in a 55% district is
projected to win with 57%.
for underachievers (1%). If
a two-term or three-or-more term incumbent’s worst performance is
below the district partisanship, the projection is 1% less than
their worst performance.
race uncontested (35%). If
a two-termer’s second election was uncontested, the incumbent is
treated like a freshman. This
variable represents the minimum winning margin by which a race is
considered uncontested race.
for long-term overachievers (60%).
If a three-term incumbent’s worst performance is better
than the partisanship of the district, the projection is reduced by
60% of the difference of partisanship and worst performance.
to better 2nd election (33%).
If a two-termer’s second election was better than their
first, then use this variable to interpolate from the stronger
performance to the weaker performance to establish the “weakest
performance” number. The
purpose of this is to give more weight to the 2nd
related to national two-party vote
2-party share (50%). Our
model makes projections based on an assumption that the national
two-party vote is evenly split between Democrats and Republicans.
To examine the projections in the event the two-party vote is
not evenly split, you can enter a different number in this cell.
related to categories
a projected result is between 47% and 53%, it is considered
(5%). If a projected
win is between 53% and 55%, it is considered a “tight” projection.
(10%). If a projected
results is between 55% and 60%, it is considered “comfortable.”
(>10%). This is not
a separate variable, as it encompasses all projections greater than
Algorithms for 4 different types of races
Use partisanship to project outcome, but then subtract
11% off projected margin.
Ex: a 62%
partisanship is a 24% margin, minus 11% = 13% margin or 56.5%
There are 3 separate cases for incumbents.
For 2002 projections, we begin by adjusting past performance to
reflect change in district partisanship from 2000 to 2002.
53.5% win in a district that changed from 52.1% to 54.3% picks up 2.2%
and is treated as a 55.7% result.
If past performance was below partisanship,
the projection equals the past performance + 1/3 the difference with
partisanship. (Projection stronger than past performance.)
performance 42.5%, partisanship 48.5%, projection = 44.5%
If past performance was better than
partisanship, the projection equals past performance – 2/3 of the
difference with partisanship. (Projection
weaker than past performance.)
performance 48.5%, partisanship 42.5%, projection = 44.5%
election was uncontested, treat first election as like a freshman.
If 2nd election
contested, then consider weakest of past 2 performances.
If the 2nd
election was stronger than the first, adjust the past performance by
interpolating from the stronger performance toward the weaker
performance based on the variable.
If past performance
< partisanship, projection = performance - 2%
If past performance
> partisanship, projection = past performance - 2/3 difference with
Long termers (3 or more)
Take weakest of past
If < p-ship,
projection = performance - 2%.
If > p-ship,
projection = interpolation from past performance back toward p-ship
based on variable.
If an incumbent was uncontested
by a major party in each of the past two elections, then
projection = district
partisanship + 3%
of party and margin
The projection is a
percent (0-100) referring to likely Dem vote.
Based on that, we
project a party and range (landslide, comfortable, competitive or no
projection of party: