Category Archives: Predictions

The 2016 Electoral Map

The Aggregated 2016 Electoral Map

 

One week to go!

Hillary Clinton has a strong electoral lead — 347 to 191 — and she is close in a few states where Donald Trump leads, such as Georgia and Missouri.  Consistent with polls and the election narrative, Clinton is en route to handing Trump a resounding electoral loss.

Here’s the predicted 2016 electoral map:


Click the map to create your own at 270toWin.com

The below table shows current projections for battleground and other close states in the 2016 electoral map.

State Clinton Trump Johnson Stein Clinton Chance of Winning Trump Chance of Winning
Arizona 39.04% 46.88% 7.24% 7.29% 25.8% 74.2%
Colorado 46.62% 40.04% 9.48% 3.56% 92.1% 7.9%
Florida 49.11% 46.06% 4.63% 1.48% 70.0% 30.0%
Georgia 44.51% 47.05% 8.18% 0.14% 43.7% 56.3%
Indiana 40.48% 47.15% 11.72% 0.78% 10.20% 89.80%
Iowa 45.44% 45.34% 6.78% 2.49% 48.3% 51.7%
Maine 46.94% 38.45% 7.46% 7.24% 90.00% 10.00%
Michigan 49.64% 39.56% 7.36% 3.51% 91.90% 8.10%
Minnesota 47.75% 39.91% 5.82% 6.52% 81.40% 18.60%
Missouri 43.40% 48.07% 6.63% 1.37% 38.6% 61.4%
North Carolina 48.58% 45.45% 5.82% 0.01% 65.4% 34.6%
New Hampshire 48.16% 41.25% 8.49% 2.88% 93.0% 7.0%
Nevada 48.87% 42.96% 7.08% 1.18% 79.2% 20.8%
Ohio 46.88% 44.99% 6.40% 1.82% 69.1% 30.9%
Pennsylvania 50.15% 42.96% 5.16% 1.78% 87.8% 12.2%
Virginia 51.49% 40.69% 7.13% 2.03% 87.5% 12.5%
Wisconsin 49.86% 41.61% 5.86% 2.60% 66.3% 33.7%


Structural Model Method

Independent of candidate characteristics, the Republican Party should fare well in 2016 due to Democratic Party fatigue (only once since 1952 has the same party held the White House for more than 8 years in a row).  However, Donald Trump’s historically low favorability ratings may very well cost him the presidency — including candidate favorability in the structural model hurts Trump to the tune of 60 electoral votes, enough to flip the election from a close Republican victory to a Clinton rout.

The structural model takes data from the 2000, 2004, 2008, and 2012 presidential elections to run a linear regression that determines the relationship between a handful of variables, including state demographics, and number of Democratic public officials, and the Democratic vote share.  It is developed by averaging two approaches: one which ignores candidate favorability and a second which includes in the regression the difference between Hillary Clinton and Donald Trump’s net favorability (the results from those models can be found here and here.  Clearly, Trump’s historically low favorability ratings could potentially cost him the election).

The structural model assesses the underlying electoral landscape separate from campaign actions.  By accounting for factors such as the state partisan voter index (developed by the Cook Political Report), the percent of House seats occupied by a Democrat, and region, we can understand how states are inclined to vote without campaign activities or candidate quirks.  Of course, considering Clinton and Trump have high unfavorable ratings, a pure structural analysis will likely miss the mark (hence averaging it with a structural model that includes favorability).  We have also developed a state battleground model to analyze poll results.

The structural model serves as a baseline.  We can expect these, or similar, results if the campaign ended today.  Between now and November 8, one variable will be adjusted: the difference between Clinton and Trump’s net favorabilities.  Numbers are from Gallup.

Overall, the model explains around 94 percent of the vote share variation during the four elections.

Predicting third party candidate vote shares is difficult because they fared poorly in previous elections, but polls indicate 2016 will be different.  Regression models won’t work.  Instead, using a Libertarian and Green Voter Index, vote shares for Gary Johnson and Jill Stein can be modeled.  The voter indices approximate each state’s inclination to vote for a Libertarian/Green Party candidate by taking state results from the past four elections and dividing them by the LP/GP national result.  This index can then be multiplied by Johnson and Stein’s national polling average to estimate their vote share in any given state.

An example should clarify the method (the following numbers are all made up): Say in Alabama the Libertarian candidate received 0.5% in 2000, 0.25% in 2004, 1% in 2008, and 2% in 2012.  Nationally, that candidate earned 1% in 2000, .50% in 2004, 1.5% in 2008, and 3% in 2012.  The index for each year is 0.5, 0.5, .67, and .67.  Averaging the four, Alabama would have a Libertarian Vote Index value of 0.59.  To estimate Gary Johnson’s 2016 vote share in Alabama, I multiple 0.59 by his national polling average (which I have weighted to account for pollster accuracy and date).

With a Libertarian and Green Party candidate included, Clinton and Trump vote shares need to be adjusted.  To determine how much to subtract from each, I find the difference in polling averages between the weighted Clinton vs. Trump average and the weighted Clinton vs. Trump vs. Johnson vs. Stein polling averages.  From there, I divide the difference between each candidate’s polling average by the total number of percentage points lost between Clinton and Trump.  Their initial vote share estimates are then subtracted from the difference quotient multiplied by expected Johnson and Stein vote shares.

These values will obviously change as Johnson and Stein’s poll numbers fluctuate and the difference between the two polling averages changes.  As such, this model will be updated weekly (assuming new polls are released during the week).

State Poll Method

This model is developed through a simple process: Take the cross-tabs of each state poll and look at response by race, gender, and party identification.  Those results are multiplied by inferred electoral composition of each group (determined by a linear extension of the trends displayed in 2004, 2008, and 2012).  Demographic breakdown (race, gender, and party ID) is averaged and then multiplied by pollster rating (numeric values assigned based on the 538 assessment of polling outlets) and 1 divided by the days until the election from the poll’s end (this means that recent polls are weighted more than older polls).  Results are then multiplied so the numbers are sensible (ie, so that when added together, the numbers are equal to the sum of poll values in the RealClearPolitics average).

Aggregate Model

This model aggregates and weights the structural and state poll maps.  Initially, the two are weighted equally, but as states are polled more and election day nears, the battleground states model is dynamically given a larger say in the aggregate.  The structural model without candidate favorability sheds weight faster than does the structural model that includes candidate favorability.

To determine win probabilities, each state’s expected vote tally is simulated 1,000,000 times, varying candidate strength among different races, gender, party, and expected third party vote.  Doing so allows the model to account for polling error — by varying strength among demographic subgroups, the model analyzes what might happen if Clinton or Trump fares, for instance, unexpectedly well with black voters (an outcome that could flip Georgia to Clinton or allow Trump to win Pennsylvania).

 

*Old Updates*

July 20 Update: Our model continues to favor Hillary Clinton though polls, national and state, are beginning to tighten.  The structural model, which accounts for candidate favorability, gives Clinton a large edge.  Clinton’s unfavorable ratings have risen whereas Trump, with the aid of the Republican National Convention, saw his favorable ratings rise a couple of points.  However, Clinton’s margins in state polls, while shrinking, when combined with the structural model yields a comfortable lead.  Neither candidate is consistently crossing 45% in state polls, a clear sign that voters are dissatisfied with their choices this November.

Is Indiana in play?  The model saw Clinton’s chances in Indiana skyrocket this past week.  Can she actually compete in the traditionally red state?  Most likely not, though Trump’s selection of Indiana Governor Mike Pence as his running mate and joint rally in that state, plus his commitment to spending money defending the state’s 11 electoral votes, indicates that the Trump campaign might not be comfortable in its slim lead there.  Indiana’s past proclivity to vote for a libertarian candidate leads to a high expected result for Gary Johnson in the state.  Our models show that when Johnson is included in national polls, Trump loses slightly more support than does Clinton.  Those two instances intimate a close race in Indiana, one which might not bear fruit in November.  State polls are needed.

How might the RNC affect the race?  It’s too soon for polls to reflect Trump gains from the RNC, though his net favorability, tracked by Gallup, has risen throughout the week.  Polls released over the weekend and the beginning of the next week will likely show a closer race, with the Democratic National Convention next week similarly giving Clinton a bump.  In the weeks after the conventions polls should stabilize and begin to reflect the true nature of the race.

June 29 update: In the last week, Donald Trump’s net favorability numbers rose by around 4 points while Hillary Clinton’s fell by the same amount.  That net differential helped Trump gain a couple of points in the structural model, narrowing Clinton’s lead in Florida, Ohio, and Iowa and allowing Trump to expand his margin in Indiana and Missouri to double digits.

North Carolina remains in Trump’s corner by a couple points.  Georgia and Arizona, two states Clinton supporters think might turn blue this cycle, both favor Trump by 9 points.  Here the state polls differ from the structural model: Our state poll model shows Trump three points in the Copper State, but the structural model gives him a larger edge.

July 6 update: The holiday weekend meant few polls released this past week.  National numbers continue to strongly favor Hillary Clinton and state polls largely back up that data (though more are needed).  This next week will be interesting as polls will capture the effects of Donald Trump’s latest Twitter snafu and the potential fallout from Clinton’s email investigation conclusion.  

North Carolina is currently anyone’s game, as evidenced by Clinton campaigning there with President Barack Obama and Trump holding a rally in the state that same night.  Other efforts to expand the electoral map, for both campaigns, are not yet looking good.  Arizona and Georgia, two states in which some Clinton folks believe she will be competitive, still strongly back Trump; Wisconsin and Pennsylvania, states Trump believes he can flip, are pro-Clinton at this time.  Ohio remains very close and Nevada, while still favoring Clinton, is showing a very tight race in state polls.

Gary Johnson is still forecasted to do well for a third-party candidate.  His national numbers are approaching double digits, though his state polling is rather low (or non-existent — a number of surveys fail to include his name).  Currently, third-party candidates actually hurt Clinton more than Trump, perhaps indicating that a few points of her support comes strictly from people voting against Trump (not for Clinton).   

July 13 update: North Carolina has flipped from slightly favoring Donald Trump to favoring Hillary Clinton by a percentage point.  The state’s 15 electoral votes put Clinton at 347 and Trump at 191.  Aside from Indiana, which has tightened this week as Trump (and Clinton’s) favorability dipped, this map is the same as the 2008 electoral map.  

In recent days, national and state polls have reflected a close race.  Contemporary Quinnipiac University polls show tight, if not tied, races in Ohio, Pennsylvania, and Florida.  Our models, which look at weighted and aggregated polls, still show Clinton with a slim lead in those states and a likely victory.  That said, if the polls continue to show a close race in said states, our model will quickly reflect the new reality.

Heading into Cleveland, Trump must try to further unite his party.  He currently receives between 70 and 90% of the Republican vote in most states whereas Clinton generally receives 80-95% of the Democratic vote.  Trump must boost his numbers among Republicans; he still has room to grow in that area.  Clinton has yet to reach her ceiling among independents, a number of whom likely supported Bernie Sanders in the primary and are still making their way to the Clinton camp.  Sanders’ recent endorsement of Clinton may hasten that process.

Some fallout from Clinton’s email scandal has been noted.  Her favorability numbers declined this week and polls post-James Comey’s decision not to recommend charges have shown her shedding a couple of points to Trump.  However, it doesn’t seem like Trump successfully capitalized on the announcement.  Time will tell whether he can keep salient Clinton’s email scandal.

The Republican Convention will likely boost Trump’s poll numbers a little bit.  That likely won’t be reflected next week, but rather during the week of the Democratic National Convention.  Trump unveiling his vice-president might also pad his numbers among Republicans.  As summer wears on, the excitement continues — check back next week for the updated model!

Election 2016: State Polls Model

Assessing State Polls

*June 29 Update*  Hillary Clinton has narrowly pulled ahead in North Carolina.  Her leads in other states have slightly expanded in the past week, largely following the trend in national polls.  The gender gap is currently favoring Clinton — though Donald Trump tends to do well with men, Clinton does even better with females.  Trump is still struggling to consolidate Republican support.  He’s polling in the high 70s to low 80s with Republicans throughout the states, bleeding some support to Gary Johnson (LP) and Clinton.  To win, he’ll need to earn their support.

The PoliticalEdu state polls model uses polling data to analyze individual states in the 2016 presidential race.  It accompanies the structural model and is combined with it in the aggregated 2016 electoral map model.

This model is developed through a simple process: Take the cross-tabs of each state poll and look at response by race, gender, and party identification.  Those results are multiplied by inferred electoral composition of each group (determined by a linear extension of the trends displayed in 2004, 2008, and 2012).  Demographic breakdown (race, gender, and party ID) is averaged and then multiplied by pollster rating (numeric values assigned based on the 538 assessment of polling outlets) and 1 divided by the days until the election from the poll’s end (this means that recent polls are weighted more than older polls).  Results are then multiplied so the numbers are sensible (ie, so that when added together, the numbers are equal to the sum of poll values in the RealClearPolitics average).

Naturally, this model only applies to states that have been polled.  Many have not, leading to a number of grey “undecided” states.  Those will hopefully be filled in as the election approaches and more states are polled.

The model’s results, shown below, are favorable to Hillary Clinton.  Thus far, she is faring well in state polls; however, it is still early and much can change between now and November.


Click the map to create your own at 270toWin.com

Poll-based states’ predicted results:

State Clinton Trump Johnson Stein
Arkansas 36.90% 47.84% 5.26% 0.00%
Arizona 41.11% 44.95% 3.47% 0.00%
Florida 45.40% 41.20% 3.47% 0.97%
Georgia 41.08% 44.23% 3.72% 0.00%
Iowa 45.81% 41.66% 0.00% 0.00%
Michigan 44.89% 39.89% 14.03% 0.00%
North Carolina 44.42% 44.01% 4.35% 1.07%
New Hampshire 46.57% 40.40% 0.00% 0.00%
Ohio 45.00% 40.03% 3.19% 1.02%
Pennsylvania 47.04% 42.33% 3.44% 1.21%
Texas 33.20% 41.53% 0.00% 0.00%
Virginia 42.28% 39.63% 3.82% 0.00%
Wisconsin 48.26% 40.44% 4.28% 0.60%

(You’ll notice these numbers do not add up to 100% — the polls released have options for “don’t know/other” and “wouldn’t vote,” thus preventing the candidates from adding to 1.  The number of “don’t know” respondents should decrease as election day approaches.)

This post will be updated as more polls are released!

Electoral Map 2016 — Structural Model

Electoral Map 2016: The Structural Model

PoliticalEdu is developing three electoral models for 2016.  The first, the structural model, takes data from the 2000, 2004, 2008, and 2012 presidential elections to run a linear regression that determines the relationship between a handful of variables, including state demographics, and number of Democratic public officials, and the Democratic vote share.  It is developed by averaging two approaches: one which ignores candidate favorability and a second which includes in the regression the difference between Hillary Clinton and Donald Trump’s net favorability (the results from those models can be found here and here.  Clearly, Trump’s historically low favorability ratings could potentially cost him the election).

The structural model assesses the underlying electoral landscape separate from campaign actions.  By accounting for factors such as the state partisan voter index (developed by the Cook Political Report), the percent of House seats occupied by a Democrat, and region, we can understand how states are inclined to vote without campaign activities or candidate quirks.  Of course, considering Clinton and Trump have high unfavorable ratings, a pure structural analysis will likely miss the mark (hence averaging it with a structural model that includes favorability).  We have also developed a state battleground model to analyze poll results.

The structural model serves as a baseline.  We can expect these, or similar, results if the campaign ended today.  Between now and November 8, one variable will be adjusted: the difference between Clinton and Trump’s net favorabilities.  Numbers are from Gallup.

Overall, the model explains around 94 percent of the vote share variation during the four elections.

Predicting third party candidate vote shares is difficult because they fared poorly in previous elections, but polls indicate 2016 will be different.  Regression models won’t work.  Instead, using a Libertarian and Green Voter Index, vote shares for Gary Johnson and Jill Stein can be modeled.  The voter indices approximate each state’s inclination to vote for a Libertarian/Green Party candidate by taking state results from the past four elections and dividing them by the LP/GP national result.  This index can then be multiplied by Johnson and Stein’s national polling average to estimate their vote share in any given state.

An example should clarify the method (the following numbers are all made up): Say in Alabama the Libertarian candidate received 0.5% in 2000, 0.25% in 2004, 1% in 2008, and 2% in 2012.  Nationally, that candidate earned 1% in 2000, .50% in 2004, 1.5% in 2008, and 3% in 2012.  The index for each year is 0.5, 0.5, .67, and .67.  Averaging the four, Alabama would have a Libertarian Vote Index value of 0.59.  To estimate Gary Johnson’s 2016 vote share in Alabama, I multiple 0.59 by his national polling average (which I have weighted to account for pollster accuracy and date).

With a Libertarian and Green Party candidate included, Clinton and Trump vote shares need to be adjusted.  To determine how much to subtract from each, I find the difference in polling averages between the weighted Clinton vs. Trump average and the weighted Clinton vs. Trump vs. Johnson vs. Stein polling averages.  From there, I divide the difference between each candidate’s polling average by the total number of percentage points lost between Clinton and Trump.  Their initial vote share estimates are then subtracted from the difference quotient multiplied by expected Johnson and Stein vote shares.

These values will obviously change as Johnson and Stein’s poll numbers fluctuate and the difference between the two polling averages changes.  As such, this model will be updated weekly (assuming new polls are released during the week).

Including Johnson, this is the electoral map 2016:

 

electoral map 2016

 

The map belies the closeness of many states.  Here is a table of states that could very easily change the election.

Clinton +5-7.5 Clinton +2.5-4.9 Clinton +0-2.4
Colorado Iowa Florida
New Hampshire Ohio
Pennsylvania
Wisconsin
Trump +0-2.4 Trump +2.5-4.9 Trump +5-7.5
North Carolina Indiana
Missouri

This post will be updated!

wisconsin democratic primary predictions

Wisconsin Democratic Primary Predictions

We’re back with our Democratic prediction model, which fared very well during Western Saturday (it correctly predicted the winner in each of the Alaska, Hawaii, and Washington caucuses and its vote share estimates also fell close to the actual results).  While those results likely did not change the trajectory of the race, they have certainly infused Bernie Sanders with momentum: In the past week, Wisconsin polls flipped from having Clinton up 6 points to Sanders being up an average of 5 points.

Our Wisconsin Democratic primary predictions show two different (and simultaneously expected) results.  The table below depicts win probabilities for the two candidates.  It largely aligns and mimics the polls — Sanders has a clear advantage and is indubitably favored, but not overwhelmingly so (a win probability one would expect with a candidate leading the polls by just more than the margin of error).

Hillary Clinton Win Probability


Bernie Sanders Win Probability


Wisconsin 42% 58%

However, the vote share model tells a different story.  The vote share Wisconsin Democratic primary predictions point to a decisive, landslide victory for Sanders.  Our vote share model relies heavily on demographics and those of Wisconsin trend favorably to Sanders — the state is overwhelmingly white (82 percent) with a very small African American and Hispanic population (6 and 5.6 percent, respectively).  These demographics are similar to those of Minnesota, a neighboring state which Sanders handily won (with 62 percent of the vote; Minnesota also favored Sanders because it was a caucus).  Sanders fares very well with white voters and their large presence in the state’s electorate leads to the model advantaging him in the primary.  In other words, if he’s to make up the delegate gap, Wisconsin is very favorable terrain to net a large number of them.

Hillary Clinton Vote Share


Bernie Sanders Vote Share


Wisconsin 47% 53%

Will our predictions bear out?  Based on polls, it seems so, though given Sanders’ recent momentum and financial resources (which could fund a substantial last-minute ad blitz), it would not be surprising to see Sanders win by slightly larger margins.  Considering that Wisconsin is 82 percent white, the predicted margin is actually rather disappointing for Sanders – favorable demographics in a medium sized state offer him an increasingly rare opportunity to pick up a large amount of delegates and begin to meaningfully close his deficit.  We predict the below delegate allocation:

Hillary Clinton Delegate Expectation


Bernie Sanders Delegate Expectation


Wisconsin 40 46

These targets, again, seem reasonable given the polls.  If Sanders earns more than 46 delegates from the primary, it will be a good day for him.  If he passes 50, it will be a very good day for Sanders (though, unless indicative of beating polls and expectations, the single victory here will not alter any race dynamics).

As always, take these numbers with grains of salt as they reflecting underlying electoral conditions, not the campaigns or the candidates or momentum or news, etc.  These estimates may well be wrong (we fully admit that) and in the case they are, we’ll go right back to the drawing board to refine and edit our models.  Any comments about these forecasts or our models are welcomed!

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democratic primary predictions and forecasts

Democratic Model

If you’ve been on our site, you’ve likely seen our Democratic primary predictions.  This post explains our models and how we come up with our Democratic forecasts.

Our first model predicts state winners; it uses a logistic regression and runs simulations for each election 1000 times.

The state winner model uses the following independent variables: whether the contest is a caucus, state percent white, black, and Hispanic, state GDP per capita, and the state’s average age.  The dependent variable is a Clinton victory (a logistic model with a Clinton victory coded “1”).  Here are its predictions for the primary season as well as the each primary/caucus’s actual winner:

State


Hillary Clinton Win Probability


Bernie Sanders Win Probability


Actual Winner


IA 0.44 0.56 Clinton
NH 0.19 0.81 Sanders
NV 0.70 0.30 Clinton
SC 0.99 0.01 Clinton
AL 0.99 0.01 Clinton
AR 0.83 0.17 Clinton
CO 0.48 0.52 Sanders
GA 0.99 0.01 Clinton
MA 0.75 0.25 Clinton
MN 0.10 0.90 Sanders
OK 0.15 0.85 Sanders
TN 0.86 0.14 Clinton
TX 0.97 0.03 Clinton
VT 0.12 0.88 Sanders
VA 0.93 0.07 Clinton
KS 0.25 0.75 Sanders
LA 1.00 0.00 Clinton
NE 0.20 0.80 Sanders
ME 0.14 0.86 Sanders
MI 0.43 0.57 Sanders
MS 0.97 0.03 Clinton
FL 0.96 0.04 Clinton
IL 0.98 0.02 Clinton
MO 0.84 0.16 Clinton
NC 0.99 0.01 Clinton
OH 0.84 0.16 Clinton
AZ 0.85 0.15 Clinton
ID 0.08 0.92 Sanders
UT 0.09 0.91 Sanders
AK 0.13 0.87 Sanders
HI 0.16 0.84 Sanders
WA 0.17 0.83 Sanders

For Democratic primary vote share predictions, I use a dummy variable for caucus, state percent African American, white, and Hispanic, state GDP per capita, percent of the state population between 18 and 25, a dummy variable for whether Clinton is predicted to win (found above), and a dummy variable for the South.  The dependent variable is her actual vote share.  This approach explains around 93 percent of vote share variation.  Its predictions as well as actual outcomes are shown below:

State


Hillary Clinton Vote Share


Actual Clinton Vote Share


IA 0.44 0.5
NH 0.32 0.38
NV 0.58 0.53
SC 0.73 0.74
AL 0.71 0.78
AR 0.59 0.66
CO 0.41 0.4
GA 0.74 0.71
MA 0.53 0.5
MN 0.37 0.38
OK 0.38 0.42
TN 0.67 0.66
TX 0.64 0.65
VT 0.20 0.14
VA 0.60 0.64
KS 0.36 0.32
LA 0.71 0.71
NE 0.36 0.43
ME 0.36 0.36
MI 0.51 0.48
MS 0.78 0.83
FL 0.72 0.64
IL 0.51 0.51
MO 0.51 0.5
NC 0.66 0.55
OH 0.60 0.57
AZ 0.52 0.58
ID 0.26 0.21
UT 0.30 0.2
AK 0.26 0.18
HI 0.21 0.3
WA 0.32 0.27

Each contest yields new data points that are then put into the model.  The two tables shown depict initial predictions, not outputs from after the model has considered new information and learned from it (which, when backtested, presents more accurate results than our first estimates – no surprise there).  Our Democratic primary predictions become more accurate as n (completed primaries/caucuses) increases, leading to a higher degree of confidence with which we can forecast the primaries.

Feel free to comment with questions or suggestions – we’re always looking to improve our model!

washington democratic caucus

Democratic Forecasts: Alaska, Hawaii, and Washington

Our model correctly predicted each of the 3/22 contests and is now 28/29 for the “season.”  Below we show our forecasts for the Democratic caucuses in Alaska, Hawaii, and Washington:

State


Hillary Clinton Win Probability


Bernie Sanders Win Probability


Alaska 13% 87%
Hawaii 16% 84%
Washington 17% 83%

Below is our prediction for caucus vote shares.  The model explains 96 percent of the variation between vote shares, so it is not perfect, but still highly predictive.  These numbers will not be perfect and will likely miss actual shares by 2-5 percentage points.  However, they provide strong vote share estimates and yield important delegate targets for each state.

State


Hillary Clinton Vote Share


Bernie Sanders Vote Share


Alaska 26% 74%
Hawaii 26% 74%
Washington 32% 68%

Which leads us to delegate targets for the day:

State


Clinton Delegate Target


Sanders Delegate Target


Alaska 4 12
Hawaii 6 19
Washington


32


69


Total 42 100

Clinton should hope to win 42 delegates and Sanders 100.  Any numbers above/below these estimates would prove to be a good/bad day for the candidates.  Sanders is down around 300 pledged delegates – netting 58 over Clinton would close that gap and bring him some needed momentum as the map moves to his stronger states.  (Of course, netting 58 delegates does not put him in the lead and does not climb him out of his delegate hole – it simply moves him ever so slightly closer to Clinton.  Sanders needs to perform well continuously and win large states, not just small ones like Idaho and Utah).

Saturday looks to be strong for Sanders.  We’ll see how the day unfolds.

arizona primary voter suppression

3/22 Democratic Primary Predictions

Using PoliticalEdu’s Democrat primary model – which relies heavily on state demographics and is 25/26 so far – we have our predictions for the March 22 contests in Arizona, Idaho, and Utah.  Keep in mind these show the win probability, not forecasted vote shares (and so little information regarding delegates can be gleaned from this model; a vote share model is in the works).

State


Hillary Clinton Win Probability


Bernie Sanders Win Probability


Arizona 85% 15%
Idaho 8% 92%
Utah 9% 91%