the data diary

The final pre-election forecast gives Kamala Harris a >99% chance of beating Donald Trump in Massachusetts.

Presidential probabilities

Each day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races. Each candidate’s probability of winning is the proportion of simulations that they’ve won.

Forecasted election-day voteshare

The model first constructs a polling average, pooling data across similar states when polls are sparse. It then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.

pollster

date

population

mode

sample size

harris

trump

margin

University of New Hampshire Survey Center

2 November

Likely voters

Probability Panel

699

63.8%

36.2%

D +27.6%

MassINC Polling Group

1 November

Likely voters

Online Panel/Text-to-Web

535

66.4%

33.6%

D +32.7%

Emerson College

26 October

Likely voters

Online Panel/Text-to-Web

974

61.8%

38.2%

D +23.6%

University of Massachusetts Department of Political Science/YouGov

10 October

Adults

Online Panel

602

65.1%

34.9%

D +30.2%

Suffolk University

5 October

Likely voters

Live Phone

467

65.5%

34.5%

D +31.0%

MassINC Polling Group

18 September

Likely voters

Live Phone/Text-to-Web

784

64.3%

35.7%

D +28.6%

University of New Hampshire Survey Center

16 September

Likely voters

Probability Panel

525

66.7%

33.3%

D +33.3%

1–7 of 7 rows

State similarities

The model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another. Similar states are more likely to share polling biases and see similar shifts in polling trendlines.

Sources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com

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