We describe the creation and quality assurance of a dataset containing nearly all available precinct-level election results from the 2016, 2018, and 2020 American elections. Precincts are the smallest level of election administration, and election results at this granularity are needed to address many important questions. However, election results are individually reported by each state with little standardization or data quality assurance. We have collected, cleaned, and standardized precinct-level election results from every available race above the very local level in almost every state across the last three national election years. Our data include nearly every candidate for president, US Congress, governor, or state legislator, and hundreds of thousands of precinct-level results for judicial races, other statewide races, and even local races and ballot initiatives. In this article we describe the process of finding this information and standardizing it. Then we aggregate the precinct-level results up to geographies that have official totals, and show that our totals never differ from the official nationwide data by more than 0.457%.

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I derive the probability that a vote cast in an Instant Runoff Voting election will change the election winner. I show that there can be two types of pivotal event: direct pivotality, in which a voter causes a candidate to win by ranking them, and indirect pivotality, in which a voter causes one candidate to win by ranking some other candidate. This suggests a reason that voters should be allowed to rank at most four candidates. I identify all pivotal events in terms of the ballots that a voter expects to be cast, and then I compute those probabilities in a common framework for voting games. I provide pseudocode, and work through an example of calculating pivotal probabilities. I then compare the probability of casting a pivotal vote in Instant Runoff Voting to single-vote plurality, and show that the incentives to vote strategically are similar in these two systems.

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