Graphing Russia’s Election Fraud

Following Russia’s parliamentary elections on December 4, a link was posted to Reddit reporting an impossibly high turnout (99.51%) and near unanimous support (99.48%) for Putin’s ruling party, United Russia, in the last location one would expect it: the republic of Chechnya. Even if relations with the secessionist region have improved since the Second Chechen War, both the turnout and United Russia’s vote share are a complete joke. This absurdity prompted a more thorough examination of all regions, many of which were also plagued by irregularities. In this post, I will give some detailed visualizations of both region- and precinct- level election data, and point out some highly likely instances of fraud.


First blood: Visualizing Correlations

To start things off, here is a post I wrote a week ago about a task I had working at CPPIB: Visualizing Correlations.

…correlations in finance are difficult to estimate. If you take all the historical data you can get your hands on, you might be missing out on some recent correlation drifts. If you take too little, you are subjected to estimation error. The problem went as follows: 5 assets, 5 different ways of calculating correlation (varying time horizons and weight structures), and 1 “target” correlation matrix; the objective is to quickly spot when live correlations deviate too much from the target.

Read the full post at