Here is the opensource data extracted and uploaded to github repository where source code and data is stored.

The developer (favstats) has extracted data by county across the US. Election Count by County

The CSV file can be imported into MS Excel. Assuming absentee_max_ballots is the maximum ballots sent out per county. Then look at (absentee_max_ballots - absentee_votes) which should be positive but in certain counties there is a negative result such as Los Angeles, San Diego, and Orange Counties. This is where I believe much of the fraud occurred. You can apply this same simple subtraction of (absentee_max_ballots - absentee_votes) across every county in the US.

**The negative number implies more absentee ballots were counted than were sent out for that county. ** Assuming the numbers are correct and my interpretation of the headers (i.e.: absentee_max_balltos, absentee_votes) is correct.

This only takes <10 minutes to find.

Here is the opensource data extracted and uploaded to github repository where source code and data is stored.

The developer (favstats) has extracted data by county across the US. Election Count by County

The CSV file can be imported into MS Excel. Assuming absentee_max_ballots is the maximum ballots sent out per county. Then look at (absentee_max_ballots - absentee_votes) which should be positive but in certain counties there is a negative result such as Los Angeles, San Diego, and Orange Counties. This is where I believe much of the fraud occurred. You can apply this same simple subtraction of (absentee_max_ballots - absentee_votes) across every county in the US.

This only takes <10 minutes to find.