diff --git a/scripts/postprocessing/scoring.py b/scripts/postprocessing/scoring.py
new file mode 100644
index 00000000..362a1c63
--- /dev/null
+++ b/scripts/postprocessing/scoring.py
@@ -0,0 +1,65 @@
+# -*- coding: utf-8 -*-
+from dataclasses import dataclass
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+
+@dataclass
+class Voter:
+    """
+    Represents a single voter.
+    This tabulates the number of good votes, total votes,
+    and points.
+    We only put well-behaved people on the scoreboard and filter out the badly behaved ones
+    """
+
+    uid: Any
+    num_votes: int
+    num_good_votes: int
+    total_points: int
+
+    def voter_quality(self):
+        return self.num_good_votes / self.num_votes
+
+    def is_well_behaved(self, threshhold):
+        return self.voter_quality() > threshhold
+
+
+def score_update(new_vote: int, consensus: npt.ArrayLike, voter_data: Voter) -> Voter:
+    """
+    This function returns the new "quality score" and points for a voter.
+    I.e. a voter casts a vote on a question.
+    The consensus is the array of all votes cast by all voters for that question
+    We then update the voter data using the new information
+
+        Parameters:
+            new_vote (int): the index of the vote cast by the voter
+            consensus (ArrayLike): all votes cast for this question
+            voter_data (Voter): a "Voter" object that represents the person casting the "new_vote"
+
+        Returns:
+            updated_voter (Voter): the new "quality score" and points for the voter
+    """
+    # produces the ranking of votes, e.g. for [100,300,200] it returns [0, 2, 1],
+    # since 100 is the lowest, 300 the highest and 200 the middle value
+    consensus_ranking = np.argsort(np.argsort(consensus))
+    new_points = consensus_ranking[new_vote] + voter_data.total_points
+    # we need to correct for 0 indexing, if you are closer to "right" than "wrong" of the conensus,
+    # it's a good vote
+    new_good_votes = int(consensus_ranking[new_vote] > (len(consensus) - 1) / 2) + voter_data.num_good_votes
+    new_num_votes = voter_data.num_votes + 1
+    return Voter(voter_data.uid, new_num_votes, new_good_votes, new_points)
+
+
+if __name__ == "__main__":
+    demo_voter = Voter("abc", 10, 2, 6)
+    new_vote = 3
+    consensus = np.array([200, 300, 100, 500])
+    print(demo_voter)
+    print("best   vote", score_update(new_vote, consensus, demo_voter))
+    new_vote = 2
+    print("worst  vote", score_update(new_vote, consensus, demo_voter))
+    new_vote = 1
+    print("medium vote", score_update(new_vote, consensus, demo_voter))