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Scoring and Ranking

by LucidPlexus
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A weighted score anyone can rebuild, test and explain. Two A4 pages on turning several columns into one ranking, worked on the 80 pending applications in Worth Backing?, in DuckDB SQL and pandas. Every result printed is the real one. It covers…
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A weighted score anyone can rebuild, test and explain.

Two A4 pages on turning several columns into one ranking, worked on the 80 pending applications in Worth Backing?, in DuckDB SQL and pandas. Every result printed is the real one.

It covers one row per candidate, text numbers with units, min-max, z-scores and percentile ranks side by side, normalising against a fixed reference and within a group, policies for missing values, ties and tiebreakers, how stable a top 10 is when the weights move (1,000 random weightings), and the points behind each pick.

It also shows a trap that gives a wrong ranking without an error: percent_rank() gives a missing value a percentile: in DuckDB an application with no revenue outranks the largest revenue in the data, and in SQL Server or MySQL the trap runs the other way.

PDF to print, and an HTML version to read on screen. The weights illustrate the method; the sheet says so, and the book's own scorecard is built in its Lesson 15.

#cheat-sheet #data-analysis #pandas #sql #startups #window-functions
Type Cheat Sheet
Pages 2
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