Category: Analytics

This is the last post in my accidental Fourier-Motzkin trilogy (part one: the method; part two: the baseball lineups), and it’s about the bill coming due. Because when you use elimination to throw variables out of a model, the mathematics is very clear about what you keep — and very quiet about what you lose. I found out exactly what I’d lost the first time DraftKings rejected a lineup my spreadsheet swore was legal. The story runs straight through a beautiful piece of combinatorics called Hall’s marriage theorem, so we’ll pick that up along the way.

In my last post about Fourier-Motzkin elimination I promised a real example involving baseball, and I keep my promises. This is the story of how a dusty 19th-century math trick ended up picking my daily fantasy lineups — and how I stumbled into a genuine Fourier-Motzkin elimination example without fully realizing that’s what I was doing until much later.

Continue reading “A Fourier-Motzkin Elimination Example: DraftKings Lineups”

Fourier-Motzkin elimination is one of those methods that gets a page and a half in the linear programming textbooks, right before the simplex method shows up and steals the whole show. I was first introduced to it while working as an OR analyst at Cargill, and my honest first reaction was: nice, but why would I ever use this? It took me years (and one very stubborn model) to change my mind. This post is the explanation I wish someone had given me back then: what the method actually does, how the mechanics work, and a small worked example showing a model before and after the elimination.

Continue reading “Fourier-Motzkin Elimination: An Old Trick I Still Use”