Personal blog on early retirement, tradelines, and analytics projects. I write about what I learn as I am learning it.
analytics
In trying to keep the title short I left out that I am talking about computer sports rankings made with artificial neural networks (competitive neural networks to be more specific), as initially explained in an earlier post. That original post was based on a paper that didn’t effectively assess the home/visitor advantage (if there is one) so here I am proposing a way to address the issue.
The US Constitution establishes an indirect system to elect the president and vice president. People elect a college (a group) of electors that then elects the president. This used to be a non-issue and non-event for ages up until in November of 2000 the election was so close in Florida that it required waiting for a few weeks and a Supreme Court decision to decide the winner. Another eventful election happened in 2016 (sans the Supreme Court intervention).
Continue reading “Electoral College Optimization”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.
Continue reading “Hall’s Marriage Theorem Meets the DraftKings Entry Form”The usual image of an artificial neural network:
What follows is a paper that I wrote in the Spring of 2001 for an “Introduction to Neural Networks” class that I took as part of my Master’s degree. It is mostly a review of someone else’s paper on the subject, except that I wrote the network in Excel and ran it on that year’s football season games. Fun, fun.
Continue reading “A neural network approach to college football rankings”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”