Personal blog on early retirement, selling tradelines, and analytics projects. I write about what I learn as I am learning it.
analytics
Perhaps the defining factor in a competitive neural network is its transfer function. In the case of networks used to rank sports teams, at the very least, the transfer function will be driven by the results between the pairs of teams. However, the use of the result in the function can take many forms. I will explore them.
In my previous and first post about the electoral college, I tried to show how it is possible to get the necessary 270 votes to win the election in the college and do it winning the popular election in a minority of the states. Now we model the electoral college as a knapsack problem.
My approach was a bit simplistic (heuristic) and now I will show how to model the electoral college as an example of a very well-known problem in mathematical programming: the knapsack problem. In the knapsack problem, you want to fill the knapsack with a few items. Each item has a weight and some value to you, and you want to pack the knapsack with as much value as possible within the weight the knapsack (and your own back!) can hold.
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”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”