Now, in addition to the College Football Rankings that I publish here and I introduced and explained in an earlier post, I moved from a site I dropped (Sportheory.com) into this site a ranking of College Basketball teams calculated with a neural network.
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 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 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”
