analytics
I was introduced to the shortest superstring problem by a request I got on Fiverr. The request turned out to be a waste of time but I really enjoyed learning about it.
The shortest common superstring problem aims to find a string with a minimal length that contains every string in a given set.
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 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.
