The problem with developing a point spread betting system for football teams is that you can't initially use a neural net. This is because backfitting raw stat data could lead to poor forecasting, or handicapping. What you have to do is come up with a method where you rank teams first and then feed them into a neural net system to forecast the estimated handicap or point spread.
Why? Teams change over the season; they may lose a key player to injury or evolve their strategies per game. Although neural nets are fantastic, they can't cope with these paradigm shifts easily. However, if they are fed a ranking system that gets updated weekly based on data from played games, those rankings can be fed into the model and compared to opposing teams in the next game.
Now the trick is to develop this ranking system without backfitting data. How do you do this? It's not easy but the route to use in my mind is to use neural net clustering to first identify if any pieces of data seem to drive the point spread. Once you know that, then its a matter of devising a mathematical model to help you rank the teams.
FYI, I am using EasyNN Plus for this project. I may or may not post my data files.
From around the Social Web!
Want to leave a comment?
If you want to give me some feedback on this post, please contact me
via email or on Twitter