I wanted to take a moment and say thanks to Tibor, one of my 12 readers, for forwarding me some really interesting research papers on modeling NFL and NBA games with neural nets. There are some really good nuggets of information in those papers, especially the discussion on setting the right momentum and learning rates.
I used that information to fiddle around with my neural net model and I'm posting some recent results from my DRAFT NFL neural net point spread model. As you can see, the model is pretty good at determining if the home or visiting team will win (a negative sign means the home team wins) but the predicted spreads are way off relative to actual spreads.
This leads me back to developing some sort of ranking system to feed the model, which I wrote about in my "Thoughts on Ranking Football Teams" post. The good news is that the research papers that Tibor sent me allude to a type of football match system where the model learns the results of previous games and then applies its statistical analysis to new match ups. Despite this good nugget of information, I feel that I have a long way to go to get something solid before the season starts.
In the interest of science, and because I love my 12 readers, I'm uploading my EasyNN Plus data file for this particular model. However, you'll have to have the full version of EasyNN Plus to use this file because the model uses 980+ example rows and the test version only allows you 100 rows. If you follow the link above and buy EasyNN Plus from there, I will get a small commission from Steve.
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