== Neural Market Trends ==

Predicting Winners in Football


nfl-logoI've made some serious headway last week in analyzing NFL football data to model game point spreads.  I was able to determine with great accuracy (84%), using backfitting, what team would win a matchup. I did this by building a backpropogation model from 2007-2008 data with about 20 matchups as the prediction set.  The only bad thing I discovered was that the point spread prediction was way off.

As I wrote before, backfitting is NOT the way to go for building this type of  model and the fact that the magnitude of the actual point spread was off bore that out.  You might be asking yourself right about now, "if this is not the way to go, why did Tom do this?" 

I did this because I'm still in my data discovery phase looking for relationships and tinkering around.  Now that I understand various data relationships and can detemine the winners relatively well, the next step, and perhaps the hardest now, is determining the ranking system.  I suspect that the ranking system will help get the game spreads closer to what they should be for future games.  If all goes well I should be making spread calls on this blog for the next season as a way to determine if my model is worth a sh*t.


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