> The last results, after remodeling the problem, came to be on par with the odds themselves. Around 37%. This time my AI was as good as consensus at betting ha

> The last results, after remodeling the problem, came to be on par with the odds themselves. Around 37%. This time my AI was as good as consensus at betting harness racing. using the same dataset as betters with a NN is not really better than running a regression on it, so this doesn't really surprise me. nn are able to find patterns with things others than historical series. taking this forward would need to combine historical race sets external sources that could have correlated information available that's not easy to extract by simple regression, like news and articles whenever available or meteorological data or whatever, just feed it everything and let the nn sort itself the useless data out