Quote:
Originally Posted by heehaww
Ok so then if I understand you correctly, nothing weird would have to be going on for this to be a real effect. As an extreme example, if I try to predict the winner based only on who has the better starting pitcher, that will be a -EV model because that's not the only factor. But that doesn't mean that factoring the starting pitcher is a blanket -EV thing to do or that it's not a real factor. It just means that I gave that factor the wrong weight relative to other factors.
Theoretical question:
Imagine that for a certain sport one used their domain expertise to isolate 3 stats (A, B, and C) they think will have strong predictive value. They perform a linear regression to get the ideal weighting of each stat, and when backtesting wagers of that 3-feature model it has a positive return on investment. However, when they look back at the regression report they notice that stat C has a p-value greater than 0.05 so they remove it as a feature from the model, but then the wagering backtest has a slightly lower ROI, say around 0.5 to 1% less.
Would you assign more importance to the information provided by the p-value and continue to leave stat C out of the model, or would you assign more importance to the backtest ROI and leave stat C in the model?