A 1.8% increase in expected K rate is huge — whether you are a baseball GM or a fantasy player.

]]>1. One potential problem is that essentially you are fitting these observational data and then trying to use them to predict future performances. All these really tell is is that your model fits *these* data quite well, but we don’t really have any sense of how the model plays out going forward. It would be more compelling if you randomly took half your cases to fit the original model and then used the coefficients from that model to predict other half of the cases. We could then see what the R2 would be on this other half to see if it retains its predictive power.

2. It could be the case that once you control for K% BB% becomes insignificant. so you may want to run a model with both included.

3. The magnitude of the BB% coefficient is about 2/3 of the size of the K% variable suggesting that K% has an effect about 50% larger than BB%.

4. Finally, it seems that these effects are relatively small. If I understand your metrics correctly, a 1% increase in K% corresponds to a .18% increase in regular season K% (with Marcel held constant). So a 10% increase in Spring K% corresponds to a 1.8% increase regular season K%.

Despite my suggestions, this is a very interesting first step. Well done!

]]>Spring to season ERA: 0.035

Spring to Marcel ERA: .02