Anyone who is expert in modern statistical methods may want to have a look at this fellow's master's thesis:
I'm pretty versed in certain areas of math, but am relatively weak in stats and this paper on forecasting baseball players is well beyond my pay grade.
He comes from a strong computer science program, so I consider this a little better vetted than the average collection of formulas and tables.
I am intrigued by the visualizations he provides, although not able to fully absorb what they intend to impart. If I can't understand the pretty pictures, what hope is there for the actual content.
My main point in posting this is to say that the cutting-edge in baseball analytics is a lot farther out there than people sometimes realize. It's not just a matter of whipping together some formulas, tweaking the coefficients to attain some new level of precision. Moreover, the guys who can do this are not inexpensive to hire - this guy's LinkedIn Page suggests to me that a baseball team can't hire him for an intern's wages, which has been my impression of past hiring practices by the Twins in the area.
It seems to me that a minimum standard for the Twins' analytics staff is that they have someone who can understand this paper - preferably several who can at least debate knowledgeably on the methods that this author uses. If they can't, they will continue to play catch-up with the teams that can do this. Maybe this paper is where the cutting edge is, maybe it isn't - but I want my Twins to know. Small differences in player evaluation will determine who makes the winning offers for the right free agents and trade acquisitions, and who pounce first during the amateurs draft.
If Twins personnel browse through this site, I urge them to pay attention to this paper. The subject matter concerns only major-leaguers, but the methods look like they could be applied to young'uns too, and that might be a bigger payoff.