This is a complicated aspect. Back in the 70s or 80s, investment firms started hiring Physics majors with no background in finance - the so-called Rocket Scientists. This paid off handsomely, as those noobs brought in a fresh perspective that the old guard didn't have the mathematical chops to carry out. Actual ignorance of the field they're being hired into isn't an actual positive. The idea was that it's easier to teach Finance to someone with a background in applied Calculus, than to teach Calc to the Finance guys. Nowadays pretty much any MBA program involves exposure to the "Quants", and those who go on and specialize in quantitative methods need to be pretty good at higher math. Same here. To a degree, it's probably easier to teach the rudiments of baseball talent evaluation to someone with highly evolved stats skills, than to teach what's in the linked research paper to baseball experts. Eventually, there will be a meeting somewhere in the middle, where future front offices take this particular skillset for granted as necessary to their jobs. My view is that there doesn't have to be friction. The master's thesis in that paper looks like it used data available from SABR, stats concerning measured performance. But, you could just as easily use data from an internal database, involving quality judgements like swing quality, or even just "The Good Face" - even if the numbers have no precision, someone with a 10 on the Good Face scale and another with a 1 conveys some meaning. The statistical tools are good at locating correlations, and it might very well be possible to capture some value out of what scouts have always instinctively felt. If that initiative panned out, scouts would continue to use their expertise to judge prospects by their Face, or (more likely) by their swing. The Parker Hagemans of the world actually should fit in very well with the framework a good analytics team would construct. All the statistical methods in the world won't buy you anything if you lack quality input.