Mid-Tier Hitters I Like
I’ve always struggled to understand the “ceiling” projections that accompany prospects. It’s not that I don’t get the concept — 95th- or 99th-percentile outcomes aren’t the same for everyone, and that’s interesting. And it’s not that I don’t think some prospects have higher ceilings than others. It’s merely that I have a hard time discerning which types of prospects have the greatest chance of being superstars. For every Bryce Harper where the talent smacks you in the face, there’s a Mookie Betts. Fine, it’s not a one-to-one ratio, but plenty of prospects exceed their supposed ceilings, and I’m no good at figuring out which ones are the most likely to do so.
Because of this deficiency, I’ve always looked at prospects slightly differently. I tend to look for players who have a good chance at becoming average regulars, assuming that the best way to find prospects who’ll go completely ham and turn into MVP candidates is to find as many minor leaguers who have the skills to make the big leagues as possible.
This process has led to some successes — like Eric Longenhagen, I was optimistic about Randy Arozarena when the Rays acquired him in 2019. It has also led to some failures — like former FanGraphs editor Carson Cistulli, I’m still waiting for Max Schrock to ascend to batting-title contention. This year, I’ve been working on putting a little more intellectual rigor behind my process, and Eric and Meg Rowley were kind enough to let me share the Prospects Week stage to yammer on about it.
As befits any Ben Clemens project, I used a combination of statistical modeling, careful observation, and semi-rigorous gibberish to synthesize a group of hitters I’m interested in. If you want to ignore the methodology and just get to the sweet, sweet list, I totally understand, but for everyone else, let’s talk about that “statistical modeling” part.
The natural impulse when looking at minor league statistics is to apply some kind of translation. Strike out 15% of the time in Double A? You’ll strike out 23% of the time in the majors. Nine percent walks and a .330 BABIP? That’ll be 6% and .295, thank you very much. Then you add a little age-related improvement dust, and bam, major league prediction. One problem with that method: it’s terrible and doesn’t work. Read the rest of this entry »