In the first part we used our not-yet-fully-revealed but “fully-operational” … [*its a trap! not really*] … **Flowchart Profiler** to zero in on Nelson Cruz’ non-random skill set.

We found:

- Cruz has the all-important power factor: consistently hitting the ball hard in the air.
- Cruz does not consistently hit line drives for hits-in-play.
- Cruz does not show consistent plate skills to draw walks and avoid strikeouts.

So our profile is YNN (yes-no-no) in that hierarchical order.

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When we apply that profile to our database of 537 relatively successful MLB hitters during the period for which we have batted-ball data (2002-14), we get this distribution of offense measured by OPS:

And when we smooth that out into a “normal distribution” it looks as so:

A normal distribution is in the shape of the famous “bell curve” and it indicates that outcomes will gravitate around the mean (the peak of the curve) and that variation from the mean will fall mostly within one or two “standard deviations” from the mean. More specifically, 68% of the outcomes will fall within one standard deviation of the mean and 95% of the outcomes will fall within two standard deviations.

The fact that we can get a normal distribution within each subcategory indicates that we’re doing a decent job of narrowing down the variation to the more “random” factors.

So **what do we learn about Nelson Cruz?**

Among those with Cruz’ skill-set (Power: yes; Line Drives: no; Plate Skills: no), the mean is .754 OPS and the standard deviation is .051. That means the great majority of players with that skill-set will fall between .703 and .805 in OPS.

As we can see, Cruz is at .829, which is one-and-a-half standard deviations above the mean.

To date, then, **Cruz has “out-performed” the typical “one-dimensional power guy” by a fairly considerable margin,** but not so much that he’s “off the curve.”

Will he “regress to the mean”?

To get at that we need to go a bit deeper. Has he been lucky or good or both?

Part 3 here.

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Spec, this OPS bell curve as a way to measure regression is the coolest thing I’ve seen in a bit. So Cruz gets an A in maximizing his skills, but can expect a slugging heavy .750 OPS from an average player with his skill set?

Yes. Mark Trumbo is almost right on the mean. Dayan Viciedo is less than a standard deviation below it.

But guys like Braun, Stanton and Napoli fall into the top of this class as well, so you can have success with this profile.

Interestingly, Justin Upton is at the far right of the bell curve (.834) and B.J. Upton well on the left (.725) when they both fall into this skill-set group.

Its interesting to compare B.J. to Justin Upton. Justin has a slightly bettery eye. B.J. is at a lifetime 2.5 strikeouts over walks, Justin is at 2.3 strikeouts over walks. The two hit about the same number of doubles and triples. Both players have immense power, with B.J. recording 110-113 m.p.h. off the bat, and Justin regularly recording 114 m.p.h. off the bat and hitting 117 m.p.h. as his best hit.

Justin just strikes out a little less and crushes it a little bit more. One is going to get paid zillions, while the other is on an albatross contract. There is a lesson here somewhere, and it goes beyond that chicks dig the long ball. Just being a little bit better at what you do can create an exponential increase in your value.

The other lesson is that B.J. has clearly under performed since Justin joined his team. I think he has wilted under the pressure of being the weak sister Upton (Kate’s a powerhouse) and might raise some eyebrows with a fresh start.

Just noodlin’.