An Analytical Look at the 29 NCAA Division One Starting Pitchers Who Cracked the Top 100 2014 Draft Prospects List of Baseball America or

To prepare for my work as a scouting director in the Minor League Ball mock draft and collect an additional level of info on these arms beyond what can be gleaned from accessible scouting reports, I organized certain details of all 2014 plate appearances against these 29 NCAA pitchers into spreadsheets and evaluated them just as I occasionally do with minor league pitchers.

My performance evaluation system classifies each fair, non-bunt batted ball against a pitcher into 1 of 10 general outcome categories (popup, pull-third groundball, center-third groundball, opposite-third groundball, pull-third line drive, ..., opposite-third outfield flyball) and charges the pitcher with the number of runs that the event is worth, on average (in minor league baseball, in this case; the run values per event range from a low of -0.27 runs for a popup to a high of +0.38 runs for a pull-third line drive). Ordinarily I would grade the batted ball performance of each pitcher versus all qualifying conference SPs and then rank them based on run avoidance per batted ball to see how well that prospect fared on contact. But for the sake of time (capturing and compiling the data of a ridiculous number of otherwise irrelevant plate appearances), I simply compared the batted ball performance of these 29 top draft prospects and ranked those 29 on that. It is worth mentioning that, unlike with MiLB game data, the batted ball type is not provided for NCAA base hits that reach the outfield. Because of that I assume that the singles hit to a particular part of the outfield have the same GB/LD/OFFB distribution that is typical for hits of that type to the same location in minor league baseball, and so on for the doubles, triples, and homers hit to each outfield location. Those assumptions impact only about a third of the pitcher's batted ball performance, as the outs (which are referenced by the NCAA as being of a specific batted ball type and direction) carry the bulk of the weight in their batted ball assessment. So, as an example, if a pitcher was typically better than average at avoiding line drives on their hits allowed to the 3 zones of the outfield, they probably fared a bit better, though not drastically so, than the value in the tables that follow would suggest (ballpark numbers, literally).

Since BB% and K% norms vary between conferences, I rated each prospect's BB+HBP% and K% versus their fellow conference SP's rates (that info was relatively easy to collect from the stats portal). So keep in mind in the tables that follow that a pitcher's control performance (BB+HBP%) and their strikeout performance (K%) are being rated among conference peers whereas their batted ball performance is being rated among the top draft prospect arms.

The ratings that follow are expressed on a 20-to-80 scale with 50 being average and values above 50 being better than average. Percentiles indicating the percentage of peers that they stand to be bettering on each rating are included in parentheses. I've added an Overall Performance Rating that shows how their overall performance would grade out among collegiate SPs based on the individual skill-specific control, strikeout, and batted ball ratings.



And below are the results ranked from best overall performance to worst, with the very good values emphasized in green (plus 1 standard deviation or better) and the very bad emphasized in red (minus 1 standard deviation or worse) .


The most startling observation may be how little Aaron Brown statistically resembles an outfielder who pitches once a week (his strikeout rating may be inflated a touch here based on the absence of a premiere strikeout artist among his conference SP peers this season). At the opposite end of the spectrum, pro success seems an unlikely outcome for Chris Ellis if those 2014 numbers are reflective of his true skillset.


Fun with the Numbers: MLB SP Comparables

Now that I have these numbers on each I can compare their control, strikeout, and batted ball performance ratings to the same numbers of two hundred one 2013 MLB SP to see who they performed most similarly to, again relative to conference/league standards (analogy-wise, think NCAA SP : NCAA Conference : : American League SP : American League).

Here are the most similar matches, with the relative strength of the match expressed as a percentage in parentheses.


Of the 5829 matches evaluated here (29 NCAA SP x 201 MLB SP), the one between 2014 Sean Newcomb and 2013 Jose Fernandez was the strongest at 99.1%. Curiously, Fernandez also happened to be the top match for Brown (96%) and Zech Lemond (97%). Ryan Dempster cracks the comp list thrice also, most prominently in the case of Tyler Beede (albeit a relatively weak best match at 90%). Francisco Liriano seems a logical match for Carlos Rodon in that both are heavily-slider-biased southpaws. Austin Gomber excelled at all 3 skill ratings during the spring and early summer, and that puts him performing in his conference à la Felix Hernandez in the 2013 American League. Poor lad Brett Graves gets linked up with the 2013 version of Joe Blanton. We would not expect a flattering comp for Ellis based on the earlier numbers, and his turns out to be the 2013 version of Ryan Vogelsong.

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