Stephanie McMahon Q&A:

Pitching by the Numbers: On the fly

The supposedly sabermetrically savvy are outsmarting themselves when it comes to fly ball pitchers.

Let's start with the king of all the new-age pitching stats – batting average on balls in play (BABIP, but if you pronounce that as one word, you are a dork). I like the stat, generally. Our pitching projections can be much sharper as a result of it. But it's not only less useful in projecting extreme fly ball pitchers, it's actually harmful.

The reason is that fly ball pitchers should be expected to have a much lower BABIP than league average for the simple reason that fly balls are significantly less likely to become hits than grounders. How less likely can be gleaned from this chart of the most extreme fly ball pitchers in baseball last year (minimum 20 starts):

Player Team GB/FB AB H HR K SF BABIP
Guillermo Moscoso Oak 0.49 469 98 14 71 3 0.219
Jered Weaver LAA 0.66 857 182 20 198 5 0.254
Josh Collmenter Ari 0.70 530 129 16 89 2 0.266
Ted Lilly LAD 0.71 723 172 28 158 5 0.268
Colby Lewis Tex 0.71 767 187 35 169 5 0.270
Brandon Beachy Atl 0.75 529 125 16 169 5 0.317
Jeremy Hellickson TB 0.77 695 146 21 117 2 0.224
Scott Baker Min 0.77 500 125 15 120 2 0.301
Bruce Chen KC 0.79 589 152 18 97 5 0.283
Javier Vazquez Fla 0.82 732 178 21 162 6 0.286
J.A. Happ Hou 0.82 592 157 21 134 8 0.311
Brett Cecil Tor 0.83 476 122 22 87 5 0.272
Michael Pineda Sea 0.84 629 133 18 173 3 0.263
Tim Wakefield Bos 0.87 545 148 22 83 7 0.286
Wade Davis TB 0.88 712 190 23 105 7 0.286
Alexi Ogando Tex 0.91 628 147 16 124 3 0.268
Jason Vargas Sea 0.91 787 205 22 131 4 0.289
Shaun Marcum Mil 0.92 753 175 22 158 6 0.267
Freddy Garcia NYY 0.92 564 151 16 95 7 0.298
Tommy Hanson Atl 0.93 484 106 17 142 3 0.274
Brandon Morrow Tor 0.94 683 162 21 203 9 0.307
Randy Wolf Mil 0.95 805 214 23 134 6 0.295
Bronson Arroyo Cin 0.96 793 227 46 108 5 0.283
Justin Verlander Det 0.99 904 174 24 250 3 0.238
Josh Tomlin Cle 0.99 634 157 24 89 3 0.255
Ian Kennedy Ari 0.99 818 186 19 198 9 0.278
Josh Beckett Bos 1.00 693 146 21 175 5 0.252
Aaron Harang SD 1.00 651 175 20 124 2 0.306
James McDonald Pit 1.00 657 176 24 142 5 0.310

The BABIP expectation for this group should be .276 (their average) and even lower (.270) for the those lower than the average GB/FB ratio of 0.87 (it's .280 for those with a higher rate, but still 1.0 or better).

But yet all these pitchers are generally recalculated to have the generic, league-average BABIP and resulting expected ERA. (I do realize that fly balls that are hits are about 10 times more likely to result in extra bases, so let's table xFIP for now.)

I promise you that I am not representing Jeremy Hellickson. I had no shares, at least not until doing this research (after which I acquired him for Rickie Weeks in a dynasty league). But I understand your suspicions after last week's column on missed swing rates. Commenters noted how that piece didn't address his lucky BABIP. (Why would it?) Well, here you go. Hellickson wasn't so lucky after all – especially when you factor in that 16.2% of his fly balls were infield pop-ups (second highest rate in the league, behind only Ted Lilly). That's about 20 more automatic outs than we could expect – about as good as Ks (versus the 16 or so outs we should have expected with regular old fly balls).

Another key note about extreme fly ball pitchers: Their expected rate of homers allowed as a percentage of fly balls allowed is typically better than the league average rate, too. Perhaps I'll expand on this in a future column. But you need to know it right now if you are drafting because those xFIP ERAs recalculate homers and unfairly inflate them for many of these types, I do believe. Think about it logically – the fly ball pitcher is exerting control over the at bat when the hitter hits a fly ball. He's basically won. When a ground ball pitcher allows a fly ball, something by definition has gone wrong and the hitter is presumably significantly more likely to make solid contact.

So discount the reported "luck" factor for the pitchers above. Many of your owners and most stat heads will be undervaluing them. Don't be like them. Also, circle the guys who had a high BABIP despite their GB/FB suggests. They were likely far more unlucky than it first seems (though check their respective line drive rates, too). And, yes, by implication ground ball pitchers are unfairly rewarded by the luck adjusters. But more on that later, too.

Michael Salfino (Twitter @MichaelSalfino) is a quantative sports analyst whose writing regularly appears in the Wall Street Journal. His New York sports musings can also be found at SNYWhyGuys.

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