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Trying to project Vogelsong, Part 2 in Giants Primer, Part III

April 4, 2012

Yesterday I posted this overwrought thing about Ryan Vogelsong, which still somehow necessitated a follow up, a.k.a. this. Basically, I compared Vogey to similar pitchers in some key areas and found that his results exceeded his statistics. My primary evidence was a table, which I will reproduce below:

The table is sorted from highest to lowest swinging strike percentage (SwStr%), because with only a season’s worth of data it is the most reliable statistic.* I also included strikeout rate (K%) and walk rate (BB%), because those two are pretty reliable as far as pitching stats go. Also, K% correlates well with SwStr% (as you can imagine), so any discrepancy between those two could be enlightening. Finally, I included Fielding Independent Pitching (FIP), a metric that aims to disentangle pitching performance from the defense behind the pitcher and is presented in the same format as ERA; and ERA-, a stat normalized so that a pitcher with a league-average ERA scores 100 and pitchers 10 percent better or worse than average would score 90 and 110, respectively.

*A brief explanation why: SwStr% measures one possible outcome of a pitch. Most other statistics, like K%, measure one possible outcome of a plate appearance (PA). Since pitches occur more frequently than PAs (indeed, a PA is nothing but a series of pitches), there will always be more data on pitches than PAs. Once the set of data is large enough, you can operate at the level of the PA and not lose any accuracy. But with only one season of data to work with, particularly for pitchers, statistics concerning PAs are just too dadgum unreliable to use in projecting future performance.

The table demonstrated that Vogelsong’s results in terms of ERA- were elite (28% better than average), yet his underlying statistics ranged from average to poor. His SwStr% was especially troubling, not only because it was so low, but because it suggests that his K% should be lower than it was, and it was only average. One way that this incongruity can be sustained is if Vogelsong has excellent command of his best pitches, which would result in a greater share of called (as opposed to swinging) strikeouts and make up for a lack of stuff. In order to find out if this is the case, I turn to heat maps.

As detailed here by people much smarter than I, heat maps represent where a pitcher threw his pitches. They are created using PITCHf/x technology and can be sorted by type of pitch thrown and the handedness of the batter. Vogelsong’s heat maps for his curveball, which he used as his out pitch in 2011, look exactly like this:

The heat maps portray pitches from the catcher’s perspective. The white box is your run-of-the-mill strike zone. The frequency with which Vogey’s curve was thrown to a certain location is represented by the color gradient. Dark blue areas mark infrequent locations, followed by light blue, green and yellow in ascending order of frequency; gray means the pitch was never thrown there.

As you can see, Vogelsong did a great job of of keeping his curveball down and away from hitters. If he missed, he missed in the dirt. Rarely did he hang a fat deuce over the middle of the plate (baseball is weird). In short, these heat maps confirm that Vogelsong has excellent control of his curveball. Now let’s look at his two-seam fastball, statistically his best pitch.

According to these heat maps, Vogelsong kept his two-seamer away from lefties and rode it in on righties. This is sound pitching sense: a two-seamer will usually sink and move toward same-handed batters. For lefties, the pitch at first appears like a fastball away, but it runs away from them, outside the zone. If they swing, they will only strike the ball weakly; if they take it, there is a chance it might catch the outside corner and be called a strike (especially with a catcher adept at framing pitches). For righties, the pitch at first appears like an inside fastball, and it only runs further inside from there. Most good hitters can turn on and rip a normal inside fastball, so this is a hard pitch to lay off, but if they swing at this pitch they will likely get jammed.

Another thing that is evident from these heat maps is how well Vogelsong executed this game plan. It’s easy enough to articulate, but to actually throw the pitch so consistently in the same location is a challenge many major league pitchers struggle with. I will spare you the visual, but the pitchers near Vogelsong at the bottom of the table above—Wade Davis, Tim Stauffer and Chad Billingsley—did not control their pitches nearly as well. (Click the links and see for yourself.)

In fact, Vogelsong commanded his best pitches better than the pitchers at the top of the table—Cole Hamels, James Shields, Jeremy Hellickson and Ricky Romero—commanded theirs. Of course, those pitchers have better stuff than Vogey, and thus don’t rely on placement as much as he does. (All four share a great changeup, which a good pitch no matter where it’s thrown so long as the pitcher’s arm action resembles that of a fastball. If you look at their heat maps for the changeup, you will see that it’s thrown all over the strike zone, but that doesn’t take away from its effectiveness.)

It is the command of his two-seamer and curveball that makes me more optimistic on Vogelsong than most. Surely, hitters will have a better scouting report on him after a year back in the majors, and his success will depend partly on how he adjusts to the adjustments they make. Also, there is the factor of pitcher-catcher chemistry to consider.

Is this man the key to everything? No, probably not. Let's ask him. "Mr. Whiteside, are you the key to everything?"

Vogelsong started 28 games last year; of those, Eli Whiteside caught 15, Chris Stewart seven, Buster Posey four and Hector Sanchez two. Besides the ineffable chemistry, it is possible that Whiteside brings other advantages, such as a superior pitch-framing technique. In this threepart series on hardballtimes.com, Max Marchi demonstrates how valuable pitch-framing can be and which catchers excel at this rather Zen way to fool an umpire into calling a strike (Jose Molina is the best). Whiteside isn’t mentioned in Marchi’s study because he doesn’t play enough, but he is generally considered a good defensive catcher (last year’s struggles notwithstanding), so it is eminently possible that he can frame pitches well enough to turn a substantial amount of balls into strikes. For a pitcher who works the corners like Vogelsong, that can mean the difference between an easy inning and a rally, an All-Star year and an average one. Posey has a reputation for being a good catcher as well, but I’m inclined to give Whiteside the edge because for many years his careers has depended on him being superb defensively. That edge is slight, and this point can become moot if the Giants decide to keep Whiteside as the backup catcher.

Vogelsong will regress, but not as much as most people predict. He will be an above-average pitcher, which is damn good for the fourth spot in the rotation.

Final prediction: Time for me to sack up and spit out some numbers. I will present my prediction alongside some other noteworthy projection systems, for shits and giggles and shiggles.

Predictor-man, -bot or  -lady – GS, IP, K, W-L, ERA
Bill James – 29 GS, 196 IP, 161 K, 10-12, 4.09 ERA
RotoChamp – n/a, 175 IP, 136 K, 11-8, 3.55 ERA
Marcel – 23 GS, 137 IP, 114 K, 10-7, 3.43 ERA
Professor Trelawney – In his tea, I have seen the Grim! With my Inner Eye! This can only mean death!
Fangraphs Fans (n=25) – 28 GS, 171 IP, 123 K, 10-10, 3.95 ERA
ZiPS – 25 GS, 153 IP, 119 K, 8-10, 4.18 ERA
Marciano – 28 GS, 180 IP, 121 K, 13-9, 3.62 ERA

Thanks a bunch for reading all the way down to here. I know it was tough at times. One could even say that you pushed it to the limit. I certainly pushed the limits of smooth transitions (Hell, who am I kidding? I blew past the limit.) with this shit paragraph.

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2 Comments
  1. Molls Balls' Balls permalink

    These articles are the tops, just as good as anything on FanGraphs. If I knew any better I’d say you’re as well-versed in baseball statistics as Dirtbag is in HU stats.

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