Fly Ball vs. Ground Ball Pitchers: What We Might Expect in 2018

Conventional wisdom suggests ground ball pitchers are superior to fly ball pitchers. Indeed, budding Cincinnati Reds ace Luis Castillo has fantasy (and regular) baseball fans all kinds of excited about not only his whiff-inducing capabilities, having struck out 27 percent of hitters in 2017, but also his worm-killing tendencies, having generated ground balls on almost 60 percent of the balls in play he allowed. His potentially lethal combination of whiffs and grounders certainly warrants optimism.

However, the San Francisco Giants’ Matt Cain — and, more recently, the Toronto Blue Jays’ Marco Estrada — rewrote the book about how fly balls and contact management intersect. (The Athletic’s Eno Sarris chronicled the phenomenon in his tribute to the former Giant.) Unfortunately, they wrote it in indecipherable gibberish, leaving us to piece together exactly how these two succeeded where others couldn’t and maybe shouldn’t have.

In general, how much better are ground ball pitchers than fly ball pitchers — if at all? Do ground balls deserve the amount of admiration they elicit, or are the prevailing perceptions of ground ball pitchers misguided? Using PITCHf/x data, I binned pitchers by their ground ball percentage (GB%) and investigated their pitch usage and outcome trends to draw conclusions about the relative effectiveness of ground balls to fly balls.

The PITCHf/x data tracks more than 7.3 million pitches thrown during the last 11 seasons (2007-17). I grouped pitchers together by their ground ball rates in bins spanning four percentage points each (i.e., 43 percent to 47 percent), centered on the roughly major league-average rate of 45 percent. The following research relates general trends by ground ball rate; investigates the importance of velocity to inducing ground balls; describes how trends have changed over time, especially during the recent and current “juiced” ball era; converts, so to speak, the results to reflect the technologically fancier Statcast Era; and anticipates how all of this might play out during the 2018 season.

Pitch Mix and Outcomes

Table 1 depicts pitch distribution by ground ball rate bin.

Table 1. Pitch Type Usage
GB% Bin Fourseam Sinker Cutter Curve Slider Change Splitter Screwball Knuckleball
<31% 27% 11% 5% 15% 19% 20% 3% 0% 0%
31-35% 24% 13% 6% 15% 18% 21% 2% 0% 0%
35-39% 23% 14% 6% 16% 18% 20% 3% 0% 0%
39-43% 22% 16% 7% 16% 17% 19% 3% 0% 0%
43-47% 21% 17% 7% 16% 17% 19% 3% 0% 0%
47-51% 22% 18% 7% 15% 17% 18% 3% 0% 0%
51-55% 21% 19% 6% 15% 17% 19% 3% 0% 0%
>55% 21% 21% 5% 12% 18% 19% 3% 0% 0%
Average Usage 22% 16% 6% 15% 17% 19% 3% 0% 0%
SOURCE: PITCHf/x (2007-17)

Fly ball pitchers throw slightly more off-speed pitches — namely, sliders and change-ups — but not so much more often to make it noteworthy. Rather, fly ball pitchers tend to rely significantly more on four-seam fastballs; ground ball pitchers, on sinkers. From 10,000 feet, this isn’t surprising at all: sinkers typically generate the most ground balls, and four-seam fastballs, the fewest. There’s certainly more to it than just pitch selection — command likely plays a large role as well — but for the average pitcher, one stands above the other. Moreover, the margin between the two pitches is pretty dramatic.

Table 2. GB% by Pitch Type
Pitch Type GB%
Splitter* 54.6%
Sinker 53.6%
Curve 50.6%
Change 49.1%
Screwball 47.6%
Cutter 45.2%
Slider 45.0%
Knuckleball 44.5%
Fourseam 35.6%
Average 45.1%
SOURCE: PITCHf/x (2007-17)
*Splitters thrown only 3% of the time, per Table 1.

However, the trade-off in ground balls between four-seamers and sinkers corresponds cultivates an inverse relationship with swinging strike rates (SwStr%), or “whiff rates,” as well. Four-seamers, despite allowing a ton of fly balls, produce almost 50 percent more whiffs than sinkers.

Table 3. SwStr% by Pitch Type
Pitch Type SwStr%
Sinker 5.4%
Fourseam 8.0%
Cutter 10.5%
Knuckleball 10.7%
Curve 11.9%
Screwball 11.9%
Change 15.4%
Slider 16.2%
Splitter 17.6%
Average 10.1%
SOURCE: PITCHf/x (2007-17)

(There’s probably another discussion to be had here about how much pitchers should actually rely on four-seamers to play up the effectiveness of their superior secondary offerings.)

Alas, the dynamic between four-seamers and sinkers is such that fly ball pitchers tend to induce more whiffs — and, thus, more strikeouts — than do ground ball pitchers.

Table 4. The GB-Whiff Dynamic
GB% Bin SwStr% K%
<31% 11.1% 24.5%
31-35% 10.7% 23.3%
35-39% 10.3% 22.2%
39-43% 10.1% 21.9%
43-47% 10.0% 21.8%
47-51% 9.7% 21.0%
51-55% 9.7% 20.7%
>55% 9.8% 20.2%
Average 10.1% 21.7%
SOURCE: PITCHf/x (2007-17)

A couple of ticks added to or subtracted from a pitcher’s strikeout rate doesn’t seem like much. But among pitchers worthy of rostering in standard fantasy baseball leagues, a couple of ticks can mean everything. (Or, well, not everything, but a lot. It can mean a lot.)

Batted Ball Distribution and Outcomes

At this point, in a vacuum, the fly ball pitchers carry the slightest advantage: they induce more whiffs and, thus, fewer balls in play, creating fewer opportunities for hitters to inflict damage upon them. Of course, not all balls in play are created equal. And, at a very basic back-of-the-trading-card level of analysis, fly ball pitchers continue to carry a simple advantage: they allow a slightly lower batting average. That’s because, given they allow much steeper launch angles (to be explored in more detail shortly), they produce significantly more infield pop flies, which are as good as automatic outs.

Table 5. Batting Average Allowed
GB% Bin BAA PU%
<31% 0.2465 12%
31-35% 0.2494 11%
35-39% 0.2560 9%
39-43% 0.2584 8%
43-47% 0.2576 7%
47-51% 0.2593 6%
51-55% 0.2574 5%
>55% 0.2552 4%
Average 0.2566 7%
SOURCE: PITCHf/x (2007-17)
PU% = pop-up (infield pop fly) percentage = IFFB% * FB%, using FanGraphs metrics.

Truthfully, there’s kind of a parabolic flex to this — batting average allowed peaks around the major league-average ground ball rate and erodes as it approaches the extremes. However, it appears to erode more swiftly as pitches adopt an extreme fly ball bent.

Until now, we’ve lauded the merits of the fly ball approach, especially at the extremes. This is where it falls apart, almost exclusively at the hands of one important infallible sabermetric truism: home runs, when calculated as a ratio of fly balls, are predictably unpredictable. There exists too much noise in reliably predicting any given player’s single-season home run-to-fly ball ratio (HR/FB)*, yet the numbers are remarkably steady in aggregate, falling tightly between 14.5 percent and 14.9 percent. (*Note: Here, HR/FB uses outfield fly balls as the denominator as opposed to all fly balls. In other words, it removes infield fly balls and therefore inflates HR/FB rates relative to how they appear at FanGraphs.)

For all intents and purposes, any given fly ball has a 14.8 percent chance of leaving the park, whether you’re an extreme fly ball pitcher or an extreme ground ball pitcher. Accordingly, fly ball pitchers inherently allow more home runs, both as a percentage of balls in play and at-bats. The relationships are nearly linear — which, assuming a constant HR/FB rate, should be the case.

Table 6. The HR/FB Conundrum
GB% Bin HR/OFFB HR/BIP HR/AB
<31% 14.8% 5.3% 4.0%
31-35% 14.7% 4.8% 3.7%
35-39% 14.6% 4.5% 3.5%
39-43% 14.9% 4.1% 3.2%
43-47% 14.6% 3.7% 2.9%
47-51% 14.9% 3.4% 2.7%
51-55% 14.8% 3.1% 2.4%
>55% 14.5% 2.5% 2.0%
Average 14.8% 3.8% 3.0%
SOURCE: PITCHf/x (2007-17)
OFFB = outfield fly balls = FB% * (1 – IFFB%), using FanGraphs metrics.

All of this manifests in much worse batted ball outcomes for fly ball pitchers, erasing the slim gains from whiffs and pop-ups, and then some. Table 7 shows the marginal increases in damage inflicted by additional fly balls as demonstrated by steadily increasing isolated power (ISO) and slugging (SLG) percentages.

Table 7. Death from Fly Balls
GB% Bin BA ISO SLG
<31% 0.2465 0.1862 0.4327
31-35% 0.2494 0.1732 0.4226
35-39% 0.2560 0.1674 0.4234
39-43% 0.2584 0.1603 0.4186
43-47% 0.2576 0.1489 0.4064
47-51% 0.2593 0.1408 0.4000
51-55% 0.2574 0.1319 0.3893
>55% 0.2552 0.1138 0.3690
Average 0.2566 0.1507 0.4073
SOURCE: PITCHf/x (2007-17)

Granted, this says nothing of any particular pitcher. No one falls perfectly along these linear depictions of very generalized pitching profiles. There’s volatility in skill, and there’s volatility in random variance. But if ever you were to choose between similarly valued commodities with only ground ball rates as your only source of distinction, you may want to lean toward the heavier ground ball rate. It will result in a higher batting average on balls in play (BABIP), but the damage inflicted by each of those batted balls will be notably lesser. (And, anecdotally speaking, fewer fly balls could mean steadier performances, more likely succumbing to death by BABIP rather than death by HR/FB.)

The Importance of Velocity

Truth be told, velocity bears little importance upon either a pitcher’s ability to induce ground balls or a pitcher’s ability to limit home runs. The average pitcher whose fastball clocks in at any given normal fastball velocity can expect a ground ball rate between 42 percent and 44 percent and a HR/FB rate between 13.8 percent and 15.3 percent on his fastball specifically (i.e., irrespective of all other pitch offerings). There does exist a slight, albeit steady, upward climb in line drives as fastball velocity increases, as shown in Table 8, but it comes with the territory.

Table 8. Relationship with Velocity
MPH Bin GB% LD% HR/FB
< 87 44.2% 21.0% 14.7%
88 43.0% 21.1% 15.3%
89 42.8% 21.7% 14.9%
90 43.9% 22.0% 15.0%
91 43.6% 22.4% 14.6%
92 42.8% 22.5% 14.5%
93 42.8% 22.7% 14.7%
94 42.5% 22.9% 14.2%
95 42.6% 23.2% 14.4%
96 43.4% 23.5% 13.8%
97 47.1% 23.5% 14.3%
> 98 43.8% 23.8% 13.5%
Average 43.2% 22.5% 14.6%
SOURCE: PITCHf/x (2007-17)
Pitcher velocities are rounded to the nearest mile per hour (MPH).

Sorry, this subsection was anticlimactic.

Change Through the Years (Looking at You, Juiced Ball Era)

Tables 9 through 12 show how the home run landscape has changed over the last decade-plus through the lens of several aforementioned advanced metrics: ISO, HR/FB, HR/BIP, and HR/AB. This longitudinal look affirms many of the axioms previously discussed.

Table 9. GB% Over the Years: ISO
GB% \ Season 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
<31% 0.175 0.186 0.187 0.167 0.177 0.175 0.173 0.180 0.196 0.202 0.222
31-35% 0.172 0.165 0.183 0.172 0.160 0.172 0.154 0.160 0.173 0.189 0.198
35-39% 0.170 0.175 0.165 0.162 0.163 0.161 0.159 0.150 0.179 0.180 0.185
39-43% 0.166 0.158 0.159 0.153 0.152 0.166 0.155 0.145 0.161 0.171 0.179
43-47% 0.137 0.151 0.155 0.146 0.140 0.153 0.142 0.131 0.148 0.160 0.169
47-51% 0.153 0.145 0.143 0.134 0.133 0.140 0.136 0.125 0.137 0.151 0.158
51-55% 0.131 0.123 0.132 0.127 0.135 0.139 0.124 0.123 0.134 0.147 0.133
>55% 0.110 0.111 0.124 0.111 0.108 0.117 0.106 0.100 0.112 0.120 0.134
SOURCE: PITCHf/x (2007-17)

 

Table 10. GB% Over the Years: HR/FB
GB% \ Season 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
<31% 11.1% 12.7% 13.3% 11.6% 12.8% 13.3% 14.8% 16.1% 17.6% 20.1% 22.1%
31-35% 12.4% 12.8% 13.6% 12.7% 11.6% 13.5% 13.7% 14.6% 17.2% 20.6% 20.1%
35-39% 12.9% 13.1% 12.8% 12.3% 12.8% 13.6% 14.8% 14.1% 20.3% 20.0% 20.0%
39-43% 13.4% 12.9% 12.9% 12.1% 12.0% 14.4% 15.1% 14.3% 19.3% 20.4% 21.1%
43-47% 11.1% 12.5% 13.3% 12.6% 12.1% 13.7% 14.7% 13.5% 17.7% 19.4% 21.7%
47-51% 13.7% 12.3% 13.3% 11.9% 11.4% 13.5% 15.6% 14.8% 18.0% 19.9% 21.6%
51-55% 12.9% 12.1% 12.5% 12.2% 13.0% 14.0% 14.4% 15.6% 19.3% 21.4% 18.4%
>55% 12.1% 11.6% 13.0% 12.0% 10.7% 13.1% 14.2% 14.1% 20.2% 19.6% 23.9%
SOURCE: PITCHf/x (2007-17)

 

Table 11. GB% Over the Years: HR/BIP
GB% \ Season 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
<31% 4.4% 4.9% 5.1% 4.5% 5.2% 5.2% 5.0% 5.1% 5.6% 6.2% 7.0%
31-35% 4.4% 4.5% 4.8% 4.5% 4.2% 4.7% 4.1% 4.3% 4.7% 5.9% 5.9%
35-39% 4.3% 4.3% 4.2% 4.1% 4.2% 4.5% 4.3% 3.8% 4.9% 5.1% 5.5%
39-43% 4.1% 3.9% 4.0% 3.7% 3.7% 4.4% 4.0% 3.6% 4.3% 4.8% 5.2%
43-47% 3.1% 3.5% 3.7% 3.6% 3.4% 3.9% 3.5% 3.1% 3.7% 4.3% 4.8%
47-51% 3.5% 3.2% 3.4% 3.1% 3.0% 3.4% 3.3% 2.9% 3.3% 3.9% 4.3%
51-55% 2.9% 2.9% 2.9% 2.9% 3.1% 3.3% 2.9% 2.7% 3.2% 3.7% 3.4%
>55% 2.3% 2.3% 2.7% 2.4% 2.1% 2.6% 2.3% 2.1% 2.6% 2.9% 3.4%
SOURCE: PITCHf/x (2007-17)

 

Table 12. GB% Over the Years: HR/AB
GB% \ Season 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
<31% 3.5% 3.8% 3.9% 3.4% 3.9% 3.8% 3.7% 3.9% 4.4% 4.6% 5.3%
31-35% 3.4% 3.4% 3.8% 3.5% 3.2% 3.6% 3.1% 3.3% 3.6% 4.4% 4.5%
35-39% 3.4% 3.5% 3.4% 3.3% 3.3% 3.4% 3.3% 2.9% 3.7% 3.9% 4.1%
39-43% 3.3% 3.1% 3.1% 3.0% 2.9% 3.4% 3.1% 2.8% 3.3% 3.7% 3.9%
43-47% 2.5% 2.9% 3.0% 2.8% 2.7% 3.0% 2.7% 2.4% 2.9% 3.3% 3.6%
47-51% 2.9% 2.6% 2.7% 2.5% 2.4% 2.7% 2.6% 2.3% 2.6% 3.1% 3.3%
51-55% 2.4% 2.3% 2.4% 2.3% 2.5% 2.6% 2.3% 2.1% 2.5% 2.9% 2.6%
>55% 1.9% 1.9% 2.2% 2.0% 1.7% 2.1% 1.9% 1.7% 2.0% 2.2% 2.7%
SOURCE: PITCHf/x (2007-17)

The tables reflect trends mentioned earlier. Pitchers who induce fewer ground balls allow more home runs, whether on a per-at-bat or per-ball in play basis, and it has remained true for years (not to mention decades or, perhaps, the entire history of professional baseball). Table 10, which describes how HR/FB has changed since 2007, shows the equality of HR/FB across binned ground ball rates but not from year to year. The juiced ball phenomenon is well-documented (1) (2) (3) (4), and its impacts scream at you from the right side of the table. It’s interesting, though, at least to this author, that HR/FB rates remain impartial despite the juice.

Of additional interest to this author and, perhaps, also to the reader: Table 9 captures how dramatically hitter power has increased over the years; contemporary hitters are hitting home runs on fly balls almost twice as often as they were a decade ago. Granted, some of that correlates with the juiced ball, but absent the juiced ball, the trend remains. However, the corresponding increase in strikeout rates, prior to 2015, had kept the home runs (as percentages of batted balls and at-bats) impressively steady.

Like a fountain, home runs serve as inflow and strikeouts as the drain, creating the illusion that the water level remains unchanged, like a stagnant pond. I understood the correlation between home runs and strikeouts (“selling out for power”) but had not understood the magnitude of its effects over the years until framed in this manner.

Accommodating PITCHf/x in the Statcast EraTM

In January, FanGraphs’/The Hardball Times’ Jeff Zimmerman converted cryptic spring training data into ground ball rates and, later, launch angles — the backbone of the modern sabermetric movement fueled by Statcast data. For the time being, ground ball rates and launch angles can peacefully cohabit the sabermetric space in which we work. In the event that a researcher relies exclusively on launch angles — or, for whatever reason, discounts the validity of PITCHf/x or Baseball Info Solutions (BIS) batted ball data — one can quickly and reliably convert launch angles to ground balls (and vice versa) using the following equation. Using joined 2017 Statcast and BIS data spanning 570 pitchers:

GB% = 0.5414*[Launch Angle] — 0.0091
Adjusted R2: 0.495

What to Expect for 2018

Expect more of the same. It’s outside my scope to predict what will happen to the structural integrity of baseballs next season, but it’d be unwise betting against Commissioner Rob Manfred to issue an order to de-juice them. That said, the amount of juice in the balls doesn’t change facts that have withstood the test of time (and external, juicy forces): fly ball pitchers allow more home runs and, therefore, hitters to inflict more damage per batted ball.

Somewhere in this balancing act, there’s an equilibrium point at which a fly ball pitcher must generate ‘X’ percent more strikeouts to outperform a ground ball pitcher who induces ‘Y’ percent more ground balls. That’s not an answer I have for you today — and, while it would be nice to know, it would not be a particularly helpful answer anyway given the random variance that permeates every performance every season and makes baseball the (mostly) unpredictable sport we love.


Currently investigating the relationship between pitcher effectiveness and beard density. Biased toward a nicely rolled baseball pant. Three-time FSWA finalist, one-time winner. Featured in this year's Lindy's Sports' Fantasy Baseball magazine. Doing everything I can to better understand (fantasy) baseball using only publicly available data.
newest oldest most voted
bjoak
Member
bjoak

No idea why you’re not just normalizing for strikeouts and focussing on the value of groundballs. Seems like a moot point to say GB pitchers strike out less if you’re discussing Luis Castillo.

wash_hts_gold
Member
wash_hts_gold

“but it’d be unwise betting against Commissioner Rob Manfred to issue an order to de-juice them” …. I’m honestly not sure what this statement means.

It would be wise for one to bet on Manfred ordering de-juicing?