Archive for BABIP

In rounding out my series on the most important factors influencing components of BABIP, I will be looking into what most affects a hitter’s batting average on ground balls. So far, the results of these analyses have been consistent. Launch angle has been the main driver of BABIP for pitchers, and it has been for hitters as well, at least when they are launching flyballs. The story is a different one, though, when hitters put the ball on the ground. Ground ball launch angle was not a significant factor in determining a hitter’s ground ball batting average, and neither was ground ball exit velocity.

Whether or not a hitter pulls grounders has much to say about whether that player will hit for average on grounders. There is a negative relationship between these variables that is significant at p < .0001 and with a Pearson’s r of .27. Even more important is how fast the hitter is. The Pearson’s r for the positive correlation between average sprint speed and ground ball batting average was .45.
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In a pair of recent columns, I looked into what factors have impacted flyball BABIP (or FB BABIP) and ground ball batting average for pitchers, and those analyses were linked by a common finding. Whether pitchers are allowing balls that are in play in the air or on the ground, the launch angles of those batted balls go a long way towards explaining whether they become base hits. Now I am turning my attention to flyball BABIP for hitters, and the trend continues. While flyball pull rate, average flyball distance and average exit velocity, both on flyballs and line drives combined and on flyballs alone, did not have significant relationships with FB BABIP for hitters, average flyball launch angle (FB LA) turned out to be a statistically significant factor yet again (p < .0006, r = .19)
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In the second installment in my series on the factors impacting components of BABIP, I move on from flyball BABIP for pitchers to ground ball batting average for pitchers. This analysis produced one result that really surprised me: whether or not a pitcher has a tendency to allowed pulled grounders does not have much of an impact on the ground ball batting average they allow. I didn’t anticipate this, because hitters put up a collective .180 batting average on pulled grounders in 2019, but a .306 average on all other grounders. For pitchers who allowed at least 225 grounders in seasons between 2015 and 2019 (n=286), the negative relationship between pull rate and ground ball batting average allowed (GB Avg) was significant at p < .05, but with just an .012 Pearson’s r.
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A little more than a year ago, Alex Chamberlain and I looked into what type of impact a slew of Statcast measures had on a pitcher’s overall BABIP rate. Hard-hit rate and exit velocity on ground balls (EV GB) had the strongest correlations, but it seemed unlikely that the latter would have much to say about which pitchers would be best at limiting hits on flyballs in play. In general, it seems that BABIP could be influenced by different factors depending on the type of batted ball.

So let’s test that out. This column is the first in a series of four where I will be looking at the impact of various measures on flyball BABIP and ground ball Avg, both for pitchers and hitters. I’m kicking this off with an analysis of flyball BABIP for pitchers.
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Note: As was the case in a previous analysis of Statcast measures and their correlation with power metrics for hitters, I owe a debt of gratitude to Alex Chamberlain. He did a lot of heavy lifting for this column, running the correlations and discussing interpretations with me.

It won’t be the first or last time, but I did a silly thing on Twitter. In announcing a pick for the Pitcher List Experts Mock, I decided to tout the player I chose by citing one of his achievements, as captured by a Statcast metric.

Fun fact: Trevor Williams has the 3rd-lowest avg. exit velocity allowed on flies and liners over the last two seasons (min. 400 BBE), behind only deGrom and Kluber. Just took him in Rd 21 of the #PLExpertsMock. @JustinMasonFWFB, you’re up!

— Al Melchior (@almelchiorBB) December 13, 2018

(Justin, by the way, made his pick very promptly.)
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In a fairly surprising turn of events, the Rockies demoted Jon Gray Saturday. Gray has arguably been baseball’s most enigmatic pitcher this year, posting a career-worst 5.77 ERA supported by career-best peripherals — e.g., a 13.4% swinging strike rate (SwStr%) underpinning a 28.9% strikeout rate (K%), and fielding independent metrics of 2.78 xFIP, 3.08 FIP, and 3.15 SIERA. Given our most basic sabermetric understandings of baseball, Gray should be a very good pitcher, even if he pitches half his starts at hitters’ paradise Coors Field.

I have written about how a common-breed Rockies pitcher’s peripherals might be penalized for calling Coors Field home (Gray inspired this bit of research as well). FIP metrics generally underestimate ERA by anywhere from 0.8 to 1.3 runs for home starts (compared to 0.0 to 0.2 runs for road starts), suggesting that Rockies pitchers may underperform (a) their FIPs by 0.35 runs or (b) their SIERAs by 0.65 runs — given error bars, maybe more.

Still, that doesn’t explain why Gray’s ERA is nearly 6 right now. I shed light on the ridiculousness of the move; his strand rate (LOB%) is suppressed and his batting average on balls in play (BABIP) is elevated, even compared to his uniquely bad baselines. I’m not sure there’s much more to it.

Nick Mariano of RotoBaller noted here that Gray’s fastball has been incredibly hittable since his debut and especially this year. Despite my thoughts on the inevitability of regression in Gray’s favor, I wanted to pursue Mariano’s train of thought a little further. Gray’s fastball is bad, but how bad? And why?

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It’s hard to put a good spin on Gary Sanchez rolling into late June as the seventh-ranked catcher in Roto value (per ESPN’s Player Rater), but while his season has been disappointing, it has its bright side. For one thing, at least he is having a better fantasy season than Willson Contreras. For another, he leads all catchers in runs (35) and is second in home runs (13) and RBI (39).

It’s clearly Sanchez’s .194 batting average that is holding him back, and now that he is mired in a 6 for 62 slump, it’s heading in the wrong direction. He is a bit off last season’s home run pace and his strikeout rate has risen slightly from 22.9 percent in 2017 to 24.6 percent so far in 2018, but his real problem is what he is doing on balls in play.
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Long has the relationship between pitcher performance and batted ball metrics been dubious. The Sabermetric community has a solid understanding of why, fundamentally, a pitcher is good or bad. Strikeouts are good. Walks are bad. Hits by pitch are also bad. Home runs allowed are especially bad. So on, so forth. And by no means are batted ball metrics useless. It’s how we know ground balls allowed are superior to fly balls allowed, for example.

The community had hoped, however, that more granular batted ball metrics would help us better explain some of the more nuanced elements of pitcher performance, including those related to luck, such as batting average on balls in play (BABIP) and the percentage of home runs per fly ball (HR/FB). Since their introduction to the public sphere in 2015, and even with the inclusion of more granular Statcast data in 2016, any relationships that might exist between the physics and outcomes for batted balls during an individual pitcher’s season are still poorly explained. The following table depicts the correlations between pitcher BABIP and various batted ball metrics, sorted by the strength of the relationship (all qualified seasons, 2007-17, n = 898):

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The second-best part about Tommy Pham is I can basically recycle this post I wrote about Domingo Santana three and a half weeks ago. Like, I could replace Santana’s name with Pham’s throughout it and you wouldn’t blink. Pham, through his first 628 plate appearances, has hit a home run on more than 28% of his fly balls (28% HR/FB); if sustained for another 72 PA, it would be the third-best mark through a player’s first 700 PA in the last 15 years (among more than 600 qualified hitters).

The best part about Tommy Pham, though, is something Santana doesn’t have, and it’s something more than skin deep. Depending on whom you ask, Pham has swung at pitches outside the zone only 19.8% (BIS), 22.2% (Pitch Info) or 22.9% (PITCHf/x) of the time. Those rank, in order, 6th, 11th and 18th among 205 hitters with at least 250 PA — in other words, the 95th percentile (for the former two) or at least the 90th (for the lattermost). In short, he forces pitchers to pitch to him. Few in the game have been more selective, and few in the game have shown this much power this early in a career. (“Early,” by number of games, obviously, because Pham, at 29, is hella old for a guy who barely has a full season’s worth of PA.) The coincidence of his selectivity and his power is nice, to say the least.

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This is a long one. I appreciate your patience in advance.

Mike Podhorzer, I and sporadic others have — but primarily Mike has — carried the torch on developing ‘expected’ metrics, such as xBABIP (expected batting average on balls in play), xHR/FB (expected home run-to-fly ball ratio) and xK% (expected strikeout rate), all and the rest of which can be found here. For the uninitiated, these xMetrics help describe how a hitter or pitcher should have performed based on various measurements of the events that unfolded and typically are more predictive of future performance than the original metric. They’re not perfect, but, like other advanced metrics, they give us a better understanding of player performance and ability.

Each metric — xHR/FB, xK%, etc. — has formulas for both hitters and pitchers, with the hitter metrics typically having stronger correlations than those for pitchers. Unfortunately, pitcher xBABIP has always eluded us. It’s inappropriate to repurpose hitter xBABIP for pitchers, but it’s because the model coefficients (weights) would be different, not because the theory underpinning the model is flawed.

That’s the problem, though: hard hits, line drives, infield fly balls — these all should affect a pitcher’s BABIP allowed. Our intuition begs it to be true. Yet there’s a resounding lack of evidence that suggest otherwise. The correlation between BABIP and hard-hit rate (Hard%), line drive rate (LD%) and infield fly ball rate (IFFB%), among others, borders on nonexistent:

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