Archive for BABIP

The fantasy community is down on Justin Upton. I get it, but it’s a little strange to me given our collective penchant for recency bias. Upton had a monster second half and finished the season an almost-perfect replica of his usual self. (The operative qualifier being “almost.” We’ll get to that in a second.) Sure, it was a rocky year, but hey, Joey Votto had one, too. Dude was batting .213 with a 27 percent strikeout rate (K%) through May…

Right, so Upton was an almost-perfect replica of himself. In a vacuum, his production looks nearly identical to his typical annual accomplishment, down to nearly every statistic except for his batting average on balls in play (BABIP). In my investigation of his woes, I noticed his uncharacteristically low infield hit rate (IFH%). Here’s a list of hitters with higher infield hit rates than Justin Upton in 2016:

• Salvador Perez
• Kendrys Morales
• Albert Pujols
• One hundred and thirty five other players not surnamed Ortiz, Mauer, Seager, Granderson, Cabrera, Votto or Kemp

Yes, Upton ranked among the bottom 6 percent of hitters in terms of infield hits. If there’s a single bone to pick about Upton’s season — well, aside from the insane volatility — it’s that his BABIP failed to get back on track, continuing to linger at a league-average mark. It seems a trend has emerged; accordingly, it’s easy to accept said trend as a new normal, as a resignation of Upton’s fifth tool.

I’m here to make the classic* Infield Hit Rate Defense, or IHRD, as it’s known in the infield hit community.**

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Robbie Ray is already shaping up to be one of 2017’s most contentious starting pitchers headed into draft day. (This isn’t even my first time writing about him in the last half-year.) His 28-percent strikeout rate (K%) and 3.45 xFIP scream of an elite starter, but his 4.90 ERA and 1.47 WHIP, sustained during more than 170 innings pitched, seem to say otherwise.

Analysts and laymen who have expressed optimism about Ray have done so in regard to his alleged hittability. That 1.47 WHIP didn’t come from nowhere: his .352 batting average on balls in play (BABIP) got him there. You’ll hear a variety of arguments: he struggles on his third time through the zone; he lacks a quality third, or maybe even second, pitch; and so on. I’m not here to argue the validity of those sentiments.

I want to talk exclusively about Ray’s BABIP. Well, his sinker, too. And maybe even his strand rate (LOB%)… But mostly his BABIP. Please, have a seat. I don’t want to fluster you.

Ray’s .352 BABIP in 2016 was the second-worst of the last 15 years. That’s out of 1,281 individual player-seasons posted by qualified starting pitchers. His BABIP was historically bad — strange, you’d think, for a pitcher who has quickly demonstrated a lot of promise. So, I want to approach this whole BABIP thing in a vacuum. Let’s just look at the facts — not even alternative facts, but real facts!

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Yesterday, FanGraphs made public its splits leaderboard, which the authors have been able to test and refine in private for some time now. It’s an incredible tool. If you haven’t checked it out, you should. If you haven’t thanked Sean Dolinar for building it, you should. (If you have any preliminary feedback, leave it in the comments and I’ll pass it along.)

There are a seemingly infinite number of ways to cross-cut data in endlessly fascinating ways. Splits by handedness, situation by outs, situation by leverage, situation by defensive alignment (shift or no shift!) — the list goes on. But the thing that most interested me immediately was understanding the implications of more granular batted ball data.

Two tools I once refined/created — xBABIP and xISO — rely almost exclusively on Baseball Info Solutions (BIS) batted ball data. Yet they were limited in their capabilities because of the limited nature of the data: we knew each hitter’s contact quality (hard/medium/pull) and contact direction (pull/center/oppo) but now how the trios intersected. But, ah, the splits leaderboard.

The following tables depict the batting average on balls in play (BABIP), isolated power (ISO), and home runs per fly ball (HR/FB) in 2016 by each cross-section. Read the rest of this entry »

Purposes of this post:

1. Glass half-full: To update a year-old xBABIP equation that estimates a hitter’s batting average on balls in play (BABIP) based on his batted ball data;
2. Glass half-empty: To pay restitution to readers and beautiful human Mike Podhorzer for damages incommensurable, wrought by the careless oversight of the initial version of the equation;
3. Regardless, a glass with some amount of water in it: To provide, for those lacking attention or care, the updated version of equation aforementioned, found here for one’s immediate gratification sans linguistic obstruction.

An Explanation

Last year, when FanGraphs obtained Baseball Info Solutions (BIS) batted ball data, I was, in a word, jazzed. Kind of like the Statcast revolution now (but not nearly as popular or flashy), FanGraphs in conjunction with BIS bestowed upon the sabermetric community more great tools to describe and predict player performance. I developed an equation to estimate a hitter’s expected batting average on balls in play, or xBABIP. This was not an original idea — other iterations of xBABIP already existed — and I freely admitted then (and now) that my equation was not necessarily superior to any other. My equation simply offered new value by (1) using the new batted ball data and (2) incorporating metrics from a single, easy-to-locate source to make easier the calculation of the equation.

Unfortunately, the equation has long been overdue for a makeover — right from the very start, basically. See, in my attempt to create a relatively simple equation, I overlooked a critical element of the data that has produced imperfect xBABIP estimates. In an attempt to make this a learning experience for anyone who cares, let’s take a look at batted ball data by season at the league level.

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A few weeks ago, I dug through some PITCHf/x data, courtesy of Baseball Savant, and calculated the BABIP (batting average on balls in play) on ground balls to the pull side by velocity for hitters by handedness in 2015. There are a lot of prepositional phrases in that last sentence, but instead of trying to further clarify it, I’ll summarize the findings: right-handed batters hit for a higher batting average on pulled ground balls at every batted ball velocity than did left-handed batters in 2015.

It’s a long time coming, and I’m here to present the same analysis but for ground balls to the opposite field.

But, first, some quick housekeeping. Following my recent ADP (average draft position) research, I asked readers to predict which players’ end-of-season (EOS) rankings would outshine their ADPs for 2016, given some certain conditions. Twenty-seven readers responded and the results are in, ranked by frequency of votes:

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Yesterday, I borrowed PITCHf/x data from Baseball Savant to investigate how changes in batted ball velocity affected batting average on balls in play (BABIP) to a hitter’s pull side. If you’re too lazy to click, the short of it is: more velocity coincides with a better batting average. However! Lefties consistently fare worse than righties on ground balls to the pull side at all batted ball velocities.

This phenomenon can perhaps be attributed to the defensive shift. Or to the ease with which second and first basemen can convert singular outs at first base compared to their shortstop and third base counterparts due to the distance (and, thus, difficulty) of the throw. Or, most likely, to both.

But that’s not why I’m here. I’m not in the business to speculate — not today, at least. I’m just here to provide the facts in the form of some numbers I crunched in Microsoft Excel that, if you read yesterday’s post, you will probably find interesting. It has a nifty graph, if words aren’t your thing.

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Baseball Savant, a website maintained by Daren Willman, is a thing of beauty. Aside from some great leaderboards and applications, Willman hosts a database of PITCHf/x data. Using it without a game plan is like entering the Amazon without a machete — it can be unwieldy and overwhelming. Navigating just right bears ample fruit, however. I would like to share some of my fruit with you.

Because in 2015, PITCHf/x data began including batted ball velocity for most balls in (and out!) of play. Batting average on balls in play (BABIP) is a critical component to player success, and while there has been plenty of focus on it in the last decade — more so than, say, pitch framing, which is a popular but still-raw area of research — the baseball community would still benefit from a better understanding of BABIP, especially in light of more frequent employment of defensive shifts.

Intuition tells us that a harder-hit ball in play will have a greater probability of resulting in a hit. (Indeed, my expected BABIP equation from last year that helps corroborate such a claim.) Specifically, in regard to ground balls and defensive shifts, a hard-hit grounder will have a much greater chance of clearing a crowded first-base line than would a softly hit grounder.

Enter Baseball Savant and its very granular PITCHf/x data. Read the rest of this entry »

Where the defensive shift and batting average on balls in play (BABIP) intersect intrigues me, but I’ve had a hard time figuring out a way to quantify it without having some sort of access to shift data. Despite advances Major League Baseball has made in measuring and collection data, not all of this information is publicly available or easily accessible, even if you know someone who knows someone (this guy).

But I think I finally had some kind of breakthrough or epiphany or what-have-you. It would be a time-intensive approach — a problem for a lazy person (this guy) — but it would be worth it to, perhaps, chip away at the relatively enigmatic BABIP with only publicly available tools at our disposal.

More than four months ago, I posted an expected BABIP (xBABIP) equation that is not necessarily better than any other that exists but does use strictly publicly available data. Here, I expand.

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I like a lot of pitchers who have unperformed this year. With strikeout and walk rates (K%, BB%) of 20.5 percent and 7.0 percent, respectively, Drew Hutchison delivers everything I want from a mid-rotation fantasy starter. With a 5.19 ERA and a 1.47 WHIP, however, he delivers a flaming bag of feces to my doorstep.

The same can be said for Taijuan Walker who, after a terribly rough start to the season, dazzled for seven straight starts before recently tossing three stinkers. With plate discipline ratios better than Hutchison’s and just 22 years old, Walker demonstrates the skill set and ceiling that have earned him consensus top-20 honors on prospect lists from 2012 through 2014. Yet his 5.06 ERA and 1.29 WHIP have left fantasy owners not only disappointed but also reeling.

Hutchison and Walker share a common trait: their ERAs dwarf their fielding independent pitching (FIP) statistics. FIP was designed to demonstrate a pitcher’s true performance in light of the events he can control — that is, events independent of balls put into play at the mercy of the defense supporting him (among other things).

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I committed Matt Holliday to my disabled list Monday, marking the 14th(!!!!) DL move I’ve made for my primary team this season. Perhaps the state of my team is implied by the length of its disabled list. If not, I’ll make it clear: my team has been bad. Pretty darn bad.

All of my drafts were especially poor. I drafted the same terrible, injured, underachieving players in every league, so it has been generally a nightmare all around. The hole I dug for myself is deep. Kyle Lohse broke ground on said hole with an 8-run Opening Day outing that lasted all of 3-1/3 innings, and we never looked back. Woe is me. Alas, it’s barely the second week of June, and I have already resorted to my Hail Mary play: buy low on everyone in sight.

Calling it “buying low,” however, is a bit misleading. It’s a shallow league, so there is arguably a stronger incentive for owners to cut bait on underachieving name-brand players in order to ride the hot streaks of unknown quantities, given they crop up more abundantly. What I’m actually doing, then, is loading up on underachievers from waivers. My team is already underachieving. These guys are already underachieving. How much worse could it get?

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