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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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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Updated Feb. 25, 2017

Aug. 16, 2016: Updated Alex’s xBABIP equation and added Andrew Dominijanni’s xISO equation.
May 23, 2016: Published.

Jump around in this post:
Hitter metrics: xBABIP | xISO | xHR/FB | xOBA | xK%
Pitcher metrics: xHR/FB | xLOB% | xK% | xBB%

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *Do you frequently use RotoGraphs’ “X” (expected) metrics? Do you wish they were easier to find? Have you ever commented to ask if they could be added to the leaderboards or at least wished they were all located in one spot? If so, you may want to…

BOOKMARK THIS PAGE!

I don’t know if there will ever be a time when FanGraphs has a leaderboard devoted to “X” metrics. The fantasy analysts at RotoGraphs have taken a largely vigilante approach to creating descriptive and predictive expected metrics over the years. Moreover, each metric typically undergoes an iterative process by which we improve it when new data is made publicly available to the authors.

So, this is it. This is my best attempt, on behalf of RotoGraphs’ staff and at the polite and enthusiastic behest of its readers, to centralize the freshest versions of the relevant metrics the RotoGraphs staff most frequently cites. I have also built primitive Microsoft Excel-based calculators for some (but not all) of the metrics that crunch the numbers as long as you provide the appropriate inputs. It should save us all an extra minute or two and preserve our sanity a little bit.

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So, Jackie Bradley Jr. hit another home run last night. It’s his seventh of the season and his sixth since May 5. That’s six home runs in 13 games to go with a .404/.474/.762 line. All the while, he extended his hitting streak to 24 games. It’s pretty crazy. He’s on a tear, for sure.

But I’ve seen some overenthusiastic Tweets about his breakout being legitimate. That, I don’t fully understand. He doesn’t seem like a fundamentally different hitter than the JBJ we saw in 2013. Or 2014. Or 2015.

Obviously, the .271 ISO (isolated power) is impressive for a relatively tiny dude. It’s not far off his .249 ISO from last year, so it looks like it might be something sustainable. His strikeout rate (K%) is way down, too, which is undoubtedly a boon to his triple-slash line. But red flags abound with JBJ, all (or most) of which I will detail here.

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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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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 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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You may have noticed that FanGraphs now feeds batted ball data, courtesy of Baseball Info Solutions, into its leaderboards. The day the data appeared, my mind buzzed with ways they could be useful in improving our understanding of a hitter’s batting average on balls in play (BABIP).

Mike Podhorzer already augmented previous attempts at devising an equation for expected batting average on balls in play (xBABIP) for hitters by incorporating elements of a hitter’s power, speed, plate discipline and batted ball tendencies. So, with fresh numbers in hand, I embarked on a journey to further improve the ever-evolving xBABIP. However, I sought to do so by using only batted ball data. Basically, I intended to develop a convenient xBABIP equation, one that can be computed using almost entirely variables found on the same page.

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