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 »

Early last season, I introduced an equation that calculates a hitter’s expected isolated power, or xISO. Since I haven’t discussed it all so far this season, and we’re halfway through June, I figured now is better than never.

Before I proceed: Andrew Dominijanni expanded on my research about a month ago. He incorporated exit velocity, sourced from the new Statcast data hosted at Baseball Savant. The model better explains the variance in the data and has slightly better predictive (year-to-year) qualities, making it the optimal choice.

Andrew’s version of xISO generates a pretty simple calculation, but I’m feeling especially lazy today, and I can find hard-hit rate (Hard%), pull rate (Pull%) and fly ball rate (FB%) all on FanGraphs’ batted ball leaderboards.

Read the rest of this entry »

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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Back in May, I introduced an equation that would calculate expected isolated power (thus, “xISO”) numbers for hitters based on their batted ball profile. The idea was to generate an equation that could accurately describe for how much power a hitter should be hitting based entirely on publicly available data (provided to FanGraphs by Baseball Info Solutions), as opposed to proprietary data, so all fantasy baseball enthusiasts could use it.

I won’t get into the nitty gritty again — you can click on the link in the first sentence if you want to open that can of worms — but I will provide the equation again for posterity:
a
xISO = –.1396 + .1814*Pull% + .5136*Hard% + .2344*FB%

I’ll provide a table of xISOs for all qualified hitters below and deliver some insight regarding potential buy-lows and sell-highs.

Read the rest of this entry »

I am humbled by the interest in my expected isolated power (xISO) equation since its inception. A fairly simple but helpful tool, I take solace in hoping it maybe has helped one fantasy owner identify a smart buy-low candidate — or reluctantly cut bait on Carlos Gonzalez. I also appreciate the feedback and recommendations for improvement and expansion. I will take care to consider their implementation when I have more available time.

It’s important to remember that Steamer and ZiPS also provide updated and rest-of-season (RoS) projections for most players. Hitter ISOs aren’t listed on the page listing all projections — only slugging (SLG) and batting average (BA) are included, forcing the user to perform some light arithmetic — but ISOs are listed on each hitter’s personal page. And they are not fundamentally different from the expected ISOs I have calculated for you today (spoiler alert). For reference, I crunched the correlation coefficients of every qualified hitter’s current xISO versus his current Steamer and ZiPS ISO projections:

Read the rest of this entry »

Last week, I introduced xISO, a metric that calculates a player’s expected isolated power based on his batted ball profile (per FanGraphs’ recently added batted ball data courtesy of Baseball Info Solutions). Having looked at a handful of underachieving National League outfielders for its induction, I’ll expand the analysis of xISO here today.

I’ll reiterate some key points. I used all 12 years’ worth of batted ball data for all player-seasons in which a hitter qualified for the batting title. The OLS regression specified pull rate (Pull%), hard-hit rate (Hard%) and fly ball rate (FB%) as explanatory variables and produced the following equation, which I deliberately omitted last week:

Read the rest of this entry »

Man, I am having a blast with the Baseball Info Solutions batted ball data that was recently added to the batted ball leaderboards. Sure, there are reasons to complain: the batted ball spray and contact quality statistics lack context, leaving you in the dark about how spray and contact intersect. For example, there’s Hard%, and there’s LD%, but how many of a hitter’s balls in play are hard line drives? (You can actually find this data on individual player pages under the “Splits” tab — just not on the leaderboards.)

Just because the available data aren’t as granular as one might wish they were doesn’t make them worthless or unusable. Yesterday, I demonstrated that we can still achieve small gains in our understanding of batting average on balls in play (BABIP) using the new data.

Read the rest of this entry »