Archive for expected ISO

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:
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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.

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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:

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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:

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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.

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