Archive for isolated power

Not so long ago, when it came to pull-heavy power hitters, there was no equal to Brian Dozier. Based on developments currently unfolding this season, the genre could explode in 2020.

Back in 2016, Dozier was unique among major league hitters in his ability to pull flyballs at an extremely high rate consistently. Among hitters who launched at least 100 flies in both 2015 and 2016, Dozier was the only batter to exceed a 35 percent pull rate on flyballs in both seasons. He essentially lapped the field, surpassing a 40 percent rate in both campaigns. Then Dozier was the only hitter to pull off the feat in both 2016 and 2017.
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I recently wrote about Statcast measures that were highly correlated with power metrics, such as HR/FB and ISO. The research confirmed that several Statcast measures could be useful tools for identifying undervaled power sources. One factor I ignored in that analysis was pull rate, but in taking a belated look at it, I found that one of the apparently strong relationships gets notably weaker when we control for a hitter’s pull tendencies.

In general, exit velocity on flyballs and line drives turned out to be strongly correlated with ISO for hitters with at least 150 batted ball events in 2018. However, when you isolate the top 10 percent of the sample in terms of pull rate, the relationship is still meaningful, but it’s not quite as strong. That could have implications for how we view two of last season’s top power hitters, Jose Ramirez and Alex Bregman.
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As a Twitter dork, I’m exposed to a lot of discussion about swinging strike rates (SwStr%), so much so that it almost feels like it has supplanted xFIP (or other comparable metrics) as a catch-all way to evaluate pitchers. Dude has a 12.5% whiff rate! Sweet. It’s not for naught — swinging strike rate bears a strong correlation to strikeout rate (K%), which comprises substantial portions of the regression equations that underpin the aforementioned xFIP and its counterparts. Swinging strike rate’s correlation to the following metrics (using data from the last five years of 714 pitchers who threw at least 100 innings in a given season):

• K%: r = 0.83
• SIERA*: r = 0.61
• xFIP*: r = 0.55
• FIP: r = 0.50
• ERA: r = 0.40

(*See footnote.)

It also correlates strongly year over year (among 392 player-seasons during the same timeframe in which the pitcher threw 100 innings in the current and subsequent seasons):

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I planned to include Rhys Hoskins in my blind résumés post from Monday, but I couldn’t find any realistic comps for him. Part of the problem is no one does for a full season what Hoskins did for 50 games. Part of the problem, also, is no one does for a full season what Hoskins would be expected to do for a full season, based on his peripherals. It’s a fairly unique skill set (although let’s not conflate “unique” with “the best” or any kind of superlative like that… yet).

Hoskins had himself a real, real nice debut. This isn’t the first time you’ve read about him in the last couple of months and it will be far from the last. Andrew Perpetua, for all intents and purposes, regressed his batted balls from 2017 and he still would’ve had an awesome season. In Eno Sarris’ heart, as well as mine, Hoskins was the runner-up National League Rookie of the Year to Cody Bellinger.

Hoskins had himself a real, real conveniently sized debut as well. His playing exactly 50 games prevents me from arbitrarily choosing a cutoff and having to justify it. A cutoff for what, you ask? Well, Hoskins, in exactly 50 games, posted a .359 isolated power (ISO) while swinging and missing only 7.1% of the time. He struck out a fair deal, but he also walked a ton. Take this snapshot of a season and, as aforementioned, you’ll be hard-pressed to find comps.

Which is exactly why I set out on a very pseudo-scientific quest to find any of Hoskins’ contemporaries who have done this — this, being the aforementioned 50 games of a .350-ish ISO and a 7%-ish swinging strike rate (SwStr%) — at any point in their careers (or within windows of their careers that I’ve curated). I’m winging it here, plucking names from my brain who have elite power and at least above-average plate discipline (assuming Hoskins might, but it’s not a foregone conclusion) and scouring their careers for similar streaks. Any omitted hitters are a product of my lack of memory or imagination, not of malice. Except for Giancarlo Stanton.

Mike Trout

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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.

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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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