Archive for underachiever

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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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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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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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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