Archive for Meta Analysis

It’s time to get nerdy. Very nerdy. While I’m going to post some auction values end the article’s end (I just lost 99% of the readers), the focus of the words is to concentrate on setting a pitcher-hitter split by just using Standings Gain Points (SGP). It’s not going to work with every league type. Not even close but it’s a theoretical solution which can help an owner the best chance to get the most value out of every draft pick or auction dollar. It’s definitely a better option when trying to set in-season values.

The following process is definitely ripe for discussion and argument. While writing The Process, Tanner and I debated this idea and even brought in others to determine if the pitcher-hitter split can be done by using SGP values. We published without including the concept as we were still ironing it out. We are still not in 100% agreement but here is the idea.

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Yesterday, I identified and discussed six hitters who appeared near the top of my barrels per true fly ball (Brls/TFB) leaderboard. As a reminder, Brls/TFB is one of the primary components of my xHR/FB rate equation and is calculated by taking the Statcast Barrels count and dividing by the batter’s fly balls minus infield flies. Today, let’s check in on the hitters who surprisingly ranked closer to the bottom of the leaderboard, rather than the top.

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Two weeks ago, I discussed the hitters who enjoyed surges and suffered declines in their barrels per true fly ball (Brls/TFB) rates, a metric that’s one of the primary components of my xHR/FB rate equation. Today, let’s identify and discuss some surprises at or near the top of the Brls/TFB leaderboard. Some of these hitters did post high HR/FB rate, so those hitters might not be so surprising as Brls/TFB leaders, but rather the rankign validates the success.

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Yesterday, I identified and discussed the hitters who increased their fly ball pull percentage (FBP%) most from 2017 to 2018. Pulling the ball more frequently typically fuels power growth, so a significant decline could be the cause of a power dip. So let’s find out which hitters with at least 30 fly balls enjoyed a FBP% decline of at least 10%.

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Alas, we have reached the final 2018 pair of recaps of the primary components of my xHR/FB rate equation, fly ball pull percentage (FBP%). Simply, it’s easier to hit a homer to the pull side. Why? Because the fences are closer down the lines than to center field. Plus, hitters are typically able to generate more power when pulling the ball, so they hit their pulled flies harder, plus those pulled flies don’t have to travel as far to jump over the fence for a dinger. So let’s find out which hitters with at least 30 fly balls enjoyed a FBP% surge of at least 10%.

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Shortly after the onset of last season, I dug into pitch-level statistics to see how much swinging strike rate (SwStr%), ground ball rate (GB%), and isolated power (ISO) varied by pitch type. I felt inspired after analyzing Madison Bumgarner before the 2018 season and noticed his fastball, once elite, was utterly broken after his dirt bike accident. (See his 2018 player caption and this July post in which I followed up MadBum’s lack of progress.) I felt encouraged by the praise the post received from readers and fellow analysts alike for the clarity it provided. I’d like to think it helped move the needle, even if only slightly, in terms of how we evaluate pitchers.

I wanted to refresh the guts of that post for the 2018 season with additional metrics. There’s not much else to discuss; this’ll be short and sweet. (I’ll toss in some gratuitous high-level analysis following these tables.)

Notes:

• All data is courtesy of PITCHf/x via Baseball Prospectus
• All tables present average rates for starting pitchers only
• Due to pitch tracking/stringing not being perfectly precise, the numbers below are highly accurate but not completely so and may not align exactly with FanGraphs’ batted ball data (for example, Baseball Info Solution strings far fewer line drives than does PITCHf/x)
• Click headers to sort!

Batted ball outcomes by pitch:

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On Thursday, I identified and discussed the hitters(with at least 30 fly balls in each year) who increased their average fly ball distance (AFBD) marks the most from 2017 to 2018. Now let’s take a look at the biggest decliners. If a hitter on this list suffered a dramatic drop in HR/FB rate, this is likely why.

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Yesterday, I devised a new expected strikeout rate for pitchers and used it to identify qualified starting pitchers who over- or under-performed in 2018. I’m reluctant to make out the exercise to be more than it is. I simply wanted to take the most intuitive approach to describing a pitcher’s strikeout rate (K%): by using the plate discipline exhibited by opposing hitters. Today, I seek to do the same for hitters. I can tell you now the discussion will be much more qualitative than quantitative.

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Yesterday, I identified and discussed the hitters that increased their barrels per true fly ball rate (Brls/TFB), a metric that makes up one of my xHR/FB rate components. Let’s now take a gander at those whose Brls/TFB rates declined most from 2017 to 2018. We’ll limit the list to declines of at least 10%.

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A year ago, I introduced the newest version of my xHR/FB rate equation. One of its components is barrels per true by ball rate (Brls/TFB), which divides the Statcast Barrels count by fly balls – pop-ups. The use of true fly balls, rather than the batted ball events (BBE) count the Baseball Savant leaderboard shows us, pits all types of hitters on the same playing field, instead of unfairly penalizing extreme ground ball hitters or benefiting extreme fly ball hitters.

Let’s take a look at all hitters with at least 30 true fly balls that increased their Brls/TFB rate by at least 10%. Then I’ll discuss the interesting names.

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