Hi! Disclaimer: In this post I use raw Statcast data to calculate expected batting average (xBA). Evidently the raw data do not include the sprint speed adjustment that the Statcast folks said they made. That adjustment only shows up on player pages and in the search. This explains why it seemed to me an adjustment had not been made! The xBA values on player pages are much closer than the raw values and look similar to what I have presented below, and it explains my confusion herein regarding the matter.

Thanks for reading!

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It has always seemed rather obvious to me that Statcast’s expected batting average (xBA) failed to properly account for a hitter’s speed (“sprint speed”). It seemed like fast hitters routinely outperformed their xBAs while slower hitters underperformed. In looking at a Statcast-era leaderboard (2015-21) of differentials between actual and expected batting averages on ground balls, obvious names rise to the top: Delino DeShields, Dee Strange-Gordon, Eduardo Núñez, Billy Hamilton, Jose Altuve, Jonathan Villar, Norichika Aoki, Mallex Smith, Jean Segura, Adam Eaton, Starling Marte… the list of players who have historically outperformed their xBAs by the widest margins are (were) all elite speedsters. At the other end of the spectrum, post-prime sluggers: Justin Smoak, Chris Davis, Logan Morrison, Jay Bruce, Kendrys Morales, etc. etc.

I thought this exact phenomenon, which is not a revelation by now, had once nudged the Statcast team to apply a sprint speed adjustment to xBA. Apparently, this happened sometime between the 2018 and 2019 seasons. Here’s the original snippet, which I very lightly edited for clarity:

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Note: During the All-Star break, at least Paul and I are going to break down pitcher results and they will all be in this article for easy reference. The article will grow throughout the week as more and reports get done. Please submit pitcher recommendations in the comments making sure it’s only actionable pitchers (no Cole’s or Giolito’s).

Background

For those coming back from a two-year submarine tour, here is some background on the current state of the sticky substance ban. Pitchers who use sticky stuff on baseballs create more spin and more spin can lead to more strikeouts for certain pitches. Someone(s) felt it was finally important to start enforcing the rule after decades of turning a blind eye. Here is the time timeline for the crackdown. Read the rest of this entry »

Last week, I quickly introduced my updated hitter xK% equation thanks to commenter suggestions. Let’s now put the new equation into action and update my potential hitter K% improver list. The original list used the earlier version of this equation and can be found here. As you might have expected, many of the same names made this new list. The xK% equation is updated, but the result isn’t dramatically different than it had been. So I won’t be discussing the names I did last week, just the new ones.

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One of the best things about being a FanGraphs author is the ability to receive feedback and immediately act on suggestions from readers smarter than me to improve my analysis. I’m no math or data wiz, but have learned so much from just trying to develop equations over all these years that I could actually play one decently on the Internet now. A week ago, I shared a long overdue update of my hitter xK% equation. It incorporated metrics from Baseball-Reference.com, and performed darn well, clocking in with a 0.941 adjusted R-squared. Yet, it was still ripe for improvement.

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Yesterday, I used my newly unmasked hitter xK% equation to identify and discuss the hitters who have most underperformed the metric. Today, let’s now look into the biggest overperformers, or hitters who may be deserving of a higher strikeout rate right now.

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A whopping eight years ago, I shared the hitter xK% metric I developed using a couple of our plate discipline metrics. It was quite good, using only three variables, but still had a strong R-squared of 0.81. Since then, I haven’t discussed it all that much, but still use it to help formulate my Pod Projections. However, I have actually been using an updated version that I had never shared and it’s even better. The comments on my recent xwOBA articles inspired me to finally reveal the latest and greatest version of the hitter xK% metric.

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Today’s mini-study is brought to you by Austin Gomber and Tarik Skubal. Both struggled to start the season with April ERA’s over 6.00, but I noticed both have righted the ship with May ERA’s near 3.00. Excessive walks caused their early struggles but both got them under control.

With the regression always looming, I wondered how much of the May gains should be given back in June. After looking through the data, not as much as I expected. Read the rest of this entry »

Today, I’m going to take a stab to answer the simple question:

“Do hitters perform better with regular at-bats?”

While the question is simple, finding an acceptable answer is not so easy. While my final approach is far from “perfect”, it verifies the statement is true to a small extent.

The concept behind the rule-of-thumb is that with regular at-bats, a hitter can get into a groove and perform better. Usually, the idea references a hitter in a platoon.

Besides being in a platoon, he could be in the National League and used primarily as a pinch hitter. Anyone going from mainly being a pinch hitter to a full-time role will no longer occur the pinch hitter penalty.
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Today’s post is brought to you by Vince Velasquez who is schedule to face the Marlins. When I decide which pitchers to target, I look for who is facing some of the weaker offenses and Velasquez popped for being interesting (3.68 ERA, 11.4 K/9) and available. I noticed his 15% K% (5.8 BB/9) and moved on. He broke my simple rule of not rostering a player with a walk rate over 10% BB%. But is 10% the right value? After diving into the numbers, it’s not a bad rule, but I’ll try to shrink that number down.

I use 10% because it’s simple to remember. The equivalent is 3.9 BB/9 which could be rounded up to an even 4.0 BB/9. The deal is that while simple rules help, I’d like to have every advantage. For example, I used to use a 50% GB% to show an advantage. The deal is that between 50% and 55% GB, the pitcher doesn’t gain any advantage in double plays and additional high flyballs (i.e. outs).

The other reason I picked 10% is that only a few pitchers reach this total. Of the 68 qualified starters this season, nine have a walk rate of 10% or higher.
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A while back on Twitter, Razzball’s Rudy Gamble made the following comment in a discussion of Vladimir Guerrero Jr. and his high groundball rate.

trying to think of any plus power guys that never figured out how to reduce a ton of GBs.

maikel franco is the only one that comes to mind with hosmer a possible second

— Rudy Gamble (@rudygamble) March 1, 2021

Vlad’s career groundball rate in just 900 PA is 50.5% which would rank as the 5th highest of 75 hitters. Over the same sample, his 118.9 mph Max Exit Velocity is the highest. It’s like he’s been blasting holes in the infield. So coming back to Rudy’s comment, will a power hitter, like Vlad, eventually get the ball in the air. While the answer is some, it’s likely not enough to make a difference.
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