After more than two days of pouring over pitcher batted ball data, I better write something before I am fired. In the end, I found nothing groundbreaking. Popups and groundballs good… everything else (i.e. line drives and flyballs) is bad. The change I’d recommend going forward is to move to a more granular grading of batted to bins, like barrels, based on outcomes. Read the rest of this entry »
I’ve been trying to get to the root cause of how much can a pitcher limit hard contact. I’ve had some hits and misses, but today I’ll finish going over the possible inputs.
I’ve already investigated the groundball/flyball issue and generate weak contact when hitters make contact with pitches out of the strike zone in this series and previously looked into two-strike counts and found nothing. Here are the five possible causes.
So it’s time to finish the list to see if changing speeds and horizontal movement help limit hard contact.
Recently, I started working through predicting which pitchers limit hard contact and by how much. Today, I dive into if contact out of the strike zone can be predictable and if that contact is weaker than contact in the strike zone. First, I need to go over a couple of concepts
The division of credit for WAR goes with 50% to hitting and 43% to pitching and 7% to fielding. People way smarter than I have determined that split.
Major Note: For simplicity, I’m going to adjust the pitching percentage up to 100% so the fielders are allocated 14% of the credit for what happens when a pitcher is on the mound.
On top of the fielding allocation, not every batter puts the ball in play with the league at an 8.7% BB% and 23.4% K% this season. So now, 68% of all at-bats end with a ball in play with 14% points of that 68% goes to the fielders and 54% (68%-14%) to the pitcher’s batted ball talent. So it works out that 79% (54%/68%) of a pitcher’s batted ball results should be attributed to him. Read the rest of this entry »
Sometimes conclusions to tough questions just don’t sit right, especially when the answer is “We don’t really know.”. How pitchers control batted balls has never had a simple definitive answer. I’m going to give it another shot.
I have some ideas of what might be a cause, but I want to start with a blank slate. What’s got me diving back in is the following table from a recent article of mine.
While a few percentage points of difference may not seem like much, I expected a lot more regression to the mean with my limited sample size. With just the above information, I felt I needed to re-investigate the subject. I know that some of the regression amounts have previously existed, but I wanted to dive in with some fresh eyes and new batted ball data. Read the rest of this entry »
For the second week in a row, I had to voice my amazement that JT Brubaker was cut in the NFBC Main Event. While Brubaker’s results have not been great (4.95 ERA), there are several signs that point to him being closer to a 4.00 ERA pitcher. The stat that sticks out is his 1.9 HR/9. The home runs have him with a 4.98 FIP while his xFIP is a full run lower at 3.98. I wanted to see if I should blindly assume that his home run rate will drop. With the expected drop, will his FIP and ERA regress downward to his xFIP? Also, are there any measurable traits that make a pitcher more home run prone? I ended up with a “maybe” and a solid “no”.
Brubaker isn’t the only pitcher who fits this mold. Bailey Ober has a 2.2 HR/9. His 4.99 ERA is almost identical to his 5.18 while his xFIP is down at 4.12. Another is Yusei Kikuchi (1.6 HR/9, 4.37 FIP, 3.47 xFIP). Adbert Alzolay (2.0 HR/9, 5.03 FIP, 3.89 xFIP). The season is over halfway over and fantasy managers are losing patience. Read the rest of this entry »
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.
So, this post reinvents the wheel a bit. Perhaps it can serve as a mini-primer or -tutorial for you. At the very least it can serve as further validation of the work that the folks at Statcast completed and instituted a couple of years ago. Just keep in mind that the original post below remains intact, completely unedited.
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:
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).
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.
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.
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.
