We Know More About the Swing Now, but What Else Is Missing?

Gregory Fisher-USA TODAY Sports

It’s been a fun couple of weeks seeing all the work that has been done as a result of Statcast’s expanding into bat tracking. The great thing about this game is that there is always more to learn. With the addition of bat speed and swing length, we now have a better idea of telling the story of a player’s swing, but there is still so much more to tap into.

Back when I was using a Blast Motion bat sensor on a daily basis, I was exposed to every component of the swing that you could think of. Bat speed was one of them, but that only scratched the surface. There were pieces explaining my path at different points in the swing, how long it took my barrel to meet the plane of the ball, where in space that happened, and so much more. For a while, the public data available was focused on the outcome. What was the pitch? What was the result? What was the exit velocity and/or launch angle? With this new update, we’re progressing toward the how. How fast did the player swing? How long was their swing? We can now tie that in with the result, but there are additional details needed to understand the full scope of how results happened. That’ll be the focus of this piece.

First, it’s important to highlight the great work that has already been done explaining the new data we have and what the information tells (and doesn’t tell) us about the swing. Ben Clemens explained some applications of the new metrics and what their relationship with performance is on a macro scale. One thing Ben mentioned that resonated with me is thinking about the new (and old) information as inputs for us to use to understand performance rather than the answers themselves. Each piece works together to tell a story, whether that be league wide or player specific. Basically, these are pieces of information that need additional context.

Relatedly, Patrick Dubuque and Stephen Sutton-Brown from Baseball Prospectus, provided a great analysis of how to put bat speed into the context of pitch counts, from the perspective of both the hitter and pitcher. And there is more beyond just these two, including Noah Woodward’s Substack post about bat speed, swing length, and understanding what they mean and how they contribute to the swing.

Woodward touched on a few components of the swing that I’ve talked about in previous work that we still don’t have comprehensive data on from Statcast: contact point and attack angle. Swing variability, swing adjustability, having A and B swings, etc. are all extremely important to being successful at the big league level. If you have a hole in your swing, generally speaking, pitchers will expose you, so having multiple high-quality swings is going to set you up to have consistent success, just ask Triston Casas. Swing-by-swing data on attack angle, vertical and horizontal bat angle, and point of contact will all help the public understanding of swing variability, or when and how the swing changes in general.

Let’s start with attack angle. This is the angle of the bat path at contact, relative to the ground. As your bat travels through the zone, it creates a trajectory. To optimize your chances of hitting the ball in the air, the bat should be on an upward trajectory at contact, meaning you should have a positive attack angle. One component of swing variability is creating a positive attack angle at different heights, widths, and depths. You pretty much just want to be able to manipulate your barrel to move upward no matter where the pitch is. To get a better idea of what attack angle looks like, let’s look at a video from David Adler outlining a swing from Oneil Cruz:

While attack angle is officially measured as the angle of the path at contact, seeing the path leading up to contact can tell us what kind of depth the hitter creates. In this clip, the angle of the path changes as it moves from behind Cruz’s body to in front of it. This illuminates how attack angle is dependent on point of contact. In general, the farther in front of the plate your bat is, the easier it is to create a positive attack angle. However, this thread from Driveline’s Director of Hitting, Tanner Stokey, discusses the importance of creating bat speed deep in the zone. The best hitters create their peak speeds in tight windows. Like all facets of baseball, swinging is about striking a balance of creating high levels of bat speed and positive attack angles. You don’t want to have a one dimensional swing that is focused on high bat speed while ignoring the need to create ideal bat angles both deep in the zone and in front of the plate.

Depending on how you start your swing and enter the zone, it takes time to turn your barrel over into an upward slope. For many hitters, the bat needs to travel a greater distance to create the positive attack angle that leads to optimal contact. This, of course, takes more time. But, as Robert Orr pointed out last week in his piece on the relationship between pulled fly balls and swing length, a long swing isn’t necessarily a bad thing; it’s really just another data point. With access to attack angle, we could better tell the story of how a hitter like Isaac Paredes creates depth in his swing while often making ideal contact far out in front of the plate, versus a hitter who makes contact out in front without creating the necessary depth in their swing to avoid major holes.

At the same time, it’s still possible to create a positive attack angle deeper in the hitting zone. To get there, you need to make movements that aren’t easy to do while generating bat speed and controlling your body. Some hitters with great mobility use lateral torso bend — they lean toward their back leg right before contact — to get their barrel on an upward slope deep in the hitting zone. Think of Shohei Ohtani or Edouard Julien:

These two have unorthodox skills that allow them to launch pitches high in the air to the opposite field. With point of contact and attack angle, we’d be able to quantify how different they really are from their peers on top of the visual analysis.

Then there are hitters who create flatter (but still positive) attack angles with a path that stays on a similar plane throughout their swing. They get on plane with the ball early and don’t do much to change their path throughout the swing. It’s nearly impossible to do this with a steep swing. Juan Soto is a great example of this, even if he is more powerful than the other hitters with this swing style. Here is a great angle that illustrates what I’m referencing:

Soto’s vertical entry angle (angle of the bat relative to the ground at the beginning of the downswing) isn’t far off from his attack angle. You can see how much this swing contrasts with that of Cruz, who is a big dude with a narrow stance. Because of that, his bat path is vertically oriented, and his bat needs to travel a greater distance to get on plane with the ball. With more detailed information of barrel angles at different points in the swing, we would know more about how hitters like Soto and Cruz vary from one another when it comes to getting and staying on plane.

This has been a ton of information all at once, so I’ll leave you with one last tidbit. Depending on the hitter, the angle of the path at contact can be very different from the angle of the barrel at contact (relative to the ground), known as vertical bat angle. While I’ve cited average vertical bat angle from SwingGraphs on several occasions, I’ve always focused on putting the metric into context because it varies based on several factors. Luis Arraez and Aaron Judge can have similar average vertical bat angles, but that doesn’t tell us anything about how different their swings are. We know the metric depends on pitch height, but even that alone isn’t enough to explain why Judge is a launcher and Arraez is a sprayer. As we learned earlier, each data point is an input and isn’t meant to be used alone.

There is no question teams have been using, monitoring, and applying these data to scout and develop players for years now, but despite all the metrics that we have, the information on the public side is still lagging. Ideally, in future years, we will gain access to more swing data so that we can better understand the game we love.


Looking into the Heart Zone

Kevin Jairaj-USA TODAY Sports

For years now, a simple message has been gaining traction in major league bullpens and pitching labs: Just throw it down the middle. As big league pitches have gotten speedier and bendier, the people who throw them have been increasingly advised to trust their stuff, stop nibbling around the edges, and attack the heart of the zone. Adam Berry wrote about the Rays adopting this approach in 2021. In 2022, Bryan Adams superfan Justin Choi looked into the numbers and noted, “In each season since 2015, when Statcast data became public, hitters have accumulated a negative run value against down-the-middle fastballs.” Last year, Stephanie Apstein documented the phenomenon in Baltimore, while Hannah Keyser and Zach Crizer did the same on a league-wide basis, describing the Rays model thusly:

Step 1: Develop unhittable stuff
Step 2: Let it rip down the middle
Step 3: Win

Just last week, Jeff Fletcher wrote that after trying and failing to get their pitchers to attack the zone more often, the Angels started putting their pitchers in the box to face their own arsenal, courtesy of a Trajekt pitching machine. “I knew my pitches were good,” said José Soriano through an interpreter, “but when I faced myself, I find out they’re really good. So I have more trust in my stuff now.” Pitches right down the middle are called meatballs for a reason, but if you’ve ever watched peak Max Scherzer demolish the heart of the other team’s lineup by simply pumping 97-mph fastballs across the heart of the plate, none of this comes as a galloping shock.

Still, I wondered whether I could find data to back up this shift in mindset. Are pitchers really attacking the zone more often? And are better pitching staffs (or staffs with better stuff) really attacking the middle of the plate more often? After all, the Angels rank 22nd in Stuff+ and 14th in PitchingBot Stuff, not to mention near the bottom in ERA, FIP, and xFIP. If they feel this good about their stuff, I’d imagine that every team does. Read the rest of this entry »


Dan Szymborski FanGraphs Chat – 5/23/24

12:01
Avatar Dan Szymborski: Greetings!

12:02
Avatar Dan Szymborski: I’m sad to report that the chili I was making last week when we chatted did not turn out well.

12:02
Avatar Dan Szymborski: I got careless with the salt and the whole thing was way too salty.

12:02
Avatar Dan Szymborski: So I had to eat it with VERY improper cheese and sour cream added in to mute the saltiness.

12:03
Joe: Can Gil be a front line guy? Or Schmidt? Both have looked great

12:04
Avatar Dan Szymborski: Both are certainly making their cases. Pitching development is weird, so when a guys’ working out a lot of the time it’s just him working out

Read the rest of this entry »


Emmanuel Clase Is Cuttering a Swath of Destruction

David Richard-USA TODAY Sports

Since the dawn of time, there’s always been at least one elite major league closer who’s thrown the cutter almost exclusively. By “dawn of time” I mean the mid-1990s, of course, but I think we can all agree that civilization only truly began when humankind discovered frosted tips and cargo shorts. First there was Mariano Rivera, then Kenley Jansen, and now that everything from the ’90s is back in style, there’s Emmanuel Clase.

Clase has been a crucial part of Cleveland’s surprising run to first place in the AL Central; he’s recorded the win or the save in 18 of the Guardians’ 33 victories, and he’s fifth among relievers in WPA. Cleveland’s record in one-run games is 8-6, which isn’t particularly freakish, but the Guardians are 8-2 in one-run games when Clase pitches, and 0-4 when he doesn’t.

Here’s another fun one: Clase is on pace for the first 50-save season in MLB since Edwin Díaz in 2018, and 3.4 WAR, which would be the most by a Guardians reliever since 1988. WAR wasn’t even a stat back then! Read the rest of this entry »


No, You Can’t Trade Your Newfound Reliever for a Shiny Prospect

Rick Scuteri-USA TODAY Sports

I’m writing this article for selfish reasons. Every Monday, I chat with FanGraphs readers (come hang out with us! But not next Monday, because it’s a holiday). Four or five times per chat, someone asks a variation of the same question: “Should my team trade this reliever who has been better than expected to a contender for a huge haul?” Four or five times per chat, I say that they should, but that no one would trade with them. So now, I’m trying to put some numbers to it.

The first argument against doing this is fairly simple: Reliever performance doesn’t work that way. To measure this analytically, I took a bunch of recent seasons (2019, 2021, 2022, and 2023) and split them into two. I looked at the correlation between first-half numbers and second-half numbers for every reliever we listed as qualified in the first half of those seasons. I was looking for a simple question: How much can we infer about second-half numbers based on first-half numbers?

The answer, unsurprisingly, is “not very much.” There’s an obvious problem. Relievers simply don’t pitch very many innings. Last year, Jake Bird led all relievers in innings pitched at the All-Star break, with 53.1. Most relievers had meaningfully fewer innings. They didn’t pitch a ton of innings in the second half, either, because that’s just not how relief pitching works. Only 20 relievers threw 70 or more innings last year.
Read the rest of this entry »


What if the Rockies Only Threw Knuckleballs?

Isaiah J. Downing-USA TODAY Sports

On the first knuckleball thrown at Coors Field in 16 years, Matt Waldron hit home plate umpire Bill Miller right in the nuts.

Nobody — not Waldron, not his catcher Kyle Higashioka, not Miller — appeared to know where the ball was going. Despite Higashioka frequently (and understandably) struggling to track the flight of the ball throughout the rest of the night, Waldron delivered a career-best performance, allowing just one run over six innings.

Perhaps the most surprising part of his performance was the setting. Since 2008, knuckleballers have dodged outings at Coors Field, which sits 5,200 feet above sea level. Conventional wisdom dictates that knuckleballs at altitude are a bad idea, as Cy Young-winning knuckleballer R.A. Dickey told Dave Krieger back in 2012. Read the rest of this entry »


Job Posting: Miami Marlins – Senior Analyst and Analyst

Direct Links (Please see full job postings below):

Senior Analyst
Analyst


Senior Analyst

Marlins – Manager
Miami · FL
Player Operations: Team Administration/Operations

Role Summary:
As a Senior Analyst specializing in predictive modeling, you will play a pivotal role in transforming data into actionable insights to guide critical decisions across the organization. You will be responsible for developing and deploying sophisticated Bayesian models and automating workflows for cloud-based model deployment. Your role involves collaborating with cross-functional teams and aiding in player evaluation and strategic decision-making.

Key Responsibilities:

  • Lead the development and deployment of advanced predictive models using Bayesian methods.
  • Collaborate with cross-functional teams to integrate predictive modeling into cloud-based applications and tools for baseball decision support.
  • Mentor and provide technical guidance to junior analysts in the department, fostering a culture of continuous learning and innovation.
  • Stay abreast of emerging trends and technologies in data science and baseball analytics, identifying opportunities for innovation and improvement.

Qualifications:

  • Bachelor’s degree or equivalent experience in statistics, mathematics, computer science, or a related quantitative field.
  • 5+ years of experience building and deploying predictive models, with expertise in Bayesian methods using technologies such as Stan or PyMC.
  • Proven track record of success in productionizing models in cloud environments, with experience in platforms such as AWS, Azure, Snowflake, or Google Cloud.
  • Strong proficiency in statistical programming languages such as R or Python, as well as SQL for data manipulation and analysis.
  • Excellent communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
  • Passion for baseball and a deep understanding of sabermetrics and player evaluation methodologies.

Nice to Haves:

  • Advanced degree (Master’s or Ph.D.) in a quantitative field.
  • Familiarity with additional statistical techniques such as spatial statistics or time series analysis.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, age, disability, gender identity, marital, or veteran status, or any other protected status.

Job Questions:

  1. What is one baseball question you’d like to research? Please describe how you’d answer that question. (Max 300 words)

To Apply:
To apply, please follow this link.


Analyst

Marlins – Manager
Miami · FL
Player Operations: Team Administration/Operations

Role Summary:
As an Analyst specializing in predictive modeling within our Baseball Operations department, you will contribute significantly to data-driven decision-making processes. Your role involves developing and implementing advanced statistical models, collaborating with cross-functional teams, and aiding in player evaluation and strategic decision-making.

Key Responsibilities:

  • Develop and implement predictive models utilizing baseball data.
  • Collaborate with cross-functional teams to integrate statistical analyses into cloud-based applications and tools for baseball decision support.
  • Stay updated with emerging trends and technologies in data science and baseball analytics, identifying areas for innovation and improvement.
  • Provide support to Senior Analysts in model development and data analysis tasks.

Qualifications:

  • Bachelor’s degree in statistics, mathematics, computer science, or a related quantitative field.
  • 3+ years of experience in building and deploying statistical models.
  • Proficiency in statistical programming languages such as R or Python, as well as SQL for data manipulation.
  • Strong communication skills to convey technical concepts effectively.
  • Passion for baseball and familiarity with sabermetrics and player evaluation methodologies.

Nice to Haves:

  • Advanced degree (Master’s or Ph.D.) in a quantitative field.
  • Familiarity with additional statistical techniques such as spatial statistics or time series analysis.
  • Experience with Bayesian methods using technologies such as Stan or PyMC.
  • Familiarity with cloud environments such as AWS, Azure, Snowflake, or Google Cloud.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, age, disability, gender identify, marital or veteran status, or any other protected status.

To Apply:
To apply, please follow this link.

The content in this posting was created and provided solely by the Miami Marlins.


Job Posting: Miami Marlins – Data Engineer

Data Engineer

Marlins – Entry Level
Miami · FL
Player Operations: Team Administration/Operations

Position Summary:
The Miami Marlins are seeking a full-time Data Engineer for the Baseball Analytics department. The candidate will be responsible for designing, implementing, and optimizing ETL processes that ingest, validate, and organize baseball data. The Data Engineer will support the information requirements of our Baseball Operations deparments. Strong applicants will have experience with modern data processes and database management, with extensive knowledge of both SQL and Python.

Responsibilities:

  • Continuously improve the department’s access to information; design, develop, and optimize ETL processes to ingest data from new data sources.
  • Improve completeness, cleanliness, and timeliness of existing data sources.
  • Write automated, production-quality Python and SQL scripts using effective code practices.
  • Maintain high data quality standards. Proactively identify, diagnose, and resolve data issues.
  • Learn, extend, and improve the existing database architecture – ensuring data is well organized for end-users and easy to connect to other data sources.
  • Maintain a version-controlled code repository of ETL scripts.
  • Collaborate with Baseball Operations staff to understand our organization’s information needs.
  • Prioritize workflows effectively and share relevant expertise to best support data users.

Qualifications & Requirements:

  • Strong work ethic, attention to detail, and ability to self-direct.
  • Passion for engineering development, creativity, intellectual curiosity.
  • Excellent interpersonal, verbal, and written communication skills.
  • Demonstrated experience with SQL and Python.
  • Demonstrated experience with ETL/ELT processes and database management.
  • Experience working with data in various formats including JSON, CSV, etc.
  • Degree in Computer Science, Information Systems, or equivalent.
  • Understanding of and passion for baseball and baseball research.
  • Ability to work extended hours including evenings, weekends, and holidays.

Nice to Haves:

  • Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect.
  • Familiarity with cloud computing platforms such as Snowflake, GCP, or AWS.
  • Knowledge of container-based environments, including Docker and Kubernetes.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, age, disability, gender identity, marital or veteran status, or any other protected class.

To Apply:
To apply, please follow this link.

The content in this posting was created and provided solely by the Miami Marlins.


The Yankees Rotation Has Stepped Up in Gerrit Cole’s Absence

Brad Penner-USA TODAY Sports

NEW YORK — On Tuesday afternoon, Gerrit Cole donned the pinstripes and took the mound at Yankee Stadium, not for his long-awaited season debut, but for a key milestone in his rehab: his first live batting practice session since a bout of nerve inflammation in his right elbow sidelined him in mid-March. The reigning AL Cy Young winner is still at least a few weeks away from returning, but in his absence — and in the face of considerable uncertainty given last year’s performances — his fellow starters have stepped up to help the Yankees into the AL East lead and the American League’s best record.

In front of an empty ballpark but an audience of teammates, coaches, and media, Cole — who eschewed his batting practice jersey in favor of the real thing “because I miss it” — faced teammates Jahmai Jones (a righty) and Oswaldo Cabrera (a switch-hitter batting lefty) from behind an L-screen. He threw 22 pitches, working through his full five-pitch arsenal, and by his own admission, the adrenaline from the setting led him to push his velocity to 96 mph, a point where pitching coach Matt Blake told him to back off. “Matt yelled at me, so I had to throw it like 90 a few times to even it back out,” he quipped afterwards.

“To me, he looked very much in control, with easy velocity,” said manager Aaron Boone of Cole’s session. The ace is eligible to come off the 60-day injured list later this month, but his rehab isn’t far enough for that to be realistic. As for a return in June, Boone indicated that it was a possibility, “but I don’t want to get ahead of ourselves.” Assuming Cole’s recovery from the session goes as planned, he’ll probably throw a couple more BP sessions before heading out on a rehab assignment, which given the math of building up a pitch count points to a late June return. Read the rest of this entry »


Kevin Kelly Is a Tampa Bay Find With a ‘Unique Look’

Nathan Ray Seebeck-USA TODAY Sports

Kevin Kelly is proving to be yet another diamond in the rough for the Tampa Bay Rays. Acquired from the Cleveland Guardians via the Colorado Rockies in the December 2022 Rule 5 draft, the 26-year-old right-hander has since logged a 3.14 ERA and a 3.24 FIP in 73 appearances out of the Rays bullpen. Attacking the strike zone from a low arm slot, Kelly has fanned 74 batters while allowing 70 hits and just 16 walks over 86 innings.

His prospect profile was modest at best. A 19th-round pick in the 2019 draft out of James Madison University, Kelly was unranked prior to changing organizations, and going into last year he was conservatively assigned a 40 FV and a no. 27 ranking on our Rays list. Which isn’t to say that Eric Longenhagen didn’t recognize Kelly’s potential. Pointing to the side-slinger’s east-west arsenal and ability to keep the ball out of the air, Longenhagen wrote that Kelly had a chance to stick on Tampa Bay’s roster and be “a great option out of the bullpen when you need a ground ball to get out of a jam.”

Inducing worm-killers is indeed one of Kelly’s greatest strengths. Per Statcast, his 48.2% ground ball rate ranked in the 78th percentile last season, and this year he’s currently in the 91st percentile at 55.6%. And it’s not as though he doesn’t miss a reasonable amount of bats. His strikeout rate might not be anything to write home about, but at 23.0% it dwells in middle of the pack of major league hurlers.

According to Tampa Bay pitching coach Kyle Snyder, the righty reliever’s success is based on multiple factors. Read the rest of this entry »