Almost every coach or hitting guy has had that moment: They’re working with someone on a swing change, and the athlete can’t complete the movement. Not a single cue seems to register, video hasn’t helped, or maybe the kid will never make it. So after weeks of trying to help this guy, you just decide that maybe he’s just got what he’s got at this point, and then you move onto the next guy.

I know I’ve had those moments, more times than I would like to admit. I’ve played at the JC level and at D1. I’ve coached JV high school, JC, and a summer team in the WCL littered with good D1 talent. Each of these teams has had at least one of those guys, usually more, that are eager to get better, but they just can’t seem to make a change no matter what you throw at them.

I can’t tell you that they didn’t get better because of a lack of want or “buy in”; they were limited physically, and I couldn’t see it.

When you’re working with an athlete, he typically trusts you and wants to accomplish the ideas you’re throwing at him. But bringing up that same video of Barry Bonds to each player and saying “See that!? That’s what you need to work on!” in most cases doesn’t help anything.

It’s this situation where I would step in and say, “You’re wrong.” Now don’t get me wrong, video can be a great tool for an athlete, and it’s great to have videos of professional swings, but this can misapply certain lessons.

I wouldn’t disagree that the kid has work to do; I would simply point that that we don’t genuinely know as coaches that he can even do that physically. If everyone could move like Barry Bonds, why aren’t there many swings like his in the MLB? Why isn’t there a Bonds, Trout, and Griffey swing on each team? Because everyone moves differently.

Taking a Lesson From TPI

In April I went to the Titleist Performance Institute (TPI) Level 1 seminar. TPI is the leader in golf when it comes to building tour-level golf players. What they do better than anyone else is figure out exactly how their athletes move, then build a swing that is fit to their physical capabilities. I wanted to learn what they knew about the golf swing to see what could be applied to baseball.

TPI is great at getting golfers to move differently through various mobility exercises, strength programming, and PT work so that they can create a new swing with their new gains in mobility and/or stability. It also emphasized the vital role that objective measurements in screening can have on an athlete’s development.

At the level 1 seminar, I learned how to screen an athlete and what correlations it will have with his golf swing based on findings from the screen. But I also learned how to create a better form of communication with the athlete by explaining his movement deficiencies and what influences it might have on his swing.

TPI truly emphasized two things:

1. Coaches need to be aware of movement limitations and how they can help fix them.
2. Coaches also need to focus on how they communicate what they know with their athletes

Most importantly, it opened my mind to movement deficiencies and how much they affect a hitters movements/swing capabilities. Now a lot of you are thinking that golf and baseball are totally different sports. While this is true, the physical limitations in each athlete’s body can easily be transferred from the golf swing to a baseball swing.

Introducing the Assessment with a Focus on Communication

When I got back to Driveline, I immediately screened every hitter we had in the gym. I binge watched exercises and would try them on myself every morning before working out. Then I would program exercises to help our hitters get into better positions or movement patterns based on what I found in their screen.

The result of this screening is the creation of an individualized warm-up and recovery that each hitter has every day to help keep his body working at the optimal level. Creating an individualized program for each hitter is a two-way street for both the athlete and trainer.

Athletes should know the importance of their program and take it seriously if they plan on making substantial changes in the cage. If they are just speeding through the motions and not getting the most out of their exercises, you can expect them to see the same problems arise in the cage. Coaches need to effectively communicate what they see and explain the why behind what we’re telling them to do.

Besides that, a sense of ownership has to be given to athletes, because if they don’t take their part of the job seriously, how are we supposed to do ours? It has been very important to convey to guys when showing them their exercises, what it means, why it’s been prescribed, and how it will help them be a better hitter. Rather than tell a guy, “Hey, do these exercises every day, it’ll help,” you have to be more diligent in your explanation:

“Hey, you failed the lower-quarter rotation test, and for the past couple weeks we’ve been trying to get you to load into your back hip with no success. Try these exercises out; they should help open the ROM in your hips and allow you to get into that spot easier.”

Having the ability to deliver that message to athletes is huge when it comes to getting them locked in on what you’re trying to help them with. Of course some guys, the deep thinkers, need a separate approach so they aren’t looking at a 95 mph two seam and thinking about their gluteus medius being primed. More importantly, knowing the results of the screen and how each athlete moves are extremely important for trainers and coaches. Now when anyone is struggling in the gym, before opening my mouth, I’ll pull up their screen and see if i can figure out why they are failing so hard today. It has helped not only me but also our entire staff to grasp this concept, instead of hitting the panic button and rushing a guy over to the tee to work on some constraint or feel idea we might have.

This also helps teach athletes that getting better isn’t just about more swings; it’s about taking care of their bodies to put themselves in a better position to succeed.

Implementing a team approach is extremely important when it comes to truly helping athletes achieve success. If a hitter is doing all of his mobility/stability warm-up and recovery, and we’re working on the same swing change for a couple weeks now and nothing seems to be working, then there has to be consistent communication with the PT and strength staff. When using these lines of communication, language is critical!

When I come to a strength trainer or PT and say, “Hey we’re struggling with Ricky losing his barrel, get him in so we can make some changes,” and another hitting trainer comes to the same person and says, “Hey Ricky is scooping hard, can you work on him and see if we can change that?” it is going to be confusing to that staff member. The trainers might be telling them the same problem, but it could be received differently. How are they supposed to know what swing characteristic dictates which body movement?

To someone that’s not watching hitters every day, those two swing explanations could be taken as two totally different movements or problems. That’s where consistent language between each team (skill, PT, strength) must be implemented. Just one misunderstanding can set an athlete back onto the carousel of failure and frustration. While each team member thinks they’re “helping,” the athlete is just getting thrown more terms at him, which delays his path to success. The baseball community as a whole needs to do better in adopting a set of terminology for swing characteristics. But with the amount of bickering and wasted time that would cost, we’ve implemented our own terms in house. We’ve got to start somewhere.

Getting Baseball Specific

Following my trip to TPI, our entire hitting staff went down to Oceanside, California, the home of TPI, to the OnBaseU Level 1 seminar. OnBaseU was created by the same man who created TPI, Dr. Greg Rose. Dr. Rose created a new screen specifically for hitting, and he showed us how these traits in the body could impact a baseball swing. Having gone through the TPI course, I was ecstatic to get down to Oceanside and learn the screen for baseball. Our other hitting trainers (Tanner Stokey and Max Dutto) were blown away at the knowledge they had just received, and we were all eager to get back and begin screening everyone all over again. As we move forward with this process of individualizing programs for both hitting, strength, and movement, we see that hitters must be monitored more closely than we had thought.

Of course, hitters don’t have the injury risk that pitchers do, but eventually the game catches up with you, and then you go through those 0-20 cold spells. Why is that? Maybe, pitchers have found a formula to get you out; maybe, you’re pressing and swinging at bad pitches. Or maybe, there’s been something that has changed in your body where you can’t move the way you moved a couple weeks ago. All of these are very likely possibilities, but the easiest one to start with should be the body. Knowing where an athlete’s body is when he is hitting his best will give you the ability to see what problems he might be having when he’s struggling. It’s basically a cheat sheet for his swing (not Barry’s swing).

So, when that player comes to you and he’s been struggling, you can take him through a screen and show him how his external rotation on his back shoulder is much more limited than it used to be. Therefore, he might be casting his hands early or having to commit to pitches too soon. Or, you could find nothing and begin to work on the approach he needs to take to the plate based on how he’s been getting pitched. No more throwing stuff at the wall and seeing what sticks; the screening helps you find direction. Find out how an athlete’s body is working and create a plan of attack with your strength, PT, and skill staff to get him back on track.

You see it in cages all across the world. The athlete goes 0-20, and he’s in the cage working on the tee, taking countless swings to get himself back together, trying new drills and new ideas he’s heard from his friends. All of a sudden, he’s got a band tied to a pole behind him and wrapped around his waist, a band stretched off the knob of his bat to his front foot, trying to hit beach balls bouncing on the ground with a broomstick.

Nobody wants to be a part of that, and nobody needs to go through that soul-searching moment when the answer could have been to roll out their glutes and do some 90/90 before hitting each day, and the problem would solve itself. The body is an incredible machine, but when it’s not operating properly, there will be issues in the system. So, periodically hitters will be taken aside when they’re struggling and run through a few different test that might be the issue, before even addressing the swing. The answer could be as simple as doing a variation of planks for a minute, and they’re back to getting competitive swings off. Other times when the answer isn’t so easy, the team approach comes in extremely valuable, so we can catch the issue early and get them back to where they need to be.

This article was written by Driveline Hitting Trainer Max Gordon

The post Assessing Hitters and the Importance of Communication appeared first on Driveline Baseball.

This spring we will open the Driveline Research lab to city public schools if they want to get their high school pitchers screened at no charge in our motion capture lab and learn more about the science of baseball.

WHEN: Late Winter, Spring 2019 – likely weekday evenings and some Saturdays
WHO: Coaches from inner-city, public high schools local to Seattle prioritized
WHERE: Driveline Research Lab, Kent, WA (South Puget Sound suburb of Seattle)
WHY: If you are interested in the science of baseball and getting motion capture screening reports on your pitchers at no charge

Pitchers will be required to sign informed consent documents with their parent/guardian signatures (NO EXCEPTIONS) and dress in sliders/shoes to throw off our indoor mound.

Due to heavy demand, we will be prioritizing inner-city and public high schools local to Seattle first, then consider geographically diverse locations with the main priority always being based on need and complications with funding. It is the primary intention of this project to give back to inner-city and public schools who lack baseball resources with a secondary intention of widening a research base of high school-aged pitchers to help put a dent in baseball pitching-related injuries that are accelerating nationwide.

ATHLETES: We strongly, STRONGLY recommend bringing this idea to your coach instead of filling out this form. We CANNOT accommodate many – if any – high school athletes who apply through this form. If your coach is unresponsive or combative, you can fill out this form and provide further clarification in the free-form comment section.

How to Apply

To apply for consideration, please fill out the following Google Form:

APPLY FOR DRIVELINE PUBLIC HIGH SCHOOL FREE PITCHER ASSESSMENTS

We will be in touch as we collect applications. Due to anticipated demand, not all who apply will be selected and not all who apply will get a personalized response. Thanks for your patience and interest in this program!

The post High School Baseball Assessments – Free for Seattle Public Schools appeared first on Driveline Baseball.

The third annual Driveline Pro Day will be Sunday, January 13th. First bullet at 10:30 AM at the Driveline Baseball main facility, Unit 2-4. This year we will have both hitters and pitchers with live at-bats and bullpens. HD video + analytical data (Trackman, Rapsodo, HitTrax) will be sent out to all teams who send representatives.

Full list with contact info available upon request from scouts.

Driveline Baseball – Training Facility
19612 70th Avenue South, Unit 2-4
Kent, WA 98032
(425) 523-4030

Address and more contact info available at the contact page.

Scouts are all welcome to video from any angle they like. Seats behind home plate can be a bit limited. All pitches will be recorded using our Stalker Pro Radar gun, tuned to be right-on with the tenths digit turned on. We will either have Rapsodo data or Trackman data (potentially both depending on vendor availability) for our pitchers, and Rapsodo / HitTrax data for our hitters.

Thanks to everyone who continues to attend and make this a great event. We’ll have bottled water and coffee available as usual.

How to Register as a Scout

If you plan on attending, please email kyle@drivelinebaseball.com from your MLB-affiliated email account.

The post 3rd Annual Driveline Pro Day, 1/13/19 @ 10:30 AM appeared first on Driveline Baseball.

A pro pitcher recently reached out to me to discuss our program and whether or not it was right for him. He was worried that due to his arm action – and history of injuries – that adding load via weighted baseballs and/or PlyoCare balls could make him more susceptible to injury. On the other hand, though, this pitcher is very accomplished and considered a pretty good minor league prospect and doesn’t want to make gross changes to his mechanics since he’s already pretty successful.

I told him that it was a great question and a conundrum many great pitchers face – you want to get better, but you don’t want to negatively impact the things you already do well. It’s especially thorny when you are talking about durability or potentially reducing the chance of injury – it doesn’t help if you become more durable but you stop getting people out!

The biomechanics lab in action

Fortunately our training can be scaled up and down depending on the level of change you’re looking to make. A few examples from least aggressive change to most aggressive changes (focusing on durability, velocity, and overall throwing efficiency here) could look like:

• Free agent minor leaguer
• Organizational pitcher
• Mid-level prospect
• Solid prospect
• Elite prospect
• Up-and-down MLB pitcher
• Average MLB pitcher
• Elite MLB pitcher

The free agent minor league pitcher should be willing to take on more risk to potentially attain more reward. The Elite MLB Pitcher, on the other hand, would be a fool to expose himself to significant risk – the rewards are simply not worth it!

For the pro who wrote in, he falls between Solid and Elite prospect in the minor leagues, with a snag – he has an injury history and an unorthodox arm action. How would we approach this? It is important to take into account the person’s feelings as well – no matter what we think about the player, he’s a human being with a history of pitching at high levels and feelings about his career! In this case, the pro is interested in the work we do as told to him by other Elite prospects he hangs out with, but still naturally skeptical about doing anything different. There’s nothing wrong with this; in fact, that’s great! This pitcher is taking his career into his own hands and thinking critically about training.

Non-Invasive Durability Focused Training

We start all of our athletes off with a comprehensive assessment process which includes:

• Motion capture / biomechanics screening in our precise marker-based lab with 20+ cameras
• Strength and power assessment using velocity-based training and Keiser Fitness equipment
• Physical therapy and skill-based manual screening from qualified experts like our on-staff DPT
• Pitch profiling and skill analysis using Trackman / Rapsodo and high-speed video shot from our Edgertronic cameras

From the data we receive and put into TRAQ, our internal tools package that powers both Remote Training and tracks everything at Driveline, we have automated insights on how we can improve both performance and durability through a variety of means. An expert trainer sits down with the athlete and goes over the data, insights, and the path that makes the most sense given where the athlete is in his career. This is the part where computers cannot reliably replace humans – expert coaching is still required to get the most out of the data.

For an athlete who is looking to get better but not do anything “crazy,” we would keep the volume and intensity low and focus around lower-skilled drills while monitoring output over time. Through our new biomechanical pipeline developed by our R&D department, we process nearly 200+ variables automatically and come up with profiles to target the highest risk-reward items using our Athlete Typing initiatives using advanced clustering and statistical analysis principles.

We may assign a bunch of corrective exercises, heavy throwing drills at low speeds, force acceptance work, breathing patterning, recommend manual therapy techniques, improving specific power outputs – the list goes on and on. The power of TRAQ and our clustering algorithms at Driveline Baseball and Driveline Research means our coaches work faster, more efficiently, and give athletes targeted programming that is more specific to their needs with less guessing.

Over time, we can monitor our Efficiency metric, which calculates your ball velocity as a function of a combination of torques and injury predictors we have in our system. From the kinetic loads we get, we can tell if you are producing velocity more or less efficiently than before, and where we should take the training next. Forward Dynamics Analysis will eventually let us get even more precise here with soft tissue injury projection – something Driveline Research is working on every day in the labs.

This article was written by Kyle Boddy – Founder and Director of R&D.

The post Improving Durability of Pitchers Using a Biomechanics Lab appeared first on Driveline Baseball.

While we wrap up an exciting 2018, we have even more to offer coaches who will be at ABCA this coming year in Dallas.

Big plans for ABCA:

1. DrivelinePlus live event
2. Expo Theater talks from Founder Kyle Boddy and Director of Hitting Jason Ochart
3. ABCA exclusive discounts
4. DrivelinePlus event-booth presentations

DrivelinePlus Live

We are bringing our recently launched DrivelinePlus to ABCA. For coaches that have to recruit and run the operations of a program, it’s harder than ever to stay current on the technology and data that are accelerating every year. This is why we built DrivelinePLUS. We’re democratizing access to the most important concepts in every facet of baseball with in-depth, how-to videos, so coaches can stay ahead of the competition and develop better baseball players.

January 4 7-9 PM Escondido 1 & 2 Room

Over the course of two hours, various members of the Driveline staff will give presentations and be available for Q&As on some of baseball’s hottest topics. We will give coaches an in-person opportunity to interact with our staff, and the talks will be made available in the following weeks to members of DrivelinePlus.

DrivelinePlus will also be available for $175 as an exclusive discount for the first 100 purchasers at ABCA.

Expo Theatre Presentations

Both Jason Ochart and Kyle Boddy will have feature presentations in the expo theatre.

Jason will be presenting alongside Jeff Albert of the St. Louis Cardinals to discuss how to develop hitters in the modern game.

The talk will be in the expo theatre on January 4 at 9:45 am.

Kyle Boddy will be participating in a pitching discussion alongside Nate Yeskie of Oregon State, Robert Woodard of University of North Carolina, and Bryan Conger or Tarleton State University.

The talk will be in the expo theatre on January 4 at 10:45 am.

Exclusive Equipment Discounts

Some of our most popular products will be discounted to $55: Plyocare ball set, weighted ball set, trampoline, and leather wrist weights.

Booth Presentations

We will be available throughout the conference at event booth #1623. We will also be hosting a multitude of mini-presentations from all of our departments—hitting, pitching, strength, and R & D—throughout the event.

Make sure to stop by the booth to see what we have been up to in the last year and what we have in store for the future.

We look forward to seeing everyone at ABCA!

The post ABCA 2019 appeared first on Driveline Baseball.

No one can deny that baseball has undergone a drastic change in the last few years. An increase in technology and the ability to measure things we previously couldn’t has turned things on their heads.

While it can sometimes be hard to separate the exact effects of different technologies and their effects on players from randomness (hello, juiced balls), there is no doubt that the information new technology produces is here to stay.

You can get a good look at how teams are valuing this new information by the positions they are creating and who they are hiring. Whether it’s the Diamondbacks hiring Dan Haren last year as a pitching strategist or the Rays creating a process and analytics coach, teams are looking to shrink the gap between what the front office sees and what makes its way to the players.

There has also been an increase in hires from the college ranks, in part because the good college coach often has to learn the language of the tech and numbers while successfully communicating those finding to the players, which are main traits front offices are looking for.

But introducing more technology and analytics inherently gets at the source of the old-school vs new-school debate, which amounts to whether analytics and tech have a place in the game. This, in reality, is not an either-or proposition, and you need pieces from both sides to effectively help your athletes.

Regardless of which position you take, we need to admit a couple things.

1. Baseball, especially player development, is difficult and still has a lot of unknowns.
2. The new information we have available (via tech) helps fill in specific gaps.
3. Filling in gaps of what was previously unknown can help players develop and end up performing better on the field.

Since these new changes aren’t going away, we’re going to look at some aspects of how they’re changing the game and what coaches can do to stay ahead. Let’s look at the following areas:

• What gets defined as truth has changed.
• Why coaches need to go deeper into the data, using launch angle and spin rate as examples.
• How technology may change how coaches communicate.

The “Truth” Has Changed

It’s very simple to see how the “truth” of the swing or pitching motion was applied not too long ago (and to be honest, often still is applied today). Previously, much of the “truth” in baseball instruction could be repeated verbatim: a famous player says a specific cue or feel put him over the top, so that’s the truth.

• “Famous player says he does X move.”
• “Coaches hear famous player’s description of X.”
• “Coaches reiterate that description verbatim to instruct their players.”

However, an increase in technology and information has changed how we define “real” compared to “feel” along with how we communicate those concepts.

In the past, if a hitter said the key to his success was staying down to the ball, then that feel was not only accepted as the feel that made him best but also as the truth of how he was moving. New technology is driving a wedge between what feels are and the actual measurements of how a player moves.

At some point, objective measurement came in conflict with strongly held beliefs.

If a player is struggling to hit, he usually defaults to focusing on the cues and actions that he thinks are important. For example, if a player is in a power slump, this may mean that he spends more time working on staying down to the ball, or whatever he believes is the best cue for him.

An analyst may describe this player as having difficulty with his exit velocity and launch angle, which is likely a fair statement if the player is struggling with power. But a coach and player may get upset when they hear that he needs to hit the ball hard in the air, because that wasn’t the feel he thought helped him before. This creates conflict because the measurements (launch angle, exit velocity) are overlapped with feels (staying down to the ball).

It should be expected, then, to hear that some players don’t like the word “launch angle,” likely in part because it’s being communicated to them as a feel they should have instead of a descriptive measure.

The key here is that exit velocity and launch angle are descriptive terms, like many of the “new” terms that technology and analytics have created. They are not the cues or feels, and making this distinction is vital.

Coaches are going to be expected to know the measurements (exit velocity and launch angle) and then guide the player to the drills and cues that he needs to get back to a high level of performance.

The conflict is coming because we can’t separate the cues and feels of an athlete with the new descriptive measures that describe his performance.

Not defining or separating the fact that a player can say he doesn’t focus on launch angle is missing the key point. It’s stuck in the time when cues and feels are descriptive and accurate because they are the “truth.” But since cues and feels aren’t descriptive measures, we now have better ways to measure the previously unknowable, and it hurts both coaches and players when we can’t accurately distinguish between the two.

Exit velocity, launch angle, and spin rate are the truth because they are descriptive measurements, just like pitching velocity and a player’s 60 time.

Distinguishing the two allows us to admit what is actually happening. A player can have a consistent launch angle with his “truth” (i.e. “feel”), being that he things he swings down, because we’re in a place where both are true. The measurements are accurate, and the player holds on to the feeling of the movements he needs to be successful.

However, both can’t be accepted as true if we can’t first draw a line between measurements and feels.

Going Beyond the Surface to Fill the Gaps

The goal in coaching and player development is always to help players improve. Technology and analytics contribute to this goal by simply helping fill in the gaps.

We can take a look at how some of these new stats—launch angle and spin rate—can help coaches and at how diving just below the surface can help coaches even more.

So, if we divide the line between what an athlete feels and what the measurements are, we can start to see the value in the measurements.

As mentioned before, a player can struggle by not hitting the ball well. This can be divided into what he feels and how he moves (some of which can be measured by technology such as a bat sensor or KVest).

Hitting Example: Launch Angle

New metrics like launch angle can help fill in the gaps of how a player is performing and what he does well when he’s on and where he is when struggling. Launch angle can fit right in as a descriptive measure, along with other things that coaches regularly talk about. Just because launch angle might not be every players favorite term, doesn’t mean it can’t benefit a coach:

• Beyond average launch angle, a coach can look at the standard deviation, or distribution of hits, to see how players get to that average.
• Launch angle can also be affected by the types of pitches a player is swinging at; a decrease in LA can come from expanding the strike zone and swinging at a pitcher’s pitches. Similarly, an increase in LA can come from a player swinging at pitches he hits best.
• A mechanical adjustment may be needed (and can be measured by a bat sensor), but point of contact can also have an effect on launch angle. We know that home runs are more likely to be hit out in front of the plate, so when a player is struggling, you might see that he’s taking a cue like “let the ball get deep” too far.

Measurements like launch angle provide more context and can be used with other technology to get a more accurate representation of how a player is performing. This means the cues a coach says should depend on what the measurements show.

Pitching Example: Spin Rate

It isn’t uncommon for pitching coaches to intuitively suggest that a player may be cutting his fastball. Using technology such as Trackman, Rapsodo, the Diamond Kinetics ball, and a camera, coaches can actually see if that’s happening.

• A pitcher may be cutting his fastball, which may be seen in spin rate but is more likely to be seen in spin-axis changes.
• You can also look at ball-tracking information to see how that has changed the pitch movement, which is going to be more reliable than a coach’s eye.
• A camera can give the pitcher a visual of how he is releasing the ball, and a coach can use that to make more specific changes.

Now, this doesn’t mean that the term “spin rate” or “spin axis” is what the player needs to be told. But knowing what those are and how they are relevant means you are in a better position to make changes faster. This is especially true when you are comparing using technology versus just using your eyes.

Using technology can help shorten the feedback loops players and coaches go through in order to make adjustments faster. Video and spin-rate data can change someone’s perspective, especially compared to just verbal cues, but video, spin-rate data, and cues all work together in the end.

Coaches need to be able to find the change and then figure out, find, or piece together the reason behind the change.

All that involves communication with a player. The role of future coaches is to use information and technology to give more context and drive more specific adjustments. As you can tell, this moves past the “hear cue, repeat cue” that coaches have relied on for so long.

Communicating, or Not Communicating, Data to Players

One of the new things with data and technology is that what you learn may be best communicated directly or indirectly. This means that not everything known or learned about numbers or technology needs to be directly stated to players.

Some of the data serves as more background data justifying the “why” you want to do something instead of something you would repeat word for word to your athletes.

All of these new metrics describe how a player has performed but you need coaching to help maintain a high performance or turn around a poor performance.

There is a good chance that athletes will also be split on whether they want to receive more information or not. Some will want to have everything thrown at them; others just want to be pointed in the right direction.

The beauty of understanding what new technology and analytics can do is knowing that you can deploy it whenever necessary to fit the needs of your athletes and not simply to cater to those who work best with a more non-technical approach.

The point of the data and technology is to know more of the “why” and get a player to an improved state faster. Whether a coach explains all of the background knowledge on spin rate, or any other metric, depends on the topic and the athlete.

The Coach of the Future (Starting Now)

There is now more information available than ever before on the best athletes in the world and what they do. If we want to help improve our players, looking at what information is available is a good start.

Of course, intangibles such as being a good teammate, high character, having good grades, always hustling, and being baseball smart are still important. But those things aren’t going to be your ticket to the next level alone. You need those, plus numbers that are good enough or show promise.

This is why there will always be a role for traditional scouting to go along with the measurements that are now available.

However, the coaches of the future will be judged on the metrics and if the can make positive changes in their athletes.

These new coaches don’t need to know everything about a specific technology or metric, but they need to know what it is and how to ask good questions.

So, what we have to do is be honest with what we don’t know and realize there are more tools available now to fill in our knowledge gaps.

Much of this either-or debate of whether analytics or technology has a place in the game comes down to a misunderstanding of the coaching process and the prevalence of confusing descriptive measurements with player feels or cues.

In the end, technology will simply supplement the best coaches. It’ll help fill in the gaps and create better feedback loops for players. But the style, or translation, that the data will be communicated to the players will likely be the most desired trait of all.

This article was written by Project Manager Michael O’Connell

The post Technology, Communication, and the Future of Coaching appeared first on Driveline Baseball.

With new baseball technology readily available and widely used, it is becoming more important to be proficient in using the new information available to players and coaches. This information comes from batted-ball feedback (HitTrax/Rapsodo), bat-motion feedback (Blast, Diamond Kinetics), and movement feedback in biomechanics or force plate data (K-Vest, Boditrak). As this new, objective feedback becomes widespread, the advantage will be in developing a system to present the most relevant information in order to improve a hitter’s overall game. This means collecting data on players in order to find what is “good” and “bad” and then develop a plan to target the hitter’s deficiencies and further develop his strengths.

The first step in this process is to develop a test/retest system to assess, develop, and retest in order to find out those who are improving and how they are improving. Once this system is built, both athletes and coaches will learn and develop more efficiently, as objective feedback in multiple aspects of the game will assist the athlete in pairing “feels” to results and the coach in finding what methods or drills work for each athlete.

This article details the test/retest system implemented at Driveline and the importance of tracking development and communication between coach and player. We also cover how to implement a similar system across an organization, along with executing a retest system on a budget.

Driveline Hitting “Data Collection”

Many familiar to baseball social media have realized the assessment process has received a great deal of attention—and with good reason. Without this, you cannot expect to believe that a coach can form an athlete’s development plan without data and objective feedback. An assessment process is clearly important because it provides objective data of athlete’s training goals, allowing the athlete to understand their deficiencies, set a development plan, and achieve a desired result.

This article focuses on sustaining the development plan formulated through the initial assessment through a retest system performed bi-weekly. Every other Friday, we scrap our regular hitting day plans and perform a “Data Collection Day,” in which we use all the feedback tools Driveline has. This means lining up HitTrax, Rapsodo (for batted ball spin), Blast Motion, K-Vest, and high-speed video with Edgertronic cameras.

The goal is to give athletes feedback on their swings by pairing body and bat motion to a batted-ball outcome and have it all wrapped together with Edgertronic footage. Each hitter will take 6-8 swings in a controlled environment (flips) with a K-Vest strapped to his body, a Blast Motion sensor on the knob of his bat, and HitTrax and Rapsodo recording each batted-ball outcome. This data is collected by the hitting training and R&D staff and then analyzed further by our R&D team. When it comes to research, these days serve many functions, but in terms of coaching they provide feedback in the form of data paired to video in order to more precisely track our hitters’ progress.

Once the data is collected, the hitters receive feedback in the form of exit velocity, bat speed and other bat-motion metrics, a K-Vest comprehensive report, and video of each swing. This information may or may not be useful on its own, but that is where our hitting training staff comes in. They make sense of the data, show how the hitter is making progress, and make adjustments to his development program.

“Swing Design” and Its Role in Retesting

Once the original assessment is completed, the hitter has a personalized development plan. This is a huge step towards taking ownership of personal development, but the program will not function to its potential if the hitter is not continually assessed and held accountable throughout the training program. This is where our swing-design sessions come into play.

During our swing-design sessions—a 90-minute time slot where hitters get one-on-one time with me and other hitting trainers—the data collected during these retest days plays a crucial role. Based on the data, we formulate a daily hitting plan for coaching hitters on how to use batted-ball feedback, developing an intention and task to accomplish during group work, and further integrating strength and physical therapy. Also, our ability to assess a hitter’s movement quality and patterns through an OnBaseU screen allows us to monitor how he is moving and make positive adjustments according to what the hitter is physically capable of.

Let’s look a quick example of how this process takes place.

Through the original assessment it was deemed that this particular hitter struggles to pull the ball and do damage in the air—despite a big, strong frame. As a bat-first corner infielder, it is imperative this deficiency is cleaned up if the hitter wants to maximize his time on the field.

From simply watching him hit initially, it was simple to hypothesize that he struggled with doing damage in the air because of the bat-path issues which possibly stemmed from mobility and posture deficiencies. The Blast Motion data confirmed the bat path with a steep attack angle and low bat speed, despite the ability to accelerate and create power quickly.

Through our OnBaseU screen and K-Vest data, it was clear that mobility issues and posture had to be addressed. OnBaseU revealed a lack of rotational ability in the hips and thoracic spine while the hitter was able to bend and extend with the torso and hips. The performance graphs on K-Vest showed a lack of torso bend (only seven degrees of torso forward bend at heel strike), likely causing a steep bat path and inability to get the ball in the air for any production.

This led to the conclusion to initially clean up this hitter’s stance by adding a little more bend at the waist and being cued to maintain this posture throughout the swing. It is difficult to lift the ball when setting up in a straight up-and-down fashion, so we chose to attack the lowest hanging fruit by strengthening the movement patterns that the hitter was already capable of. We then adjusted posture in order to increase the athlete’s ability to pursue a deeper, more positive path to the ball.

From this point forward, the hitter is aware of his plan to make slight mechanical adjustments. He was given a specific time period to work with a coach and focus internally in order to acquire a new pattern. After a completed session, the hitter has a feel for his new pattern and an awareness of what his daily workouts will consist of going forward. This means we developed an intent and external focus designed to help the hitter accomplish the new goals over time. (For this particular hitter, his intent was centering balls over 90 mph and over a 16-degree launch angle, and his new goal was to do more damage in the air.)

At this point, it is crucial to continue to collect data and track the hitter’s progress. While participating in data collection days, in-gym hitters are asked to sign up for swing-design sessions weekly or bi-weekly in order to track and show progress and maintain accountability to the plan and adjust it, if necessary.

The adjustments and plan developed for this particular hitter represent a great start and could possibly accelerate his learning curve and development as a hitter by-itself. However, a plan means next to nothing if it is not constantly executed, tracked, and adjusted. Making changes one day and leaving them alone is one thing, but it’s another to find out if the adjustments or plan is working, along with keeping the hitter accountable. So, it is imperative the hitter receives feedback constantly and has quality coaching along the way.

Implementing a Retest System in Your Organization

A retest system is vital to a development program, but there are many questions around the new tech available and how to allocate coaching manpower in order collect data and effectively track athletes’ progress. An organization (school, travel team, facility, professional team) must decide what data is important to them, along with implementing the data-collection technology necessary to test and retest their hitters.

The new tech in baseball is well documented but requires serious financial investment and a coaching staff proficient in its use. If you are an organization with the capabilities to implement batted-ball, bat-motion, biomechanic, and high-speed video feedback, there is no reason that these tools should not be used to their fullest extent. This means not only purchasing the feedback and data-collection tools but also employing coaches that have proven effective at using them or educating other coaches.

To summarize, the Test/Retest system for hitters at Driveline contains four key aspects:

1. Collecting meaningful data (Assessment)
2. Analyzing that data and providing clear goals to the player (Assessment Meeting)
3. Retesting (Data Collection Days)
4. Coaching and communication (Swing-Design Sessions)

These four pillars cover all bases in terms of development. It is imperative that a team or organization collects baseline data, assesses that data, communicates development goals to athletes, and retests the athletes’ capabilities along the path to their development goals.

The baseline of implementing this type of system throughout an organization is the synchronization of the coaching staff and the players. This means an effective communication system must be established in order to relay the information to the hitter. If the information is not relayed to the hitter, he cannot track his own progress and can lose sight of the initial goals. Along with being held accountable, the player’s development should accelerate due to the clear, stated goals that need to be achieved and the process of achieving them.

Developing this communication pipeline is up to each team or organization, but a good start is setting a schedule for data collection and creating time slots where coaches can coach and communicate development plans with players in one on one or small group settings. This means that once data is collected, coaches should be allocated time slots with each player, or groups of players, to begin practicing and achieving specific development goals based on what the data says after initial assessment and periodic retesting.

Many coaches have been putting players into practice groups by certain deficiencies. For example, if a group of hitters display poor disassociation skills and another group displays poor bat speed, a coach can separate these players and coach them as individuals, since the drills and programming are likely to be the same.

Each organization will have different structure and technology available, but the main factors in developing a retest system is creating a schedule for data collection and establishing a communication pipeline through individual practice slots with coach and player.

Retest System on a Budget

For a coach with few resources available that many professional teams or big-time college teams have, the question of assessing and tracking progress becomes much more difficult. While understanding of the importance of collecting meaningful data and communicating it constantly to the player is necessary, a system must be developed that employs the resources currently available.

Assuming a coach has access to Excel or Google Sheets, a radar gun, batting cage, launch-angle strings, and a blast motion sensor, many meaningful data points can be tracked and communicated to players over time. Since bat speed and exit velocity have extremely strong correlations to each other and hitting performance, it is a good idea to start with those two metrics (Radar gun for EV and Blast for bat speed). The launch-angle strings enable a coach to track launch angle with exit velocity and bat speed, and this can be used to track “barrels,” or balls hit over a certain exit velocity and launch-angle threshold. A good place to start for many college or high school coaches is balls hit over 90 mph and between 15-30 degrees. A coach can tighten or expand these parameters as he sees fit.

Maintaining the principles of testing and retesting hitters, there are many possible scenarios available to execute a retest system. This is up to the individual coach and players, but when testing, retesting, and coaching on a budget, the best and cheapest place to start is with exit velocity, bat speed, and launch angle. Assessing these metrics will enable you to customize a quality coaching plan for each player and track it over time.

Conclusion

Retesting hitters and tracking progress is vital and serves multiple purposes for both players and coaches. The foundation of an effective retest system is the player and coach working together to share feedback and execute a development plan customized to accelerate the development of the player. There are many options when it comes to collecting and tracking data that are meaningful, but the importance of creating a communication and coaching pipeline that enables both player and coach to track progress over time cannot be overstated.

This article was written by Hitting Trainer Max Dutto

If you enjoyed this article and a DrivelinePlus member, you will likely enjoy some of our DrivelinePLUS videos on:

Launch Angle Strings

Setup for K-Vest

In-Depth Hitting Assessment Part 1: Batted Ball Profile

In-Depth Hitting Assessment Part 2: Blast Motion Metrics

In-Depth Hitting Assessment Part 3: K-Vest Biomechanics

In-Depth Hitting Assessment Part 4: Integrating Strength and Overload/Underload Components

A Deeper Look at K-Vest Data

The post Constructing a Test/Retest System for Hitters appeared first on Driveline Baseball.

What Is Blast?

Blast Motion Baseball by Blast Motion, Inc. is a motion-capture sensor for the bat that measures various data points with every swing. The version that we use at Driveline measures 14 data points:

1.) Attack Angle                                                          8.) Rotational Acceleration

2.) Bat Speed                                                              9.) Connection Score

3.) Blast Factor                                                          10.) Early Connection

4.) Power                                                                     11.) Connection at Impact

5.) Plane Score                                                           12.) Peak Hand Speed

6.) On-Plane Efficiency                                            13.) Vertical Bat Angle

7.) Rotation Score                                                      14.) Time to Contact

A detailed version of each of these metrics can be found on Blast Motion’s website, but most of the measurements should be self-explanatory.

As technology in baseball continues to evolve and become more accepted, Blast Motion sensors have become extremely popular because they are affordable and easy to use. Blast Motion, Inc. does a great job of making their applications accessible for players, coaches, and parents. The flow of information is seamless and combined with their industry-leading results, it is clear why they continue to be the best in wearable bat-sensor technology.

In 2016, they became the Official Bat Sensor Technology of Major League Baseball, and for the 2017 MiLB season, they were approved for in-game use at the Rookie affiliate level in the Gulf Coast League (GCL) and Arizona League (AZL). Beginning with the 2018 MiLB season, they were approved for in-game use at all levels. Their public partnership with the Houston Astros has also sparked their popularity. As the Houston Astros claimed the 2017 World Series, other teams started to take notice as Blast Motion sensors were a staple in the Astros’ hitting programs. Clearly the Astros see value in the measurements if they committed to a partnership while using the sensor in games and practice.

Get out the rye bread and mustard, grandma…it’s GRAND SALAMI time!! OSCAR CAMPOS!! His 2nd career pro HR is a delicious one, and the ‘Cats lead the Connecticut Tigers 4-0. Follow along on MiLB GameDay:https://t.co/wloaXDfkh4 pic.twitter.com/6GF5jHsY0n

— Tri-City ValleyCats (@ValleyCats) August 7, 2018

pic.twitter.com/pX7et2zTLC

— Quad Cities River Bandits (@QCRiverBandits) September 2, 2018

with the 3-run #Astros No. 7 prospect Seth Beer goes deep to left in the 6th to give the @bcreekastros a 3-0 lead as they look to eliminate @WSDashBaseball and advance to the Carolina League finals. Follow live: https://t.co/2r3XIyI89o pic.twitter.com/sKix0e5acH

— MLB Pipeline (@MLBPipeline) September 8, 2018

As all five of the Astros’ top MiLB affiliates made it to their respective league playoffs—with Double-A Corpus Christi and Triple-A Fresno making it to the championship round and Class A Short Season Tri-City ValleyCats and Class A Advanced Buies Creek winning their league titles—the rest of the MLB started to notice. Blast Motion recently announced that the Minnesota Twins will officially partner with them as well. Sensor data is becoming more widely used in the professional baseball ranks and is extremely popular among the amateur baseball crowd.

How Blast Motion Is Used At Driveline

Blast Motion sensors are a regular tool in Driveline’s hitting programs. We use this technology—along with HitTrax, Rapsodo Hitting, K-Coach Baseball (K-Vest) and video—when we assess athletes. We may also use specific tools for more targeted changes throughout an athlete’s stay and retest them every few weeks. This allows us to acquire large data sets for analysis. Comparing data from these sources allows us to identify all swing deficiencies and track hitter progress over time.

One of my weekly responsibilities is consolidating all of the individual hitters’ Blast Motion data from the previous week and comparing it to their overall data. My main focus is to determine how well a hitter performed last week compared to his overall numbers and make adjustments for the upcoming week of training.

Of all the Blast Motion metrics, we tend focus on Bat Speed, Attack Angle, and Time to Contact. For Bat Speed, we look at an athlete’s peak, average, and median values. For Attack Angle, we look at average and median values. Only the average value is compiled in Time to Contact. From the Blast Motion website, a user is able to export all of his data in a single sheet. Using tools created by the R&D staff at Driveline, we can then quickly gather these values and compare athletes’ overall numbers to their numbers from the past week. With all of the hitters at Driveline, this gives us the essential information for our trainers to determine who is making progress and who needs to change their hitting schedule. This is a great way for trainers with multiple hitters to keep track of their progress simply and effectively.

Knowing this, we wanted to look at comparing some common practices that hitters participate in and see what the differences are in Blast measurements. This led us to the research project below, which compared metrics from hitting off a tee, front toss, and a pitching machine.

Comparing Blast Motion Metrics on Tee, Front Toss, and Machine

In this case study, we wanted to see how swings react when under different stressors or environments. Not every swing a hitter takes from the tee or front toss in a controlled environment is the same compared to when he faces a live pitcher. This case study attempted to simulate an in-game environment by using a machine as a substitute for live pitching.

Over the course of a day, we took twelve Driveline hitters of near equal age/skill level and split them into three groups of four. In each group, we had hitters take two rounds of ten off the tee, front toss, and machine, for a total of sixty swings using their game bat for the entire duration. The machine was set at the same velocity/distance for each of the groups for consistency, and the tee was moved each swing. Our machine setting used the average right-handed release point with an 84 mph 4-seam fastball, which simulates a 91 mph 4-seam fastball.  

Results

Individual hitter data can be found in a Google sheet here.

Analysis

In our statistical analysis, we looked for overall metric summary values for each individual hitter and for all combined hitters throughout the three stations. We also ran comparison tests for the three categories to see if hitters had significantly higher values among them.

Four categories had significant differences throughout: Attack Angle, Vertical Bat Angle, Bat Speed, and Time to Contact.

Looking at the summary values, we see that Attack Angle took a near 6-degree jump from 10.88 degrees at the tee to 16.84 degrees at front toss. Attack Angle is defined by Blast Motion as, “the angle of the bat’s path, at impact, relative to horizontal.” It then went down a near 7-degrees at machine work. This may be expected in front toss since front toss is a more relaxed and controlled environment, which means that hitters will look to elevate more and put their best swing on the ball. Eight hitters had a significantly higher attack angle on front toss compared to the machine. Vertical Bat Angle, defined as “the angle of the bat with respect to horizontal at the moment of impact” or “the location of the barrel of the bat relative to the knob of the bat at impact” had 9 hitters with significantly higher front toss values than tee values.

Other notable metrics that changed were Bat Speed and Time to Contact. Bat Speed, “the observed speed of the sweet spot of the bat at impact” decreased a small amount from tee to front toss, but then noticeably dropped from front toss to the machine. Eight hitters had a significantly higher bat speed on the tee than the machine. Seven hitters also had a significantly higher bat speed on front toss than the machine.

This is expected as hitters have less time to react as pitch speed increases. Nine hitters had a significantly higher Time to Contact, “the elapsed time between start of downswing and impact,” on the tee than the machine. Higher pitch speed means less time to react. A hitter having less time to react means that his Time to Contact will need to decrease to make up for it. As a result of the hitter having to react quickly to start his swing, bat speed will decrease. This is normal and expected. It is no secret that higher-velocity pitching exposes swing weaknesses.

Practical Takeaways

All of the previously mentioned metrics also bring up the fact that swings can change based on the drill. There may be different positions that a hitter’s body gets into when swinging off of a tee, compared to front toss, compared to a machine. While the Blast sensor cannot measure those changes directly—meaning we can’t say changes came from specific kinematic differences—we can see that there are differences in how the bat is moving.

We also look at the Time to Contact metric and realize that it is likely going to change based on the depth that contact is made, as well as different body movements. Hitting off a machine is likely to result in a wider range of depth that contact is made, compared to hitting off a tee or front toss.

This may help coaches think of how they are spending their practice time and what changes they are trying to make. We can break the down the types of drills that hitters often spend time working on and rank them by difficulty:

Dry Work -> Tee -> Soft Toss -> Front Toss -> BP -> Machine Work -> Live

Each drill type can be scaled in difficulty as well.

From there, we can start to look at how much time their players are spending on certain drills and then look how far away those are from live game.

Spending most of an off-season working on swing changes on the tee or in front toss may be a way for hitters to see if they’re making progress. But those changes they make may not be desirable, or practical, when they face live pitching. This means the percentage of time that hitters spend on different drills is vitally important.

This should also affect how coaches and players consider using technology to screen hitters. Coaches and players will get more accurate screening data if they can capture it closer to a live-game situation.

Final Thoughts

The goal of each hitter should be to have his best swing at every single pitch. As hitting coaches, we provide programs and implementation for a hitter to do that and transfer over those practice numbers to an in-game setting. Practice may tell one story, but hitting against live-pitching tells the real story. Blast Motion allows us to quantify swing quality, and as in-game sensor data potentially becomes more readily available at Driveline, it will be very interesting to compare it to a practice setting. Then we can conduct official research studies to quantify how live pitching truly affects swing mechanics and look at further nuances in the metrics themselves for how they may change depending on different practice situations. Conducting official research studies using Blast Motion on how live pitching affects swing quality will allow us to accurately identify weaknesses and adjust practice accordingly to maximize in-game hitting performance.

This article was written by Research & Development Intern, Griffin Gowdey and edited by Michael O’Connell

Griffin spent the 2018 MiLB season with the Buies Creek Astros, the Class A Advanced or “High-A” affiliate, in the Carolina League as a Minor League Technology Apprentice, where he was responsible for all of their in-game video and data collection as well as overseeing all of the Blast Motion sensors.

The post Comparing Tee, Front Toss, and Machine Swings with a Bat Sensor appeared first on Driveline Baseball.

A “laminar express” pitch is a two-seam fastball whose term was coined by Trevor Bauer years ago when he developed a pitch that had dramatically more movement to the arm side than he previously would have had. It was the opinion of coaches at Driveline Baseball that this was happening due to the laminar/turbulent effect on a baseball, as first discussed in regarding baseball by Dr. Alan Nathan on The Hardball Times (images are unfortunately broken on this older post) in 2012, with the underlying science discovered by Dr. Rod Cross and demonstrated in this Veritasium video.

Dr. Nathan was able to derive coefficients for the components of movement with regard to acceleration when the boundary layer of air is asymmetrical:

From that, I can use the experimental relationship between the rotation rate and the Magnus force to estimate the magnitude of the latter. Since the movement is determined from the components of acceleration, and since the Magnus contribution can be estimated, the additional acceleration due to the roughness asymmetry can also be estimated.

Here is what I find for the forces, all normalized to the weight of the ball:

gravity = 1.00
drag = 0.78
Magnus = 0.32
roughness = 0.63

Boundary layer asymmetry – laminar/turbulent airflow – was responsible for quite a high percentage of acceleration change per Dr. Nathan’s calculations, which was tremendously interesting!

The science behind such movement of a baseball was exciting to analyze and actually deploy in a game situation, which Trevor was able to do after months of practice. Eric Jagers demonstrated clear evidence that two pitches that had dramatically different movement on high-fidelity high-speed video from our Edgertronic cameras could read vastly differently on a launch monitor device like Rapsodo, with the numbers being dramatically different and outside the tolerances for margin of error – driving up the statistically likelihood of the effect existing.

Eric’s trial below features a chart of two fastballs thrown with nearly identical vertical and horizontal break as measured by Rapsodo. The launch monitor detects the ball’s spin rate, spin direction, and velocity and recalculates trajectory based on a physics model – hence why it thinks both pitches shown in the video have nearly identical movement when in reality they are quite different.

You could chalk the “misread” up to an error on Rapsodo, but this is extremely uncommon on clean takes with the Rapsodo launch monitor, and when errors do occur, they almost always report missing data or seriously incorrect reads.

In December 2018, Dr. Barton Smith from Utah State University wrote about how a “laminar express” pitch might work.

The idea of a “Laminar Express” is to cause the flow on one side of the ball to be laminar, and thus have an early separation from the ball surface, while the other side is turbulent and has a later separation. The difference in these separations would cause a lateral force on the ball.

I’ve sketched the situation below. The pitch requires a 2-seam orientation, which produces large areas of smooth surface on the two sides of the ball. As I pointed out in the pressure gradient post, if a seam is near the front of the ball, it will disturb the boundary layer, but it will return to smooth, laminar flow because of the strong, favorable pressure gradient there. This is the case for the seam on the first base side near the front of the ball. The seam on the first base side near the back has no impact, because the boundary layer has already separated there.

Despite all this work and tweetstorms around the effect, it was unclear to many people – including Dr. Nathan himself – if the “laminar” effect was truly happening, and how to measure it.

So, what did we do? We decided to visit Dr. Barton Smith in Logan, Utah ourselves and subject some of our coaches to a grueling trial of throwing hundreds of pitches with the correct laminar orientation through his very specialized and expensive Particle Image Velocimetry machine.

Science in the Mountains of Utah

On January 15, 2019, a team from Driveline Baseball including Eric Jagers, Kyle Boddy, Joe Marsh and Dean Jackson visited the USU Experimental Fluid Dynamics Laboratory to attempt to capture the air velocity field around a “Laminar Express” pitch. Dr. Barton Smith, Nazmus Sakib, and Andrew Smith (not pictured) took the measurements.

From left to right: Dean Jackson, Kyle Boddy, Barton Smith, Nazmus Sakib, Eric Jagers and Joe Marsh.

(You can find Dr. Barton Smith’s post on the topic on his blog, Baseball Aero.)

Here’s what it looks like in high-speed when throwing baseballs into Dr. Smith’s amazing contraption!

Joe Marsh slinging some laminar express pitches into the PIV machine.

I won’t reproduce all of Dr. Smith’s work, but I’ll post a sweet image from his PIV analysis as well as his opinion which he tweeted out:

The figure below shows a PIV dataset of one Laminar Express pitch. Note that this is viewed from above. We successfully captured 3 Laminar Express pitches during the day. All three showed an important feature: the wake is tilted upward (to the left from the pitcher’s view). In the dataset below, the boundary layer on top appears laminar, in that it separates from the ball near 12 o’clock. On the other hand, on the other side of the ball, the flow is turbulent, and remains attached to the ball far longer. The net result is a tilted wake. The pattern fits my sketch from a month ago. This ball has a downward force on it (in the data frame of reference) which is to the right from the pitcher’s frame of reference. The PIV data near the ball are poor (and there is a large region of missing data at the bottom of the ball), but this does not affect my conclusions.

The preliminary results from the @EngineeringUSU PIV air velocity measurements of "Laminar Express" pitches in conjunction with @DrivelineBB. I am now a believer. This thing exists. Comments/questions welcome.

— 𝔹𝕒𝕣𝕥𝕠𝕟 𝕊𝕞𝕚𝕥𝕙 (@NotRealCertain) January 20, 2019

Conclusion: So What?

It’s a rich tradition of Driveline R&D dating back a decade now to replicate results we already knew were true. Some people would call this “wasting time,” but it’s actually the soul of science to confirm things, not always seek novelty. In this case, we were pretty sure our high-speed video, launch monitor numbers, and anecdotal experience just playing catch indicated that we were right, but science generally demands a higher bar to clear. Dr. Barton Smith’s lab at Utah State allowed us the possibility to delve deeper and see the mechanism by which the “laminar express” really does work, and the results astounded all of us (except for Dr. Barton Smith, who called his shot and was basically correct).

The end result is that we know how to add additional movement not currently tracked or understood by models used by Trackman, Rapsodo, or Flightscope (all for different reasons due to different underlying technologies and assumptions, but that’s a post for another day), and that’s really damn cool. It’s not every day you get to throw baseballs in an expensive materials science laboratory and do hardcore science, finding out things that no one else has discovered, but in January 2019, a bunch of nerds from Driveline Baseball got to do just that.

The post Laminar Express: Baseball Science Behind the Two-Seam Fastball appeared first on Driveline Baseball.

With increasing accessibility to tools such as Rapsodo, Trackman, and Flightscope at all levels of the game, spin rate and spin axis have largely dominated much of the recent conversation revolving around pitch design and repertoire development. This greater emphasis should come as no surprise given the relatively strong initial links between spin rate and descriptive measures of performance.

However, despite the large amounts of energy and attention being invested into looking at spin rate by coaches and athletes alike, many questions still linger in the public realm regarding the most fundamental aspects of spin rate.

Given that we now have four full years of Statcast data and a handful of recently published papers to analyze, we can start tackling some of the most basic questions about generating spin that were left unanswered just a few years ago.

How Do We Spin the Baseball?

Dating to as far back as Stevenson (1985), researchers have been interested in examining finger kinematics and kinetics during the release sequence of overhand throws. Most recently, a paper by Kinoshita et al. (2017) found that pitchers impart three “peaks” of force on the baseball during the delivery corresponding with max external rotation, the ball-rolling phase, and what we’ll describe as spin creation.

Essentially, as the arm accelerates, the ball produces more force on the fingers, which if unmatched, would likely cause the ball to fling backwards at any point during the delivery (Matsuo et al., 2017). Since we do not want this to occur, we subconsciously balance this force with our fingers by imparting a normal force on the ball in the opposite direction (Hore and Watts, 2011).

As we get to the end of this process, the thumb slips off the baseball roughly 6-10ms before release (Matsuo et al., 2017), allowing the ball to roll up our fingertips so that we can accelerate the ball towards the target and impart “shear force” (or tangential force) on the baseball about 3-5ms before release. It is hypothesized that this production of tangential force on the baseball just before release is both how and when spin is generated (Kinoshita et al., 2017).

Picture from Matsuo et al

Can I Increase My Spin Rate?

It might surprise you to learn that there are actually a few straightforward ways to increase your spin rate.

Increasing Velocity

As mentioned on this blog, velocity is linearly related to spin rate from an individual perspective, so increasing your velocity is a surefire way to increase spin.

However, the reasons as to why spin and velocity are correlated in such a manner are a bit less clear. According to Kanosue et al. (2014), “the angle at which the fingertips reached forward over the ball during the top-spin phase (arm acceleration) was highly correlated with ball spin.” The researchers speculate that because higher-velocity pitches need to have a lower vertical release angle to reach home plate as a strike (all else equal), the palm of the throwing hand would have to be angled farther downward at release, thus causing the fingertips to flex farther over the ball. Indeed, when we plot vertical release angle by spin rate once controlling for a pitcher’s individual average using 2018 MLB data, we do see some evidence of this effect.

Alternatively, since shear force was found to closely mirror resultant forces (the forces imparted on the ball to keep it in our hands), it could also be the case that as velocity increases, so too does the force of the baseball on the pitcher’s fingers. As a result, forces generated by the pitcher’s fingers are also magnified, thus increasing the ability of the pitcher to impart greater shear force before release. An illustration of this hypothesis is shown below.

In all likelihood, both of these theories have validity in explaining the link between velocity and spin rate and are at least somewhat interrelated with one another.

Adjusting Spin Axis

Beyond simply increasing velocity, we can also impart more relative cut on the baseball to increase our spin rate. This holds true not only for fastballs, but also across most pitch types as well.

The reasons as to why this occurs are a bit unknown given that forearm and wrist kinematics are difficult to measure and have often been overlooked in biomechanical research. We do know that pitch types with increased amounts of cut, such as curveballs, generally have greater supination of the forearm, ulnar deviation, and wrist flexion when compared to pitches with natural run or fade (Solomito et al., 2014). Perhaps one, two, or all three of these traits can explain why more cut equates to more spin, but more research is necessary to identify potential root causes.

Less Certain Ways of Potentially Increasing Spin

Finger Strength

If generating shear force is likely the main producer of spin rate, then the role of finger strength in generating shear force (particularly of the index and middle finger) should not be overlooked. Within the Kinoshita study, it was found that pitchers impart finger forces on the ball close to their strength limit (>80%) while pitching at max intent. From a perspective of purely force production, one could reasonably make the connection that increasing finger strength could have the potential to help an athlete generate more shear force on the baseball.  

Despite this fairly straightforward link, to our knowledge there has been only one study which has investigated finger strength and release spin (Woods, Spaniol, & Bonnette 2018). Perhaps counterintuitively, the authors found a negative correlation with spin and finger strength, which can likely be attributed to the fact that grip and intent did not seem to be controlled for and that pitchers were asked to throw curveballs rather than fastballs.

More work is needed to investigate the correlation between spin rate and finger strength, which is a subject we look forward to researching in greater detail moving forward.

Friction

Given that we know pitchers have only a handful of milliseconds to impart shear force on the baseball, any additional adhesive properties or friction between the fingers and the ball is likely going to be valuable in helping generate spin (Kinoshita et al., 2017).  

But achieving maximum levels friction and adhesion ultimately depend on anatomical and biological factors that are typically outside of an athlete’s control (Spinner, Wiechert, & Gorb, 2016). Attributes such as finger length, age, and sex (though not fingerprints) ultimately become key components in being able to impart spin on a baseball. As a result, it is easy to see why there is a common belief that all pitchers have their own inherent spin rate that is difficult to alter.

Complicating matters further, friction is also impacted by skin hydration levels, which are ever changing and often a function of the surrounding environment (Adams et al., 2013). For example, as air becomes drier, pitchers intuitively know to blow on their hands to apply moisture. Vice versa, as moisture is in the air and athletes begin to sweat, they know to apply rosin to minimize moisture.

It has been found that all athletes have their own optimal moisture level on their fingers to maximize their friction coefficient, meaning that there is no “one size fits all” formula with foreign substances to impart more spin (Adams, Briscoe, & Johnson, 2007). Instead, pitchers should find the right combination of legal substances available to them at any given time to obtain the ideal moisture levels to feel comfortable and impart spin on the baseball.

Can I Decrease My Spin Rate?

Changing Grips

Besides taking something off a pitch, it is widely known that using a grip that either splits your index and middle fingers or that incorporates your ring finger likely helps decrease your spin rate, even when controlling for velocity. This is something that we have tested in the past, finding that both a split finger grip and three-finger grip decreased Bauer Units when compared to more traditional fastball grips.

While the splitfinger effect is a bit easier to understand (try applying force to the palm of your opposite hand using both a regular and splitfinger grip), the three-finger finding is perhaps a little more peculiar given that more fingers should equate to more friction, and thus more spin.

Fortunately, we can look back to the individual forces produced by each finger during arm acceleration provided within the Kinoshita paper to understand why using the ring finger mitigates spin.

Looking at the illustration below, we find that the ring finger, when measured against the index finger, middle finger, and thumb, imparts about half as much shear force onto the baseball, on average, compared to the index and middle finger. The ring finger also has no “peak” of shear force just before ball release, which has been hypothesized as the main link in generating spin.  

So, in both cases, it seems as though each grip constrains our ability to impart absolute shear force on the ball in a unique way, thus limiting our ability to impart spin while holding intent levels equal. This lends credence to the idea that there is no grip that works best for everybody, as each pitcher will have different measures of finger strengths, finger lengths, and frictional forces that interact with the ball in a specific way.

Where Does This Leave Us Today?

Although we now know more about generating spin than we did a few years prior, there is a decent amount of work still needed to be done if we want to fully understand spin and how to develop it. Moving forward, investigating interactions between friction, finger strength, and grips and how they relate to changing spin should provide us with a better understanding of how we can improve the pitching quality of individual athletes.

The future is bright for pitch design, and it seems like we’re only scratching the surface.

This article was written by Dan Aucoin

The post How Do We Generate Spin? appeared first on Driveline Baseball.

This post from our Coaches Series was written by Justin James, Head Coach at Point Loma Nazarene University. He previously served as Assistant Coach at University of California, San Diego.

In prior blog posts, I covered implementing the Driveline Pitching Program for first-time coaches: 30 days Before a Season Starts, Year-Round Programming, and a Review of First Year Implementation. In this post, I cover implementation from a head coach’s perspective who is considering his development approach from the offense and pitching side.

I have used the Driveline model for several years with my pitching staff, with excellent returns and development. When hired as a head coach this summer, I knew I wanted the same approach from the offensive end that I took with the pitching staff. Keeping track of swings, testing, overall intent, and structure are what I believed to be the best approaches. Overload and underload training works, and I’ve seen it help my previous players.

Surveying a Team in the Fall: Pitching

Every year, it’s vitally important that coaches look back at what players did over the summer and plan out where they want to be in-season.

The pitching staff I took over was on an extended-rest period. This meant two things: the new members of the staff would have no experience with Driveline throwing protocols and demands, and the returners would have to on-ramp entirely again because of the very limited throwing over the summer.

Because of this, the first month of individuals was entirely dedicated to education of the protocols: light pen work and breaking down their movement patterns. On top of this, we included a four-day lifting split that was executed by our S&C coach Erik Pederson.

From the pitching side, I decided not to test anything in their first fall except their PlyoCare drills in order to establish a baseline and help them understand what their perceived exertion scale looks and feels like. Like mentioned in previous posts, it’s hard to know what your recovery exertion should be when you never record their recovery days and the same can be said on their max intent days.

Recording this information allows for better max intent, recovery, and blend days. You can see we had some tweaks to their specific days to account for over working and possibly under working on certain days. This was the plan that I felt best prepared our pitchers for the season.

Surveying a Team in the Fall: Hitting

We also decided that our hitting training needed to be re-hauled. We knew that we wanted to focus on intent, strength, and re-organization of mechanics and decided to do that through the Driveline Axe bat Underload/Overload program. Literally the first thing I did when hired as the head coach at Point Loma was order the Driveline Team Axe Bat Hitting System. It was the best purchase of the fall, without question. It gave variety, direction, and structure to our training on the offensive end. My assistant Jeff Calhoon was in charge of running the program and mastering the techniques to make it fit our needs and time constraints. He was willing to implement, continue his knowledge, and adjust to our players’ needs, and this paid dividends.

On the offensive end, we tested them over six different weeks. This included regular tee, rocker tee, and walk-in tee drills. Each drill was executed with barrel-loaded, hand-loaded, and underload bats. We tested both pull side and opposite field numbers to further connect the “why” to the drills and what we were trying to teach in our approach. The overall team’s improvement over the fall was obvious. We also kept note on what days we lifted or conditioned because this would skew our numbers; however, working on strength and pushing ourselves is the whole point of the fall, so we kept all numbers as is but documented those days for our own training knowledge (future tweaks to how/when we train/test). Here is our fall team numbers and a few examples of players who stood out.  

Each Player Was Also Given a Profile for Motivation and Tracking:

As you can see, major improvements occurred in some individuals and gave our team and ourselves a road map to development.

Practice Plan

One of the main deterrents to this type of training is a lack of baseline knowledge, cost, and the overall time it takes to implement in a daily or weekly practice schedule. Step one in my opinion is prioritizing what’s most important to you and your program. Our approach is to make our best players better through challenging demands and drills while also keeping track of their habits and cage work. More importantly is bringing the middle-tier players up to become more impactful when they are given the opportunity. This might be younger guys that project playing a year or two out or guys that simply need to learn a different way to be aggressive and get stronger.

With this in mind, it’s actually became easier to fit everything in. One of the best strategies was to eliminate wasted time from the pitching staff (shagging or sitting around). We simply started off the day hitting (BP and other offensive drills), and typically most programs finish with BP. The reason this worked best was it allowed our pitching staff to have ample time to warm up and get in ample long-toss time. Most days, this got them ready to go in between BP groups 3-4, and if done early, it allowed for mental work or help shag the last bit of BP.

The timing also helped by going straight into team defense with limited cool down. It gave my hitting coach, Jeff Calhoon, plenty of time to get what he needed done and same on the pitching end. During this approach, it also allowed for testing to be one of the offensive rotations to keep accountability up, instead of a wasted shag group or sloppy cage rotation. This also could be done easily during individual work to maximize gains in the limited time allowed per NCAA rules. Another aspect that goes unmentioned is it allows for easier “offseason” training plans (such as Christmas, Thanksgiving, or summer) that I have found very beneficial.

Finally, it pays off when both sides are training the same way. Hitters and pitchers both try to compete within their positions for gains against other teammates and themselves regularly. After my first fall as a HC, I, without question, highly recommend adding the DL/Axe training on top of the pitching DL protocols.    

The post Coaches Series: Pairing Hitting with Pitching appeared first on Driveline Baseball.

In a previous article, we discussed the difference between cues and measurements as well as how technology is changing baseball. We now know that instead of simply repeating cues verbatim to players, learning more background knowledge of certain topics is needed. One of those topics is spin rate. As you’ll see today there are lots of layers that go into understanding how a pitch is effective.

We’ve previously covered some basics of fastball spin rate and spin rate of off-speed pitches, but it’s time for another look.

In this article, we first look at high-spin fastballs and ball axis, and then we move deeper into the complex nature of spin, axis, and movement. We also explain some of the nuance with high spin and its relationship to movement. This article intends to show how a concept, like spin rate, can scale from relatively simple to more complex when you really dive into the metrics.

While the available information and what’s relevant to each player scales per level, equipment, and knowledge, today we aim to show what you can learn from even relatively simple explanations, which will open the possibility to learn more down the road.

Basics of Spin Rate: A Recap

When a fastball is thrown with backspin, we can explain its vertical movement with Magnus force. The ball spins, pushing the air downward behind it and creating and equal, opposite force upward. So a good part of the movement of a fastball depends on how fast or how slow, called the rate, it is spinning.

This can have effects on hitters, which we’ve observed through research using a pitching machine with different spin rates.

From: The Effect of Fastball Backspin Rate on Baseball Hitting Accuracy: Higuchi, et al., 2013

Picture from: Baseball Spin and Pitchers’ Performance: Kanosue, et al. (Open Access)

Now the fastest spin rate used in the study above is faster than what we’ve seen pitchers throw, but the point still stands: hitters are more likely to swing under a high-spin fastball. We also see that hitters tend to hit more fly balls and have more swings and misses on high-spin fastballs, which is why they’re more likely to hit low-spin fastballs on the ground.

We can also look at the relationship of fastball spin rate (while holding velocity constant) and its relationship to swinging strike percentage and average launch angle.

Now, based on reading the above chart, it would be easy to conclude that high-spin fastballs should be thrown high in the zone and low-spin fastballs should be thrown low in the zone. However, this conclusion isn’t so simple, and we need to look deeper to find better context.

First, you’d need to control for velocity. We’ve seen that spin rate increases with fastball velocity, but it’s also easy to misapply basic lessons—especially if you are comparing pitchers with different velocities. This is largely why we like to use Bauer Units, to better compare fastball spin rates, because you can have pitchers that throw the same spin rates, and whether they are high or low depends on velocity.

Below is a simple example, showing how pitchers with the same spin rate but different velocities have different Bauer Units.

Understanding Bauer Units and how to judge spin with them gives coaches further context of the spin rate of fastballs. But, especially with fastballs, ball axis also needs to be taken into account—and it’s often forgotten.

Fastballs and Spin Axis

There is a distinction between a high-spin fastball that has a high degree of vertical movement and one that does not. This is because of the ball’s axis.

So, it’s not just the spin that we want to focus on. We want to focus on the spin, what the axis is, and how that is related to the movement of the pitch. Even more specific, you can have two pitches with the same velocity and spin rate but have different movement profiles based on the axis of the ball.

Below is a pitch with a “1:00” spin axis and a 95% spin efficiency, based on Rapsodo metrics.

Below is a pitch with the same 1:00 axis and a 50% spin efficiency, based on Rapsodo metrics, giving it more cut relative to the pitch with 95% spin efficiency.

Now, video and some published research can tell us that the spin axis of each pitch significantly correlates with the orientation of the hands and fingers just before and at ball release. Which makes having a camera especially important when trying to piece together what ball tracking technology is saying.

When looking closer to see if a pitcher cuts his fastball we need to understand that some pitchers have essentially grown up cutting the ball when they throw a fastball, which is incredibly difficult to change when they’ve thrown thousands if not millions of pitches with that pattern. Other pitchers may have slight fluctuations in their release point, or fingers at release, that can change their spin axis, which can be adjusted given proper video and spin-rate feedback. This is important because one of the differences between amateurs and professional pitchers is the consistency of their spin axis.

The movement of a fastball will also be affected by a pitcher’s arm slot, or release point. As arm slot changes, so does the orientation of the hands and fingers at release, thus affecting the axis of the ball.

Assuming pure transverse spin, a pitcher who throws from “over the top” will have a nearly horizontal spin axis. A high percentage of backspin with a horizontal axis causes most of the spin deflection to be in the positive vertical direction. (That is to say, basically up.) With a more traditional three-fourths slot, we start to see more similar horizontal and vertical break values. This can be attributed to the axis shifting to a more diagonal position—meaning more sidespin. Lastly, a “low slot” or sidearm pitcher, will likely have a fastball spin axis that’s almost purely vertical. As a result, nearly all the movement will be lateral to the arm side.

As you can see, high spin rate by itself does not mean more positive vertical break. In this case, the pitch movement largely depends on the axis which the ball revolves around.

This was examined in 2015 in the Baseball Prospectus annual report. They used Brooks Baseball data along with arm angle measures of 25 pitchers (provided by ASMI). They ended up finding a correlation of .75 between four-seam fastball angle and arm angle and a correlation of .79 between two-seam fastball (sinker) movement and arm angle.

Lastly, how spin axis is measured depends on the technology used to track ball flight.

Both Trackman and Rapsodo measure spin axis but in different ways. Trackman follows the entirety of ball flight and infers spin axis from the trajectory. Rapsodo calculates spin axis directly, but infers trajectory based on the axis itself. Each method has its strengths and shortcomings, for example laminar flow isn’t accurately accounted for, but the main advantage of Rapsodo is that it can accurately measure spin efficiency. This is very helpful for designing pitches.

In short, knowing a pitcher’s spin rate on his fastball is important, but knowing his spin axis is also important because that gives you more context for how the pitch moves and how he can use it, or if it may be beneficial to try and change it.

Tying the Influence of Spin Rate and Spin Axis Together: An Even Deeper Look

Beyond the relationships mentioned above, we also know that spin axis influences both spin rate and spin efficiency. Essentially, the more you cut the ball, the more total spin you’re able to generate, often at the expense of transverse spin.

This means that a high-velocity pitcher who naturally cuts the baseball may have a significantly higher raw spin rate than a low-velocity pitcher with natural arm-side run, despite not actually being better at generating spin. In other words, a high-velocity, over-the-top pitcher who is actually less able to use a combination of finger forces and friction to add spin on the ball at release could produce a higher spin rate than a low velocity, sidearm pitcher who can generate spin well.

To control for this, we created Spin+, which attempts to predict a player’s spin rate, using MLB Statcast data, based on both spin axis and velocity. This allows us to isolate only the rotations per minute generated from factors outside of velocity and axis.

Spin+ is useful because we can compare the spin rates of pitchers like Andrew Triggs—who had an average spin rate in 2018 of 2,414 rpm with just an 89 mph fastball and a completely vertical spin axis (in x-z direction) of 269 degrees—with Carl Edwards Jr.—who throws a 94.5 mph fastball that averages 2,658 rpm and an almost completely horizontal axis of 173 degrees.

Although Edwards Jr. boasted the second highest fastball spin rate of any pitcher in the big leagues in 2018 with at least 75 fastballs thrown, Spin+ believes Triggs’ ability to spin the baseball is actually ~90 rpm higher than Edwards Jr.’s, once you control for the advantage that Edwards Jr. has with regards to velocity and axis.

The main takeaway is that raw spin rate can be deceiving without additional context—particularly in physically maturing pitchers where velocity may jump periodically, and spin axis may be more volatile. In designing a pitch, it is important consider how an athlete’s spin rate might change based on the adjustments you’re trying to make. Therefore, it’s important to not only monitor the raw spin rate but the axis as well. Video can also be helpful in this case to get a better idea of how the ball is coming off the fingers.

Applying Spin Rate and Spin Efficiency to Pitch Design

Additionally, we can also apply the Spin+ methodology to spin efficiency, which like raw spin also shares a relationship with spin axis and velocity. In combining both internal measurements of Spin+ and spin efficiency, we’re able to gain a much better understanding of where a pitcher has the potential to improve based on his underlying data.

To highlight an example using Statcast data in the illustration below, we see the top ten fastballs in 2018 that scored highest in our “pitch potential” metric. This metric is designed to identify fastballs in the big leagues with the greatest potential to increase total movement based on a pitcher’s inherent spin characteristics. By having a pitcher’s predicted and actual spin rate alongside his predicted and actual spin efficiency percentage, we’re able to compare what we’d expect his transverse spin rate should be compared to what actually occurs.

For example, in looking at the 2018 leaderboard above, we see that Tyson Ross threw his two-seamer (FT) with an additional 347 RPMs of raw spin compared to what we would predict given the velo and axis on the pitch. With this information, we can then calculate a Spin Efficiency percentage, which expects that Ross would generate ~2,190 rpm of transverse spin on his two-seamer during the 2018 season.

However, in looking at his output for the 2018 season, we see that Ross came up about 291 rpm short of that figure and only 14 rpm higher than what we would expect an average pitcher with an average spin rate would produce at his axis and velo. This means that Ross is essentially throwing his two-seamer (and four-seamer [FF]) with just league-average movement, despite being amongst the league leaders in generating spin.

Now, in spite of leaving movement on the table, this does not necessarily mean that Ross, or any other pitcher on the list above, has a “poor” design on their fastball. Rather, as you may have noticed, most of these pitchers are sinkerballers by trade, and any increase in transverse spin would impart more “carry” onto their fastballs. By killing movement via gyro spin, they are essentially making their sinkers or two-seamers “heavier,” which almost certainly better plays to their approach of how to get batters out.

This highlights a more parsimonious relationship between spin rate, spin efficiency, and the “right” approach to designing a fastball. There really is no a + b = c magic formula where player x needs to throw a fastball with a specific axis and efficiency percentage to have more success. Rather, we need to consider the constraints of the individual alongside a proper game plan to formulate something that will work best for an athlete’s specific skill set.

Conclusion

As you can see, there is a lot of depth to the discussion around spin rate. The rate that a pitch spins matters, as does the axis. But the axis can change depending on arm slot and the orientation of the hand and fingers at ball release. Beyond even that, you can get even more granular when looking into MLB spin-rate data.

Your head might be spinning from the more complicated sections at the end, but there are a few key points for coaches and players.

• Spin rate is important to understand, because it can affect the movement of the pitch, which helps determines the amount of whiffs and types batted ball outcomes a pitcher gives up.
• If you’re looking to judge a pitchers fastball, you’ll need to look past the raw spin and control for velocity. Bauer Units are a good way to do that, this is especially important for pitchers with outlier fastballs velocities, either lower or higher.
• Next you’ll need to look at the pitches spin axis. The spin axis, along with a movement profile, will tell you more about a pitch than just spin rate alone.
• There is a large amount of MLB data available that can tell you even more about different pitchers and their repertoires. We’re just starting to scratch the surface of what it means and how it’s most useful.

Lastly, we need to take a data-driven approach when asking a player to make a specific adjustment on how to throw a pitch. This assures both the coach and athlete that the time and effort spent making said adjustments do not go to waste.

This takes some getting use to, but recognizing what pitches have the potential to be good or bad is the first step in using technology. Ultimately, knowing whether a pitch is ‘good’ depends not only on the spin, axis, and movement profile of the pitch, but also how it performs in game.

As a result, being able to utilize technology to objectively assess a given pitch within the context of relevant metrics is vital to get the most out of a player’s given abilities.

This article was co-written by Dan Aucoin, Michael O’Connell, and Eric Jagers

The post A Deeper Dive into Fastball Spin Rate appeared first on Driveline Baseball.

While the beginning of the technology revolution in baseball analytics may have started on the pitching side, hitting is catching up fast.

More people and teams than ever have been using objective, precision-measuring technology to evaluate swing outcomes (batted ball technology like Rapsodo Hitting and HitTrax), swing characteristics (BlastMotion), kinematic movements of swings (K-Vest), and a few other categories that we ambiguously refer to here in a blanket cover-all reference.

We use all these tools at Driveline and they generate a ton of instant, usable data. But what is even more trendy than using any one of these tools by themselves?

Using two or more of them at the same time—and time syncing them to inspect on a swing-by-swing basis.

While we’ve been building a growing database of tools stretching across at least three categories, today we discuss timestamping individual HitTrax and Blast swings over the last few months. Below are some general descriptive results and relationships from around 9,000 swings of over 42 individual players.

Intra-Column Correlations Among Blast Values

First, let’s look at the relationship of the Blast column values to each other. Now there’s not that much literature on what these values should be and how they relate to each other.

Below are some descriptive stats for each of the Blast metrics and the intra-correlations between the metrics:

There are some extremely high correlations here between metrics, with not too much variable of a change in magnitude.

In particular, plane score seems to increase and decrease very closely with on-plane efficiency; the same relationship exists between rotation score and rotational acceleration. A look at the Bland-Altman plots shows an interesting possibility that there may be a constant scalar multiplier turning one metric into another. In other words, only one of each pair of metrics may be especially necessary when evaluating a swing or a set of swings.

Also of interest, and somewhat intuitive for the believers in bat speed, are the very high correlations with bat speed and power, as well as Blast Factor and the peak-hand speed.

There are also healthy, negative (and again intuitive if you can visualize what happens as you cut down on the time it takes to make contact) time-to-contact correlations with the rotation metrics, bat speed, power, and peak-hand speed.

Inter-Correlations with Blast and HitTrax

Now let’s fold in some HitTrax metrics. To keep it simple, here’s a general view of Blast correlations with some of the main HitTrax metrics.

The magnitude of the correlations may not be visible at first glance (especially not when compared to the daunting Blast intra-correlations), but since we’re looking at a sample size of 9,000 pitches, correlations become statistically significant at a very low level. Some of the ones that are particularly relevant include the following:

• Exit velocity increases (in order of strength) with Blast’s power metric, bat speed, peak-hand speed, and the rotation metrics, while decreasing with time to contact.
• Launch angle has a positive significant correlation with attack angle which, while completely different metrics, are often either confused for one another or believed to not be correlated at all.
• Correlations aren’t an end-all–be-all of measuring how these metrics interact of course.

Values by Binning (Level, Pt of Contact, EV, LA)

We can also bin these metrics by skill level and relevant context to gain preliminary insight into what swing characteristics differentiate pros from amateurs and how these metrics can be impacted by specific pitch tendencies.

In the table above, we see that both exit velocity and bat speed increase at a significant margin as the level of competition rises. Launch angle and attack angle are similar between our college and professional trainees, but each metric is significantly lower in our HS trainees from a relative perspective.

By examining more detailed Blast Metrics by competition level, higher skilled athletes seem to have a lower time to contact, a higher collision efficiency score, and a tendency to let the ball travel deeper when making contact.

By breaking up these metrics by pitch velocity at contact, we see that as a pitch comes in hotter, launch angle increases as attack angle decreases. Of course, if we’re trying to roughly match the plane of a given pitch to maximize outcomes at the plate, and slower pitches descend at a steeper angle (holding location equal), this intuitively makes sense. Interestingly, metrics such as plane efficiency, time to contact, and depth of contact were relatively unaffected by pitch velocity.

When hitting the ball farther out in front of home plate, batters tend to make contact with a higher attack angle, a longer time to contact, a worse plane efficiency score, and a flatter vertical bat angle. You can can also see that EV peaks at somewhere roughly between 22-25 inches out in front of the backend of home plate in our sample, something that has been confirmed by Hit F/X data at the big league level.

Lastly, we can break pitches up by vertical and horizontal location at contact to see how batters adjust their swing to accommodate pitches in different locations in and around the zone.

We see that as location height increases, launch angle and attack angle also increase at the expense of bat speed. Going up the ladder also negatively influences plane efficiency score, increases time to contact, and causes hitters to make contact farther out in front.

While there are less interactions between swing metrics and changes in horizontal pitch location (from a catcher’s perspective), it does look like a batter’s attack angle peaks and pitches travel deeper when a pitch is located down the middle. Connection score and plane efficiency also score better, on average, as pitch location moves farther away from a batter.

In general, this initial breakdown of Blast and HitTrax data shows us how important it is to consider the context of a given pitch and batter skill level when interpreting a player’s given Blast and HitTrax metrics. Before diving into intra-player analysis, exploring ways to control for different variables seems important in objectively assessing an athlete’s swing.

Averages per Players (Controlling for Individualized Means)

Since the whole sample spanned over 40 athletes, all with their own swings and tendencies, it’s worth looking at the means across the athletes as well—along with an initial exploration of a linear mixed-model analysis with the athlete serving as the random effect—to explore the possibility of the non-independent, repeated measures by the same athlete influencing our quantitative findings.

First, here’s some of the more commonly investigated metrics and their respective descriptive statistics for a list of the anonymized athletes:

From left to right, the metrics are as follows

• peak and average exit velocity
• peak and average bat speed
• peak and average rotational acceleration
• peak and average peak hand speed
• peak and average power
• the average and standard deviation launch angle
• the average and standard deviation attack angle

Here are some regressions ran against these means across a few different combinations, with the respective proportion of variance they cover. While peak and average EV both show high correlations across the metrics we look at here (bat speed, rotational acceleration, power, and peak hand speed), it’s interesting that one’s peak EV seems to correlate higher in all four cases.

We run the risk of correlations being uneven across different subjects and correlation of averages being a potential misrepresentation of the nature of the data. The correlation coefficient is defined as E[(X−μX)(Y−μY)]/σX σY. Even though it makes sense to take averages in this situation, by definition this changes the relationship between the x’s and μX and y’s and μy.

So to kind of loop back and make conclusions on some of these relationships, we also ran a linear mixed-model analysis on the whole dataset of swings, controlling for the pitcher as the random effect and then running a Wald Chi Square Test to determine the significance of the explanatory variable or the fixed effect across all the swings.

Yup, those are all extremely significant at any conceivable alpha level.

Now that we ironed out some long-thought ideas with some concrete numerical findings, we look forward to explaining some more complex ideas and relationships in Part 2 of the Blast-Hittrax data mash-up blog.

Co-Authored by Alex Caravan and Dan Aucoin

The post Pairing Blast and Hittrax Data appeared first on Driveline Baseball.

This past summer at Driveline, we had more than 200 athletes pass through our gym, and each of them went through a full biomechanics assessment. That’s a lot of data, a lot of arm paths, a lot of lower-half mechanics, a lot of kinematic sequences, and a lot of arm stress.

From the information gathered, we’ve been able not only to figure out averages and baselines but also a better idea for target metrics that we have identified as meaningful or actionable. Knowing what averages to look for when correcting an athlete’s arm action, or what a good lead leg block looks like, is essential to correcting mechanical flaws.

We won’t go too much into how we interpret biomechanics reports for athletes, but we’ll share some observations and provide a big picture look at the data.

What Can We Learn From 300+ Biomechanics Captures?

We had more than 200 athletes and more than 300 captures, but we had to do a bit of data cleaning. Markers falling off during captures and inconsistent mechanics can lead to high standard deviations in the signals we look at. To limit variance, we removed data with more than 5% variance in torque values.

We were left with 182 captures, an average height of 6’1”, an average weight of 195 lb., and an average fastball velocity of 81.3 mph. The table below details all of the metrics that we looked at:

That’s a lot of data.

Notes and Observations

We had an average pitch velocity over 81 mph in a motion capture lab. In the past, ASMI has classified 78 mph as “elite,” so by this definition the average athlete coming through our gym is elite. That’s something to be proud of.

On the mechanics side, we now know that the average thrower at Driveline releases the ball at about 90 degrees of shoulder abduction and about 0 degrees of shoulder horizontal abduction. This means that the throwing arm is raised neutrally out to the side, similar to a t-pose.

T-pose: arms in line with the shoulders—0 degrees of horizontal abduction—and raised to 90 degrees of abduction

So, if the throwing arm is mostly neutral and in-line with the torso, what’s extension?

Extension doesn’t come from extending your arm out in front. It is actually a result of forward trunk tilt (leaning forward), lateral trunk tilt (leaning to the side), and forward shoulder rotation. While arm drag can detract from this, making adjustments with your arm is only a small piece to the puzzle.

We also know that the average thrower at Driveline keeps his trunk closed by 10 degrees at foot contact and extends his knee by 3 degrees from foot contact to ball release. But what does that really tell us?

Not much.

The averages are just that. They set the baselines that we use in our biomechanics assessment, but we can’t draw many meaningful conclusions from seeing the averages of everything.

There are some valuable things though. Remember this video?

Basically, all that stuff remains true. We saw an average varus torque of 101 Nm, which is in-line with previous research. Assuming you have an average length forearm, that’s like wrenching yourself into external rotation with a 45 lb. plate.

Here’s one more fun tidbit: The average shoulder compression force, the force pushing your shoulder into its socket, was upwards of 1000 N of force. That’s nearly 225 lbs.!

Correlations to Velo

So, what actually correlates to velo?

The strongest correlations (r^2 > 0.2) are between kinematic velocities and joint kinetics. Simply put: move faster to throw harder.

We also see that throwing harder leads to higher joint kinetics, which actually contradicts past research. Basically, the harder you throw, the more stress you put on your arm.

Now the hard part will be figuring out how we can make an athlete rotate faster, move his arm faster, and extend his knee faster. We can save that for later.

Looking at kinematic positions, we actually see that no positional metrics were really correlated with velocity. Forward trunk tilt at ball release has the strongest correlation with an r^2 of 0.195. Past studies have linked forward trunk tilt to velocity, so there may be something there.

Studies have also linked maximum external rotation as well as shoulder horizontal abduction at foot contact to velocity, but we show only weak correlations.

We also see no correlation between torso angle at foot contact (meaning keeping the trunk closed), but that doesn’t mean you shouldn’t throw with your shoulders totally open. It just means we need to look at the data a little differently.

Positional metrics are harder to address in their relationship to throwing velocity. For example, someone can hit all the right positions, but just be moving very slow—and thus throw the ball slow. It’s a multivariate problem in this regard.

It could also be that the positional metrics simply don’t linearly scale with velocity. There’s a sort of diminishing returns when making changes to those metrics. For example, if you already have 50 degrees of scap retraction, increasing that even further may not make you throw harder. Or if your trunk is already closed off by -20 degrees at foot contact, chances are that closing yourself off even more won’t help all that much.

While you can hit all the perfect positions during a throw, if you’re not moving fast, you’re not going to throw hard. If you’re not strong, you’re not going to throw hard. There’s a multitude of factors that can contribute to velocity—being in the right positions is just one of them. This is where we start to see the real complexity of pitching mechanics.

Correlations to Torque

It could also be argued that some metrics imply that an athlete is throwing more efficiently—that is, with less stress.

There are definitely sequencing patterns more conducive to long term health and others more conducive to higher velo. Can I concretely say what those patterns look like/who displays them? Nope, but I can say that they exist. I think. Maybe. Who knows.

— Rob Hill (@Berticushill) January 24, 2019

Taking height and weight out of the equation, let’s look at correlations to normalized torques. I’ve provided the most interesting looking correlations, but for those interested, the full table is available.

To prevent the possibility of a multiple comparisons problem (since we are comparing a large number of variables) we used a Bonferroni correction in order to implement a stricter P-value cutoff.

Significant correlations are noted by the P-values with an asterisk next to them.

A couple of things make sense: There are a few correlations to kinematic velocities. The faster you move, the harder you throw, the more torque is put on your arm.

There’s also some interesting correlations to positional metrics. Lateral trunk tilt at ball release is positively correlated with varus and shoulder internal rotation torques, and shoulder abduction at ball release is negatively correlated to varus, internal rotation, and flexion torque.

Lateral trunk tilt is the amount of side tilt that an athlete has. This largely determines arm slot. Shoulder abduction is the upper arm moving in the frontal plane.

So increased lateral trunk tilt and decreased shoulder abduction at ball release both correlate with increases in varus and internal rotation torque. This is how we define “pulling off the ball”—excessive side tilt and arm dragging below the shoulder line. Here’s what that looks like in Visual3D: 

We also have decent evidence that pulling off the ball has negative effects on joint torques, which is exciting.

Maximum elbow flexion has positive correlations with elbow varus and shoulder internal rotation torques. This means that being excessively “inside 90” with the arm action could have negative effects on those torques.

Elbow flexion at max external rotation is negatively correlated with elbow flexion and pronation torques, but positively correlated with shoulder adduction torque. This potentially means that decreased elbow flexion (meaning the arm is more extended) at max external rotation—what we would call forearm flyout—could lead to an increase in both elbow flexion torque and pronation torque, but an increase in adduction torque.

Front knee flexion at ball release saw significant negative correlations to varus, shoulder internal rotation, adduction, and horizontal adduction torques. This means that having a more extended knee at ball release—something that potentially indicates a strong lead leg block—is correlated to those increased torques.

This only looks at a couple of metrics available, but it provides insight defining good and bad mechanics. If we can quantify what positions actually lead to a reduction in joint kinetics, that’s immensely valuable.

Bins

Let’s look at some comparisons. First: what’s the difference between people who throw slow and people who throw hard?

We binned athletes into five groups: >87 mph, 84-87 mph, 81-84 mph, 75-78 mph, and 75- mph. Our lowest velocity recorded was 69 mph. These were all judged under a similarly-corrected alpha level using a Bonferroni correction.

Kinematic velocities are where we see some obvious differences. From the slowest group to the fastest, trunk angular velocity, elbow extension angular velocity, and lead knee-extension velocity all saw significant increases.

Simply put: rotate faster and make your lead leg block better.

Looking at the kinetics of those who threw slow and those who threw hard, we see confirmation that throwing harder results in more stress on the arm. Even normalizing for height and weight, we see significant increases in all torques except for elbow flexion torque.

Again—throwing harder is significantly correlated with increased torques on the arm.

What if we normalize for velocity to create a velocity-to-stress efficiency metric? A higher number would mean that you are more efficient—that is you generate more velocity per unit of stress. A lower number means you are getting less velocity per unit of stress.

Here we see that all efficiencies go down in the group that threw harder. The harder you throw, the less efficient your mechanics become. Although the changes were not significant (possibly due to the smaller sample size of the two groups), the results are still interesting.

What could that mean? It’s difficult to say, but perhaps as you throw harder, mechanics become less efficient mechanics. Intra-athlete testing of throwing at 50% intent all the way up to 100% intent could provide additional insight to this. This is something we’ll continue to look at as we have more high-level pitchers throw in our lab.

Now, let’s look at lefties versus righties.

Positionally, no differences stand out. From a mechanical standpoint lefties and righties move about the same.

Looking at the kinetics, we see something interesting. When normalizing for height and weight,  varus torque, shoulder internal rotation, and shoulder adduction torque were all significantly lower in lefties.

Interestingly, average velocity by lefties was lower, which could have led to the reduced torques. When factoring in velocity, only varus torque velo efficiency was significantly correlated—which is still impressive. Lefties averaged 16.1 mph per unit of varus stress, whereas righties only got 13.2 mph per unit of varus stress.

What this means is that if you’re a righty and your arm hurts, you should consider throwing with your left arm. Is that how that works?

It’s worth noting that the sample size of lefties was on the lower side (n=25), so a larger sample size could provide us with more insight into these differences.

What’s Next?

There’s a lot we can take away from this information. We’ve shown that the faster you move, the more likely you are to throw hard. The harder you throw, the higher the torques on your arm. We’ve quantitatively shown that pulling off the ball can lead to an increase in torques on the arm.

But there are still a lot of unanswered questions.

We weren’t able to correlate any positional metrics with throwing hard. Beyond kinematic velocities, we weren’t able to really see why people throw hard.

There’s also an abundance of other metrics that we haven’t looked at. Timing and sequencing obviously plays a huge role in the delivery, which we didn’t address. We didn’t look at anything in the glove arm—can we quantify positive disconnection? Those are just two ideas to start.

The metrics examined here were generated by an older version of our pipeline. In the newer version, we hope to better answer these questions and look at some other metrics that we think could be interesting.

The pitching motion is incredibly complex system that’s reliant on many factors: strength, speed, timing, flexibility, and many others. Using an ever-expanding list of biomechanical markers and metrics, integrating force plates, and focused research studies, we can continue to enhance our understanding of the pitching motion.

Down the road, initiatives like forward dynamics and more in-depth athlete typing will hopefully provide us with a more complete picture of what makes pitchers throw and what we can do train them. We’re still only at the beginning.

This article was written by Research Director Joe Marsh

The post Biomechanics Rewind: A Look at the Numbers from the Last Six Months appeared first on Driveline Baseball.

Jeff Passan: ESPN Baseball Columnist

Jeff Passan– (@JeffPassan on Twitter) is a baseball columnist with ESPN after a 13 year stint with Yahoo Sports, and author of New York Times Bestseller The Arm: Inside the Billion-Dollar Mystery of the Most Valuable Commodity in Sports.

Jeff joins Mike and Kyle to discuss the process of creating “The Arm” and the journey he went through writing the New York Times Bestselling book. Jeff also goes into some of the hottest topics around the game including MLB’s fame problem and not connecting with the younger audience, what Kyler Murray choosing football over baseball means for multisport talents moving forward and the impact of this year’s free agency.

To view more of Jeff’s work click here

Listen now on Apple Podcasts, Google Play, Spotify, Stitcher or your preferred podcast player.

The post Driveline Baseball Podcast- EP. 16 Featuring Jeff Passan appeared first on Driveline Baseball.

As access to ball-flight data becomes more readily available and intricate theories of pitch/swing design continue to increase in popularity within the player-development community, we can easily overlook one of the most arguably fundamental concepts of the game: how batters see the ball as it heads towards home plate.  

While much has been written within the sabermetric community about tunneling, game theory, vision training, etc., few researchers have thoroughly reviewed what information sources hitters are attuned to and how these sources influence perception. Let’s take a deep dive into what we now know about gaze behavior and how that might affect our respective in-game approach.

Note: When we talk about gaze tracking, or gaze behavior, we’re talking about where a player is looking; that is, the movement of the hitter’s eyes and head as he tracks an incoming pitch.

This review is detailed and comprehensive, so if you only want a summation of the discussion, we recommend scrolling to the key takeaways at the end. For those who want all the gory details of gaze behavior in batting, let’s begin.

Intro to Batter Gaze Behavior and Basic Constraints

Research on batter gaze behavior started with the work of Hubbard and Seng in 1954, who used both qualitative observation (in-game) and cinematography (during BP) to analyze the head and eye movements of MLB and NCAA batters. Using relatively crude methods by today’s standards, the research concluded that batters tend to keep their head relatively fixated while tracking a pitch they intend to swing at (likely where the axiom “keep your head still” is derived from) and, more importantly, that eye movements of expert batters cannot track a pitch all the way to home plate.  

Given the technological limitations at the time, it took until 1984 for anyone to publish a worthwhile attempt to replicate these findings and build on Hubbard and Seng’s paper. Researchers Bahill and LaRitz analyzed several college players and one MLB subject (Brian Harper) in their ability to track a wiffle ball attached to a fishing line propelled by a motor at greater than 60 mph (as you can imagine, batters were not permitted to swing).

Bahill and LaRitz’s main findings partially confirmed the work of Hubbard and Seng: batters were generally unable to follow a pitch in their central vision as it crossed home plate, despite the relatively low velocity of the incoming pitch and the batter’s inability to swing during the experiment. As noted in the paper, the average amateur player’s gaze falls behind the ball at roughly nine feet in front of home plate, whereas MLB participant Brian Harper was able to track the pitch until roughly five and a half feet in front of home plate.

Conceptually, this makes sense based on what we now know about smooth-pursuit eye movement and angular velocities of a baseball as it travels towards home plate. Although baseball players have a superior ability to track fast moving objects compared to the general population (Uchida et al., 2013), there are a couple of things working against them that make it extremely difficult to obtain useful information from the latter part of ball flight. First, the angular velocity of an incoming pitch can reportedly reach up to 750 deg/sec as it crosses home plate (Higuchi et al., 2018), which is well above the highest reported values of smooth-pursuit eye movements in humans of 120 deg/sec (Bahill & LaRitz, 1984). Second, batters are also constrained by visuo-motor delay, or the time it takes to react to an object’s change in trajectory. For example, it takes roughly 200-250 milliseconds for us to react to an unexpected ball and check our swing (Gray, 2009) or to react to an instant change in target trajectory in a finger-tracking task (Engel & Soechting, 2000).

As a result, when batters are occluded from seeing the last 150 milliseconds of ball flight, there are no significant differences in performance than if they were able to see the entire trajectory of an incoming pitch (Higuchi et al., 2016). With these results in mind, the concept of tunneling was born to take advantage of these limitations in batter perception.

However, like most complex topics related to human behavior, there are certainly more layers to batter gaze behavior to consider.

The Expert Way to See the Ball

As mentioned above, the MLB participant in Bahill and LaRitz’s research (Brian Harper) was able to see a pitch deeper in its trajectory relative to the lesser skilled participants. In doing so, Harper demonstrated an ability to produce faster smooth-pursuit eye movements and to couple both head and eye movement together, which dismisses the head-movement axiom promoted by Hubbard and Seng. This ability stands in opposition to the collegiate batters, who were only able to use either head or eye movement when tracking a pitch, but not both.

To add an additional caveat to the findings, one amateur batter in the Bahill and LaRitz study was able to use an anticipatory saccade during ball flight, which allowed him to align his gaze with the ball as it crossed home plate. Bahill and LaRitz noted that this batter was perhaps motivated to do this in order gain additional visual feedback on where the ball would have made contact with the bat, a form of feedback that we now know is important in batting (Gray, 2009). They coined this gaze strategy as the “optimal learning strategy,” in which batters sacrifice the ability to adjust their swings during the middle portion of ball flight in order to “park their gaze” at contact.

As a result, these findings provide a much more complex picture of how batters track the ball, as multiple strategies can seemingly be used by different batters of varying skill.

To explore this further, albeit in cricket, Land and McLeod (2000) sought to breakdown the differences in gaze behavior of three batters of varying skill levels using a pitching machine. In their experiment, they found all three batters used an anticipatory saccade at ball bounce and, more interestingly, that higher skilled batters initiated their saccades sooner.  

Combing these results with those of Bahill and LaRitz, a straightforward takeaway might be that expert performers are simply better able to align their gaze ahead of the ball for the majority of ball flight, and that they employ a variety of unique skills to do this. However, we can only take away so much from these studies because they had serious design flaws.

Each participant faced some combination of below-average velocity, a pitching machine, or an inability to swing, which we know can drastically influence results between expert and novice performers (Mann, Abernethy, & Farrow, 2010). Furthermore, none of the recent studies mentioned above included truly elite participants.

To overcome these shortcomings, a 2013 cricket batting study was published by Mann, Spratford, and Abernethy, which included two elite professional cricket batters and two club-level cricket batters facing game-like velocities against a screen-based pitching simulator.

Supporting the findings of Land and McLeod as well as Bahill and LaRitz, the researchers found that expert batters initiated earlier saccades at ball-bounce and coupled both head and eye movements with greater efficiency. Expert batters were able to follow the ball with their heads egocentrically, meaning that they don’t need to move their eyes to keep the ball within their central vision.

However, instead of just simply relying on precise head movement, expert batters shifted their gaze to be farther ahead of the ball throughout the trajectory, even initiating a second anticipatory saccade just before bat-ball contact (like what Bahill and LaRitz found) so that their gaze was “parked” at contact point for the majority of trials.

On the other hand, club-level batters were much more likely to have their gaze trail behind the ball during its trajectory and were also less likely to initiate a second saccade. As a result, on good-length and short-length trials, expert batters aligned their gaze with the ball at contact on 100% and 90% of their respective trials, whereas club batters only aligned their gaze with the ball at contact on 13% and 70% of their respective trials.

Club batters also performed significantly worse by measures of contact scores, naturally providing more concrete evidence that predictive-gaze behavior and earlier/more frequent saccades is a hallmark of better batting performance and skill.

Is There Really an Expert Way to See the Ball?

Despite the solid experimental design, the 2013 study by Mann et al. also had its limitations. First, only four athletes participated in the study, so it’s difficult to say whether the gaze behavior of two elite and two club batters truly represented the general population. Second, although bounce length was varied, no curve was incorporated into the pitches that batters were told to swing at. Last, age was not controlled for, so we’re left unsure of whether this so-called expert gaze behavior is something that develops in early adolescence or later in a professional career.

To control for these issues, Sarpeshkar, Mann, and Abernethy (2017) published an excellent follow-up study with the hopes of replicating earlier findings related to gaze behavior in cricket batting. A total of 43 cricket batters were split up into four groups by skill (elite vs. club) and age (adult vs. college-aged batters). The batters faced two blocks of game-like pitches so that one block consisted of straight pitches only, whereas the other had a combination of curved and straight pitches to isolate for the effect of curved trajectories on gaze.

In looking at straight-blocked pitches only, the researchers had mixed results. They were unable to replicate the results found in Mann et al. (2013) that elite batters egocentrically tracked each pitch with their heads, because mean head angle for elite batters were relatively similar to that of club batters (3.1 deg vs. 3.7 deg). Furthermore, the researchers did not find any evidence that elite hitters initiated anticipatory saccades significantly earlier than club batters did.

However, it was shown that, on average, elite batters directed their gaze farther ahead of the ball compared to club batters (-.6 deg v. .4 deg), maintained more consistent gaze-head angle across different trials, were more likely to initiate a second saccade towards bat-ball contact, directed their gaze more towards the ball at bat-ball contact (46.1% vs. 26.1%), and had better performance at the plate. These findings held true regardless of whether an expert batter was an adult or “youth,” leading the researchers to conclude that elite gaze behavior develops during early adolescence.

But, when introducing just the possibility of curved trials into the experiment, the expert “advantage” all but disappeared to the extent that the performance of expert vs. club batters was indistinguishable. Gaze fell farther behind the ball in these trials, saccades were less frequent and delayed, and the probability of locating gaze at bat-ball contact decreased from 28.2% to 16.6%.

When actually adding curved pitches into the analysis, expert vs. club batting outcomes continued to decline and expert vs. club batting performance remained indistinguishable. However, there was evidence that expert batters were better able to functionally adapt their gaze to accommodate curving trajectories when compared to club batters. For example, elite batters directed their gaze towards “swinging” pitches more accurately than club batters on curving trajectories, and they used a higher proportion of oblique saccades than club batters did.  

So, despite years of research highlighting the correlations between better performance and hallmarks of expert gaze behavior, that relationship almost all but disappears when even the possibility of a curving pitch is introduced.

Cognition Aiding Perception

The researchers state that these results emphasize the importance of top-down influences of expectations in visual-motor behavior. For example, expert batters are better able to account for relevant context to aid in their performance (Gray, 2002) and all batters perform better with advanced information on pitch probabilities prior to a given plate appearance (Gray, 2015). Perhaps in a more realistic setting, where pitch types aren’t varied at random, expert batters are better able to use context to maximize their advanced abilities to track the baseball with or without curve.

If this is true, this has significant implications related to sequencing, because a pitcher will have to weigh randomizing pitch types in conjunction with other concepts, such as tunneling or “waste pitches,” that are also hypothesized to negatively influence a batter’s perceptual capabilities.

For batters looking to improve their ability to track a baseball, perhaps learning the pitch-type tendencies of pitchers in different situations is a worthwhile endeavor compared to other visual-training modalities. If anything, the Sarpeshkar, Mann, and Abernethy paper indicates that even if a batter has elite gaze behavior, he’ll be unlikely to take advantage of this skill if he does not have an accurate idea of what pitch might be coming.

Bringing It Back to Baseball

Since we’ve spent the past three studies talking about cricket, it’s time to transition back to baseball.

After a thirty-year freeze in the research, Fogt and Zimmerman (2014) and Fogt and Persson (2017) completed two pilot studies using collegiate batters in a controlled lab environment where tennis balls were shot from a pitching machine at roughly 76 mph.

In conditions where batters were instructed not to swing, pitches were predominantly followed through head movement initially and batters incorporated eye movement later on during ball flight when the projectiles were travelling at a faster angular velocity. Within the 2017 paper, it was found that participants used an anticipatory saccade (as shown in Bahill & LaRitz, 1984) to follow the pitch as it crossed the plate, likely to generate a comparison relating early ball flight to final pitch location.

In the swinging condition, batters again relied on mostly head movement to track pitches during ball flight and were able to align their gaze with the ball until roughly five and a half feet to home plate. The key finding here was that despite allowing their participants to swing, the collegiate batters were still able to align gaze for just as long as Brian Harper was able to in the Bahill and LaRitz study.

The researchers speculate that despite not being able to use late ball-flight information to aid in performance (as reported by Higuchi et al., 2016) it may serve batters well in future ABs to continually track pitches for as long they can.

More recently, a paper by Higuchi et al. (2018) looked at the gaze behavior of six collegiate batters when facing a pitching machine set to fast and slow speed settings (90.15 mph / 71.4 mph). The researchers broke up ball flight into 4 1/5th intervals starting at 20% to see if the magnitude of head or eye movement significantly differed throughout ball flight. In both the fast and slow speed settings, head movement significantly increased later in ball flight, whereas eye movement was relatively minimized throughout.

They also found that within their group of six participants, three stopped moving their heads once their swings were initiated, while the other three continued to follow the ball with head movement for as long as possible. In particular, one subject demonstrated the ability to align his gaze with the ball at the moment of bat-ball contact, but the other five could not. However, unlike in the blocked-straight trials of the aforementioned cricket studies, the researchers found no correlation with the alignment of gaze at bat-ball contact and batting performance.

Therefore, in considering these results in conjunction with the two papers by Fogt and Zimmerman and Fogt and Persson, it is reasonable to conclude that head movement is the predominate driver of how a batter tracks a baseball pitch. This makes sense intuitively, given what we already know about the limitations of smooth-pursuit eye movements and the speed of incoming pitches.

In fact, Higuchi et al. go one step further and suggest that rapid-eye movement during ball flight might actually be detrimental to performance in baseball batting, given that excessive changes in eye position may require too much processing time for the body to re-adjust its spatial relationship to the ball and the bat. While interesting, the Sarpeshkar, Mann, and Abernethy study indicates that we probably need a more thorough baseball-batting study that includes both expert and novice batters hitting against straight and curved trajectories at game-like velocities before we can derive a more specific conclusion such as that.

Wrapping Up the Research

Recapping over sixty years of gaze behavior research certainly leaves us with more questions than answers, but here’s what we know at this point in time.

How Do Batters Track the Ball?

More recent research has indicated that head movement plays a vital part in tracking a given pitch and that eye movement plays a minimal role at best. However, there has yet to be a published gaze-tracking study in baseball that has been able to attribute these behaviors to an increase or decrease in batting performance or against both straight and curved pitches.

How Long Do Batters Track the Ball?

There is strong evidence that the first 150ms of ball flight are vital to batting performance and that the last 150ms are irrelevant. However, we have yet to see that result replicated in an in situ environment that incorporates curved pitches and relevant game context.

This is problematic because we know in situ environments are critical to differentiating expert vs. novice performance, and we also know that some batters are able to align their gaze with the ball at contact. Furthermore, in a recent abstract published in the Journal of Sport & Exercise Psychology where two professional batters faced fifteen fastballs, fifteen curveballs, and fifteen changeups randomly ordered by type and location in a virtual environment, it was found that the temporal error at bat-ball contact could be predicted simply by the deviation of the batter’s gaze from the ball. It was also reported that temporal error was minimized when the head and ball were aligned at contact (Nakamoto, Fukuhara, & Mann, 2018).

Of course, we need these results to be published elsewhere to draw multiple conclusions from them, but this indicates that we have a lot more work to do before we can definitively say whether professional baseball batters are unequipped to track the ball over the last 150ms of ball flight and whether that information is irrelevant in hitting a baseball successfully.

Do Experts Track the Ball Differently Than Novices?

We do not exactly know how expert batters track a baseball compared to novices, because this question has not been investigated in over thirty years. In turning to cricket batting, expert batters have been found to align their gaze farther ahead of the incoming pitch, couple their gaze more closely with a pitch at bat-ball contact, and better accommodate their gaze strategies to follow curved pitches. That said, it is unclear whether any of this information translates to baseball batting.

If there is anything to take away between the differences in the gaze of expert and novice performers, it is that skilled batters may be better able to account for uncertainties in ball flight through advanced cues (Kato & Fakuda, 2002) and top-down cognitive influences (Gray, 2002). As a result, whatever advantage experts have as it relates to gaze may be leveraged during game-like context rather than a typical lab setting.

What Is Next?

In an attempt to fill this aforementioned gap in the literature, we’ve begun our own research on batter gaze behavior with the hopes of changing the way we understand how batters see the baseball. A lot of work needs to be done, but given the increasing interest in tackling more in-depth questions related to practice design and pitch sequencing, we believe that this will be a significant contribution to the baseball community when all is said and done.

A quick look into gaze tracking with @pupil_labs pic.twitter.com/wPkkjC3sEM

— Tanner Stokey (@Tstokey) January 24, 2019

This article was written by Research Analyst Dan Aucoin

The post How do Batters See the Ball? A Review of Gaze Research in Batting appeared first on Driveline Baseball.

This off-season and spring training have seen a major upheaval in the number and type of coach that is being hired into the professional ranks, while the use of technology throughout all levels of professional baseball has increased drastically.

This new level of investment demonstrates clearly that teams are valuing player development more highly than in the past. But, even with millions of dollars pouring into new technology, personnel and methods, a question remains:

Are teams investing enough?

Over the next few months, we’re sharing several articles that look at Major League player development from a different perspective relative to what you typically see on this blog.  We are going to look at the business case of developing baseball players to try and determine an appropriate level of investment by a team.

Our goal is to understand if organizations fall short in leveraging their resources to optimize a player’s career trajectory. Ultimately, we’ll attempt to evaluate how much suboptimal habits cost organizations in the long run.

Before we can accomplish that, we need to address how much a given prospect is worth to an MLB team. By performing an analysis to generate our own valuations, we can quantify the net value of any persistent inefficiencies that may exist within player development today.

Our series on The Business Case for Development begins with this feature and will have several parts:

1. What is the Value of a Prospect? An Updated Methodology
2. The Value in Optimizing Player Development From the Lens of Generating Velocity
3. Programming Risk and the Associated Outcomes of Lower Level Prospects
4. Homegrown WAR: How Much Production from Homegrown Prospects is Needed to Compete?
5. Traded In vs. Traded Out: Comparing Player Development Departments Based on the Performance of Team Switchers
6. Drafting High Ceiling Prospects: Getting Peak Value In Acquiring and Developing Talent
7. The Rise of Replacement Level and the Early Peaking Aging Curve: Is it More Difficult To Acquire Talent in the Open Market?
8. Realizing Returns on Player Development: Do Additional Coordinators and Affiliates Make the Difference?

Valuing Prospects: Full of Assumptions

Valuing prospects on a per-dollar basis is not a new concept. Creagh & DiMiceli, Zimmerman, and Edwards have previously laid out extensive blueprints on how to derive prospect valuations. However, since there is no so-called right way to calculate these figures, each piece has relied on its own set of assumptions, which have created discrepancies in the respective values reported.

Some arbitrary choices, such as choosing an appropriate estimate of $/WAR and a discount rate, are simply unavoidable given the nature of task. However, other assumptions are shortcuts of more granular analysis, because going through historical WAR and salary values on a yearly basis is tedious and potentially unreliable if not done carefully.

We have never shied away from the tedious.

Our prospect-valuation model minimizes these shortcuts to improve confidence that our results accurately depict how much a prospect is truly worth.

As an aside, the “true worth” of a prospect is best described in layman’s terms as what a team should pay to acquire that player given the price of acquiring production in the open market.

Calculating $/WAR

The first step in this process is to calculate a $/WAR figure that best represents the current standing of the market. Any aggressive or conservative estimate of $/WAR can skew prospect valuations by a relatively large margin, so it’s important to get this right.

Fortunately, Matt Swartz has provided a sound method on how to derive a reasonable proxy of $/WAR within a given season. We replicated his work and gathered the names and salaries of every player with at least six years of service time in a given season since 2010. We then paired these player seasons together with each player’s respective Fangraphs WAR (fWAR) output and summed both veteran production and salaries on a per-year basis. To account for situations in which a veteran cost a team a draft pick, we added the NPV (Net Present Value) of the attached pick to said player’s first-year salary on his new contract, because this is when teams forfeiting the pick incur that cost.

By dividing the sum of veteran salaries by the sum of veteran fWAR, we obtain relatively conservative market estimates of how much teams spent to acquire a win in the open market in a given season (with the caveat that players who signed extensions were included in our sample). We’ve compared our estimates (DL $/WAR) to Swartz’s (Swartz $/WAR) from years 2010 to 2016 to validate our 2017 and 2018 figures, shown in the table below.

Projecting 2019 $/WAR

While reassuring that our figures are consistent with ones published by Swartz, the uncertainty that surrounds the influence of the newest CBA on teams’ spending habits makes it difficult to use these historical estimates to forecast an accurate $/WAR figure for 2019. As shown above, since MLB instituted stricter spending limits and penalties on payroll expenditure in the 2017-18 offseason, the market has been in a deflationary period.

To workaround these limitations, we turned to the Steamer Projection System and gathered projected WAR and salary estimates for all veterans during the 2019 season. Given that prior research has found projection systems are overly optimistic in their forecasts of veteran production by roughly 15% and that aging curve peaks have been trending younger, we also performed this analysis on 2018 data to get a sense of how biased these figures are in the present day.

In first looking at 2018 projected $/WAR, we found that our new estimate came in at ~71% of its actual value, falling from $10.64M to $7.6M. For 2019, projected $/WAR came in a hair under $8M, which we now know is only ~70-85% of what the actual figure will be for the season. To correct for this, we multiplied $8M by 30% to obtain a $/WAR estimate of $10.4M for the upcoming 2019 season.

This $/WAR estimate ends up being significantly higher than the $8.5M and $9M suggested by  Creagh & DiMiceli and Edwards to calculate their respective prospect valuation figures. However, our $/WAR estimate is derived from actual market data rather than intuition. To justify player valuations in the $8-9M range, $/WAR would have to drop by over 21% in just a two-year period. That seems unlikely.

Furthermore, even if $/WAR on the open market hypothetically fell to levels below $10M, we believe that there are two reasons why a simple estimate of $/WAR would actually undersell the value that homegrown production provides an organization.

The Merits of Homegrown Players Vs. Free Agents

First, there is little potential for sunk costs with homegrown players because their salaries are not guaranteed beyond one season at a time.

For example, in acquiring Jonathan Schoop last year at the trade deadline, the Brewers obtained a potential buy-low bounceback performer who could have simply been non-tendered at the end of the season (at no cost to the organization) if an uptick in production did not occur. Hypothetically, had Schoop returned to his 2017 production levels, the Brewers would have likely exercised the right to retain his services for the 2019 season and extracted additional surplus value beyond the 2018 season. Thus, by acquiring Schoop, the Brewers obtained loads of upside at relatively no risk, which surely drove up the price point they had to pay the Orioles in order to receive his services.

Homegrown players also possess minor league options for 3 years, which can be used for developmental purposes (see Alex Gordon’s 2009-2010-2011 seasons) and additional roster flexibility (see the Dodgers and A’s 2018 starting rotation) that provide teams with added value that open market players cannot offer.

As a result, teams should be willing to pay more in terms of $/WAR for these players given that they possess team-friendly volatility that includes hypothetical team options after every season. As a result, their distribution of outcomes generally contain much higher upside than a typical free agent does, and the risk is mitigated by the fact that future salaries are reliant on production itself.

This doesn’t necessarily mean that a two-WAR season from a homegrown player is worth more to a team than a two-WAR season from one acquired through free agency. However, if we hold salaries equal, it does mean that teams are relatively protected over multiple years from a worst-case-scenario outcome in the case of homegrown players. This creates value for teams (either through trade or over multiple years) that should not be ignored.

Second, since payroll expenditure is now a constraint on acquiring wins in the open market for several large-market teams (who are traditionally on the steep slope of the win curve), players who generate surplus production at relatively low cost can also give teams the flexibility to spend elsewhere on their roster—which in itself has value.

For example, a team within $20M of the “cap” (err…tax threshold) would not be able to extract full surplus value if they were offered a trade for a five-WAR player at $25M per year, given the tax and additional penalties they’d be subjected to. However, this same team would be able to extract maximum surplus value from a three-WAR at $5M per year player if offered via a trade and thus would likely be willing to pay more to acquire that asset (despite each player having similar surplus values).

As a result, players who are productive at lower salaries (typically homegrown players) are likely more valuable to a given team than a simple $/WAR estimate gives them credit for. Thus, if we construct an approach to valuing prospects that uses $/WAR and select a figure below market rate, we will likely underestimate our prospect valuations by fairly significant margin.

Determining a Discount Rate

As described earlier, to calculate prospect valuations we also need to select an appropriate discount rate that reasonably estimates how much a team would trade a win today for a win a number of years in the future. Getting this number to be fairly accurate is critical because prospects can take a while to develop and fulfill their six years of club control. Any mis-estimation of the market’s discount rate can compound on itself and skew our results by millions of dollars. (See Bonilla, Bobby.)

To pinpoint a discount rate, we again turn to the work of Matt Swartz, who has previously used the difference in $/WAR spent by teams on free agents with and without draft-pick compensation attached to them in order to find a discount rate that causes both FA groups to have equal expenditure. (For more info on his methods and why the rate is estimated to be relatively high, click here.) Historically, Swartz has found that the discount rate fluctuates at around 10%, but that the value is subject to a large amount of variance on a yearly basis for a variety of reasons.

Given the variability and limitations associated with estimating a discount rate, we decided to select the 9.3% figure provided in Swartz’s initial work on discounting future values in baseball. In using 9.3% instead of 10%, we select a figure that is more closely aligned with the discount rates chosen in prior prospect-valuation research. Furthermore, this figure was calculated when draft-pick compensation was easier to tease out and when $/WAR estimates were more likely to still be linear. (In a case where $/WAR was no longer linear, Swartz’s analysis would no longer be feasible given that only a specific subset of free agents are offered QO’s.)

How Much Are Prospects Worth? Methods to Calculate Prospect Surplus WAR by Rank

With a framework for deriving $/WAR and discount rate established, we can calculate prospect values.

To do this, we use Baseball America’s historical top 100 list, as they’re the most easily accessible resource of historical prospect rankings to build out our database.

We collected BA’s rankings from 1995 to 2009 and assigned each player with a coinciding WAR value (from Fangraphs), playing-time figure (from Fangraphs), and salary (from USA Today and Fangraphs) for each season that followed a year’s respective ranking.

Building this database answers questions like, “How much WAR did Alex Rodriguez produce in 1998 at his salary just three years after ranking atop Baseball America’s Top 100 in 1995.”

This structure allows us to work with more precise measures of analysis relative to previous work on prospect valuations, which finds a prospect’s total WAR accumulated over the first X number of years of his career and then relies on several assumptions to estimate how much money that prospect will earn through arbitration and how his value will be distributed over the first several years of his career.

The alternative methodology used by previous analysts is understandable since it avoids having to use historical estimates for $/WAR. However, there is always the risk that the assumptions used end up being misguided in practice.

The most glaring mistake we note is how previous estimates have tended to distribute the WAR and salaries earned by players over the first seven to nine years of their careers equally, regardless of whether they are the first or hundredth best prospect. Since top prospects are typically closer to the big leagues than those at the bottom of the list, any one-size-fits-all distribution assumption will inherently overvalue one tier of prospects at the expense of another when redistributing and then discounting WAR. Unless separate tiers of prospects are treated differently in terms of how surplus WAR is redistributed, you are giving a relative advantage to one group over the other.

For example, in Edwards’s most recent valuation work, the production within the first nine seasons of a player’s career WAR was gathered and redistributed two years into the future. By using nine years instead of seven, Edwards likely inflates the value of top prospects, who are more likely to produce larger amounts of cumulative WAR over longer periods of time and are closer to the big leagues. To counteract that, Edwards then pushes these inflated WAR values two years into the future, which deflates the relative value of top prospects given that they are more likely to have an ETA sooner than two years.

These issues compounds when you “smooth” prospect values by rank, as the “slope” of your line will either be too sensitive or not sensitive enough, helping or hurting top-tier prospects relative to the rest of their peers.

(This compares the production of Top 10 prospects vs. Bottom 10 prospects by WAR within 2 years after being ranked)

To avoid this pitfall, it’s better to rely on more precise measures of surplus WAR that use only actual production/wages and historical $/WAR to bring surplus wins back into present day context. (Historical estimates of $/WAR pre-2006 are simply calculated from how much WAR free agents usually produce in a given season [~30%] and how big their share is in terms of the overall salary of the league [~75%.])

Once we calculate the yearly surplus value of a top 100 player before he reaches six years service time, we discount those values by 9.3% raised to each year after the coinciding rankings come out. We then translate those values into present-day dollars and sum the yearly totals by player in order to find the total discounted surplus value per player during his years of club control.

Fitting Prospects Grades to Rankings

Equipped with these newly calculated NPV metrics by prospect ranking, we next turned to Fangraphs scouting board to convert the rankings of each individual player listed on BA’s Top 100 into prospect grades. This will allow us to compare prospects based on their actual projected value, rather than a ranking system that treats the 99th and 100th best prospects differently despite both of them having the same future value.

On Fangraphs “The Board,” three different sets of prospect lists sorted by both grade and ranking are available to run a simple regression to obtain predicted prospect grades based on prospect rank. With over 2,000 players graded and ranked within that time period, we generated an estimated prospect grade ranging from 35 to 70 for players ranking 900^th or higher on a prospect leaderboard. The grade-to-ranking results are in line with what Edwards recently reported within his analysis and are provided below.

**Strikingly, if ~900 prospects across all 30 teams are graded 35 or higher, that means >75% of minor league baseball players have no expected production in the big leagues. Thus, being able to develop organizational depth into substantive value could help teams to realize large returns, something we will address in future blog posts.** 

Results

Now that we have surplus values and estimated prospect grades for each prospect listed in BA’s Top 100 from 1995 to 2009, we ran a model to smooth out our surplus values by prospect grade. Once we fit these results onto a year’s worth of prospects graded above a 35, we then binned the values by prospect grade and received the values below. For comparison purposes, we added Edwards’s estimates both at $9M/WAR (what is published on Fangraphs) and at $10.4M/WAR.

In comparing the numbers a bit more closely, we observe that figures from both models are fairly similar at the top half of the table and then diverge significantly for 45 grade prospects and below. Finding similar results between models for prospects graded 50 and above is validating and a testament to Edwards’s intuition and strong assumptions.

However, in most financial models of farm systems, the returns come from having a high hit rate among top prospects and getting value from players outside the top rankings, those ranked 45 and below. Having an accurate model for the top-end of the prospect world is good, but we need to ensure an accurate evaluation of non-elite prospects as well.

Entering the Trade Market, Validating Our Results

To test our model’s results for sub-45 prospects, we evaluated MLB trades to see if teams were exchanging wins for prospects at prices we would expect given our model’s valuations.

To avoid skewing our results by including shaky forecasts of players with more than two years of service time and more than two years on their current contract, we first looked at a handful of simple trades that occurred during this off-season. In the left-hand column you see our estimated value of NPV for the big leaguer acquired (based on a $10.4M/WAR estimate and Steamer predictions) alongside our estimates for value of prospects exchanged in the deal (Sum of DL NPV) and Edwards’s estimates for the value of prospects exchanged in the deal (bumped up to a $10.4M/WAR estimate).

**Note that Alfaro had five years of club control remaining and was most recently graded a 45. We simply multiplied his respective 45-grade value by .8 to obtain to obtain his NPV. Also, Realmuto’s Steamer projections do not include framing, which means his NPV is likely underestimated in the chart above.**

Given the small sample size and the relative noise surrounding how the market dictates player values, it’s tough to take much away from the table. Initially, it looks like our estimates might come in a little bit high on players such as Alonso and Roark while Edwards’s values seem to come in too low on Martin and Roark.

In general, these initial findings make sense, as the appropriate values for prospects ranked 45 and below seem to drop off precipitously and our model lacks prior information on the actual success of these said players to adjust for this. (The 100^th ranked prospect is typically a 50.) It is therefore reasonable to believe that our model is just overly optimistic for 45-grade prospects.

On the flipside, Edwards uses some clever methods to project surplus value in prospects outside BA’s Top 100, but these methods fail to account for players who drop out of BA’s Top 100, still make the big leagues, and end up being productive. Thus, his numbers might slightly deflate the proportion of WAR that 45-grade prospects and lower might actually deserve.

Regardless, to confirm this hypothesis we need more trades. So, we pulled the major transactions at the last trade deadline and performed a similar analysis to what was provided above. Rather than attempt to pinpoint a specific NPV value that a team acquired through MLB talent (likely impossible given the hyper-specific context in which each player was acquired), we instead decide to highlight the more interesting trades.

Initially, a $275M discrepancy may not look promising, but our models are diverging as expected. For example, our numbers seem to overshoot the players traded for Schoop, Happ, and Britton, whereas Edwards’s numbers seem to undershoot the likes of Machado, Pham, and the majority of relievers who had bundles of 40s and 35s packaged for them.

A Closer Look at Two Trades in Particular

Two other trades worth mentioning are the Chris Archer deal, which Edwards highlighted within his main article as a validation of his figures, and the Oscar Mercado deal, which is a bit peculiar but provides unique insight into how teams value prospects themselves.

In the case of the Archer deal, both models provided almost identical values once we accounted for the necessary boost in $/WAR for Edwards’s figures. This gives us strong confidence that our higher-profile prospect valuations are both fairly accurate and consistent.

Exchanging Prospects for a Prospect

The Mercado deal is perhaps both models’ most telling test because we’re dealing with a rare prospect-for-prospect trade in extenuating circumstances.

It was seen as puzzling when announced, but with Mercado being Rule 5 eligible and already on the Cardinals 40-man when 40-man spots for outfielders in St. Louis were virtually all accounted for, it was likely that the Cardinals were forced to move Mercado in order to salvage any value out of the situation.

Given this context, we’d imagine that no team would be willing to pay full price for Mercado knowing very well that St. Louis has no leverage—they’re going to lose Mercado to the waiver wire regardless. However, we also know that almost every team who projects to have an opening on their 40-man in December should be interested in acquiring Mercado given the low price point. As a result, we’d expect interested teams to drive up the price so that STL would receive and accept an offer that comes in just below Mercado’s estimated surplus value.

To test this assumption using our model, we compare the net value of a 45-grade prospect at ~$14M in exchange for two 40-grade prospects valued at ~$6.4M each. In doing so, we find that the prospect package St. Louis received from Cleveland comes in about $1.35M below Mercado’s estimated surplus value, which is about what we’d expect given the context above.

Using Edwards’s model, we estimate that a 45+ position player prospect is worth about $9.2M and that a 40-grade position player is worth about $2.3M. When you do the math, Edwards’s model favors Cleveland’s side of the deal by ~$4.6M, likely too large of a discrepancy to accurately depict how both teams valued the players exchanged.

Meeting in the Middle

Given what the trade tables tell us, the best way to model how teams actually value prospects their lower-tier prospects is to hedge in between both models for prospects graded 45 and lower. By averaging our respective 40- and 45-grade values, we hopefully clean up our overly aggressive rankings on 45-grade prospects and boost Edwards’s underreporting of 40-grade prospects and lower.

In looking at the newly added column, which incorporates our combined prospect values, we see more palatable numbers across the board, with maybe one or two exceptions sprinkled in. In revisiting the Mercado trade with our new numbers, we end up ~$1.25M off the mark this time, which is reasonable and well within an estimated margin for error given the limitations of the data that we are working with.

With added confidence in these new figures, we present the newly adjusted prospect valuation numbers below.

Where to Go From Here

Looking ahead, these new prospect valuations will be the foundation for many of the articles we intend to write on player development.

With a better understanding of the value realized by organizations when prospects improve their status from one grade to the next, we gain insight into how much is at stake when a player’s career trajectory can rise or fall based on the environment and programming he is subjected to.

We’ll take this information to evaluate player development departments, analyze appropriate programming risk, and address suboptimal behavior exhibited by professional organizations as they look to gain the most value by enhancing player development. In the long run, we feel that this analysis will provide coaches, players, and fans with better insight into how much value teams are leaving on the table.

Written by Research Analyst Dan Aucoin

The post What is the Value of a Prospect? An Updated Methodology appeared first on Driveline Baseball.

Driveline Baseball Podcast Episode 17

Driveline Baseball Podcast Episode 17: On this episode of the Driveline Baseball Podcast Mike and Kyle recap the recent trip our staff took to Utah State to work with Barton Smith (@NotRealCertain on Twitter) to do some research on fastball movement. To learn more about the experiment click here to read our blog post we wrote about the trip or check out Barton’s blog here. Joining Kyle and Mike this episode is Driveline Pitching Coordinator Eric Jagers who specializes in pitch design. Eric breaks down the process of preparing our pro clients for Driveline scout day as well as giving insight on adapting the technology we use to evaluate a pitchers arsenal and develop new pitches.

Eric Jagers: (@ericjagers on Twitter) Eric has been a Pitching Coordinator/Floor Trainer for the past year at Driveline. Over the last year Eric has worked hard on improving our pitch design process and works closely with our pro clients on developing new pitches and improving total arsenals. Eric is constantly pushing the envelope on what can happen during a pitch design and shares great information on his twitter and DrivelinePlus. Make sure and follow Eric on Twitter to stay up to date on all things pitch design related

Check out some of Eric’s work here

Episode Resources:

Letters From twitter discussion: 

Related Pitch Design Articles: A Deeper Dive into Fastball Spin Rate, How do we generate spin,

Listen now on Apple Podcasts, Google Play, Spotify, Stitcher or your preferred podcast player.

The post Driveline Baseball Podcast- EP. 17 appeared first on Driveline Baseball.

After looking further into fastballs, now is a good time to look deeper at offspeed pitches—particularly curveballs and sliders.

We’ve talked before that one of the key parts of an offspeed pitch is how the spin of the pitch aligns with the axis. If using a device like a Rapsodo, this shows up as useful or true spin. This means that raw spin rate can help you bucket a curveball or slider and can say whether they have a high, average, or low spin rate. But it doesn’t tell you too much more about the pitch.

Instead, the movement of a pitch is better described by two different categories of total spin: transverse spin and gyrospin. Transverse spin, which travels perpendicular to the direction of velo of the ball, is the main driver of magnus force. (This is more specifically described in Dr. Alan Nathan’s paper Determining 3D Spin Axis from TrackMan Data.)

Magnus force (which you are likely more familiar with) is only sensitive to the transverse spin, which travels perpendicular to the direction of the velocity of the ball (think of a four-seam axis).

The gyro spin is most easily seen by a slider, which spins like a football or a bullet from a rifle.

Bonus GIF: Dylan Bundy's Slider Grip + Release for the Bundle of joy that is @PitchingNinja pic.twitter.com/df0b4Bdvo3

— Pitcher List (@PitcherList) April 5, 2017

Because of this, the so-called true or useful spin relates to how a pitch moves, whereas gyrospin likely has little effect.

Now, while we avoid going further into the weeds on spin technicalities here, we are going to talk about how offspeed pitches are currently discussed and classified.

Generally, the way we talk about offspeed pitches falls into different categories of sliders and curveballs: for instance, a sharp curveball, a loopy curveball, a slider with bite, or one that looks flat. This largely comes from lacking a way to measure pitches until recently, meaning that coaches and players have often defaulted to describing the offspeed pitches they throw by the grip and intention of the pitch.

How one describes a pitch doesn’t always map with the movement of the pitch. We can see the difficulties of that in MLB data. We should think of offspeed pitches as more of a gradient or a continuum. They blend into one another with different variations all over. This can be both overwhelming and exciting because it creates a different way to think about a pitcher’s arsenal and how that relates to designing new or tweaking old pitches.

A Technical Look at Pitch Classification

How one is motivated to classify pitches is often a function of perspective. Traditionally, we’ve grouped segmented pitch types into common buckets—such as curveballs, sliders, changeups, etc.—which can be valuable if we’re trying to understand how a pitcher perceives fit for a given pitch within his arsenal and what his throwing intent is.

But, when working with newer ball-flight technology, we’re also afforded the opportunity to view and classify pitches from the batter’s perspective, which is arguably both more useful and descriptive in understanding the quality of a specific pitch.

For example, using 2018 MLB StatCast data, we can see that Jacob deGrom’s curveball (CB) and Chris Stratton’s slider (SL) are essentially the same pitch despite being classified differently. If we’re trying to analyze how a given batter will perform against said pitches, it likely makes very little difference to them how the pitcher describes it. From a batter’s perspective, those two pitches are perceptually equivalent (relatively speaking) and thus should be grouped together.

Vice versa, Clayton Kershaw and Matt Harvey are both listed as throwing CBs, despite the movement profile and shape of each respective pitch being quite different. If we were to run an analysis looking at what defines a “good curveball,” where these pitchers should locate their respective CB or how similar both pitchers are based on pitch type frequency, it is likely that we’d be misled if we did not separate those two pitches into separate classifiers. Scouts have understood this for years, using more specific pitch-typing nomenclature to classify pitches such as “slurves” or “roundhouse curves” more appropriately.

Run Values of 12-6 Curveballs – 2018 Season

Run Values of Slurves – 2018 Season

Run Values of All Curveballs – 2018 Season

Above are three heat maps (RHP vs. RHH) for the respective run values of 12-6 curveballs (like Kershaw), slurves (like Harvey), and MLBAM classified CBs. By getting more specific with pitch typing, we can gain a better sense of both the quality and ideal location profile of a given pitch.

Deciding how many classifications are needed to accurately bucket every pitch type is always going to be arbitrary, unless you use more rigorous clustering techniques. However, using more advanced ball-flight metrics to classify pitches more descriptively, like the chart below, will likely result in a more objective understanding of where a pitcher needs to add a pitch, where two pitches might be redundant, or where a pitch consistently needs to be to have success.

Using technology as a classification system can open up a lot of opportunities for more specific classifications. There is still time to learn the best way to classify pitches, or if there even is one. But we can clearly see that this information gives us the ability to dive significantly deeper into a pitcher’s arsenal.

Many coaches today won’t have access to this sort of information, unless they are in pro ball and are in an organization that makes this data available. But that doesn’t mean that we can’t start moving towards a different way to classify pitches. One of the more straightforward ways to do so is to think of offspeed pitch movement on a scale that is based on movement, instead of what the pitcher believes he throws with his grip.

Thinking of Offspeed as a Continuum

We’ve established that a slider can drastically differ from pitcher to pitcher. These offerings may be classified the same, but they’re on completely opposite sides of the spectrum in terms of pitch movement. Some have substantial rise with little horizontal break, whereas others may have a few inches of depth and a large amount of lateral movement. We’re essentially considering everything from a true cutter to a slurve to fall into the bandwidth of what we consider sliders.

This graphic (RHP from catcher POV) from Mike Fast effectively displays the wide array of movement that sliders possess. The slider is susceptible to dip into cutter territory, as well as plunging into the neighborhood of curveballs.

Chart from the Baseball Prospectus article – Pitching Backwards: The Case for Longform Movement

Just as we see a continuum of movement for sliders, the same exists for curveballs. Some pitchers may possess a curveball with little to no drop and minimal side-to-side movement (near gyroball). Others may display very little depth and considerable glove-side action (similar to the sweeper classification). From a spin-induced movement standpoint, these pitches aren’t even close to our curveball classifications, but they receive the same title.

For pitchers that produce solid drop on their curveballs, we often see them fall into three buckets: the slurve, the traditional curve, and the 12/6 breaker. The slurve typically possesses near equal parts horizontal and vertical while appearing more east-to-west than the conventional curve. With athletes who throw a traditional curve, the offering often exhibits more drop than sideways movement. The 12/6 breaker is the least common of the bunch. It produces little to no glove-side break with a great deal of downward action (thus the name). To drive the point home, each of the pitches described are curveballs from a traditional classification standpoint, but each has incredibly different movement profiles.

But this does require moving past describing pitches on specific grips and describing what pitches are by their movement profiles.

We can see how coaches need to live between “this grip means you throw a curveball” and the highly specific technical descriptions mentioned above. When taking a pitcher in and looking at his pitches, it is unhelpful to say he “needs to learn a curveball.” Coaches need a more specific goal of what kind of curveball a pitcher may want to learn, ignoring for a minute that some pitcher’s may have difficulty learning certain pitches because of arm slots or other reasons.

So, the feedback loop needs to be more of a back of forth of what profile a pitcher may need to gain or change a pitch to. Other considerations include how quickly or difficult it is for the pitcher to pick up that pitch and how it fits in with his other pitches. Remember, there is a difference between learning a good offspeed pitch in a vacuum and learning one that fits with pitches they might already throw.

Conclusion

We again come back to how vitally important communication is and how not having coaches and players on the same page is often a problem. We know that there tends to be a coach’s or player’s description of a pitch, followed by a technical measurement of how a pitch moves. These two don’t have to live in conflict, but there are sometimes bumps along the road.

It matters less how a player or coach describes a pitch as long as it’s clearly communicated. But there are layers of technical knowledge that are now needed to truly dive into the weeds of pitch movement to see how a pitch moves and compares to others.

This can also adjust how coaches approach teaching offspeed pitches. Instead of working with a specific grip or pitch description, a coach might take a pitcher in, have him throw on a Rapsodo, dive into the numbers, and then see a specific movement profile that may need changing or a gap in his repertoire that could use a pitch of a certain movement profile. This is just another example of how technology is changing the game and requiring a slightly different understanding of what was needed before.

This article was co-written by Michael O’Connell, Dan Aucoin, and Eric Jagers

The post A Deeper Dive into Offspeed Pitches: Pitch Classification appeared first on Driveline Baseball.

Spring training is usually a happy time of year. Everyone gets to look forward to baseball being played after a long winter. Unfortunately for the players, early season also corresponds with the highest chance of injury.

Source: Epidemiology of Major League Baseball Injuries

The above study shows a spike in total injuries at the beginning of the year. While another looked at UCL injuries from 2007-2014 found a significantly larger number of tears occurred in the first 3 months of the season. (DeFroda et al., 2016)

This may be a surprise to some considering overuse is a commonly cited reason for injuries in pitchers, but this data shows that professionals see more injuries earlier in the year.

Today, we look specifically at range-of-motion research and workload-related research to see if we can get a better sense of direction for why so many injuries may be occurring in the early part of the year. The research will be able to tell us “what” they found, while we try to piece together a hypothesis for “why.”

Ideally, this should give us a better idea of how some issues may be connected. with the hope of reducing overall injury rates in pitchers.

For the sake of this discussion, we assume that these pitchers were healthy before being injured in the spring. We already know that one of the biggest predictors of injury is previous injury. Plus, it’s theoretically possible for players to have small injuries crop up at the end of the year, resulting in their trying to manage it in the off-season and then have it get worse in spring training, thus qualifying as a spring injury when it may have been a longer-term problem involving a relatively smaller previous injury that lead to a bigger injury.

But today, we focus on the question, “What could be the cause of otherwise healthy pitchers being hurt in the beginning of the year?”

To answer that, we first dive into an explanation of the basics of workload, followed by range-of-motion research, to set a baseline of information before trying to see how they may tie together.

A Quick Description of Workload Research

In research, workload is generally split into an athlete’s acute or chronic workload. Acute workload is the “workload” that occurs over a week’s time, while chronic workload is the rolling measure of “workload” over 28 days.

You can then take the acute workload (intended to measure fatigue) and divide it by the chronic workload (intended to measure fitness) and get your acute-chronic workload.

While there is not a large amount of baseball-related workload research, there is a good amount of research in cricket and other sports, which we can take some themes from. Broadly speaking, there are some recommended ranges that athletes are suggested to stay within (0.8-1.3 is often broadly recommended, though it’s shown to differ between sports), and while more research can and should be done for baseball-specific workload, this research gives us a solid starting point.

The big idea of workload measurements is that there is a sweet spot that athletes can be in, a balance between acute and chronic workloads, that can reduce their risk of injury.

In the case of a pitcher, this means he could be both undertrained or overtrained and be at a higher risk of injury. He could have a low chronic workload and see a big spike too soon, or he could have a relatively high chronic load but have frequent big spikes in workload, meaning both undertraining and overtraining can cause injury, similar to what’s seen in cricket (Hulin et al., 2014).

When we’re talking about early-season workload issues, the main issue is having a low chronic workload (from taking time off) and then seeing a sharp increase in workload, which highlights the importance of what we call on-ramping.

Note: Throwing workload is also something that the Motus sleeve monitors, if you use their sleeve. They also factor in the intensity of the throw, which undoubtedly matters as a throw at 99% of a pitcher’s peak torque should be accounted for differently than a throw at 55% of a pitcher’s peak torque.

Early Season Throwing: On-Ramping

While baseball has generally done a good job of being more aware of overuse injuries and taking time off, there is one area in particular that it isn’t very good at: getting back into throwing. This is going to be especially relevant to the time of year that we are talking about: spring training.

Not only are professional athletes starting to compete, colleges are also starting to play games, and high school teams are either starting to practice or play games depending on where they are in the US.

At Driveline, we qualify the time that athletes go from either no throwing or low-medium intent to high-intent throwing as on-ramping. It’s the period of time where they are throwing, but they are throwing to build workload capacity and prepare for high-intent throwing. High-intent throwing includes bullpens, pull downs, plyo velos—anything that is at max intent.

This period of time, on-ramping, needs to be more specifically discussed in baseball because of its relevance to throwing workload.

We want to steadily increase an athlete’s chronic workload without seeing drastic spikes. What happens too often is athletes come back from a period of time off and then see a spike in workload (generally bullpens) that is too high.

Even in spring training, pitchers report and almost immediately have to throw multiple bullpens, which quickly turns into simulated games and then spring-training games. While the overall volume may try to stay low, the intensity can still spike.

Somewhat complicating the relationship between workload and injury, besides intensity playing a role alongside volume, is that there can be delayed effects.

Some cricket research has seen that an increase of acute workload over chronic workload can result in a high chance of injury a week after (Hulin et al., 2014). Other cricket research has seen delays of 3-4 weeks (citation).

In tying this together, there seems there may be a link between mismanaging acute/chronic workloads and early season injuries, but the possible correlation might not be immediately apparent due to the possibility of delayed effects.

A Quick Look at Range of Motion

Throwing athletes tend to have less internal rotation but more external rotation in their dominant (throwing) arms when compared to their non-dominant arms.

This is likely an adaptation to throwing, but it has been shown that there can be some small differences in ROM between arms that can end up making a big difference for chance of injury.

Total Range of motion is generally similar between arms. Picture Source

Though, when we look at ROM measurements, such as the ones provided above, it is important to consider that ROM is a fluid measure that constantly changes over time for each individual athlete. For example, there are differences between measurements before and after a pitcher has pitched: either losing internal rotation, gaining external rotation, or both (Kibler, Sciascia & Moore, 2012; Freehill et al., 2014; Case et al., 2015).

Furthermore, it has been shown that there can be ROM effects that are seen after pitching in the following days (Kibler, Sciascia & Moore, 2012). This means that a pitcher can gain external rotation and lose internal rotation immediately after a start, and the lack of internal rotation can still be seen days later.

This goes to show that there seems to be an adaptation of the body where the throwing arm has less internal and more external rotation, but there are still fluctuations in range of motion that can occur based on throwing volume and intensity.

Early Season Range-of-Motion Research

Now, we are going to look at three studies in particular that both measured a number of pitcher’s ROM measurements before the season and analyzed various deficits that were seen to be related to future injuries. Now, all three studies didn’t find the exact same results, but they did all conclude that lacking range of motion before the season may be an issue.

Below are the studies and their key points:

Decreased Shoulder External Rotation and Flexion and Greater Predictors of Injury than Internal Rotation Deficits: Analysis of 132 Pitcher-Seasons in Professional Baseball

“For continuous variables, the risk of elbow injury increased by 7% for each degree of increased shoulder ER deficit and 9% for each degree of decreased shoulder flexion.”

Deficits in Glenohumeral Passive Range of Motion Increase Risk of Elbow Injury in Professional Baseball Pitchers: A Prospective Study

Neither glenohumeral internal-rotation deficit nor external-rotation insufficiency was correlated with elbow injuries. Pitchers with deficits of >5 degrees in total rotation in their throwing shoulders had a 2.6 times greater risk for injury. Pitchers with deficits of >5 (or equal to) in flexion of the throwing shoulder had a 2.8 times greater risk for injury.

Deficits in Glenohumeral Passive Range of Motion Increase Risk of Shoulder Injury in Professional Baseball Pitchers: A Prospective Study

Pitchers with insufficient external rotation (<5 greater external rotation in the throwing shoulder) were 2.2 times more likely to be placed on the disabled list for a shoulder injury and were 4.0 times more likely to require shoulder surgery.

Note: The last two studies both lasted for 8 seasons, but researchers combined the measurements of pitchers who have multiple seasons because they didn’t find a statistical difference in between year measurements. The first study also lasted multiple seasons, but if a pitcher had multiple measurements, they counted them as separate individual seasons. Those researchers cited prior evidence showing that ROM can change between seasons. (Shanley et al., 2012)

So, we see either lacking flexion, lacking external rotation, or having less total range of motion as possible red flags.

Now, these studies only link ROM deficiencies with injury risk and do not explain why these deficiencies occurred in the first place. However, one can reasonably hypothesize that these ROM deficiencies found in higher level throwers early in the season could be related to throwing volume, or a pitcher’s early-season workload.

As mentioned above, there have been studies that have shown an increase in external rotation and decrease in internal rotation when comparing before and after measurements of pitching. This makes sense given how pitching is a very dynamic action, especially when you’re asked to throw at high velocities for long periods of time (100 or so pitches) or frequently as a reliever.

So, if some players are taking time off and limiting their workloads during the off-season, they may not have thrown enough to get their dominant arm back to a “normal” level of range of motion by the start of spring training (whatever normal level that may be.) This movement deficiency coupled with throwing too much, and at too high of an intensity too soon, may be part of the reason why we see injuries early in the year.

We don’t know exactly how this would affect throwing mechanics (if at all), but what we could be seeing is an increase in dynamic external rotation without enough passive range of motion, which may be putting an athlete’s arm at risk.

We are essentially making a few assumptions:

• Players will have some change in range of motion by taking time off from throwing. Because throwing is overhead and dynamic, this likely means a possible loss of flexion, external rotation, or an overall decrease in total range of motion.
• If you go from no throw to high intent too soon, a pitcher might have arm issues because the passive rotation hasn’t “caught up” to the dynamic rotation.
• These differences in range of motion may be a reason why acute spikes in workload are dangerous for overhead throwers.

We also need to mention that there is a difference between passive and dynamically measured range of motion. We have been discussing passive range of motion, generally measured by some sort of practitioner on a table, whereas dynamic range of motion is what would be measured in a biomechanics report.

There are differences between the two, but in one analysis that aimed to compare how closely each lined up with one another, a moderate correlation of 0.46 was found between the two measurements (Miyashita et al., 2008). The main takeaway is that there is some overlap between passive and dynamic external rotation, but it’s likely that more research is needed to fully understand the relationship between the two measurements.

It may be reasonable to hypothesize that a piece of the early-injury spike may be caused by more significant differences between passive and dynamic range of motion, specifically looking at external rotation when a pitcher starts throwing. But again, more research is needed before we can confirm this.

In theory, increasing the intensity of throwing too soon could stretch passive structures beyond what the pitchers are comfortable adjusting to. This may be especially relevant for pitchers who are lacking passive external rotation, because they may be experiencing a greater than normal difference between their passive and dynamic measurements, Which hypothesizes that range of motion plays a role in early season workload injuries specifically for pitchers.

Trying to Piece Things Together

So here’s what an answer to the previously asked question, “What could be the cause of otherwise healthy pitchers being hurt in the beginning of the year?”, may be:

• A pitcher reports to spring training, having lost some range of motion in his throwing arm, enough to cause his dominant arm to measure a less passive range of motion than his non-dominant arm. This is caused by a lack of throwing workload and mobility during the off-season.
• He gets to spring training and starts throwing bullpens 2-3 times a week in preparation for the season.
• Because of the decrease in passive range of motion and increase in throwing, he experiences a bigger than “normal” (defining “normal” as what he would experience in-season) differences between his passive and dynamic external rotation.
• Because of a decrease of off-season work and sharp increase in bullpens, the pitcher also experiences a sharper than expected increase in workload.
• The effects of differences in larger passive and dynamic external rotation are unknown, but we have seen delayed effects of increases in workload, meaning the pitcher may be at risk of an injury due in part to a lack of external rotation or flexion and what may be more appropriately termed a workload error.
• All of this leads to a pitcher being at a higher risk of injury because of a poor combination of multiple variables that have been seen to put pitchers at risk.

Limitations in Finding the Root Causes of Injuries

If this sounds too simple and too perfect to be true, it probably is. There are, of course, other reasons why a pitcher could see changes in ROM that are not caused by a lack of throwing.

For example, we’ve seen that an athlete’s non-dominant arm can see changes as well, complicating how reliable comparisons between arms are.

We also cannot rule out anatomical issues, as a pitcher could be generally more “stiff” or have more muscular tightness that restricts range of motion. 

Furthermore, injuries are multifaceted problems, and we’re only looking at a few variables: range-of-motion measures and throwing workloads. We may also be making assumptions on the relationship between passive and dynamic range of motion beyond what we have evidence for.

Lastly, the injuries mentioned during the ROM studies didn’t report the time of year when the injuries occured. However, in this article we’re making the case that ROM discrepancies in pitchers increase the risk of experiencing an early season injury based on the evidence provided in the very first study that we looked at above.

To truly see if there is a relationship between the two, we need to see some research that’s able to consistently monitor throwing workload and measure range of motion. This is a relatively tough task because it would require a longer period of time and consistent tracking.

Suppressing Early Season Injury Rate in the Future

First, there should be a closer look at the workload of pitchers who are coming off of a no-throw or little-throwing period. Second, regarding range-of-motion research, findings that see a relationship between injury and range of motion should include time of year to get a more specific view of how strong the relationship is.

So are workload injuries definitely related to range-of-motion deficits? Maybe, but there isn’t much workload research on pitchers, much less workload research paired with range of motion, but baseball is also different from other sports. The specific demands of throwing are unique compared to running or cricket, where a bowler needs to keep a straight arm.

One thing we do know is that pitchers and coaches need to spend more time focusing on how they go from not throwing to being able to start pitching again in a game.

This article was written by Technical Project Manager Michael O’Connell

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