Driveline Baseball Podcast Episode 18: On this episode of the Driveline Baseball Podcast Mike and Kyle are joined by Pitching Coordinator Eric Jagers and Former Director of Pitching Matt Daniels who is now the Coordinator of Pitching Analysis for the San Francisco Giants. Matt and Eric join the show to further a previous discussion they had on Pitch Design (you can find this previous discussion here in episode 12). This time around they are here to give their thoughts on how coaches at smaller levels on low budgets can still execute this process. They also share what are some key factors in making sure you have a successful process for Pitch Design. Kyle and Mike also discuss our recent partnership with Kinatrax and our plan moving forward with markerless motion capture. Make sure and Subscribe to the show to be first notified of when the podcast is live.

Episode Resources:

Pitch Design: You can check out more work from on Pitch Design on our Blog page or you can visit DrivelinePlus where we have multiple videos discussing all things you need to know about pitch design.

Kinatrax: brings next generation markerless motion capture technology out of the academic arena and into the real world. Check out Kinatrax website to learn more about how they are bringing markerless motion capture to baseball.

Twitter: Follow Eric Jagers and Matt Daniels on Twitter!

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

The post Driveline Baseball Podcast- EP. 18 appeared first on Driveline Baseball.

As a small or lower-level school pitcher or pitching coach, one thing that can be frustrating is the lack of resources available to create an effective game plan from game to game and hitter to hitter. You’d have basic reports from a coach that say things that can be discerned from general knowledge:

• “Lead-off guy is a base stealer so be quick to the plate if he gets on.”
• “Three-hole hitter is their dude so pitch around him and be careful.”
• “Six-to-nine guys are scrubs, so let’s attack them early in the count with the fastball.”

Thanks for the heads-up, coach.

So how do we take a deeper dive into preparing a game plan based on the team and the guy we have on the bump? It’s a pretty safe bet that if you are a college or high school coach, you do not have access to Trackman, HitTrax, Rapsodo, etc. for opposing teams, and unless you can travel and watch your opponents play, you don’t have the means to see them in action before first pitch. This leaves us with working off of word of mouth from other coaches (not entirely reliable for a multiple of reasons) and the stat sheet from the opposing team’s website. Since the stat sheet is only objective data we have access to, how can we use those numbers to provide our pitchers with a plan that isn’t filled with bias?

Annie Duke says in her book Thinking in Bets (which I highly recommend) that “Improving decision quality is about increasing our chances of good outcomes, not guaranteeing them.” She then goes on:

“Thinking in bets starts with recognizing that there are exactly two things that determine how our lives turn out: the quality of our decisions and luck. Learning to recognize the difference between the two is what thinking in bets is all about.”

This is the essential part of this process: understanding that what we are trying to do is get as much information as possible in order to make a decision that gives us the best odds of the outcome we desire. You can follow a scouting report from the best MLB R&D department and still give up a home run even if you followed it to a tee. There is always a certain amount of luck and skill involved with every decision, and it’s imperative to judge the quality of the decision not based on the outcome but rather on the odds used to make that decision.

When looking to scout off of the stat sheet, we are in the business of making a decision without having all of the information available to us. Understanding how to make the data we have actionable can give us a better look at how to approach each hitter. The first step to formulating the game plan is to understand what the strengths of each pitcher on your staff are. Trying to conform your pitchers to fit a game plan that plays against their assets is not the most advantageous strategy. For example, let’s say you have a pitcher who has a fastball that spins at an above-average level and has a true top-down curveball with a substantial amount of negative vertical break. Having him pound the bottom of the zone with a heater because a team grounds out at an above-average clip isn’t your best bet—even though intuitively it might seem that way. (See Bauer Units and Pitch Comparison for more detail.)

If you don’t have access to a Rapsodo, you can still understand an athlete’s arsenal by knowing his average velocity and having a general idea of the shape of his off-speed offerings. Also, do not overlook what pitches the athlete likes to throw. Knowing what an athlete is comfortable with and what he has had success with in the past won’t solve everything but should not be completely disregarded either.

Below is a typical stat sheet found on most teams’ websites:

Luckily, this particular conference keeps extended hitting stats as well, so we have this data available:

There are a couple aspects to note as we stiff through the stat sheet. There are typically two samples of information that coaches can choose to look at: season-to-date statistics and in-conference statistics.

Season-to-date statistics include out-of-conference production, which can occur against teams that are either better or worse relative to your conference opponents. Thus, by analyzing out-of-conference stats, we risk potentially being misled by the production of a player who may have only faced better or worse competition before conference play. Also, because cold-weather teams typically play a higher propensity of road games during out-of-conference play, season-to-date stat lines could be skewed depending on where an opposing team is from.

In contrast, looking only at in-conference statistics limits our sample size of plate appearances available for analysis. This is troublesome because there is only so much we can take away from less than a full season’s worth of plate appearances, because stats have yet to become reliable enough to draw many conclusions from them.

To address these issues, we recommend looking at both in-conference and out-of-conference plate appearances, because five in-conference plate appearances tell us more information about a player than five out-of-conference plate appearances do. However, this does not necessarily mean that five out-of-conference plate appearances (or even historical statistics) do not tell us any useful information about a player at all.

However, for simplicity sake, in-conference stats are used below. We also only look at hitters with a large-enough sample size (in this case 40+ PA, so Player A through Player J) so the rest of the team (Player K through Player Q) don’t alter numbers of the hitters you will face in the game. After you have this information, identify areas that you can leverage with each hitter. This starts by trying to find outliers in a hitter’s stats. These can start to paint a picture of the tendencies of opposing hitters. To find outliers, compare these stats against conference averages. (See below.)

K per PA- .16 Std. Dev.- .08

BB per PA-  .08 Std. Dev.- .04

GO per PA-  .22 Std. Dev.- .06

FO per PA-  .22 Std. Dev.- .06

It took a couple hours to create a the spreadsheet and run the numbers to get these averages and standard deviations. Knowing how to use Excel or Google Sheets can be great and valuable tool for coaches. For instance, Udemy has some great courses that can help you brush up on your knowledge and teach you a thing or two you didn’t know!

Once some averages are established, we can find what a hitter does well or poorly to exploit in our plan of attack. A previous blog post we are heavily referencing going forward is Choosing the Correct Pitch Sequence: Data-Driven Decisions. (If you haven’t read this article, I recommend it before going forward.)

Below is a chart pulled from Harry Pavlidis’s Updated Benchmarks for Pitch Types:

Using the data gathered from the stat sheets with this chart, we can create a somewhat individualized plan for attacking each hitter. Now, let’s take a look some examples of how we can use these numbers to our advantage.

First, Player J. We can see that Player J’s total number of Ks does not stand out among his peers. However, if you compare that against the number of PAs, he strikes out almost two standards deviations above the conference average. With the walks being slightly average and the GOs and FOs normal, we can reasonably discern that this hitter swings the bat more often than taking. This likely means that he has a propensity to whiff. With walks around league average, it’s likely that he would swing through pitches and work counts deep to accidentally draw walk. So, that being said, throwing a fastball early in the count will set his expectations for velocity, and then have your pitcher throw his best off-speed pitch. A slider would be great, due to the high whiff percentage, and since we can assume this hitter will swing, the risk of also not being able to locate this pitch is minimized.

Next, Player F. He is on the opposite end of this spectrum. The true outlier here is that he walks nearly two standard deviations above the league average. He also grounds out at a lower clip than most hitters. He also leads the team in RBIs and is close in XBHs. He is either extremely lucky or does a good job of getting barrels when he decides to swing. Let’s assume it’s the latter. Throwing a curveball early here could be great because against a good hitter this can disrupt his timing for the rest of the at-bat. Doubling up on this could be a viable option, if you’re looking to avoid serving up a heater early in the count—especially with its substantially higher watch rate over other pitches. After this, depending on the handedness of the pitcher, we can look at going for a slider or a changeup to polish him off and save the fastball for later in the count.

Now, Player H. He is pretty straight across the board for BB and K but favors one batted-ball outcome significantly. He flies out about one and a half standard deviations above league average and grounds out at about the same rate on the opposite end of the spectrum. With the information provided, only 5 XBHs in 86 PA and the chance of a HR being below 6%, the idea of trying to induce a fly ball is attractive, depending on the situation. Setting up a fastball with an off-speed offering could be an effective way to get this done, due to the FO and PU rate of this pitch.

For hitters who are fairly average across the board (Player E, for example) sticking to your pitcher’s strengths is likely your best option. Allow him to do what he does best, and as more information become available (approach, previous at-bats), you can then tailor the attack accordingly.

This information can also be valuable from a game-management standpoint. For example, there is runner on third, with one out, and Player H is up to bat. The pitcher on the mound throws his fastball with high spin and has a below average GB rate. A fly ball is the most likely outcome. Let’s also say that Player B is on-deck. He grounds out and flies out at a very similar clip but does not slug a high percentage, which lessens the fear of an extra base hit. You also have a pitcher in the pen who has an above average changeup with an exceptional GB% against same-handed hitters (and as luck would have it, he and Player B are both left-handed). If we are playing the odds, pitching around Player H to try to induce weak contact or a K is smart play. The risk of walking him is minimized, because we can bring in our lefty to face Player B to maximize the chance of rolling up a double play. Now, obviously most situations will not work out this well, but knowing more about the opposing team will lead to higher quality decisions where the odds tilt in our favor.

Clearly this should not be the only approach of how to attack hitters since there are many other factors to consider—pitchers ability, pitch flight data, hitter’s swing metrics, and batted-ball data—but it is a practical way to make more informed decisions on the bump when all of the data isn’t available.

Written by Business Associate Alex Valasek and Sabermetrics Analyst Dan Aucoin

The post How to Scout Off a Stat Sheet appeared first on Driveline Baseball.

For many years, baseball teams have employed rehab specialists to not only get players back to the field quickly following an injury, but also to help with injury prevention. In the past, physical therapists, chiropractors, athletic trainers, and others have worked with athletes at the highest levels. Recently, it has become more common to see baseball-specific training facilities pop up throughout North America. The focus of these facilities has been to get athletes the best possible quality of training to meet the specific demands of their sport. However, it hasn’t yet become common practice to see rehab specialists used in this setting as much as in the team setting.

This article highlights the benefits of using rehab specialists in a training setting, as well as how to incorporate this practice.

Breaking the Stigma

In the past, rehab specialists at the highest levels of the game have mainly focused on players returning from injury. There was not a lot of communication between the team’s athletic trainer, strength coaches, skill coaches, or other rehab specialists. Coaches usually would just get a verbal report back from a player; often, this would be conveyed in a manner that resulted in the player sitting out. As a result, players have developed a stigma of seeing their team’s athletic trainer, unless absolutely necessary, due to the fear that they could be removed from competition.

Recently, teams have prioritized a team-approach model when it comes to the health and performance of their athletes. Coaches, trainers, rehab specialists, and strength coaches are more consistently in communication with each other to determine what the best course of action is for each specific player. This is not only done reactively when a player is injured but also proactively, in hopes of both keeping the athlete on the field and maximizing that athlete’s performance.

At Driveline, Curt Rindal, our chiropractor, and I have served in this capacity for several years. We have always prioritized the health of each athlete, and as Curt and I have become more ingrained into the everyday culture of the facility, the health of the athletes has steadily improved.

Prior to moving into the facility full-time, I would come in once a week and see athletes as-needed. Since I started working here full-time, we have made it a mandatory part of each athlete’s assessment process. I see an athlete on day one or day two, collect a thorough injury and health history, and perform various testing, which allows me to see if the player has the capacity to do what our strength and throwing trainers require. After collecting that information, I discuss with the athlete what corrective work they may need and lay out a plan for how often I would like them to see me.

Once the athlete has gone through that evaluation, in addition to their throwing/hitting assessment and his strength and movement assessment, he will sit down with a strength and skill coach—who have both communicated with me about what, I feel, they need to be made aware of for that athlete—and then we develop a training plan going forward. Once a month, the athlete will have a throwing, strength, and physical therapy retest to assess their progress. We then determine what the next steps going forward are. Between these constant retesting sessions, along with multiple meetings per week between the skill coaches, strength staff, and me, we are able to keep a closer watch on all of the athletes that come through our doors and get the most out of their time here.

Integrating a Rehab Specialist

A training facility does not need a full-time rehab specialist to provide significant benefits for the health of their athletes. There are plenty of ways to incorporate a provider’s services. Here are a few of the more common ways a facility owner could do so:

1. Referrals to a nearby clinic

This is the easiest and most-cost efficient way for a facility owner to incorporate some type of rehab into his program. Many physical therapy and chiropractic clinics likely have at least one clinician that enjoys working with baseball players and understands the demands of the game. This may take some researching of the clinics in your area, but developing a relationship with that provider is ultimately worth the effort.

2. Having a provider come in part-time

This is how I started at Driveline and how we still use Curt Rindal’s services. Once or twice a week, the clinician comes into the facility, and athletes can sign up for designated slots. This can be done reactively or, ideally, this should be a way to get athletes screened by a medical provider. This could be set up a couple different ways:

• First, the owner could pay the clinician directly. This could be based on how many athletes the clinician may screen or treat each time they are in the facility.
• Second, the athletes could pay the clinician each visit. Often, this will be a set amount regardless of it was a screen or follow-up visit.

3. Having a full-time provider

In an ideal world, this is the scenario that every training facility can do. Having a provider in the facility every day that the athletes get to know can go a long way in removing the stigma of seeing a rehab specialist only for injury. This can also allow for the most consistent team approach, with every staff member communicating back and forth regarding the care of athletes.

One thing that needs to be noted when having a provider on-site is that in many states, legally, certain types of companies aren’t allowed to have medical providers on their staff. In this case, that clinician, regardless if he are working at the facility full- or part-time, will need to be an independent contractor. This is something that should be looked into prior to bringing on a provider in any capacity.

As athletes continue trying to push their bodies further and further, employing the services of a rehab specialist is a must for any coach or training facility who is serious about keeping athletes healthy while maximizing their performance. There currently is no shortage of providers out there who have a strong passion for the game and would love to provide their services in a baseball setting. Taking the steps in building a relationship with a clinician is something all coaches and trainers owe to their athletes.

This article was written by Physical Therapist Terry Phillips

The post Integrating a Rehab Specialist Into a Training Facility appeared first on Driveline Baseball.

Every hitter moves differently, depending on that hitter’s individual stability, strength, and mobility capabilities. We know that youth athletes have a limited window to developing rotational speed and power, per Dr. Greg Rose, and building that engine should be the main emphasis in a youth hitter’s training and development. Training young hitters to move fast, swing with intent, and hit the ball as hard as possible will clean up a lot of different movement patterns and inefficiencies. That being said, there are still opportunities to leverage certain constraint drills within the athlete’s training program to foster a faster adoption rate of skill acquisition and to help improve each hitter’s movement quality.

So, at what point does a coach strike the proper balance between keeping a young athlete’s workouts fun and productive and allowing the athlete exploratory training and repetitions, but also addressing any glaring inefficiencies he may see in each athlete’s individual movements? Yipping a kid up with internal cues and PVC pipe drills is not fun for the athlete, nor does it teach intent, nor does it develop the aforementioned rotational speed and power.

In this article we look at four different youth hitters that have trained in Driveline’s Youth Training Sessions and examine how a program that combines moving fast and “building the engine” can also be individualized to address the specific swing characteristics each hitter possesses while still keeping the athlete’s programming relatively simple.

The following four athletes are all middle-school aged youths getting ready for their spring seasons. All have good bat-to-ball skills and have been on All-Star teams in the past. Let’s assume they all lack strength and stability, (as most 12-14 year old youths do) but are all considered good athletes relative to their ages. Below are the Blast metrics for each hitter:

Athlete A

Athlete B

Athlete 1

Athlete 2

Based on these Blast metrics, we see that Athlete A and Athlete B both struggle to reach peak rotational speed until very late in their swings, as shown by the sub 8.0 rotational acceleration scores each possess; i.e., neither one of them rotates very well and will both likely struggle to achieve high EV at deeper points of contact.

We can also see that Athlete 1 and Athlete 2 both have laughably high attack angles, particularly for the bat speed and exit velocity capabilities each of them possesses. Both Athlete 1 and Athlete 2 are moving the bat much too “uphill” at impact with the ball, likely causing them not only to topspin a high amount of their batted balls but also to struggle with pitches in the top third of the strike zone.

Based on these metrics, tailoring individualized programs for each athlete is not difficult nor too technical. Giving each athlete a specific external task to accomplish during training with possible specific constraints is a good opportunity to simultaneously train the moving fast and “building the engine” model while still putting each athlete in an environment specifically tailored to foster improved movement patterns based around the individual athlete’s swing characteristics.

We’ll bin these athletes into two groups: rotation focus and attack angle focus. Below are two sample programming guidelines designed to address each movement deficiency while still allowing the athletes to train with intent while still putting each hitter in a challenging position to execute an external goal that will help improve movement patterns:

Rotation Focus:

• Med Ball side toss – 1 set of 8, each side
• Offset closed rope ball swings into plyowall – 1 set of 8, each side
• Tee – shuffle swings w/ barrel overloaded bat – 1 set of 12 swings
• Tee – offset closed w/ handle overloaded bat – 1 set of 12 swings
• Tee – hook’em short bat – pull side gap – 1 set of 12 swings
• Front Toss – short bat, top hand – pull side gap – 1 set of 12 swings
• Front Toss – shuffle swings w/ barrel overloaded bat – 1 set of 12 swings
• Front Toss – offset closed w/ handle overloaded bat – 1 set of 12 swings
• Front Toss – underload home run derby – 1 set of 12 swings
• Live/Machine Work – inside pitch, pull-side emphasis

For this program, specific constraint drills like offset-closed and top-hand only swings versus inside pitches will put Athlete A and Athlete B in specific positions to force their bodies to “over-rotate” (relative to how much they’re currently rotating) in order to achieve the goal of hitting the ball hard, which still remains the main cue. Having these athletes work exclusively on hitting the inside pitch to the pull side with authority should theoretically help them improve their rotational speed and power capabilities.

Attack-Angle Focus:

Note: Normally attack-angle (AA) focus means taking an athlete’s attack angle that is negative (swinging down) and improving it into the positive range. With these two athletes, their AA are far too high and need to be shaved down to between 5 and 10 degrees (based on their EV capabilities).

• Med Ball side toss – 1 set of 8, each side
• Rope ball swings into plyo wall – target at chest level – 1 set of 8, each side
• Tee – High Tee w/ barrel overloaded bat – 1 set of 12 swings
• Tee – Beltre Drill w/ barrel overloaded bat – 1 set of 12 swings
• Tee – High Tee w/ long bat from shoulder – 1 set of 12 swings
• Front Toss – overhand, short bat, top hand, top of zone, opposite field gap – 1 set of 12 swings
• Front Toss – overhand, plyos, top of zone, w/ barrel overloaded bat – 1 set of 12 swings
• Front Toss – overhand, velo, top of zone, underload bat to CF – 1 set of 12 swings – LA between 5-15 degrees
• Front Toss – overhand, velo, top of zone, game bat to CF – 1 set of 12 swings – LA between 5-15 degrees
• Live/Machine Work – velocity in the top third of the strike zone – CF emphasis

For Athlete 1 and Athlete 2, specific constraint drills like short-bat top hand, Beltre drill, and overhand front toss, with plyos, at the top of the strike zone will force their bodies to move the bat through space on a flatter plane to be able to execute flush contact with that specific pitch.

For youth hitters, moving fast, swinging with intent, and developing rotational speed and power should be the emphasis for the majority of their hitting training. However, if you are looking to challenge your youth athletes with tasks designed to expose movement holes or flaws by using specific constraints and external cues, creating simple and individualized programming for each hitter is a good way to implement new training stimuli into a youth athlete’s routine.

Written by Hitting Trainer Collin Hetzler

The post Individualized Programming for Youth Hitters appeared first on Driveline Baseball.

Driveline Baseball Podcast Episode 19: On this episode of the Driveline Baseball Podcast Mike and Kyle are joined by Driveline Pro day signee Robert Robbins. Robert was a standout from the biggest Pro day in Driveline’s history and signed a contract with the Chicago Cubs organization. Robert discusses his journey from D3 baseball all the way to the pros. Robert also talks about the things he did at Driveline that got him prepared to be at his absolute best on Pro Day. Harper Vs. Machado and who is going to get more has been a hot debate all off-season long, but now that there both signed what do Mike and Kyle think about both players. This week Mike and Kyle take a different approach to the Harper Vs. Machado debate by looking at it from a Risk standpoint. Which Franchises were best aligned to sign Harper or Machado and why are so many teams afraid to take Risks on players? Find out in this weeks brand new episode.

Episode Resources:

Driveline Pro Day: In January of this year Driveline hosted our best Pro day to date bolstering the best lineup of Pitchers and Hitters we had to offer. With a total of 7 players signed we could not be more pleased of the results. Check out a highlight video here of some of the best moments from the day. Or check out this great article written by Fangraphs on how the day went here.

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

The post Driveline Baseball Podcast- EP. 19 appeared first on Driveline Baseball.

You can’t outcue a movement deficiency. These are words we live by at Driveline Baseball and that’s what our makes our assessment process so vital for getting our athletes results. Individually, our assessments don’t provide nearly as much value as they do as a whole. However, once we combine the biomechanics report and throwing videos with the range-of-motion assessment and VBT testing, that’s where the real magic can happen.

When Mariners reliever Dan Altavilla came to Driveline, his situation was unlike many of those that come here. Most athletes come to Driveline to increase velocity, improve strength, or develop a pitch using Rapsodo and edgertronic video. While Dan did work on pitch design remotely, today we’re going to focus on how the rest of Dan’s assessment focused his programming. Considering Dan’s solid big-league numbers, a peak velocity of 100.46 mph last year, and his exceptional baseline strength, finding the biggest limiting factor was a bigger challenge for us.

After missing some time with an injury in 2018, Dan knew that he needed to work on improving mobility to his muscle-bound frame. Which raises the question, How will adding mobility decrease the chance of injury? From looking at his VBT testing, it’s easy to see that he can put a lot of force into the ground, but what happens when that force moving up the kinetic chain can’t be absorbed? This video from our Director of Performance, Sam Briend, shows how that energy is transferred from the lower half to the arm.

Assessment Findings

The first thing we identified through Dan’s range-of-motion assessment was a lack of external rotation in his hips. Lacking hip external rotation can limit a pitcher’s ability to hold torque in the rear hip as the center of mass moves down the mound. Think about the squat and deadlift cue: “screwing the feet into the floor.” The point of screwing the feet is to stabilize the hips from being pulled towards the midline. When an athlete can hold torque in his rear hip, it allows the pelvis rotation to be driven by the glute rather than femoral internal rotation. In Dan’s baseline throwing videos, you can see his inability to hold tension in his rear hip as his center of mass moves towards the plyo wall. This can lead to a late pelvis rotation after foot contact, which puts him in a bad spot to have an effective lead-leg block.

The lead-leg block is a by-product of everything that happens before foot contact. You can see the effect that pelvis rotation has on the lead leg from this sugar packet and a butter knife from our biomechanist Anthony Brady:

Another issue we found in Dan’s range-of-motion assessment was a lack of thoracic rotation. Having thoracic rotation aids in creating hip and shoulder separation. As the pelvis opens, the torso has to counter rotate in order to create separation. If an athlete lacks thoracic rotation to his counter-rotation direction, his torso will rotate at the same time as his pelvis. The thoracic rotation is what we’re able to measure in our mobility assessment; we wanted to see how that number compares to how Dan moves dynamically in his biomechanics report.

When assessing hip and shoulder separation, the timing between peak-pelvis and peak-torso angular velocities is arguably more important than the degree of separation. Dan’s biomechanics report shows that while his degree of hip-shoulder separation was sufficient at 39 degrees, the timing between peak-pelvis and peak-torso angular velocities was just 0.0188 seconds, which is on the lower end of what we see.

This is important, because if we look at the joint-by-joint approach, we know that the hips should be mobile, the lumbar spine stable, and the thoracic spine mobile. Therefore, if an athlete lacks range of motion or strength in his hips, the hips will stabilize to protect themselves, which causes a compensatory effect that forces the lumbar spine to be mobile and limits the range of motion through the thoracic spine.

Sometimes addressing a thoracic-mobility issue means addressing the hips and pelvis. Knowing that Dan lacked hip external rotation in both hips, we knew that if we worked on increasing range of motion in his hips and strengthening that new range of motion, the benefits could potentially work up the chain.

Programming

Once you’ve assessed an athlete, it’s important to take a step back and look at the big picture. As you’ll see, you can use the assessment to better target what you want to improve from when they first get to the gym to when they leave.

Warm Up

Based on the information from above, we designed a daily warm-up to attack these specific movement deficiencies. The objective of the warm-up is to get the athlete in better alignment prior to whatever activity he is going to do. It should progress from general exercises that address the athlete’s deficiencies to specific exercises that prime the body for what he is about to do.

Looking at his warm-up, we have Wall Press Dead Bug and Bear Crawl in there to address anterior core and pelvis stability that could be leading to his limited hip external rotation. Most anterior core exercises stabilize the hip external rotators. The Side Lying Windmill w/ Full Exhale is to address the lack of thoracic spine rotation.

Once we get through the “corrective exercises” we move into more movement-based exercises that prime him for the movement he’s about to do (pitching) and also address his deficiencies. That’s where the Banded Hip Abduction and Alternating Lateral Lunge w/ Reach come into play. The Banded Hip Abduction targets the gluteus medius, which is the prime mover for hip external rotation, and the Lateral Lunge is a frontal-plane exercise that helps him feel the load of the hip that he is lunging into.

Using Med Balls for Patterning

Since he had just finished a full season, we wanted to stay away from a high volume of rotational med-ball work; however, there is some value to patterning movement with med-ball exercises so early in the offseason, so we programmed more anti-rotation med-ball exercises that helped pattern the movement we were looking to improve when pitching. The Half Kneeling Med Ball Scoop Toss forced him to stabilize his lead leg as he rotates the medicine ball over it into the wall.

Once we were able to get into some more high-intensity rotation exercises, we used the Step Back Med Ball Shotput throw to help address his inability to hold torque in his rear hip, and the Split Stance Recoil Rollover Throw to Wall to add a rotational component to an exercise designed to use the lead leg to propel the trunk into flexion.

The Lifting Program

After a month of cleaning up some technique flaws, we got right into dynamic-effort work because spending too much time doing maximum-strength work would be disadvantageous for Dan based on where his strength levels were. In the one-month technique on-ramp, we lived in the four-rep range and kept the load the same each week, focusing on quality of movement.

Once we got into dynamic-effort work, he paired squats in the strength/speed velocity range (0.75-1.0 m/s or 50-60% of a one rep max) with overload jumps. Because he was so muscle bound, the extra load for the jumps allowed him to summon that extra tension for jumps before transitioning into a more true-speed phase.

As far as addressing movement deficiencies in the weight room, we used a variety of methods, including adding tempo to accessory exercises to increase stability in the hips and pairing those with mobility exercises. An example is a Barbell Romanian Deadlift with a two second isometric paired with a Pigeon Pose to Half Kneeling. The Pigeon Pose to Half Kneeling takes the traditional pigeon pose, which stretches the hip into external rotation, and adds a movement to force the athlete to move in and out of hip external rotation. The goal is to increase the range of motion with the mobility exercise and then strengthen within that new range of motion.

Overview

Clip of Dan throwing during this spring training

This a solid example of why an integrated approach is so crucial for getting athletes results. It greatly benefits athletes for their coaches in multiple domains to look over information and discuss what each athlete needs. A strength coach can’t decide to stay in his lane and just focus on getting athletes stronger, because there will eventually be an athlete like Dan who is already strong enough and needs to focus his attention on other things. A pitching coach can’t just cue an athlete to “stay in his back hip” or “rotate his pelvis” because the athlete might not have the range-of-motion capabilities to do so.

In order to work towards achieving the athlete’s goals and needs determined during the assessment process, you need an R&D department that can tell you what is happening and a High Performance department that can help fill in the gaps about why it is happening. Once those two things are solved, the High Performance department and pitching coaches can work together to develop a plan to directly attack the issues at hand.

This article was written by High Performance Trainer Kyle Rogers

The post Making Movement Changes in the Weight Room appeared first on Driveline Baseball.

In our previous biomechanics post, we looked at the data we had collected over the past six months. We looked at the averages of key metrics and what metrics correlate to velocity and torques on the arm. But we didn’t really look at how we interpret a biomechanics report for our athletes.

When designing our report, we realized that there can be an overwhelming amount of information. It’s a lot to go through, even for someone with a trained eye. But an athlete understanding his biomechanics report is a vital piece in understanding what he needs to do to get better.

This means we spent a lot of time refining our report to be as readable and comprehensible as possible. To this day, we continue to improve our report as we learn more and more about the biomechanics of pitching.

The end result is a six-page report that looks at positional metrics, velocity metrics, sequencing, and joint kinetics—or forces and torques on the arm. From our internal database of motion-capture data, we have set normative ranges that we then use to assess and interpret what changes we need to make to an athlete’s mechanics.

In this post we examine an athlete’s biomechanics report and address what sort of changes we would like to make. We won’t look at every single metric; we will just run through the red flags and explain how our trainers would use that information to make specific training recommendations. 

Page 1: Title Page

This page is straightforward. Topical data, like the date of the capture and the athlete’s height and weight, are listed, and the three pictures shown are taken of the athlete at MER in Visual3D.

Definitions of the key events the report looks at are listed as well.

Page 2: Arm Action

The second page of the report looks at joint angles at key positions in the throwing arm, what we define as the arm action.

We’ve already examined one element of this page in a previous article, namely looking at shoulder abduction: the angle between the line of the shoulders and the upper arm. Ideally, we like to see the abduction path stay relatively close to 90 degrees, or neutral with the shoulders. Too high or too low could indicate arm drag or elbow climb.

Looking at the report here, we see that this athlete has a shoulder abduction at foot plant of 103 degrees and a maximum of 135 degrees, which is more than 40 degrees higher than our in-gym average! That means our guy has a drastic amount of elbow climb—not the most efficient arm path.

The next thing we look at is shoulder horizontal abduction, which is how we quantify scap retraction or scap load. A positive number indicates that the elbow is behind the line of his shoulders; a negative number indicates the elbow is in front. A good scap load looks something like this:

Looking back at our athlete’s report, we see he has 3.76 degrees of scap retraction at foot plant (average is roughly 40 degrees), and only 4.83 degrees maximum (average is roughly 57 degrees), which are both far below average.

So we’re seeing excessive elbow climb and a lack of scap retraction. To put it another way, instead of the elbow travel backwards into scap retraction, it travels upwards as the elbow hikes up. This gives us a more precise measurement of how a pitcher is moving, which provides our pitching trainers with more specific information to use to help make a change.

So how do we fix this?

We go to our bread and butter arm action drill: the Pivot Pickoff. More specifically, we have the athlete focus on driving the elbow backwards. With this particular athlete, we also added in Scap-Retraction Throws to really focus on cuing the elbow backwards to improve that scap retraction.

Page 3: Midsection/ Lower Body Positions

The third page of the report focuses on midsection and lower body positions.

Here we note things like if the athlete stays closed and stacked, if the athlete generates good hip/shoulder separation, or if the athlete has a good lead-leg block.

For our athlete, everything looks pretty good. A negative trunk angle and negative forward trunk tilt at foot plant indicates that he stays stacked and closed, and he generates nearly 48 degrees of hip/shoulder separation, which some might call elite.

We do see 48 degrees of lateral trunk tilt at ball release, which in our last biomechanics article we found could be linked with higher torques on the arm. In this case, we can explore what the root cause of the excessive trunk tilt is.

When lateral trunk tilt occurs early in the delivery, there are several factors that may contribute. It’s worth further assessing hip mobility and strength, as a lack of either of these can result in postural shifts that affect trunk positioning. From our mobility screening, we can see if hip mobility or hip strength could be a limiting factor. Or maybe the athlete just isn’t strong enough to hold himself in position. All these factors need to be considered when making a mechanical change—it isn’t just as simple as forcing an athlete to move differently.

Lastly, looking at front knee flexion, we see that the athlete doesn’t collapse on the lead leg and is very slightly extending it by about three degrees. But looking at the graph, we see that after foot plant, the athlete sinks into the lead leg a bit before starting to push back, indicating a slight inefficiency in the lead leg. Ideally, we would like to see the knee continually extend after foot plant, indicating a clear sign for improvement.

Page 4: Kinematic Velocities

The fourth page of the report looks at the kinematic velocities of the thrower.

We note that the athlete has above average pelvis and torso rotation speeds as well as average arm speeds (elbow-extension angular velocity and shoulder internal rotation velocity).

We also see that his maximum lead-knee extension velocity (how fast the angle of the knee changes) is 242 deg/s which is below our target of ~350 deg/s. This fact, coupled with the notes above, indicate that our athlete has a subpar lead-leg block—which we noted in our previous biomechanics article has a significant correlation to throwing velocity.

Page 5: Kinematic Sequencing

The fifth page of the report looks at the kinematic sequencing of the athlete.

Although there is still very little we know about sequencing and timing, we do have a couple of things we can glean from this page.

The timing of peak-pelvis angular velocity to peak-torso angular velocity has been shown to be significantly correlated to pitching velocity. Our athlete has an average of 0.0389 seconds, which is pretty good.

Other studies have examined the time from foot plant to max external rotation and into ball release, but we have yet to see any significant correlations to velocity with those timings.

This carries into the second thing we can examine: the graph. The colored arrows correspond to when the maximum values of each metric occur. As has been talked about before, we like to see those peaks happen from the ground up: Pelvis → Torso → Elbow → Shoulder.

Speeds of each segment: red is minimum speed, green is maximum speed

Page 6: Elbow and Shoulder Kinetics

The last page of the report looks at elbow shoulder kinetics—namely elbow varus torque and shoulder internal rotation torque.

We look at three sets of torques: total torque (Nm), torque normalized for height and weight (%), and miles per hour per normalized torque (mph/ %).

We note that across the board our thrower is worse than our average thrower. He has more torque on his shoulder and elbow, even when normalizing for height and weight, and gets fewer miles per hour per unit of stress than our average thrower. Not ideal.

Now does that mean that we need to shut our thrower down because he could be more prone to injury? Absolutely not.

The calculated loads on the elbow and shoulder are the total loads on those joints, but there is no insight into the underlying muscles and how they work to protect a pitcher. Simply put, there is no insight into the actual torque on the UCL or any other ligament. Just because somebody has higher torques doesn’t necessarily mean he is more likely to get injured. There are a lot of factors at play.

Perhaps the most useful thing we can take from this page is the ability to track torques and velocity-torque efficiency over time. We can retest an athlete after he has made mechanical improvements and track any changes to the resultant forces on the elbow and shoulder. We can test an athlete before and after a season to see what kinematic and kinetic changes have occurred over the course of the year.

These changes are largely unexplored for us, and as we collect more longitudinal data, we can hopefully start to answer the questions surrounding what mechanical changes actually result in less stress and more efficient mechanics.

Putting the Report into Action

With notes in hand, we sent our athlete away to train. Our trainer will be able to look at this information, along with an athlete’s strength and mobility assessment, to put together a plan.

After a couple of months, we were able to do a retest in the biomechanics lab, where we saw significant changes to his arm action—all for the good. There was less elbow climb and more scap retraction, and we even saw slight reductions in the joint forces on his elbow and shoulder, despite throwing the same ball velocity.

This is where the real value of motion capture lies, especially for elite throwers where the mechanical discrepancies can be tiny. By routinely taking thumbprints of our athletes, we can test and retest to see what training improvements contribute to performance gains and what stimuli can hurt them, like the accumulated fatigue over the course of a 162-game season.

Going forward, as we collect more and more biomechanics captures and are able to cross-reference data with training info, we can continue to improve our understanding of what markers contribute to success, and which are associated with backslides in performance.

We are only at the beginning of tapping into the ability of the biomechanics lab.

The post How We Interpret Biomechanics Reports appeared first on Driveline Baseball.

Driveline Baseball Podcast Episode 20: On this weeks episode of the Driveline Baseball Podcast podcast Mike and Kyle kick off the podcast by discussing a recent article written by Ben Lindbergh of The Ringer titled “There’s (Almost) No Such Thing As a Top Pitching Prospect”. Mike and Kyle dissect the article and take a closer look at why almost all the top prospects in the minor leagues are hitters. This leads to a bigger discussion on the differences in developing pitching athletes and how it plays a role for all levels of athletes. To read the full article by Ben go to the episode resources below. To wrap up this week’s show Mike and Kyle discuss a recent thread on Twitter by Kyle talking about the rapid decline pitchers face as they get older. How do pitchers attack the inevitable decline in skill? Find out more by listening to the show!

Episode Resources:

Ben Lindbergh (Twitter): There’s (Almost) No Such Thing As a Top Pitching Prospect

Letters From Twitter: The Pitching Aging Curve

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

The post Driveline Baseball Podcast- EP. 20 appeared first on Driveline Baseball.

It’s fairly common to watch a game these days, see a pitcher throw 99 mph, and hear the reaction of fans be a mix of amazement and fear: amazement that certain pitchers can throw that hard, and fear that it’s unsafe and an injury is guaranteed.

Given the many misconceptions and unknowns related to velocity, this article focuses on all things velocity by taking a deep dive into what the research says about injury risk and performance.

We first look at the history of studies that try to find risk factors for UCL tears and increased torque values in baseball pitching. We see that increased velocity is typically associated as a risk factor for injury, but there are still some gaps in the research that need to be filled in.

We then look at some new research that filled some of the previous research gaps. We take a wide look at what that means and what should be researched in the future.

Lastly, we discuss the performance benefits of throwing hard and look at how the research can be balanced with the performance benefits.

Velocity and Injury Risk

With shoulder injuries decreasing and elbow injuries increasing, many have turned to velocity as the culprit (Conte, Camp & Dines, 2016) because we have seen an increase in velocity in the major leagues over the last few seasons. Some pitchers may be throwing harder as they age, but teams are also replacing older pitchers (who throw at lower velocities) with younger players who throw harder and pitch in shorter spurts. These effects can be seen when looking at velocity aging curves and yearly velocity trends over time.

Given this perceived link between velocity and injury, there has been a lot of research looking at fastball velocity and injury rate. We’ve briefly summarized the research as much as possible and arranged the list in order of the year that each piece was published.

• Olsen et al., (2006) sent surveys to adolescent pitchers who had shoulder or elbow surgery and found that athletes who threw faster than 85 mph were 2.58 times more likely to be injured. However, this was a survey, so these findings may be biased due to inaccurate self-reporting methods that potentially inflate velocity measurements.
• Bushnell et al. (2010) followed 23 pitchers over 3 seasons and found that the 3 pitchers with the fastest velocity in their sample all required arm surgery. They saw a statistical difference between the non-injured velocity (85.22) and injured group velo (89.22), but they had a small sample size and only looked at the fastest pitch thrown (that is singular, the fastest single pitch) in one spring-training game over a 3-year period.
• Whiteside et al. (2016) found six variables in their analysis that related to having UCL surgery: fewer days between consecutive games, smaller repertoire of pitches, less pronounced horizontal-release location, smaller stature, greater mean pitch speed, and greater mean pitch counts per game. They saw that a pitcher was 38% more likely to undergo elbow surgery for every 1 m/s (2.23 mph) increase in mean pitch speed. Somewhat confusingly, the authors also wrote that this finding is “an unlikely increase at the professional level,” even though their data set only looked at major league pitchers. The authors suggested the most logical explanation is that elbow experiences higher torques as velocity increases.
• Chalmers et al. (2016) found higher pitch velocity was the most predictive factor of needing UCL surgery in MLB pitchers. Height, weight, and age were also found to be secondary predictors. Peak pitch velocity was significantly higher among pre-injury pitchers compared to controls (93.93 mph vs 92.1 mph). Mean pitch velocity was also higher in the pre-injury group (87.8 mph vs 86.9 mph).
• Prodromo et al. (2016) compared 114 injured pitchers to 3780 matched controls (players of similar stature who were not injured) and found that pitchers who had higher velocities on a number of different pitch types—including fastballs, sliders, curveballs, changeups, and split-fingered— were at greater risk of needing UCL surgery in the future. The researchers found no significant difference in pitch selection between the two groups.
• DeFroda et al. (2016) looked at UCL injuries from 2007 to 2014 and saw a statistically significant difference in the mean fastball velocity of pitchers who were hurt when compared to healthy pitchers (91.7 mph versus 91.0 mph).

Unlike the studies summarized above, which have all found links between higher velocities and inflated injury risks, Keller et al. (2016) found that MLB pitchers who needed UCL surgery did not pitch at higher velocities than matched controls. Instead, the researchers found that pitchers who threw a higher percentage of fastballs were at heightened risk for injury. While interesting, this finding is somewhat peculiar considering previous research and the fact that one often assumes that pitchers who throw harder also throw their fastballs more frequently.

Velocity and Increased Torque

An important aspect to note when reading the research above is that they are all correlating velocity to injury risk, but fail to consider torque. Many of the articles state, in some form or another, that velocity is likely linked to higher torques on the arm, which is why they see a relationship between velocity and injury.

While velocity isn’t the only factor, it is a theme that keeps coming up as a risk factor for injury. It may be easy to assume that higher velocities always mean higher elbow torques, but the relationship between the two hasn’t always been very clear.

There are also some confounding factors that need to be mentioned:

Selection bias is an issue. There isn’t a sample of pitchers with the same demands throwing in the mid 70s or mid 80s. The closest comparison would be high school and college athletes, but they are not as physically developed, and their seasons are more varied and shorter than the major league season.

Workload is another factor that comes to mind, as many of the studies listed above mention days between outings as a factor. DeFroda et al. found that more UCL injuries occurred earlier in the year and that there can be differences between starting and relieving.

These studies also largely looked at the averages, but it would be interesting to look at the distribution of injuries in larger velocity buckets as well. For example, would possible injury risk increase as velocity increased if buckets of 90-92, 92-94, 94-96 were compared? Or would we see that injury risk increases after a certain point, say 94 mph, and then level off even at increased velocities?

It’s also important to point out that the significance between velocities was relatively small—with the exception being Bushnell et al. (2010) which only looked at one pitch during a spring training start. (Pitchers can experience lower velocities in spring training as they build up work capacity for the season.)

• 91.7 mph versus 91.0 mph
• 92.08 mph versus 91.33 mph
• 93.93 mph vs 92.1 mph
• 89.22 mph versus 85.22 mph

Many studies also tend to look at raw torque numbers that are not normalized to height and weight, meaning taller pitchers will have higher torque values in part because their limbs are longer. There is also little research on what the muscles are doing during the throw. Since we’ve seen torques on the elbow can handle what cadavers are able to handle, the muscles of the arm play a key role in staying healthy.

There have been previous research studies published on both college and high school pitchers that have examined the specific relationship between velocity and elbow torque. Especially relevant with the increase in injuries at those levels, researchers have found a relationship between velocity and elbow torque, but have disagreed about the strength of the relationship.

For example, Hurd et al. (2012) found a positive association between ball velocity and elbow-varus torque (r^2 = 0.373, P<0.01) when looking at 26 high school pitchers. However, the average velocity of the pitchers within their sample was 71 mph, which is quite low.

Building off of these findings, Post et al. (2015) looked at a larger and older sample of 67 collegiate pitchers who averaged an 83.5 mph on their fastball, but didn’t find a significant correlation between ball velocity and elbow-varus torque.

A major reason why researchers have traditionally found such inconsistent results when attempting to link velocity, torque, and injury together is that their methods almost always look at group-wide averages and compare a wide range of pitchers. We believe that this causes them to overlook velocity’s relationship to torque at the individual level. How an individual experiences throwing harder is arguably more important than a group-level analysis, given that each pitcher has his own physiological attributes that influences results.

New research gives us a great look at how different the data can appear when comparing pitchers to each other instead of comparing them to themselves, which is why the recent paper Fastball Velocity and Elbow-Varus Torque in Professional Baseball Pitchers by Slowik et al. is so important.

New Research Fills in the Gaps

Slowik et al.’s data was collected via a retrospective review of ASMI’s database. That is, ASMI looked back through their database and included all pitchers who threw at least 5 fastballs and had a velocity range of 5 mph. To avoid outliers, no single pitch accounted for more than half the velocity range. They ended up analyzing 64 pitchers (52 righties and 12 lefties whose average velocity ranged from 71 to 96 mph) by normalizing elbow torque to body weight and height so that they could better compare the pitchers to each other.

They found that when they looked at the group of pitchers, velocity only explained 7.6% of variance in elbow torque. However, when they looked at individual pitchers, velocity explained 95.7% of the variance.

These findings can be summed up in two main ideas:

1. If you compare multiple pitchers who throw the same mph, some will have higher torques than others. Not everyone who throws 92 mph is experiencing the same torque on their arm. Furthermore, one pitcher could throw 91 mph, another could throw 95 mph, and they both could experience the same elbow torque, on average. This clearly suggests that mechanics play a role in limiting elbow torque during a baseball pitch, but that topic is outside the scope of this post. This would also be part of the reason why the previously mentioned papers looking at torque and velocity didn’t see a standout relationship, as they were looking at the whole group and not at the individual level.

One interesting note about this finding is that we saw something similar years ago when looking at pitchers with the motus sleeve.

2. If you take one pitcher and have him throw harder, he will experience more torque on his elbow.

Of course, in Slowik et al.’s study, there were a few exceptions to this. While the full data set was not made available in the paper, the chart in the paper showing the relationships between normalized elbow torque and velocity saw some individuals stand out. A few pitchers saw no change in torque as they threw harder, while one or two actually saw a decrease as they threw harder.

In a perfect world, we would have this biomechanical data available to go with performance metrics to make personalized recommendations for pitchers. Since we don’t have that information available, we need to stay with the idea that the harder a pitcher throws, the more stress on his elbow he’ll experience. (We explain the nuance of that statement in the next section.)

So, when you’re watching a pitcher throw at the high end of his velocity spectrum, you can assume that he is experiencing higher levels of torque than usual. But that does not mean he is experiencing high levels of torque overall.

It’s unknown what intent level the pitchers were throwing at in Slowik et al. Although it may seem like a small difference, we aren’t sure if the results in this study would apply to pitchers throwing at max effort.

Drawing from Slowik et al., there are three ways that we could see results if we were only looking at pitchers throwing as hard as they could.

1. Velocity and torque increase linearly, Which means, similar to this study, the harder a pitcher throws, the more torque he experiences on his arm.
2. As velocity increases, torque increases exponentially, which means that after a certain point, pitchers that try to throw hard would see a higher increase in torque than would be seen linearly.
3. At a certain point, velocity increases but there is no significant change in torque. This suggests that at a certain point, velocity can outpace torque increases at the very highest limits of velocity.

As mentioned above, all three of these examples were seen in individual pitcher comparisons. The question worth answering is which of the three theories best represents throwing at 90, 95, 100% intensity, or whether is it still a combination of the three, similar to the research above.

We can look elsewhere for hints. Research looking at wider levels of intent off the mound found fairly stark differences in how velocity and torque changed (Slenker et al., 2014).

In a similar theme as above, we saw that pitchers could drop 10 mph when comparing pitching to flat ground, but only see a decrease of 3 Nm of elbow torque when using the motus sleeve.

We’ve also seen pitchers increase velocity with no statistically significant increase in torque when they pulldown. Pulldowns are the closest thing we have to a maximum intent throw.

The differences in the stress metric between ASMI and the motus sleeve can be explained in our validation paper on the motus sleeve.

So we’ve seen that torques outpace intent level at low intent on the mound, that large decreases in velocity on flat-ground throws results in small decreases in torques, and that large increases in velocity while throwing with a running start results in small increases in torques. So, depending on the intent level and throwing drill, torque has been shown to have a nonlinear relationship with velocity.

Research looking specifically at pitchers throwing at the higher range of intent on the mound will give us an idea on how that torque changes.

However, regardless of the throwing modality, what we see is that velocity is a risk factor for injuries in professional pitchers because it is likely that the harder one pitcher throws, the more torque he experiences. Though, we don’t yet know whether the relationship between velocity and torque changes at the highest level of intent. Because ASMI looked back retroactively, and we see that athletes tend to throw slower when they are markered up, pitchers may have likely downregulated to throwing at a lower than game-like intensity.

So, this means that players who throw harder experience higher torques and that’s why they get injured, right? That’s a good guess, and it is definitely a factor, but there is some nuance to that statement.

Velocity, Torque, and Injury

The most interesting question after looking at this research is which matters more: the raw torque number or the relative torque number? Let’s elaborate.

There is a good amount of research pointing to higher velocities being a risk factor for injury. New research shows that it’s difficult to compare torque among pitchers, but as one pitcher throws harder, he’ll experience more stress. For the first point, we used the example that a pitcher throwing 91 mph could have the same stress as a pitcher throwing 95 mph. What also is true is you could have multiple pitchers throw 95 mph and have a variety of stresses.

Looking at Slowik et al., the mean value for normalized max elbow-varus torque was 5.33% ± 0.74% body weight x height. (The torque values were normalized to height and weight for better comparison.) The full data set isn’t available, but you can narrow in at a specific velocity and see a variety of torques.

For example, if we use 42 m/s (93 mph) as an example, we can see normalized torques at 4.7%, 4.8%, 5.4%, and 6.7%. This is similar, in theme, to what we saw using the motus sleeve a few years ago. Multiple pitchers can throw at the same velocity with different torques.

mStress is further explained here.

So, the question is: Does the pitcher who throws at 4.7% normalized torque have the same risk level as the pitcher throwing at 6.7% normalized torque? Does the pitcher throwing 93 mph at 60 Nm torque have the same injury risk as the pitcher throwing 93 mph at 85 Nm? This is important because we’ve seen many more correlations between velocity and injury than torque and injury.

We’ve seen velocity correlated to injury but we’ve also seen pitchers can have different torque levels at the same velocity, so what does that say about the relationship between injury and torque?

There is also more nuance involved than just torque measurements alone. Normalized torque may be better to compare than raw numbers, but we’ve also seen in several studies that workload, days rest, and throwing volume all play a role in influencing injury risk as well.

One study was able to link pitchers that experience higher levels of torque with increased likelihood of injury, but there were some serious limitations with the methods. The paper used hi-speed video cameras to get measure the biomechanics of 23 pitchers during one Spring Training game and then followed their injury history for three years after. The trend between elbow injury and higher torque was fairly significant (P= .0547), but they did see a significant correlation between elbow injury and higher elbow-valgus torque. The only issue with these findings is getting accurate torque measures from a multi-camera setup, and not a marker system, is incredibly challenging because it’s not yet been fully validated in baseball pitching.

For example, it’s difficult to find body landmarks while a player is wearing a uniform, and there is limited validation work between markered-based measurements and camera-based measurements for high-speed movements. It is speculated that a camera-based system could likely obtain accurate body positions at certain points, but it would have added difficulty calculating accurate torque measures when compared to markered-based labs because of an inability to measure rotation, and they can lose body positions as a uniform moves.

(Note: We run a marker based lab with Optitrack cameras and have recently partnered with KinaTrax markerless camera system to validate a markered-based lab to non-markered in order to see which measures are accurate.)

Therefore, we can conclude that velocity is still an injury risk factor and the harder one pitcher throws he’ll likely have higher elbow torque, but we still don’t know if higher normalized torque matters more or if pitchers are at similar risk levels throwing at their own higher torque. There is still more research needed in that area, especially for pitchers throwing at high intent. This is also looking strictly at velocity and not including mobility, strength, workload, and mechanics as other factors that could lead to higher risk of injury.

Given the link in research and general belief that throwing harder is more dangerous, it is often suggested by both fans and researchers that pitchers should just throw slower. We’re going to investigate whether that is a realistic strategy and what the consequences of decreasing velocity could be for a pitcher in terms of performance.

Velocity Performance Benefits

Again referencing the new ASMI study, it was recommended that pitchers should vary their velocities, because the more they try to throw at high intent, the higher torques they’re experiencing. Anecdotally, some hitters have said varying velocities makes hitting specific pitchers more difficult; however, the numbers behind this theory are lacking. You may also find pitchers who say they “vary” their velocities in the sense that they don’t throw at 100% effort all the time, but this strategy does not hold true for all pitchers, and there is no evidence that this strategy improves performance.

The ASMI authors supported their recommendations by looking at pitchers who qualified for the ERA title from 2015 to 2017 and the relationship between velocity and certain metrics. They found that higher velocities in pitchers resulted in lower seasonal outputs in ERA and WHIP, and subsequently higher seasonal outputs in fWAR and bWAR as well. With older, amateur research seeing similar relationships between velocity and K/9 in both starters and relievers, there’s evidence to support that velocity and performance are closely tied together.

However, the ASMI researchers instead concluded that velocity had a weak correlation with measures of performance, given that the r^2 values reported for each of the aforementioned performance metrics ranged from 0.034 to 0.158. These numbers contain a large amount of selection bias and would likely be different if relievers were included in some way. Requiring 162 innings means that there would be less variation in ERA or WAR because the pitchers who do throw that many innings are already very good or very lucky.

We also still have to remember that velocity is also treated as a floor, and further, we don’t know what exactly makes good pitchers successful in the first place.

Other academic research has examined what metrics correlate best to FIP. In a study by Whiteside et al. (2016) it was found that ball speed was one of three main factors in predicting FIP, but that it was also only able to explain 22% of the variance in FIP, suggesting that velocity is a big factor in performance, but there are still plenty of unknowns.

Interpreting how important velocity contributes to performance also comes down to differences in ERA, FIP, or whichever metrics you use to define “successful” performance in the first place. Since we don’t currently know of any model that can explain close to 100% of the variance in seasonal pitching performance, the scale is different than in other research. Complicating matter even more, metrics like ERA have low reliability year to year because they include a large amount of luck. That is why metrics like FIP, xFIP, and SIERA were created in an attempt to get a better understanding of how a pitcher is actually performing.

It would be different if we saw a larger range of fastball velocities in the major leagues, but we see few pitchers with fastballs below 90 mph. The pitchers who do fall below that range tend to be older and above average in other areas. The fact is that pitching is complex, and being a successful pitcher involves being good at multiple skills: throwing above the velocity floor, having adequate command, having at least one good secondary pitch (more than that for starters), and quality movement of all pitches, to name a few. Zack Greinke has even gone out of his way to show how important the velocity floor is for pitchers.

Greinke tweet

The possible benefits of not throwing at high velocity is often explained in a less than straightforward way, besides the supposed health benefits. General anecdotes (usually of pitchers who are older or already incredibly good) are used as examples that pitchers can be successful at lower velocities, which may broadly be true, but may not be true to you. The sample is also skewed since nobody sees pitchers with low velocity, bad command, and bad “stuff.” Those are often the first pitchers eliminated from the pool of possible pitchers.

Being successful at (relatively) lower velocities as a pitcher is less of a conscious choice and more of a series of “if, then” statements.

IF, you have above average fastball spin rate and high vertical movement…

IF, you have standout movement…

IF, you have above average control of multiple pitches…etc.

….THEN you can be successful at relatively lower velocities.

None of those mention possible mechanical differences of throwing at lower intent, which is something that may be further expanded on in further research.

This is in essence what Mike Fast wrote about on Hardball Times in his article “Lose a Tick, Gain a Tick” in 2010. Pitchers who throw harder see a decrease in runs allowed per inning, and pitchers who throw on the lower end of the fastball velocity spectrum are often exceptional in other areas.

Asking pitchers to throw slower often means asking them to give away a competitive advantage for an unmeasurable and unknowable gain in health, which they might not be able to benefit from because throwing at lower velocity could mean worse statistics and a shorter career.

Many of the examples of pitchers having success with a lack of velocity can likely be attributed to confirmation bias and not because a statistical analysis showed an advantage. In fact, many front-office members would tell you that the injury issue is complicated and that velocity is one of, if not the most, important things for a pitcher.

Source

Velocity is still seen as a huge benefactor, even when the straight correlations may be less than impressive. Giving hitters less time to react to a pitch is a good thing.

Conclusion

There was a lot to cover in this article, and while we know a bit more about the relationship between velocity and injury risk, there are still some unknowns. As always, it helps to look at all the things we do and don’t know.

What we know

• Velocity is seen as a risk factor for injury, but it’s not the only one.
• It’s assumed that velocity is a risk factor for injury because increased velocity comes with increased elbow torque
• New research shows that pitchers who throw at similar velocities likely won’t be experiencing the same elbow torque (mechanics play a role).
• New research also shows that as a pitcher throws harder, he’ll likely to be experiencing higher elbow torque.
• Velocity is a beneficial piece to performing well.

What we don’t know: The exact relationship between injury and torque

That is, if three pitchers throw 95 mph with different torque, are they all at the same injury risk because of mph? Or are they different because of mechanics and other factors?

None of the research on velocity and injury risk makes velocity less important. Similar to other professional sports, baseball pitchers are pushing their bodies to their limits. They are going to incur a higher risk than an average Joe playing in a rec league.

This makes managing other factors so much more important because we want to get pitchers to throw hard while managing their risk. Managing mobility, strength and workload all matter greatly to every pitcher, especially to those who are either trying to maximize or maintain a high level of velocity.

Written by Technical Project Manager, Michael O’Connell

The post Injury Risk, Performance, and Velocity appeared first on Driveline Baseball.

Driveline Baseball Podcast episode 21: On this weeks episode of the Driveline Baseball Podcast Mike and Kyle discuss the new overlay tool Kyle built to display Rapsodo metrics over Edgertronic footage. How does this help our coaches in the gym? and what do our athletes think about it? Listen to the episode below to find out. We had the pleasure of welcoming on another Driveline Pro Day signee this week. Kevin Kelleher a pitcher in the LA Angels organization join us to talk about his journey from contemplating retirement to travelling over 3,000 miles to give his career one last shot. Kevin was a standout performer on Pro Day and was one of the first to be signed from the event. To wrap up this weeks show we cap it off from a twitter post from Kyle talking about Coaches not embracing competition for their jobs. Wanna get the whole episode? hit the icons below to listen to the show.

Episode Resources:

Driveline’s Edgertronic Overlay Tool: Want to see our overlay tool in action click here

Kevin Kelleher: Follow Kevin on Twitter to follow along with his journey in pro ball 

Letters From Twitter: Wanna learn more about Kyle’s thread? hit here to see where it all started

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

The post Driveline Baseball Podcast- EP. 21 appeared first on Driveline Baseball.

Integrating modern technology to help build an individualized program is arguably one of the biggest challenges facing hitting coaches today. With new tools such as K-Vest, HitTrax, Blast Motion Sensors, and OnBaseU, hitting screenings becoming common among facilities and programs, and the opportunity to develop your players as efficiently as possible has never been greater. Having the tech is the first step, but being able to take four different applications and tie them together to understand why a hitter produces a certain result is really where you can extract the most value out of these tools. But how do we, as hitting coaches, accomplish this?

The ultimate goal for each hitting coach should be to successfully integrate and communicate these data points to an athlete in his own language. That way, it is easy for a coach to help each player understand what and/or why he is doing what he’s doing, and provide him with an actionable plan to make adjustments moving forward, all backed by objective measurables.

This is also a simple method to foster buy-in from your players: “Right now you’re doing X, which is less than ideal. Given this information, I think a good plan of action would be Y, and a good goal for you to shoot for six weeks from now would be Z.”

This article explains how we pair all of these tools together to better understand why each hitter performs in a specific manner. We offer real examples of athletes we’ve ran through our assessment process to offer specific case studies of how we tie all four segments of data together to better understand hitters from a macro perspective.

Tying It All Together

With all of this different data, where do we start? It’s most effective to work backwards through our various pieces of data to gain a clear understanding of why each hitter does what he does: batted ball report, back to swing metric report, back to biomechanics report, back to mobility/stability screen.

The first step is to look at a hitter’s tendencies in his batted ball report. Hitter A displays X result in his batted ball report, which is good information to have, but why is Hitter A consistently producing this result? From there, we work backwards and look at his swing metrics. By understanding exactly how Hitter A’s bat is moving through space, you’re likely to get a clearer picture of why he is impacting the ball in a certain way. Hitter A is producing X result in his batted ball report, and this can be attributed to X score in a certain swing metric (or metrics).

This is also valuable information to have; Hitter A produces this result because his bat moves a certain way throughout his swing. But why is Hitter A moving the bat through space in this way? The next step would be to look at his kinematic sequencing and body positioning data. Hitter A is likely moving his bat through space in X way because his body is moving in this specific way. Lastly, we ask “why?” one last time: why is Hitter A’s body moving like this?

• Step 1: Hitter A consistently impacts the ball in X way.
  • seen via batted ball report.
• Step 2: Why does Hitter A consistently produce this result?
  • Through the swing metric report, we see that Hitter A’s bat is moving X way through space.
• Step 3: Why does Hitter A move his bat like this?
  • By looking at the athlete’s biomechanics report, we see that his bat moves in X way due to his body moving in X way.
• Step 4: Why does Hitter A’s body move this way?
  • Through our hitting movement screen, we’re able to see whether or not this hitter has specific mobility and/or stability issues that are causing his body to move a certain way.

Through our hitting mobility and stability screen, we can see if Hitter A moves in X way due to mobility/stability issues, or if it is just an inefficient movement.

Batted Ball Data – Example 1

A hitter came into our gym recently for assessment and to train on site for one month. Recently cut from his Division 1 collegiate program, his motivation for coming out was to go back to his collegiate program in the fall and make the team. After going through his initial seven-day assessment, we generated a batted ball profile for him. Within the batted ball profile, we found that he struggled to square up balls to the pull side of the field. When putting balls in play to the right side of the field (he’s a left-handed hitter), his average exit velocity dipped down to 76.38 mph and his average launch angle to the pull side fell to 3.50 degrees. So, when he pulled the baseball, it usually resulted in a weak ground ball.

In comparing this result with the rest of his batted ball tendencies, we found that this athlete actually hit balls harder to the opposite field, which is the opposite trend of what we see our professional hitters do. In an ideal world, you can hit the ball equally as hard to all parts of the field, but not many hitters are capable of doing that. If you’re not able to hit for equal power to all fields, you can maximize outcomes on balls in play by having your highest exit velocities at an optimal launch angle to your pull side, because batted balls in the air to the pull side are the most valuable that a hitter can produce. 

We can also see in the graph below that this hitter rarely hits anything out in front of the plate:

This observation provides us with insight into why this hitter struggles to pull balls; he hits hardly anything out in front of the plate. Generally speaking, the farther to the pull side a batted ball goes, the farther out in front of the plate the hitter will want to make contact. This allows the batter more time to rotate farther and move the bat farther through space. If you’re not impacting the ball out in front of the plate, particularly with pitches on the inner third of the strike zone, you make it more difficult on yourself to produce a good batted ball outcome.

Thus, after seeing this inefficiency in his batted ball profile, we need to ask: Why does this hitter struggle to produce optimal EV and LA metrics to his pull side?

Swing Metrics – Example 1

To dig deeper, we pull up the hitter’s swing metrics to look for any clues for why he is producing suboptimal contact to his pull side.

After looking at his swing metrics, we see that he produces a very low rotational acceleration score, meaning he doesn’t reach peak rotational speed and power very quickly. It’s also likely that this hitter relies on his arms/hands to produce the majority of his bat speed.

We also notice that his early connection scores are a bit high. This means that his barrel is very vertical at the beginning of his downswing, causing the bat to have to move farther through space to get on plane with the incoming pitch. By looking at his swing metrics, we see that he isn’t rotating as quickly as he needs to be, nor is the relationship between his spine and barrel optimal. This prevents him from having a  “tight turn” since he’ll need to be able to drive the inside pitch at optimal exit velocities or launch angles.

Had this athlete possessed elite bat speed (+72 mph), he may have been able to get away with lower rotational acceleration scores, but since his bat speed off a pitching machine at game-like velocities was 61 mph on average, these game-like velocities have exposed a couple of holes in his swing.

Kinematic Data – Example 1

Given this information, we seek to understand why this hitter moves the bat through space this way to help him make an adjustment. For more insight, we look at  how he moves his body by looking at his kinematic sequencing and body positioning. From these reports, we may be able decipher the root cause of this deficiency.

Provided below is an efficiency graph of this hitter’s kinematic sequence;

As seen in the graph above, this hitter’s kinematics are out of sequence; i.e., he isn’t transferring energy from the ground and up the chain efficiently. Ideally, you like to see the peak speed sequence go from hips, to torso, to lead arm, to bat, or 1, 2, 3, 4. In this swing, we see this hitter is actually firing his lead arm (1) before his hips (2), and his bat (3)  before his torso (4). While all of his peak speeds are within pro ranges, this inefficient sequence likely contributes to his low rotational-acceleration scores, because his hips are following his hands, rather than the other way around. As a result, his bat actually decelerates at contact, which is the opposite of what we see in elite performers. This hitter also demonstrates below-average speed gains between each segment; we’ve found that the best hitters will generally have an efficient sequence and coinciding speed gains of about 1.5 to 1.8 between each link of their chain.

Taking this a step further, we can also take a look at the hitter’s body positions at first move, heel strike, and contact. This hitter maintains good body positions at first move and heel strike, all of which are within K-Vest’s provided pro ranges (the green portion of the below circles), but this changes once he gets to contact:

This visual shows segments of this hitter’s body that are in several suboptimal positions at contact. His pelvic rotation and bend, as well as his torso rotation and bend, are all out of pro ranges at contact; he is essentially “fighting against his own body” when he impacts the ball, rather than putting his body is a proper position to generate maximum rotational speed and power.

Since his lead arm fires before his hips, and his bat fires before his torso, the hips and torso are perpetually playing “catch up.” This prevents them from  decelerating early enough in the swing, causing both to over rotate at contact in an attempt to make up for the inefficient sequence. This hitter’s pelvic and torso bend are also out of pro ranges, indicating the hitter is “bailing” or “flying out” of position instead of staying stable and rotating around his spine.

Movement Screen – Example 1

This information provides tremendous insight into why he is producing low rotational acceleration and bat speed, which, in turn, helps explain why he struggles with generating optimal contact to the pull side. While this gives us a base to work on, we’re still left searching for answers for what is causing his high early connection scores. For a better understanding of why this is happening, we defer to his mobility/stability screen.

Within his initial movement screen, we can see that he suffers from poor/limited hip mobility, which is likely contributes to why he swings out of sequence. This then may contribute to his low rotational-acceleration scores, which in turn contributes to his inability to impact balls out in front of the plate, which finally contributes to his struggles with the inside pitch and producing quality batted balls to the pull side.

We can also see that he displays poor wrist mobility, or a lack of “wrist hinge,” a deficiency that could also affect his high early-connection scores.

Lastly, we can see in the visual provided below that this athlete struggles with overall stability, which likely results in his body ending up in sub-optimal positions at contact.

Gameplan and Programming- Example 1

By working backwards through our various data collections, we now know this hitter’s lack of hip and wrist mobility, suboptimal kinematic sequencing, and body positioning at contact all likely contribute to his low rotational acceleration and high early-connection scores. In connecting abnormal rotational acceleration and early-connection scores with his struggles to produce valuable batted balls to the pull side, we’ve identified the root causes of his weaknesses at the plate.

Now we have a baseline for training this athlete and have gained a good understanding of exactly what’s going on with his body and swing. This allows us to address these inefficiencies, not only with individualized hitting programming but also with individualized mobility correctives and strength training in the weight room, as well (all of which can be accessed by the athlete via his TRAQ profile). Below is an example of a month’s worth of hitting, strength, and mobility programming specifically designed for this hitter:

To monitor the effectiveness of our program, we retest each of our hitters bi-weekly. This allows us to update our programming as necessary to provide athletes with evolving programming that is best able to improve the aspects of their swings.

Batted Ball Data – Example 2

Hitter B came to us during the most recent pro off-season after a very successful college career. After being overlooked in that year’s MLB Draft, he came to us looking to train for a month before going to a few independent ball tryouts. After going through our assessment process during his first seven days of training with us, we generated a batted ball profile for him, which is provided below:

Based on the initial batted ball data, we see that this hitter has some talent. With a peak exit velocity and hard hit rate (a “hard hit” is any ball put in play within 10% of your peak EV, per HitTrax) both well above our in-gym pro averages, this hitter hits balls hard and makes solid contact more often than not. We also see that his average LA is more than double our pro average, so when he misses balls, it is likely that he misses underneath them.

As we continue diving into Hitter B’s batted ball profile into his advanced batted ball data, we start to see some trends that are worth addressing:

We can see Hitter B’s average EV to the pull side is well in-line with our pro averages. However, when he hits the ball to center and left field (he’s a LHH), his average EV falls well below our pro averages. In addition, we can also see that Hitter B’s average LA to all three parts of the field are substantially higher than our pro averages. He produces an optimal EV and LA combination to the pull side (89 mph at 22 degrees gives you a great chance for a positive outcome), but 83.52 mph at 38 degrees to the middle of the field is essentially a lazy fly ball. Furthermore, when this hitter puts a ball in play to the opposite field, his average EV drops off quite substantially (down to 73.93 mph), and his average LA skyrockets up to nearly 55 degrees. This means that his average ball in play to the opposite field is equivalent to a routine pop up.

The obvious takeaway from the batted ball report is that while Hitter B can mash balls to the pull side, he struggles when going the other way. Taking it a step further and looking at his batted ball data sorted by field, we can see that Hitter B’s lowest EV, highest LA, and shortest distances are pitches on the outside part of the plate.

Last, we can see that Hitter B’s softest hit balls (or lowest average EV) are on balls he makes contact with deeper into the plate, towards the catcher.

Swing Metrics – Example 2

With the information gathered in Hitter B’s batted ball report, we see that this hitter has talent and can produce professional level EV. However, there are a couple of holes in his swing: he struggles to hit balls hard and on a line to the opposite field, as well as on pitches in the outer third of the strike zone.

Armed with this information and the methodology outlined in our previous example, we turn to Hitter B’s swing metrics to figure out why he is consistently producing sub optimal batted ball metrics to the left side of the diamond:

Looking at Hitter B’s swing metrics, we observe that his swing is relatively clean at first glance. He produces good bat speed (averaging in the high 60s and peaking at 83.1 mph), and maintains a healthy attack angle of 8 degrees. However, a deeper dive uncovers a below average rotational-acceleration score, an on-plane efficiency of less than 50 percent, a relatively low connection at impact score, and a relatively high time to contact score relative to his peak and average bat speed.

What insight does this information provide us? While Hitter B’s rotational-acceleration scores aren’t terrible, we want to see him hovering around 15-16 g. The below average score indicates that he generates the majority of his bat speed in the later parts of his swing, which could mean he heavily relies on his arms and hands to generate bat speed. Additionally, Hitter B’s relatively low on-plane efficiency score means that he isn’t getting on plane with the incoming pitch until the later parts of his swing. As a result, both of the scores on these metrics provide straightforward evidence as to why Hitter B struggles with optimal contact on pitches away, as well as driving the ball to the opposite field.

Additionally, and arguably most relevant, Hitter B has a connection at impact score of 80 degrees, which is lower than the 90 degree score you’d like to see. The connection at impact score measures the relationship between the hitter’s spine and the hitter’s bat at contact with the ball. Generally speaking, hitters in our gym with low connection at impact scores have a tendency to “slice” or “cut” balls to the opposite field. The ball will likely come off the bat with side spin at a higher than desirable launch angle. In this instance, this theory is supported by Hitter B’s advanced batted ball data (low EV and high LA to the opposite field, and on pitches in the outer half of the zone).

Lastly, Hitter B’s average time to contact of 0.16 is a bit higher than our pro averages in gym, indicating that his swing can sometimes be a bit “long” and/or that he makes the majority of his contact out in front of the plate. This is generally a good thing, but it’s likely this hitter is actually making contact with pitches in the outer third of the zone a bit too far out in front of the plate, rather than identifying the outside pitch, and letting the ball travel a bit deeper.

By pulling up some additional point of contact data, we can see more that supports this theory:

The vast majority of this hitter’s batted balls are impacted 6-18 inches out in front of the plate. As you can see on the X axis in the graph above, home plate is 0, anything negative is above or behind home plate, and anything positive is the distance in front of the plate at which contact is made.

Kinematic Data – Example 2

With a detailed understanding of which pitches Hitter B struggles against and why his swing characteristics contribute to those weaknesses, we can continue to work backwards to identify the root cause of these issues through how his body moves. Below is an efficiency graph of Hitter B’s kinematic sequence and speed gains:

We can see that Hitter B is out of sequence. His lead arm actually fires before his torso, which the opposite of what we would see in an efficient example. So, while he produces peak speeds and speed gains that are sufficient, the fact that his lead arm fires before his torso and most of his point of contact data is 6-18 inches out in front of the plate provides validation that this hitter relies on his arms and hands to generate the majority of his bat speed.

This data also helps explain Hitter B’s below average rotational-acceleration scores. Had he been in sequence and using his torso more efficiently, he’d have been able to produce peak bat speed more quickly, which in turn would help improve his rotational acceleration and time to contact scores.  

Taking this a step further, let’s take a look at Hitter B’s relevant body positions at contact:

While all of these body positions at contact are within K-Vest’s provided pro ranges, we can see that Hitter B’s pelvic bend, pelvic side bend, and torso side bend are on the edge of those provided pro ranges. Hitter B’s borderline pelvic side bend and torso side bend scores could be contributing to his low connection at impact scores, which is likely what’s causing him to “cut” and “slice” balls to the opposite field rather than driving them on a line.

Movement Screen – Example 2

So, what do we know so far? We know Hitter B struggles to drive the ball to the opposite field and struggles to drive the pitch on the outer third of the plate, which of course go hand in hand.  

We theorize this could be attributed to a less-than-optimal point of contact on outside pitches, a relatively low rotational-acceleration score, and a low connection at impact score. In diving deeper, we posit that these swing metric scores can be attributed to out-of-order kinematic sequencing, as well as borderline pelvic and torso side bend positions at contact.

As shown with Hitter A, truly getting to the root cause of Hitter B’s deficiencies requires us to better understand why his body moves as described above. For example, are these body movements the result of a lack of mobility/stability? Or has Hitter B just patterned himself to move like this?

For additional insight, we dive into Hitter B’s movement screen:

Looking at Hitter B’s movement screen, we see that he displays limited mobility in both pelvic and trunk rotation, but, interestingly enough, passes both arm (shoulder and elbow) movement screens. On the surface, these results provide good insight into why Hitter B fires his lead arm before his torso. Furthermore, Hitter B also displays limited thoracic spine mobility, providing more reason for why he is so apt to rely upon and fire his lead arm before his torso.

The limited pelvic rotation Hitter B displays could be a likely factor in his sub-optimal pelvic side bend at contact position. Rather than rotating his hips as much as he should, he manipulates them vertically in order to be on time and get his barrel to the ball.

Additionally, Hitter B also displays limited wrist mobility, or struggles to create sufficient “wrist hinge,” which could factor into Hitter B creating sufficient bat lag. This likely explains why the vast majority of Hitter B’s point of contact is so far out in front of the plate: his limited mobility causes him to release the barrel very early.

Now, what do we know?

• We know Hitter B displays limited pelvic and torso rotation, as well as limited wrist mobility.
• It is likely that these mobility inefficiencies contribute to this athlete’s sub-optimal sequencing (1, 3, 2, 4) and body positions at contact (pelvic and torso side bend).
• Diving deeper, these sequencing and body-position inefficiencies likely contribute to the athlete’s low rotational acceleration (11.6 g) and connection at impact scores (80 degrees).
• Tying everything together, these aforementioned swing metric scores are what’s likely causing Hitter B to struggle with pitches in the outer third of the strike zone, as well as driving the ball to the opposite field. (Low EV, high LA)

Gameplan and Programming – Example 2

With all of this information and a theory supported by several different sects of data, we generated a holistic program for this athlete to combat these identified inefficiencies. For Hitter B, we developed a hitting mobility warm-up and cool down designed to address and improve his limited pelvic, t-spine, and wrist mobility. In the weight room, a strength training program was designed to help him move more efficiently and get stronger in his specific areas of needed improvement. Lastly, Hitter B’s hitting programming was specifically designed to force movement adaptation tailored to improve his struggles with the outside pitch, driving the ball to the opposite field, and allowing for a deeper point of contact when warranted. All of which was dropped into his TRAQ profile for him to be able to access anytime, anywhere, on any device.

What’s Next

We believe in an integrated, data-driven approach to training athletes. All of the different tools and technologies we use can and should be tied in together. The more information you have on a given athlete, the better training and programming you can provide them. We also believe in incrementally retesting our athletes so we can quantify progress and improvements, and make adjustments if we’re not seeing the results we expect. 

The hitting community now has incredible tools available to them, all of which should be used in conjunction with one another. We know more about hitting now than we ever have before, but there is still much we don’t know.

The next steps in the evolution of Driveline Hitting will be validating the current technology we use via motion-capture analysis, measuring and testing hitter’s ground-reaction force, and gaze tracking each hitter’s pupils to better understand what a hitter sees when he steps in the box as he tracks pitches.

There’s a lot we don’t know, but we’re self-aware enough to know that we don’t know, and we’re on the road to finding out.

Written by Hitting Trainer Collin Hetzler

The post Pairing Various Tools and Tech Together to Better Understand Hitter Tendencies appeared first on Driveline Baseball.

In part one of The Business Case For Player Development series, we calculated prospect valuations for top tier minor leaguers and validated those estimates by looking at several trades throughout MLB. Building on that work, we use those valuations in this post to model the net-present value of improving prospects by providing them with an individualized training program. Through this analysis, we quantify how much money organizations leave on the table by not getting their pitchers fully equipped with a custom training plan based on a thorough assessment.  

The State of Player Development in Professional Baseball

With front offices expanding and marketplaces becoming more efficient, professional baseball has become a copycat industry, of sorts. Successful teams have seen their employees poached and ideas copied, all in an attempt by competitors to nullify a team’s perceived advantage as quickly as it is capitalized on.

With this in mind, it is not surprising that more teams have begun heavily investing in player development after witnessing the Houston Astros’ rise from laughing stock to perennial contender. Taboo only a few years ago, Rapsodo units and Edgertronic cameras are now commonplace in one of baseball’s last frontiers where low-hanging fruit still exists—the backfields.

While the inclusion of flashy technology to develop pitches is certainly a welcome change in a stubborn industry, it has the potential to cause one to overlook the single most important attribute to a pitcher’s success: velocity.

For example, despite teams investing more money into technology, it is still common practice for them to prescribe cookie-cutter throwing programs that cater to an unrelenting minor-league schedule before the specific needs of the individual.

By failing to provide players with an individually designed throwing program based on personalized assessment, teams inevitably fall short in designing an optimal development plan for their pitchers.

Using in-gym data from Driveline and several MLB metrics to isolate the relationship between changing velocities and future production, we quantify how much this suboptimal behavior costs teams in terms of Net Present Value (NPV).

Can We Develop Velocity in Professional Athletes?

To set the baseline for our analysis, we first need to look at whether we can significantly improve the velocity of professional pitchers using a data-driven assess-train-reassess model.

To address this, we gathered data on 58 high-level athletes who went through our velocity development program within the past 18 months. All players averaged at least 88 mph on their fastballs during their initial mound velocity, were 19 years or older at the time of their assessments, and trained at Driveline for at least three weeks. The cumulative averages of the athletes who met this criteria are provided below:

Now, developing velocity is not a linear process and not every athlete is guaranteed to see results. To gain a better feel for what the distribution of the changes in velocity were for each pitcher in our sample, we binned outcomes in .5 mph increments and smoothed the buckets by frequencies:

As shown above, 41.95% of all athletes in our subset gained at least 1 mph from entry to exit mound velocity, whereas only 18.39% of athletes lost over 1 mph.

Thus, despite arguments from days gone by, it’s clear that velocity levels can increase over time by instituting proper training methods in a pitcher’s routine. With this finding, we can show that even small increases in velocity have positive effects in production and overall value.

Translating Changes in Velocity to Production

While improving velocity is a net positive, it’s important to acknowledge that velocity alone does not get batters out. So, if we are interested in quantifying how an uptick or downtick in velocity impacts a player’s projected net value to an organization, we must consider a pitcher’s entire projection in the process.

To do that, we turned to the publicly available Steamer Projections system, which has been providing yearly MLB pre-season projections for fastball velocity (FBv) and RA9 (or at least runs and innings pitched) since 2013.

The idea in this exercise is simple; we controlled for changing run environments on a year-to-year basis and found the difference between projected FBv and actual FBv (min 50 IP) as well as projected RA9 and actual RA9 for each individual pitcher at the MLB level. We expected that a pitcher who threw his fastball slower than expected in a given season would have worse outcomes than previously projected, and vice versa.

This relationship is exactly what we found over a sample of 1,838 athletes. More specifically, a pitcher who out performed his fastball velocity projection by +1 mph produced an RA9 that was .2443 points lower than expected, on average.

(This chart shows that as velocity increases above expectations, ERA, RA9, and FIP all decrease while K% increases.)

To scale this finding to WAR, we took a hypothetical player, held his IP constant at 75 (a hedge between a starter and reliever), and calculated his WAR before and after a .2443 point reduction in RA9. We found that adding 1 mph to a player’s FBv increased the expected production of said player by ~.25-.35 WAR per season, depending on whether he was a starter or reliever.

Translating Updated Talent Levels to Prospect Grades  

With estimates that relate changes in velocity to changes in performance, Kiley McDaniel’s scouting scale provides us with a link between the expected production level of a prospect at maturation (WAR) and his overall prospect grade (FV).

For most noteworthy prospects, McDaniels estimated that a .5 WAR change in projected talent level coincided with a half-grade increase or decrease in FV. Thus, we can deduce that a +2 mph increase in FB velocity causes a player’s talent level to increase by ~.5 wins and 5 points in FV.

The process here is generally straightforward. Say, for example, we have a 50 FV prospect that averaged 92 mph on his FB last season and gained 2 mph during the most recent off-season. Given the findings above, we would expect that this prospect’s projected RA9 would drop by ~.5 points due to the increase in velocity. Over the course of the season, we estimate this to be worth ~.5 WAR of production, which would boost his yearly projection from 2 WAR/year to 2.5 WAR/year. Using the chart above, this change in production would be the equivalent of moving his grade from 50 FV to 55 FV.

Simulating Value That Teams Leave on the Table

Since we now have a link between throwing harder and production from an individual standpoint, we next set out to apply this finding on an organizational level. This allows us to determine how much a league-average farm system could improve their overall production level if they provided their top prospects with a customized throwing plan.  

To gain a proxy of a league-average farm system, we obtained both the number and quality of pitching prospects graded 40 FV and above for systems ranked 12th – 18th from 2016 to 2018 by Fangraphs.

The selected farm systems contained an average of approximately 13 pitchers with a grade of 40 or higher. The distribution of grades for these prospects is shown in the table below:

With these frequencies at our disposal, we created a multinomial simulation that generated 13,000 pseudo pitching prospects across 1,000 “league-average” farm systems. These prospects were designed to be representative of a prototypical pitching prospect that had only been exposed to traditional throwing programs in their past.

(The graph above shows the average grade of a corresponding prospect rank within each farm system.)

To apply the impact that Driveline’s throwing program could have on these generated prospects, we created an additional multinomial simulation by using the results from our velocity development program to find expected changes in their velocity via our program. By linking changes in velocity with a coinciding change in future value, we obtained a before and after snapshot of future value for each prospect after having gone through a Driveline program.

The Initial Results

We converted these FV grades to NPV values and then averaged the change in surplus value for each farm system before and after the Driveline throwing program. From our experiment, it was found that an organization increased the NPV of their farm system by approximately $38 million after having their prospects go through a velocity development program. This is roughly the equivalent of adding a 55-FV prospect to a farm system or having two 45-FV prospects jump to a 50 FV.

(Above is a hypothetical farm system both before and after the Driveline throwing program.)

Scaling Back Our Estimates

While a $38 million estimate might be a little eye opening initially, we believe that this estimate is likely inflated by a few shortcomings in our initial methods that need to be addressed.

First, our initial methods do not consider that some prospects would have likely seen changes in their velocity had they just continued with their traditional throwing program. In other words, hypothetical prospects went through a Driveline throwing program and either gained or lost velocity based on the expected results of our program. The control group, however, had their velocity stay the same despite the fact that they would have been training as well. Thus, this needs to be accounted for.

Second, although the sample of players used to build our Driveline throwing program simulator was comprised of many professional athletes, the average player within our sample was not representative of a 45-FV prospect or higher. As a result, our initial estimate is likely biased upwards because higher-level prospects are more likely to be closer to their velocity ceiling than those in our sample. This makes marginal velocity gains for top prospects more difficult to obtain than our simulation led to believe.

(Graphic above is taken from our Summer 2018 Pitching Analysis)

To adjust for both of these shortcomings, we regressed our initial results by 25% and threw out any prospects in our sample graded 45 FV or higher. While both somewhat arbitrary concessions, we think these additional steps tackle both limitations head on.

For example, in regressing our changes in NPV by 25%, we acknowledge that pitchers can either develop or lose velocity by sticking with a more traditional program. That said, we also recognize that changes in velocity are likely to be less volatile for players using traditional training methods than for players using unfamiliar training modalities.

In removing any prospects graded 45 or higher, we remove a subset of pitchers that predominantly already throw hard and have had large amounts of success training with traditional methods. Furthermore, this subset of prospects would likely have more jurisdiction over their own program, and thus they might be less likely to follow through on a new training stimulus that is foreign to them.

Final Results

With only 40-grade pitching prospects remaining within our hypothetical sample of athletes, we repeat the same methodology as described above and regress the changes in NPV for each farm system by 25%. With these adjustments, the average change in NPV for each farm system drops down to ~$11 million with a wide distribution that is skewed leftward.

This conservative $11 million estimate is still a large sum of money that most teams have not cashed in on, and it does not consider other elements of value that individualized throwing programs provide teams, which we will address in future pieces.

For example, the methods above do not give individualized throwing programs to prospects graded 35 FV and below, who constitute 80-85% of all Minor League Baseball players. This subset of players should be able leverage their individualized programs more so than top prospects, given that they benefit the most from increasing outcome volatility. The same can be said for AAAA players who are heading into the final stages of their careers and high profile prospects labeled as projectable, whose value is predicated on improving velocity.

The estimate above also does not consider the value of programming daily warm-up and recovery modalities in preventing injuries, which keeps players on the field longer and mitigates significant rehab costs for organizations in the long run.

A Better Framework for Development

Given the influx of dollars now being spent on player development, it is clear that giving pitchers a development plan that addresses their individual deficiencies and helps develop velocity has a massive ROI for organizations. The reason for this is straightforward: the upside of increasing the volatility of outcomes for 40-FV prospects and lower will always outweigh the risk.

Rather than having these players continue to accrue 25 starts season after season, perhaps it is time teams re-evaluate and provide their athletes with a plan that is going to make them more likely to reach their goal of making it to the big leagues.

After all, it has been about 10 years since we learned that when you gain a tick, you lose one, so how long will it be before teams decide to put this research into practice?

Written by sabermetircs analyst Dan Aucoin.

The post The Value in Developing Velocity in the Minors appeared first on Driveline Baseball.

Driveline Baseball Podcast episode 22: On this weeks episode of the Driveline Baseball Podcast we are pleased to welcome on Trevor and his father Warren Bauer to discuss their experience using Edgertronic cameras to train and how their process has changed over time. They also discuss how Kyle and Trevor first met and how it blossomed into the relationship it is today. From five Casio cameras mounted on walls to a box that shoots 2000 frames per second tune in to the show to see how tech integration and adapting new tech has shaped Trevor’s career. To Wrap up this week’s show Kyle and Mike discuss a tweet thread by our Research Analyst Dan Aucoin on how front offices struggle to get coaches on the same page for developing players. Mike and Kyle take a deeper look at this issue and why communication is a roadblock for many teams.

Episode Resources:

Trevor Bauer: To see more of the work Trevor has done with cameras over the years check out his Youtube page for some interesting videos. Trevor has also Co-founded a company called WatchMomentum that tells players stories and allows athletes to express themselves how they see fit. To see some of these stories click here

Edgertronic Cameras: Wanna learn more about how we use these cameras? Check out some of the blogs we have written to get the most out of this tool here. Or check the Edgertronic development kit we sell on our site.

Letters From Twitter: Wanna learn more about Dan’s thread? hit here to see where it all started

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

The post Driveline Baseball Podcast- EP. 22 appeared first on Driveline Baseball.

One of the greatest benefits our assessment provides is how it better informs our staff when developing and implementing throwing programming.

It is one thing for an athlete to have all of his assessment data at his fingertips and to understand precisely what was uncovered. But it is another to actually identify the specific programming needed to attack those inefficiencies.

In a previous post, we discussed how we utilize the assessment and weight room to make movement changes. We will now discuss how we design throwing programming to create changes.

An invaluable piece of our assessment process is the motion-capture report our biomechanics lab generates. This gives us distinct kinematic positions, sequencing, and velocities for each athlete as well as joint kinetics. Our training staff can then use this data to better construct specific throwing programming designed to improve mechanical efficiency and positively impact on-field performance.

Truth data is important because it strips away stylistic nuance while giving us objective feedback using basic physics and biomechanical principles. Using our motion-capture lab is the ideal way not only to inform training programs but also can be leveraged in the following weeks or months to evaluate progress.

To explore this further, we can look at an athlete who trained with us this past off-season.

The Athlete

This particular athlete arrived at the facility following the conclusion of his first professional season in September to get assessed. He also went through a motion-capture retest shortly before leaving for spring training. This made for a unique opportunity to see what changes occurred and to discuss what throwing programming was designed based on the initial assessment findings.

His first season of professional baseball saw his velocity drop dramatically and performance suffered a bit because of this. At the season’s end, his average fastball velocity for the year was 88.6 mph, which is a far cry from what he was used to during his successful college career.

Initial Motion Capture Findings

Taking a look at the athlete’s initial biomechanics assessment data, we saw a few significant inefficiencies in both the upper and lower half.

We first see that his arm wasn’t deploying very efficiently. Having 50 degrees of elbow flexion at foot contact and a maximum average of only 82 degrees indicates that his elbow never gets inside 90 degrees. When elbow extension happens too early force is not applied efficiently.

This minimal elbow flexion is also usually linked to low scap retraction at foot contact. Scap load or scap retraction is quantified in our motion-capture report above by horizontal shoulder abduction. It is a measurement of the angle between the humerus and the torso in what would be the transverse plane.

This athlete only gets roughly 4 degrees of shoulder horizontal abduction (scap retraction) at foot contact, which is well below average. While kinematic positions themselves may not necessarily have a strong correlation with velocity, they can influence kinematic velocities and joint kinetics. In other words, the ability to move fast, apply force properly and transfer energy efficiently are all keys to throwing hard and that is, in part, made possible by getting in good positions.

Finally, we see the arm is late getting up and that shoulder abduction is a bit low at foot contact: 77 degrees. It then climbs up aggressively to 95 degrees at ball release. Combined with the low scap retraction, this means his throwing arm is very extended at foot contact and his elbow climbs quickly into ball release. It may also mean that his arm is spending energy traveling up instead of to the plate.

Looking at the lower half, we see his pelvis is pretty closed at foot strike, as indicated by the pelvis angle being -0.51 degrees. If the lead leg hits without the pelvis being open enough, the athlete may not be able to maximize the lead leg’s ability to facilitate rotation. This can negatively impact pelvis rotational velocities, which potentially hurts the rotational velocities of the segments up the chain.

Here is what his initial assessment looked like:

Initial Assessment notes:

Primary concern: Arm action has room to improve.
– Elbow never gets inside 90 degrees (max 82 degs of flexion, early forearm flyout)
– limited elbow flexion linked to low scap retraction at foot plant (10 degs)
– landing closed with hips. pic.twitter.com/KbEkTkpmlm

— Anthony Brady MS, CSCS (@BaseballFreak_9) March 1, 2019

Programming

Throwing programming was built around the information produced from the athlete’s initial assessment with a heavy reliance on the motion-capture data.

The obvious performance need was to increase fastball velocity. Increasing fastball velocity would be one of the most impactful ways to improve on-field results. However, to achieve that we needed to address the aforementioned movement issues.

First Training Block – Strength/Mobility Focus

We did see some range-of-motion and strength deficits were present. A lot of these are typical for an athlete after a long season, but deficiencies in strength and mobility certainly could have been contributing to some of the suboptimal movements. This is why dedicating specific training time to restoring range of motion along with focused strength work is so important early on—especially before moving into higher volume or high-intent throwing.

We took about two and a half weeks off from all throwing before moving into another two weeks of recovery-only work. This period involved low RPE pivot pickoffs and reverse throws with plyocare balls and some light catch play.

Second Training Block – Arm Action Focus

After taking time off and knowing we needed to insure fitness levels and work capacity were built back up appropriately, the following five weeks were designed to on-ramp the athlete and start patterning a more efficient arm action.

This phase was filled with hybrid days primarily. This meant the athlete performed all of the plyocare drills at moderate intensity and eventually 90% RPE. Most long toss days did not include any type of a compression phase initially. As we got closer to the conclusion of this training block, they were introduced during his 90% RPE work.

We made some modifications to this athlete’s plyocare work in an effort to influence the mechanical changes we were looking for. The first of which was eliminating rockers and replacing them with a step back. During rockers, he just wasn’t putting himself in a great position. His lead knee was tracking forward and he pushed into early hip extension. We made the decision that a step back was a better fit as it allowed his lower half to sequence better.

We also cued him to think about the lower half working more rotationally during this drill to encourage the pelvis to get open more.

The other modification we made to his plyocare work was adding scap-retraction throws.

This is an overload-specialized drill that is a simplified version of the pivot pickoff. It is designed to help the athlete feel as though the elbow is dominating the pickup phase of the throw rather than the hand. It allows the athlete to drive the elbow back to create scap retraction on the throwing side, which can also help him hold counter rotation better.

With greater scap retraction, this athlete could work his arm up into the driveline phase of the throw more efficiently, eliminating the forearm flyout we saw in his motion capture as well as the low shoulder-abduction issues. We also had him start a bit more supinated with his hand during pivot pickoffs to help further influence scap retraction.

Third Training Block – High-Intent Work

After adequate time to on-ramp sufficiently and periodic high-speed video retests, we moved into high-intent work. We primarily worked with two high-intent days a week: plyocare throwing at 100% RPE and pulldowns.

At this point, the athlete had not been on a mound for quite some time. We decided adding some moderate intensity mound work into his programming would beneficial.

This meant that after completing his high-intent work, he would move to the mound and throw anywhere from 10-15 fastballs at around 70-80% RPE. Velocity was capped and we used the radar gun to monitor intent. This would get the him back on the mound, effectively shortening any mound-blending phase that is usually necessary, and helped better blend the new, more efficient movements to the slope.

Fourth Training Block – Pitch Development

We transferred to the mound completely in early January. The earlier mound work afforded us the opportunity to focus on a variety of pitch-development initiatives during this training block.

We made one final change to his plyocare work. We saw he was struggling to control his center of mass appropriately. His back foot was not staying anchored, and he was not able to stay in his rear hip. Early extension can prevent the glute from being the primary driver of pelvis rotation.

To attempt to address this, we added in some modified drop steps to his plyocare routine to help sequencing and get him to hold tension in the rear hip longer.

We cued him to rotate the lower half as fast as possible and for the back foot to feel as though he were spreading the floor—a similar cue we use in the weight room. Ideally, we see a slightly more vertical shin angle, but this was a good starting point.

Velocity on the mound was now as high as 93.9 mph. We felt confident that we could move to pitch design and attempt to optimize the rest of his arsenal.

Even pitch design relies heavily on the data we collected during his first few days in the gym. We do a baseline mound assessment where the athlete throws a short bullpen, usually on their fifth day in the facility. We collect velocity readings and capture pitch metrics with Rapsodo for each pitch type. This information would end up informing our pitch design sessions in the lab months later.

Most of the time was spent on his slider since it would be the primary breaking ball and has the highest usage percentage behind the heater. Looking at baseline data, we saw his slider was extremely inconsistent.

The plan was to create a slider with extremely low spin efficiency. Get as close to a pure gyro as we could. This would kill lift on the pitch, and since he didn’t throw his breaking ball with above average raw spin, it would allow gravity to do the work. Some simple trial and error with the Edgertronic really helped speed the process up. Explaining to the athlete what we needed the ball to do, spin like a bullet in this case, really seemed to be all the cueing necessary.

Motion Capture Retest Findings

After five months of training with us, this athlete went through a motion-capture retest. We usually do these retests with athletes every six weeks depending on the individual trainee, but for this athlete, it made sense to wait until he was back on the mound.

This time around, we see a much more efficient arm action. Forearm flyout is no longer present as elbow flexion is now 110 degrees at foot plant with a maximum average of 125. Shoulder abduction is better as well. He is now at about 86 degrees at foot plant and moves to around 90 at ball release.

Scap retraction was greatly improved. We now see 43 degrees of horizontal shoulder abduction at foot plant. This is a significant difference from his initial assessment. Getting good horizontal shoulder abduction like this also helps the athlete hold counter rotation in the upper half more effectively. Scap load allows for t-spine extension, which is critical in allowing the scap to tilt. This helps transfer energy upward from the trunk to the shoulder as it goes into external rotation.

Overall, this athlete went from having some significant issues to a pretty clean and efficient arm action.

Here is a look at his initial assessment (red) and his retest (green) overlaid:

So how did we do?

It didn’t happen overnight. But check out that new arm action

– more elbow flexion, arm gets inside 90 degrees (82 degs 110 degs)
– more scap retraction into foot plant (10 degs 43 degs)

Not seeing the pronounced forearm flyout from earlier. pic.twitter.com/h2o8a145qP

— Anthony Brady MS, CSCS (@BaseballFreak_9) March 1, 2019

In the lower half, we were looking to get the hips open earlier. In his initial assessment, the hips were extremely closed. In his retest, the pelvis is more open at foot plant. This is going to help facilitate better lower half rotation and sequence his pelvis and trunk more efficiently.

Lower half and sequencing changes.

It’s not much, but it’s enough. on the right, we can see that the hips are opening just a bit earlier into foot plant, about a difference of 20 degrees, which is enough to facilitate that lower half rotation. pic.twitter.com/hzAHjUWi86

— Anthony Brady MS, CSCS (@BaseballFreak_9) March 1, 2019

Returning to Competition

Leading up to facing hitters, he had been working around 90-92 mph and touching 93 mph, but we weren’t sure what to expect during live at-bats.

The above image is a screen grab from this athlete’s Trackman data from his first live at-bats in the facility. You can see he averaged 94 mph and touched 95 mph. Fastball velocity was living well above the 88.6 mph mark, which he averaged last season.

You can see below that the slider movement profile and pitch metrics we wanted the athlete to work within were something he was able to replicate against a hitter.

He eventually reported back that spring training was going well and he had touched 96 mph in a spring training game. Perhaps more importantly, he was feeling great and recovering exceptionally well between appearances.

Wrap Up

Biomechanics Breakdown time! Showing off the power of a fully integrated assessment process that utilizes biomechanics data to drive programming decisions

Got a spicy assessment to retest success story for ya, courtesy of @billhezel.

Red = assessment (Oct)
Green = retest (Feb) pic.twitter.com/r4uVbn469W

— Anthony Brady MS, CSCS (@BaseballFreak_9) March 1, 2019

None of these changes would have been possible without the initial assessment and the information it provides. It is deservingly the primary driver in determining how we build throwing programming for our athletes.

Having the ability to retest athletes to verify if positive changes have taken place is not only vital to our core training methodology but also helpful in validating its effectiveness. It also demonstrates how important communication is among departments when training athletes. This is executed extremely well at Driveline. Our high performance team and skill coaches communicate regularly to pair skill and strength/mobility work appropriately. This was certainly the case here.

Player development is not simply teaching a pitcher a slider or getting him to deadlift. Those are simply small pieces to it. Development truly starts with an assessment. Without a comprehensive initial assessment and an integrated approach to training encompassing all departments (medical, skill, strength, and R&D), athlete results will be limited.

Building out systems that help facilitate this approach is essential for any organization that prioritizes development. Getting an assessment is great. Uncovering what deficiencies an athlete has that prevent him from moving well or performing at his optimal level is certainly important. However, the athlete needs individualized programming that targets those inefficiencies and an organization that can support him throughout its execution.

Written by Pitching Coordinator Bill Hezel

The post Leveraging Assessment Data to Build Throwing Programming appeared first on Driveline Baseball.

For years, the game of baseball’s training environment hasn’t mimicked what occurs during a game. Throwing fastballs or curveballs every other pitch in batting practice hardly creates a random environment for pitches that hitters may see in a game. Though rotating between two pitches does get us further away from “block” training, it still lets the player know what is coming.

At Driveline, we try to challenge our hitters with consistent “machine work,” where we are have them constantly hit off the machine, chasing exit velocity and consistent barrel contact. But we run into the same problems mentioned above with this type of training: After a hitter has seen the pitch once, he knows exactly what it’s going to do and when it will arrive. Block training has been used in baseball for years simply because it’s easier to facilitate by coaches and trainers. Players will likely get in more reps, but are they learning and developing from those reps?  

Block versus Randomized Training

A quick story can easily explain the difference between “block” and “randomized” training. Two golfers are on a practice putting green. One of them is a PGA tour player, and the other is his close friend that rivals the tour player in skill but hasn’t yet made it onto the PGA Tour. Now, the friend lines up his putt from 4 feet away and strikes 100 putts from 4 feet, he then strikes 100 putts from 6 feet, and 100 from 8 feet. This takes him about 3 hours to complete all 300 putts, because he is taking his time and making each stroke count. Meanwhile, the PGA Tour player drops 3 balls on the side of the green and putts from the same spot to 3 different cups. The Tour player then continues to bounce around all over the green to random cups from random angles and distances. In the 3 hours he’s on the green, he only rolls 30 putts. His friend comes off the practice green frustrated and says to his swing coach “I don’t understand, I work harder than almost every player on the Tour. I just rolled 300 putts in the time he rolled 30. Why is it I still can’t break through and make it on the PGA Tour?” The trainer replies, “You rolled 100 from 3 feet, 100 from 6 feet, and 100 from 8 feet, essentially you only rolled 3 putts in 3 hours.” He continues, “He rolled 30 different putts from different areas and you continued to strike the same ball an extra 99 times.” Once our brain has been given the job of rolling a 3 foot putt, it simply ingrains that one motor pattern. By randomizing your training, you will have a better ability to adapting to new situations and stimulus to complete the task at hand.

Yes, I am claiming the way that we have been going about our training isn’t the most conducive for creating elite hitters. A problem that we face, like other facilities and programs across the world, is time and arm health. Would I like to have each hitter hit live every single day? Absolutely! The problem is that isn’t realistic in our current format for training. Also it can be difficult to work on mechanical swing changes while facing a live arm. Our pitchers are working on gaining velocity, strength, movement patterns, and at times designing new pitches. We simply can’t pull them in to throw live every day without issue.

So then comes the question, Who is in their mound-work phase and how many are there? Do we have enough arms to be continuously throwing live for 5-6 hours a day? The answer is no, not even close. Therefore, we are stuck back in our block training ways, running competitive pitches through the machine at hitters for hours on end. Luckily, our machine isn’t perfect, so during a round of 10 swings, only 3-4 pitches will be in the same location of the strike zone. In reality, they are only gaining a handful of quality reps each round in this environment, since they know what is coming out of the machine.

Instituting Short Boxes

One thing we do have at Driveline Baseball is a lot of retired baseball players that still want to compete and mix it up. The combination of having former ball players on staff and the need for more randomized training created Short-Box Tuesdays. Every Tuesday, a schedule is created for which Driveline employees are throwing to which group. The only requirements are that the pitcher keeps fastballs under 77 mph, and that’s all. On Tuesday, we bring in the short mound used for throwing into the plyo-wall and set it up at 50 ft. Hitters will get 2 rounds of flips to work on whatever drills or feels they want. Then for the next 45-50 min., Driveline employees will be committed to making at-bats as realistic and competitive as possible.

The first few weeks were not promising in terms of success for the hitters. It’s hard to blame them since they have been conditioned to see multiple pitches at the same speed, spin rate, and break. Now they are having to account for picking up release points, more rhythm, and, most importantly, different pitches. They are in an environment where they have to hit, not try and PR off an 88 mph cutter from the machine.

From the employee’s standpoint, this isn’t a tough ask. They get a break from a normal day to go compete, and since we’re at a shorter distance, it’s easier to keep it competitive at a lower intensity. Yes, arms still get sore, but with 6 days between outings and a lowered intensity, bouncing back for the next Tuesday isn’t much of a problem.  However, if a thrower doesn’t have the power to give hitters consistent live looks, then I suggest supplementing pitchers bullpens or flat-ground/short-box days with live at-bats (given their arm health). If it’s a short box, it can give pitchers a controlled and comfortable environment to play with a pitch they have been working on but are not yet prepared to throw in-game.

Having a coaching staff that can mix pitches in batting practice should be mandatory at this point. For instance, at least one adequate righty and lefty practicing with the ability to mix speed and location during normal batting practice will help tremendously in preparing them to compete. Making batting practice as game-like as possible will pay huge dividends come game time. Plenty of hitters need to “feel good” before they go out and compete. While confidence is something incredibly valuable to bring to the plate, a hitter’s rounds of “feel good” batting practice taken 45 minutes ago shouldn’t be the determining factor in how he walks to home plate. I would rather my hitters have their brains firing faster and adjusting to pitches on the fly, rather than toss 40 mph pitches in there so they can hit it into outer space and “feel good” about themselves.

We have even gone far enough as almost completely taking away underhand front toss flips in favor of an exaggerated front toss length, over-the-top throw. What we’re looking for is something far enough away where we can get a full arm swing, but close enough that we can locate each pitch in the location we’re aiming for. This gives the hitters a more realistic timing mechanism to work off and makes our standard front toss a little harder and more variable.

The front toss portion of our training is directed towards making swing changes and creating better feel for what each athlete is trying to accomplish in a relatively controlled environment. We don’t want to have guys go straight onto the machine and try and feel out if they’re getting their pelvis to start its rotation on time while trying to hit a slider. So while there is a time and place for working on mechanics and developing new feels, as soon as you have put in the work for what you’re targeting, challenge it with a more chaotic environment.

Balancing Challenging Practice and Tee Work

Since we constantly look for ways to make training more game like, let’s look at tee work. At long last, we find ourselves hitting a ball placed in an area we choose. It doesn’t move, and we spend hours on end tirelessly perfecting our craft at something that has very little translation to the sport we play.

I have never been to a driving range and seen a golfer throw a golf ball up and hit it when working on his distance wedge game. Don’t get me wrong, the tee is an incredible tool to work on things like sequence or feels. But once you have begun to accomplish your goal with the tee, immediately try and challenge it with a moving ball. Hitters say all the time, I can hit this ball 10/10 off the tee perfectly, but when I get to BP I just can’t seem to do it. More often than not, it’s because the feel they are working on was never challenged while it was fresh in their brains after tee work.

Slowly working swing changes from the tee, to constraints in front toss, to overhand toss, to BP, and finally to the machine is a way for an athlete to continuously be challenged on seeing if his new pattern is breaking down or not. You can see that if you go from tee straight to the machine or in-game situations, it’s going to be hard for whatever swing change to hold up.

Baseball must start working away from its “it’s just easier this way” mindset. If you’re really trying to create better and more competitive hitters, you must challenge them in the environment that closely resembles their sport. Part of this may involve more competitive batting practice from pitching machines, but it’s also incredibly valuable to mix in live pitchers.

Coaches that don’t start looking for different avenues to develop their players because they just print off last year’s practice plans are going to fall behind. Those that take the development and training of their players seriously will always find ways to make practices and repetitions resemble more game-like experiences. So, let the players fail so they can learn how to adjust. Nobody should hit .800 in BP, and no scouts should have to worry about if they’re “a 5 o’clock hitter for a 6 o’clock game.”

Written by Hitting Coordinator Max Gordon

The post Randomized and Blocked Training: Balancing Different Types of Hitting Practice appeared first on Driveline Baseball.

Driveline Baseball Podcast episode 23: On this weeks episode of the Driveline Baseball Podcast we have part 2 of the Trevor and Warren Bauer interview. In part 2 Kyle and the Bauer duo explore what baseball will look like in the near future as tech integration plays a bigger role inside the professional ranks as well as the players responsibility for improving themselves. Hit the links below to give part 2 a lesson. This week we also bring on our Technical Project Manager Michael O’Connell to discuss a recent study published by American Sports Medicine Institute, Birmingham, AL. What does throwing harder mean for pitchers long term health and what is the risk and reward that comes with higher velocities? Found out in episode 23.

Episode Resources:

Trevor Bauer: To see more of the work Trevor has done with cameras over the years check out his Youtube page for some interesting videos. Trevor has also Co-founded a company called WatchMomentum that tells players stories and allows athletes to express themselves how they see fit. To see some of these stories click here

Edgertronic Cameras: Wanna learn more about how we use these cameras? Check out some of the blogs we have written to get the most out of this tool here. Or check the Edgertronic development kit we sell on our site.

Sunday Thunder-Nuggets: You can check out the study discussed this week by clicking here

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

The post Driveline Baseball Podcast- EP. 23 appeared first on Driveline Baseball.

In our last piece, we took a surface level look at the first publicly available pairing of Blast and HitTrax data. In this piece, we take a look at commonly held, specific beliefs that have been touched upon by our hitting trainers and see whether the data backs it up. We also take a more nuanced view of a popular sabermetric proxy for evaluating attack angle via unsupervised learning.

Since our last post, we’ve added over 2000 sample swings of paired HitTrax and Blast data.

This piece includes a very granular examination of some very specific contentions, so to save time, here are the direct theses we are examining:

• Contention A: Hitters with higher early connection degrees in the top of the zone have less productive BBEs (Batted Ball events).
• Contention B: Athletes likely have high EV on pitches in the lower third of the zone and low EV on pitches in the top third of the zone (for connection_at_impact > 100).
• Contention C: Athletes take too long to reach peak rotational speed during swing and will likely struggle to achieve high EV at deeper points of contact (for rotational_acceleration_<15).
• Contention D: The 1/8th rule average LA makes for a suitable proxy for attack angle on a macro scale (per hitter). (More detail below but on exploring methods for reverse engineering attack angles.)

One final disclaimer: these findings are meant to be purely numerical with robust statistical merit rather than any sort of coaching indicator or lesson. The first three contentions are commonly believed both internally and among other analytically minded hitting instructors, and so now is the time to assess these claims.

A few quick definitions of the Blast metrics we discuss in this piece are necessary for comprehension:

• Early connection is measured in degrees as the relationship between body tilt and vertical bat angle (the angle of the barrel of the bat relative to the knob of the bat at impact) at the start of the downswing.
• Connection at impact measures the same relationship as early connection but at impact instead of the start of the downswing.
• Rotational acceleration is measured as the speed with which the bat accelerates into the swing plane.
• Attack angle is measured as the angle of the bat’s path at impact, relative to horizontal with a positive value indicating swinging up.

Ok, with a few disclaimers and definitions out of the way, let’s roll up our sleeves.

Contention A

Contention A has already been touched upon in the public Twittersphere.

Fires me up when @maxgordon40 hits me up with a theory about Early Connection Deg (Blast metric) and fires me up even more when his theory is validated by d@tA.

Gordo proposes that hitters with higher early connection deg’s in the top of the zone might have less productive BBEs pic.twitter.com/nAf4QNtW1k

— Alex Caravan (@Alex_Caravan) March 7, 2019

To touch back on this, this contention seems largely true. The mean early-connection figure for HHB or Hard-Hit Balls (measured here as EV over 90) is a little lower than for the non-HHB population for swings both in the bottom and top of the zone. So if anything, we can extend the contention to hold true for the rest of the zone, keeping in mind that the sweet spots are relative: What’s too high of an early-connection degree in the bottom of the zone is of greater money value in the top of the zone.

As a side note, the top and bottom of the zone was split up accordingly and depicted below:

• The top of the zone includes the 1, 2, 3, 11, 12 split-out zones.
• The rest of the zones are in the same vein.

The zone was additionally calibrated for each hitter’s height and stance, per HitTrax.

Contention B

Athletes likely have high EV on pitches in the lower third of the zone and low EV on pitches in the top third of the zone (for connection_at_impact > 100).

First off, the similar scope of this question prompts us to recycle the same graphic.

So using the same top and bottom of the zone denotations, we now look at the kernel density (a smoothed depiction of a random variable’s probability density function, or just a fancy way to show where most of the variable’s values lie) of exit velocity (rather than investigating the swing-characteristic metric of early connection as we did in the previous contention) and split up our large data frame into four main subgroups:

It looks like there is a ~1.5 mph EV difference between the top and bottom of the zone when the connection at impact is above 100, but it looks like the gap widens when looking at the swing metrics under 100 degrees. In fact, when running a within-subject, one-factor variable ANOVA test (chosen for the singularity of the top/bottom zone factor, and the proliferation of multiple swings per 40+ different athletes), we see that the top/bottom factor is significant (with the p-value under null hypothesis F test registering < 0.000001) for swings under 100 degrees at connection of impact, whereas the subpopulation of swings with a higher connection at impact does not find the top/bottom denotation as being a significant explanatory variable for exit velocity (Pr(>F) = 0.32).

So, Contention B appears to be more valid when expanding its scope to the bottom of the zone as well.

Contention C

Athletes take too long to reach peak rotational speed during swing and will likely struggle to achieve high EV at deeper points of contact (for rotational_acceleration_<15).

Here, we’re varying three continuous metrics: rotational acceleration, point of impact depth, and exit velocity.

Having a lower rotational acceleration does seem to make it more difficult to achieve higher exit velocity at deeper points of contact—as well as at every other common point of contact. An examination of a few density plots of the two rotational acceleration populations seems to bear this out. But first, let’s look at three steadily deeper and more selective samples:

And then let’s consider a comparison at a shallow point of contact where the hitter is over two and a half feet out in front:

If anything, it looks like rotational acceleration becomes even more pivotal if the hitter is very out in front. Well there’s a happy takeaway: A higher rotational acceleration seems to speak dividends no matter where a hitter makes contact.

Contention D

The 1/8th rule average LA makes for a suitable proxy for attack angle on a macro scale (per hitter).

First, here is a little bit of explanation, since this may be a new theory to some. The idea (touched upon first here by Tom Tango and here in a community Fangraphs piece) implies that gathering a hitter’s hard-hit batted balls average launch angle is an appropriate benchmark for representing a hitter’s average attack angle. (A general proxy has been cited as the top eighth of hard-hit balls to serve as the said “hard hit batted balls” subgroup.) Now, this comparison has been analyzed and constructed roughly through thorough swing-mechanic reverse engineering, but now we have the chance to attempt to validate this method.

We have approximately 45 different hitters in our dataset but keeping in only those who have at least 40 batted ball events (so as to deal away with some small sample size snarkiness) leaves us with 39.

Then, we constructed three different attack angle proxies versus the actual average attack angle per hitter, and we calculated a few error results (mean absolute deviation and root mean square error) as well as a directional indicator of reliability (the ubiquitious R-squared).

Specifically, the proxies were computed as the following:

1. Average launch angle of the hitter across all batted balls. (The simple and easy method.)
2. The average launch angle of the 1/8th hardest hit balls (exit velocity) per hitter.
3. An averaged of methods A and B.

These are fairly interesting results. Preferably, I would have also used the median LA as a proxy metric, but in this situation, with HitTrax representing launch angle as whole integers rather than float decimals, there wasn’t a discernable difference between the median and mean values.

The real (as measured by HitTrax) attack angle has a magnitude difference with all off the proxies (a 5-degree difference with the second method and a ~8 degree difference with the third, combo method) but looks quite directionally reliable with strong correlation values. The combo method specifically looks very promising, given awareness of the magnitude difference—and to be fair, for most analysis purposes in the scope of comparison, both of intra- and inter-nature, directional reliability could well be much more valuable than a strict low RMSE or the like. Here’s a visual representation of the combo method plotted via the actual average attack angle of each individual hitter:

Now, more contentions will come soon as we fold in both Rapsodo Hitting (tons of exciting batted-ball spin explorations) and some synced K-Vest data as well. For those of you that made it to the bottom of this, congrats! The diamonding will continue in the next piece of synced batted-ball and swing-data exploration.

Written by Quantitative Analyst Alex Caravan

The post Pairing Blast and Hittrax Data Part 2: Specific Focuses appeared first on Driveline Baseball.

Driveline Baseball Podcast episode 24: On this weeks episode of the Driveline Baseball Podcast Kyle and Mike discuss the theory and process of creating our data tracking software TRAQ and how it has become an integral part in us, training athletes. Kyle and Mike breakdown the gruelling process of us breaking google sheets when tracking athletes data and how that motivated us to make something better. We also welcome back on our Technical Project Manager Michael O’Connell to discuss a recent blog article we published about increasing your spin rate. Are there ways to increase the spin rate of your pitches and if so how does it help? Listen to this weeks episode to find out!

Episode Resources:

TRAQ: To find out more about our player development software click here

Spin Rate Article: To check out our article on spin rate click here

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

The post Driveline Baseball Podcast- EP. 24 appeared first on Driveline Baseball.

As we’ve continued along with The Business Case For Player Development Series, we’ve attempted to evaluate and analyze specific subcomponents of player development in today’s rapidly changing landscape. These micro-level analyses have been useful in identifying the cost of inefficiencies that still occur within player development; however, they have also been limited when attempting to extrapolate these findings on an industry-wide perspective.

In this mini-series, we attempt to expand our focus to analyze the overall state of player development over the past seven years. More specifically, we attempt to quantify each organization’s respective ability to acquire and develop young talent within the minor leagues. These outputs will give us insight into how important a highly functioning player-development department is to win at the MLB level and extend a competitive window years into the future.

This blog post outlines the details and methods behind the creation of our estimates, as well as provides some initial findings. A subsequent post will take a deeper dive into our results and provide some key takeaways for the industry moving forward.

Is Finding a Star Nothing But Luck?

Just past the halfway mark of the 2008 season, the Milwaukee Brewers were coming off a winning streak that moved them up to second place in the NL Central. Motivated to make the playoffs for the first time in 26 years, Milwaukee’s front office orchestrated one of the most famous mid-season deals of all-time, netting CC Sabathia for Matt Laporta, Zach Jackson, Rob Bryson, and a player to be named later.

Sabathia went on a historic second half run that led Milwaukee to a coveted playoff berth, whereas Laporta, the twenty-third ranked prospect by Baseball America at the time, sputtered shortly after switching organizations.

Luckily for Cleveland, the fine print of this deal contained a gentleman’s agreement stipulating that if Milwaukee made the playoffs, Cleveland would get the option of choosing between Michael Brantley or Taylor Green as the final player exchanged in the trade.

With the Brewers clinching the wild card on the last day of the 2008 season, Cleveland took up the opportunity to choose Brantley, who went on to amass 19.7 fWAR before leaving in free agency eleven seasons later.

As it stands, the trade perfectly illustrates how good scouting, good player development, and a massive amount of luck can make a significant impact on the successes and failures of an organization. For all the praise owed to the Indians for identifying, acquiring, and developing a mid-tier prospect into an All Star, there was an equal amount of criticism due for missing on Laporta, Jackson, and Bryson.

This is one of the challenges in evaluating the outcomes of different players within a given organization: there is no telling who deserves credit for the positive or negative, or whether there is any credit worthy of being distributed at all beyond pure chance.

However, as challenging as evaluating a scouting department or a player development department may be, we believe quantifying player acquisition and development is still a worthwhile analysis to undertake. Beyond potentially helping us identify whether continual increased spending on player development is a sensible investment for teams, a robust examination can also provide us with insight into whether one organization can identify and nurture a player to the big leagues with greater success than another, considering all else equal.

Is identifying and developing young baseball players into productive big leaguers an organizational skill? Or are farm system returns merely driven by variance?

With a sample of minor league players large enough to sift through the noise, we find evidence that some organizations are indeed better suited to acquire and develop prospects of equal talent. These organizations, led by heavy investors and early adopters of progressive means of player development, have been able to extract massive amounts surplus value over their competitors during the past seven seasons.

Acquiring Yearly Prospect Grades

To obtain accurate estimates of an organization’s ability to identify and develop a player, we set out to build a large enough dataset of recently acquired minor league players that allowed us to perform the following tasks:

1)     Generate an expected NPV via Future Value (FV) for every minor league player entering professional baseball to control for organizations that have a greater opportunity to acquire higher caliber amateur prospects.

2)     Map changes in expected NPV via FV for every minor league player on a yearly basis to control for minor league players who switch teams via trades.

3)     Estimate an expected NPV for each player graduating to the MLB level to quantify the end product of a player development department.

In order to satisfy these three goals, we first needed to find a reliable source of prospect grades that went far enough back in time and could be translated into dollars on a yearly basis. Fortunately, the The Baseball America Prospect Handbook has been grading a significant number of prospects (900) using the 20-80 scale on a per year basis since 2012, providing us with enough information to get started.

Angels Top prospect Jaime Barria beat the Rangers in his MLB debut Wednesday night.

Back in our 2017 Prospect Handbook, were especially impressed with his changeup pic.twitter.com/cYLiKKsdGH

— Baseball America (@BaseballAmerica) April 12, 2018

However, upon acquiring the aforementioned handbook, some heavy lifting was needed in order for us to use the data. First, Baseball America’s (BA) Prospect Handbook grades are only available in print, so we had to manually enter 2,700 prospect names, grades, and risk factors into a spreadsheet. Second, although Baseball America’s grades are published on the familiar 20-80 scale, their methods of assigning a prospect grade to a player are based on a “Realistic Ceiling” and “Risk Factor” calculation, rather than a traditional FV grade from which our NPV estimates are derived. As a result, we needed to adjust our BA grades to better align with our own fitted estimates of prospect value. To do this, we deducted 5 points from the Realistic Ceiling of every “High” risk profile prospect and 10 points from the Realistic Ceiling of every “Very High” and “Extreme” risk profile prospect.

To validate these conversions, we first looked at how many prospects had a higher adjusted grade than the prospect ranked above them (per BA) within a given organization. We found that out of 2,700 prospects within our sample, only 159 were out of order relative to their respective ranking.

With confidence that our prospect grades were accurate, we then matched our adjusted BA grades to Fangraphs’ “THEBOARD!” FV grades for the 1,092 prospects graded by both outlets from the 2017 season onwards.

As shown above, we obtained an R^2 value of .57 when comparing the two sets of prospect lists, with our adjusted BA grades coming in roughly 1.3 points higher than Fangraphs, on average, over every paired prospect in the sample. Of the 1,092 prospects in our sample, only 54 players had larger than a half-grade discrepancy between the two resources.

We interpreted these results as an indication that many of the discrepancies in prospect grades between the two resources were likely due to a difference in opinion, rather than an unreasonable grade adjustment on our part. Thus, we were comfortable using our adjusted BA grades for the remainder of our analysis.

Extending Our Database to All Minor Leaguers

With yearly prospect grades available to us for all relevant prospects from 2013 through 2019, we then set out to expand our database to include all minor leaguers and their respective signing bonuses received (with the exception of international free agents who signed at 25 years or older or those who started their professional career in AA or higher). As we explain a bit later, this data allowed us to generate a proxy of expected FV for every minor league prospect, as driven by the market, before they put on an affiliated uniform.

While obtaining signing-bonus data for every drafted player since 2012 was relatively straightforward, using publicly available resources to acquire every international free agent’s signing bonus was extremely challenging. To overcome these difficulties, we used a blend of resources including Baseball America, Sportrac, MLB.com, and The Baseball Cube to fill in as many values as possible.

(Note that we have more lenient requirements for Cuban-born players being included in our sample, which explains the discrepancy in dollars spent during 2015.)

In comparing the bonuses of international free agents (IFAs) within our database to Fangraphs fully populated dataset, we found that we were able to obtain information on roughly 24% of all international free agent signings from 2012 through 2017. While disappointing, our database still accounted for roughly 76% of all MLB IFA bonus expenditure during that time period, as well as every prospect graded 45 FV or higher by BA since 2012.

So, although we may be missing the vast majority of signings for $100,000 or less, we do have data on the vast majority of significant signings in a given season and every high-level prospect since 2012, which supplements the main focus of our analysis.

Translating Bonuses to Future Values

With this more complete dataset at our disposal, we obtained amateur FV grades from Fangraphs’ “THE BOARD” for every available prospect prior to their respective Rule 4 draft or July 2 signing window.

We then used our previously obtained signing-bonus data to generate three separate second order polynomials to predict FV grades for every prospect in our dataset based on when they were acquired, how they were acquired, and how much signing-bonus money they received.

Our first polynomial was built off of the 545 domestic amateur prospects graded by Fangraphs from 2015 through 2018. While this sample spans across two separate CBAs in which slot allotments towards the top end of the draft changed by a significant amount, we found insignificant differences in our results when splitting the players before and after the ratification of the new CBA. As a result, we elected to keep our sample together when building our regression.

To control for inflation, we normalized each player’s bonus total to be a proportion of all bonuses spent within each player’s respective draft. For example, in 2012, Carlos Correa received a $4.8M signing bonus that accounted for 2.45% of all bonus money spent during the 2012 draft, whereas in 2018, Joey Bart received $7.025M bonus that only accounted for 2.38% of all bonus money spent during the 2018 draft. Thus, as it relates to our model, Correa would actually be considered the more expensive acquisition compared to Bart, despite receiving an absolute bonus that was $2.2M cheaper.

Beyond just bonus money, we also considered a player’s pick number and whether he was a HS, 4YR, or JUCO product; however, none of these contextual factors added any significant predictive power to the model once bonus money was accounted for.

Our second and third polynomials were built using a split sample of 173 international free agents (IFA) graded from Fangraphs from 2015 through 2018. The splitting of the sample was designed to account for the wildly changing spending habits of teams due to the imposed limits on expenditure after the most recent CBA was ratified.

Controls were added to account for nation of origin; however, that information provided no significant predictive power to our model despite there being slight evidence of a premium on Cuban players within our sample, talent being held equal.

(The large signing bonuses of Cuban players seem to suppress the Predicted FV for IFAs from other major countries by a slight margin.)

Once values were fit to each prospect in our larger database, we chose to adjust for the super inflation that occurred in 2015 and 2016 due to impending spending limits by multiplying the 2012–2014 IFA class’ signing bonuses by 1.5. After this adjustment, the number of prospects with a fitted grade of 40 FV or higher for each respective IFA class are shown below.

(Our model was unable to handle Moncada’s signing bonus, so we manually assigned him a 60 grade upon signing.)

With fitted FV estimates for all players within our database, we rounded each value to the closest traditional grade in order to obtain an expected NPV for each prospect upon entering affiliated baseball.

For international players, any prospect that signed for under $250,000, after controlling for inflation, was designated as a non-prospect and received an NPV of $0. For domestic players, any prospect that signed for under $125,000 in 2017/2018 and $100,000 in 2012–2016 was also designated as a non-prospect and received an NPV of $0.

These constraints, while somewhat arbitrary, classified roughly 55% of all acquired prospects per year in our database as non-prospects. Since prior analysis has found that roughly 75%-80% of affiliated prospects are considered non-prospects, this figure was designed to accommodate the roughly 500-700 IFA non-prospects that were missing from our dataset on a per year basis.

(A snapshot of the first 10 picks of 2016 in our DB)

Graduating to the Big Leagues

With a database containing expected NPV estimates and year-to-year changes in NPV for the vast majority of minor league players acquired since 2012, the final step in our methods was to account for prospects graduating to the MLB level.

Evaluating graduated prospects proved to be a more difficult task than initially perceived, as players who have reached the big leagues in our sample have not yet fulfilled their full 6+ years of club control. Making matters more challenging, publicly available multi-year projections of WAR tend to be unreliable and have relatively large disagreements on player evaluations.

As a result, we decided to go one by one and manually grade each graduated player based on his age, 2019 projected WAR, and previous MLB production.

Since these values comprise much of the value teams have generated over the past seven seasons, we wanted to be as transparent as possible. Thus, we have made all subjective grades used in this analysis available via the link provided here. Along with all of our MLB grades, you will also see a tab that instructs you on how to adjust each team’s final results for Value Generated, if you have any strong disagreements with our grading system. For example, if you believe that Kyle Freeland should have been a 60 FV upon graduating the big leagues, the link above provides you with the means to adjust the Rockies final values as you see necessary.

Beyond discrepancies in subjective grading, the tool linked above also allows you to adjust our final estimates of Value Generated based on hypothetical scenarios of an athlete’s development. For example, if you believe that Carlos Rodon would have been a 2.5 WAR player had he integrated his CH more effectively into his repertoire, you can adjust his final FV to a 55 and recalculate Chicago’s final values to account for this.

(A snapshot of the top 10 signees of the 2014 draft with MLB grades highlighted in gold)

Once a player relinquished prospect status, their MLB FV grade remained constant for the remaining duration of his career unless he switched organizations, established himself as an MiLB player again, and then proceeded to demonstrate a significantly different talent level upon reaching the Major Leagues with his new organization. In these specific cases, we wanted to credit the respective player development department that significantly improved a player’s overall talent level while the player was under their umbrella.

This exception was used sparsely, but it did allow us to account for players such as Max Muncy,  who, after accumulating -.7 fWAR over two seasons with the A’s, transformed himself into one of the premier power bats in the game since joining the Dodgers. Similar adjustments were applied to Chris Taylor with LAD, Caleb Smith with MIA, Matt Boyd with DET, and Tyler Olson with CLE.

(Both Chris Taylor and Max Muncy revitalized their playing careers after joining the Dodgers, a testament to the organization’s ability to acquire and develop talent over the past seven seasons. Ironically, Taylor and Muncy were drafted seven picks apart in the fifth round of the 2012 draft.)

Obtaining Baseline Results

With a complete database that included yearly changes in value for every acquired prospect in affiliated baseball since 2012, we were able to debit or credit each organization for the change in value, above or below expectation, for each prospect in their farm system signed after January 1, 2012.

(Mark Appel was rated a 55 FV prospect before entering Houston’s system as 2013’s first overall pick. After peaking as a 60 FV heading into 2015, his prospect status dropped to a 50 FV before he was traded to the Phillies prior to the 2016 season. With Appel now currently out of baseball, the Astros incurred a -$21.3M estimate of Value Generated for Appel’s development, whereas the Phillies incurred a -$25.9M estimate of Value Generated for realizing no return on an incoming 50 FV player.)

The table below shows our estimates for the raw value generated by each respective organization from 2012 through 2019.

At first glance, the position of many organizations relative to league average seems to align with what intuition would suggest. Teams at the top of the list—such as the Dodgers, Astros, Cardinals, Braves, and Yankees—have developed solid reputations as having either a strong scouting department, a strong player development department, or both. On the flipside, organizations near the bottom of the list have either found ways to build a contender without a homegrown core, or they have struggled to get talent to the majors at the rate we’d expect given their draft position, incoming talent via trades, and international free agent expenditure over the past seven years.

Aside from the order of where teams fall in this leaderboard, it is also fairly eye opening to see the absolute difference in Value Generated between the best and worst teams in terms of acquiring and developing players when controlling for incoming talent. While luck surely plays a significant factor in the $816M separating the Dodgers and White Sox in terms of Value Generated, it is telling that several of the organizations towards the bottom of our list have recently invested in many of the developmental tools the Dodgers and Astros have been using for years. All things considered, it is unlikely that only luck explains the differences in the values provided above and more work is needed to be done to quantify the true first mover’s advantage realized by the Dodgers and Astros.

Concluding Remarks

After leveraging our newly constructed database to generate descriptive estimates for each organization’s ability to acquire and develop talent over the past seven seasons, preliminary findings suggest that we have developed a valid measurement of descriptive performance to disseminate the leaders from the laggards in terms of valuing and developing players at the minor league level.

With these promising initial results, part two of our mini-series will further investigate how teams are able to generate value throughout their minor league system, whether adding expected value to a prospect’s net worth significantly impacts the success of the big league team, and whether we can strip out luck to provide legitimate measures of an organization’s efficiency regarding player acquisition and development.

The post Is Finding a Star Nothing But Luck?: Quantifying the Effectiveness of MLB Player Development appeared first on Driveline Baseball.

Can we accurately evaluate an organization’s ability to acquire and develop players relative to their competitors? In part one of our mini-series looking at the overall state of minor league player acquisition and development, we attempted to answer that question by building a database of over 8,000 minor league players in order to assess each organization’s ability to generate value. With dollar estimates of surplus value that controlled for player expenditure and expected talent levels for incoming players, we were able to identify industry leaders and laggards in player development. We were also able to quantify a preliminary estimate of monetary value realized by more progressive teams via a first mover’s advantage.

In this post, we build on our preliminary methods and analyses to examine how data-driven player acquisition and development contributes to a sustainable model of success at the major league level. Using our baseline estimates to generate more specific and reliable results, we evaluate the merits of unique team-building strategies and provide more realistic estimates of the surplus generated by each respective farm system in baseball.

Estimates > Anecdotes: Evaluating Farm Systems More Effectively

Heading into the 2015-2016 off-season, the San Diego Padres organization found themselves in a make-or-break situation. Having won 74 games the season prior with a roster built to “win-now,” the organization faced the difficult decision of choosing whether to stay the course or to rebuild internally.

The front office chose the latter, and invested heavily in amateur and minor league talent over the next few seasons. With $80M spent in International Free Agency, three top-ten picks, and a bevy of trades that netted them the likes of Fernando Tatís Jr., Chris Paddack, Francisco Mejía, and Anderson Espinoza, the Padres jumped from 25th to 1st in Baseball America’s Organizational Talent Rankings, constructing a rebuild that likely surpassed even the most optimistic of expectations.

While surely an organizational-wide effort, many of the public accolades given to the Padres for their successful rebuild were directed towards their player-development department, who transformed many young, talented athletes into some of the premier prospects in the game.    

But, given the wealth of riches that San Diego’s player-development department were gifted, do we really know whether all of the praise they’ve received is justified? For every Luis Urías and Joey Lucchesi that has been signed and developed, a rival farm director can point to several expensive international signings in the Padres system who have not yet lived up to the billing. In considering the sheer investment in minor league talent that the Padres committed to the project, did they receive the value they expected, given what was spent?

Answering this type of question is not straightforward because there have been no publicly available resources to date that have analyzed an organization’s farm system while controlling for player expenditure. As a result, fans, coaches, and even team executives have leaned on anecdotal examples of the successes and failures of different players to develop opinions on whether a team has been efficient at signing and developing young talent.

Fortunately, our Value Generated metric (introduced in part one of our mini-series) provides a solution to this issue by controlling for expected value acquired by a given farm system. Uninfluenced by the various investment strategies of teams or the subjective opinions of insiders, our metric not only provides a valuable KPI to MLB organizations, but also allows us to investigate the merits of different spending strategies used in building MLB rosters.

Who Has Developed the Most Value in the Minors?

With most of the work already done for us, our Value Generated estimates for each organization are provided below. As highlighted in part one, organizations with strong reputations in amateur scouting and player development—such as the Astros, Dodgers, and Cardinals—have realized approximately $350M to $450M in surplus value over the past seven seasons. In contrast, teams with more traditional approaches to player acquisition and development have incurred approximately $175M to $350M losses within the same time span.

(By splitting the results into subsets of 3-4 year buckets, we see how some teams like the Braves, Rays, Yankees, and Padres have trended upwards in recent years, whereas the Tigers, Rangers, Giants, and White Sox have had little success in acquiring and developing talent in the minors since 2016.)

With a staggering difference in surplus value realized between leaders and laggards, we see that the organizations toward the top of our leaderboard have maintained a consistent advantage in developing talent over the past seven seasons, while the gap between the lower performing teams and league average has widened as prospects within our sample progress to full maturation.

Who Develops More Value With Fewer Opportunities?

Equipped with our metric, we wanted to first address how farm-system rankings can be influenced by both total expenditure in minor league players and efficiency in acquisition and development. This analysis allows us to decipher the following:

• whether some organizations develop better players with less resources available while other organizations struggle to realize any ROI on highly touted prospects
• whether some teams are able to leverage their abilities of generating surplus value in the minors by continuously investing in young talent

In returning to our Padres example from above, we expect that the organization would score well in Value Generated, particularly since 2016, but see that they ranked 15th in Value Generated since 2012 and 9th overall since the start of their rebuild. In the Padres having the number-one farm system in baseball, we have evidence supporting the idea that some combination of both above-average player acquisition/development and heavy investment in prospects played a significant role in boosting their farm system to the top of the rankings.

Applying this analysis to the other 29 teams, we can further leverage the most recent Baseball America (BA) Talent Rankings to identify which organizations have invested heavily in minor league players but have still fallen short in producing homegrown value and which organizations have continued to generate surplus value, albeit with limited opportunities.  

(This table uses our Value Generated metric to obtain the difference between expected and actual 2019 BA Talent Ranking. We see that the White Sox, Padres, and Rays have talented farm systems without an exceptional amount of Value Generated, indicating that they have likely made a sizeable investment in MiLB talent during recent years. Meanwhile, the Red Sox, Cubs, and Giants have poorly rated farm systems that have likely resulted from low investment and/or poor Value Generated in recent years.)

In using a simple linear translation of 2016–2019 Value Generated to 2019 BA Talent Rankings (a Spearman’s rank correlation of .41 was found between the two), we observe some interesting takeaways. First, organizations ranked towards the bottom of our Value Generated metric—such as the Reds, White Sox, and Marlins—all received BA Talent Rankings that were 6-16 spots ahead of where we would expect, given their ability to generate value at the minor league level. Using this to conclude that each organization has invested heavily in young talent but has received relatively little value in return, we can see that constructing a home-grown competitive roster now likely requires both efficiency in player acquisition/development and heavy investment, rather than just the latter.

In an effort to address the former, both the Reds and White Sox have recently made several changes to advance the state of their player development and maximize the most out of their recently acquired young talent. However, only time will tell if these changes will improve both teams’ standing in our Value Generated rankings over the next few seasons.

On the opposite side of the spectrum, organizations found at the top of our list of Value Generated that have received a worse BA ranking than expected—such as the Red Sox, Yankees, Nationals—have been able to supplement their organizations with significant surplus value in the minor leagues, despite having “win-now” rosters. Even when considering market size, it is likely no coincidence that these organizations have had sustained success at the MLB level over the duration of our sample, given that they’ve been able to provide their big league team with cost-controlled talent or trade capital for postseason runs.  

Last, we also observe a handful of organizations—like the Cardinals, Dodgers, and Astros—who have developed superior Value Generated estimates relative to their peers and a better farm-system ranking than expected. Likely blending a strategy of continuous investment in prospects with superior scouting and player-development modalities, these organizations have seemingly developed a sustainable model of success for both present and future outcomes at the major league level. 

Does Efficiency in Development Lead to Success?

Having demonstrated that an organization’s respective BA Organizational Talent Ranking is a function of both efficiency in acquiring and developing talent and overall investment in prospects, we wanted to quantify just how important Value Generated is in making a significant impact on the overall success and long-term health of an organization.

To examine this, we calculated the correlation between winning percentage and Value Generated over the past seven seasons. We found a fairly robust correlation coefficient of .62. As shown below, some teams can outperform expectations with shrewd pickups, large budgets, luck, or aging cores. However, for most teams, getting surplus value from their farm system above expectations is a prerequisite to maintaining consistent success over an extended period of time.

To gain a sense of the savviest teams in the free-agent market and/or other means of player acquisition, we extrapolated our Value Generated metric into an expected winning percentage over the past seven seasons. This allowed us to find the difference between actual and expected average-win totals by season in order to see how much each organization outperforms expectations over time.

We see that our new leaderboard is biased in favor of large markets and teams that were built to win before the prospects in our sample fully matured. That said, there are also creative small-market value shoppers—such as Pittsburgh, Milwaukee, and Tampa Bay—who score really well by this metric.

At the bottom of the list, we see teams like the Twins and Diamondbacks—who developed strong cores over the mid 2010s, but failed to put the pieces together to build a contender—as well as the Phillies and Blue Jays, who exchanged a large amount of prospect capital to extend competitive windows in a non-sustainable manner. Other organizations found at the bottom of the list above—such as Houston, San Diego, and Atlanta—spent a large portion of the 2010s rebuilding their MLB roster from the bottom up and are now reaping the benefits moving forward.

Do Some Teams Specialize in Developing Position Players or Pitchers?

With confidence that our metric measures something related to past performance, we divided our results into buckets of position players and pitchers to further identify whether some teams have specifically struggled or excelled in developing bats or arms.

In looking at the table above, we observe some interesting outliers that align closely with our expectations. For example, our results show that Boston, Minnesota, and Oakland have had much greater success in developing hitters compared to pitchers, whereas the opposite holds true for teams like the Royals, Yankees, and Phillies.

Beyond just splits, we also can quantify observations made by other fans and analysts regarding an organization’s ability to develop a specific type of prospect. For instance, it has been well documented that the Cubs have struggled mightily to develop any pitching over the past seven years, and our Value Generated metric estimates that they have lost $126M in expected value on pitching prospects during that time frame. Meanwhile, the Astros and Yankees, who have each received high praise for integrating advanced technologies into their pitching programs, have each realized over a $200M profit in pitchers during the same window.

Can We Isolate Player Development From Scouting?  

Since amateur scouting and analytics departments also play a significant role in supplying player-development departments with athletes who are above or below expected talent upon arrival, we wanted to develop a method for analyzing player-development departments in isolation. To do so, we looked at how well certain organizations have influenced the FV’s of prospects they exchanged from other organizations via trades.

For our analysis, we assumed that any change in FV for a prospect within one year of being traded was caused by switching from one organization to another. As a result, a team would be rewarded for improving the FV of an incoming player within one year of arrival, whereas the other team would be punished by an equal amount.

For example, when Alex Jackson was traded from the Mariners to the Braves heading into the 2017 season, the Braves were able to boost his FV from a 40 to a 45 during his first year with the organization. Based on Jackson’s immediate improvement, our methods credited the Braves player-development department with $5.9M in Value Generated and debited the Mariners player-development department $5.9M in Value Generated.

Since this analysis is a zero-sum game with diminished sample sizes, some teams will benefit from trading players to organizations with worse player-development departments, whereas other teams may fall victim to random variation or trading valuable pieces before maturation. Therefore, we have to take these results in context rather than strictly at face value.

In looking at the table above, we see the Yankees, Dodgers, and Brewers have excelled at either selling high on certain prospects, targeting and developing lesser known prospects from other organizations, or trading with the right teams at the right time. In contrast, the Cubs, Rays, and White Sox have taken particularly strong hits in recent trades, with the Cubs losing Eloy Jiménez, the Rays failing to accrue much value out of their recent acquisitions in the upper levels of the minors, and the White Sox taking significant hits on the Adam Eaton, James Shields, and Chris Sale swaps.  

These Metrics Are Descriptive, But Are They Reliable?

A main concern outlined in the beginning of our initial post that still needs to be addressed is whether credit can accurately be attributed to a respective organization for the various prospect outcomes it realizes. Perhaps our Value Generated metric is simply identifying the random luck that each organization has been exposed to over the course of the past seven years and is not a reflection of an organization’s ability to acquire and develop quality MLB caliber players.

For example, although the White Sox are docked heavily in our analysis for not getting expected value out of the Adam Eaton trade, one could reason that Baseball America was a bit high on the likes of Lucas Giolito or Reynaldo López at the time of the trade compared to the rest of the industry. The public widely regarded the deal as a coup for the White Sox, but the White Sox could very well have had lower internal grades on both of the aforementioned players received and still valued that package as the best opportunity to improve their organization. As a result, our model would punish the White Sox too severely due to a lack information.

Since we do not have access to all of the details on certain transactions, interactions, or developmental plans for specific players, we cannot fairly attribute 100% of the observed credit of Value Generated to each individual team and must regress these values.

To do so, we take each team’s Value Generated on a yearly basis and calculate the reliability in our entire sample. Using the Spearman-Brown prediction formula, we find that the threshold needed to explain half the variance in our Value Generated metric is approximately 35 years, so we regress our raw values to $0 by 83.33% to account for this uncertainty.

As you can see from this table, there is still a significant amount of value generated between the best and worst organizations, despite a heavy amount of regression baked into our final estimates.

While these newer estimates of Value Generated are probably closer to the true monetary advantage that the best organizations have realized since 2012, it is also likely that these estimates are slightly too conservative given that we regressed every organization to $0 and limited each organization to only 30 minor league prospects of value per year via The Baseball America Handbook.

These methods artificially suppress our reported numbers because we know that specific organizations (particularly at the higher end of our chart) prioritize and outspend their competitors in player development by a significant margin and that they also have more than 30 players of value in the minors. As a result, organizations such as the Astros and Dodgers should have more optimistic reported estimates of Value Generated that are regressed to a higher dollar amount, relative to their peers.

As proof of this concept, competitors towards the bottom of our list—such as the Phillies, Giants, and Rangers—have all adopted significant changes within their player-development departments that align more closely with the trendsetters in the industry. In our opinion, this indicates that there were clear competitive advantages in player development realized by the Astros and Dodgers during the years in question that should be accounted for.

However, since we are constrained by the information available and want to avoid adding additional assumptions or estimates of value generated, we elected not to adjust our final values to account for these limitations. Instead, we report a conservative $135.4M difference between the best and worst organizations in MLB at acquiring and developing talent for players entering affiliate baseball after January 1, 2012.

Main Takeaways

Despite the uncertainty and chance that has always been a part of analyzing prospects, our analysis finds strong evidence that heavy investment into progressive means of player acquisition and development has provided early adopters, such as the Dodgers and Astros, with a massive competitive advantage over the rest of the league over the past seven seasons.

As teams grasp this and rapidly attempt to catch up, we can’t help but feel cautiously optimistic for the future of baseball. As players are taught to swing with more efficient swing planes, throw with better secondary pitches, and train with modalities that are actually proven to improve performance, the quality of play and the outcomes of the most talented players will surely improve.

This does not mean that there will not be any bumps in the road along the pathway to optimization. Top prospects will still fail to make it to the big leagues, amateur players will continue to be mis-evaluated, and certain developmental plans will still be ineffective—this is inevitable.

However, it is (and always has been) our position that we can put our players and coaches in the best possible position to mitigate these negative outcomes .

So, as we push ahead towards the 2019 season and beyond, this analysis shows us that the question worth answering is no longer a matter of if data-driven player acquisition and development can lead to more efficient outcomes: it is a matter of how.

As the gap inevitably closes between the best and worst organizations at developing talent, some will find a way to move ahead of the curve and some will fall even further behind. Regardless of where each team ends up finishing in the race to catch the likes of the Astros and Dodgers, it seems clear that it will be worth every penny for them to try and catch up.

Written by Sabermetrics Analyst Dan Aucoin

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