Not Adding Up | training-conditioning.com

You would have thought that the "experts" had accounted for "warm-up pitches" in their studies. This kind of throws my confidence in their research, and therefore in their conclusions, down a few notches.

Back to the drawing board fellas!!

from training-conditioning.com
Not Adding Up | training-conditioning.com:

Not Adding Up

April 18, 2018

Nationwide rules limiting pitch counts among high school athletes have not decreased pitching injuries as expected. Wondering why, a team of researchers from the University of Florida recently took a closer look at the issue.

In a cross-sectional study reported on by Orthopedics This Week, the researchers attended varsity high school baseball games for 34 Florida high schools during the 2017 season. The goal was to examine the total number of pitches thrown. Each researcher counted pitches from the mound in the bullpen before the game, warm-ups before the innings, and during the game. Long tossing in the outfield and throws made before warming up in the bullpen weren’t recorded. 

The study’s authors hypothesized that despite mandatory pitch counts during games, between 30 to 40 percent of pitches aren’t actually accounted for during outings. Additionally, the researchers expected to see a broad variation within the players’ number of bullpen pitches.

Both of these theories were supported by the study's results, which were published online by the Orthopaedic Journal of Sports Medicine. It showed that 42.4 percent of the 13,769 pitches observed for the study were not accounted for in the pitch count because they were thrown in the bullpen and during warm-ups. The study also showed a wide range in the number of bullpen pitches thrown.

“Our data revealed an unaccounted workload factor of bullpen and warm-up pitches not included in the total pitch count. We also found wide variation in the number of bullpen pitches thrown, making precise determination of pitch volume challenging among high school players,” the study’s authors wrote.

Although injuries weren’t recorded in this observational study, the findings may help explain why injuries haven’t decreased despite advances in monitoring pitch counts. Importantly, much of the prior research on pitching injuries hasn’t included bullpen and warm-up pitches in measuring throwing volume.

“Our study suggests there is a misunderstanding of the workload volume in the high school-aged baseball pitcher. Research has suggested that the occurrence of workload spikes (sudden increased volume relative to regular training volume) significantly increases risk for throwing-related injury,” the researchers wrote. 

Image by Staff Sgt. Jocelyn Rich.

'via Blog this'

Evaluating Player Fatigue with Eye Sync | Concordia University Chicago

This is forward, outside the box thinking. Great stuff!!

I could see baseball teams using this to monitor fatigue from jet lag or just generally for guys who like to "grind it out" and play every day. 

Evaluating Player Fatigue with Eye Sync -:

"With playoffs looming, the Golden State Warriors are taking a proactive approach to diagnosing exhaustion and fatigue in their athletes. To do so, they are utilizing a tool that is primarily used to detect concussions. 

The device is called Eye-Sync, and it’s from SyncThink. Warriors’ Assistant General Manager, Kirk Lacob, and Head of Sports Medicine, Chelsea Lane, teamed up to implement the tool after testing it out themselves. Designed as a virtual reality headset, Eye-Sync uses a 60-second eye-tracking test to detect any visual impairments. For this reason, it is often used to assess concussion symptoms.

However, it’s also able to determine players’ fatigue levels, Using Eye-Sync, Golden State now performs regular screenings on their players to detect for signs of accumulated fatigue, in addition to concussion symptoms.

Lacob views this as an “added bonus” and “very valuable” in preparing for the postseason, according to article from SportTechie.

“What our technology is doing is measuring [the players’ eye movement] and saying, ‘Oh yeah, they’ve improved,’” said Lacob. “Or ‘They’re back in a normal range where their eye signatures have been minimized into a range that tells us that the brain is optimized for performance now and that the effect of fatigue is no longer there.’

“That’ll help us understand what we need to do to reduce stress and fatigue on players over the last month [of the season], and that could affect our ability to push forward or scale back a little bit because our ultimate goal is to be fresh for the playoffs,” Lacob continued."

'via Blog this'

NHL commissioner Gary Bettman speaks on a panel during the 2018 MIT Sloan Sports Analytics Conference. (Photo by Patrick Daly / ESPN Images)

It's going to be tough to walk away from this completely. Teams will present it as a net positive re: performance enhancement, while the players will be somewhat wary of the negative feedback and it's use against them. Double-edged sword. But it seems inevitable that we will see FitBit for athletes, coming soon. 

from sporttechie.com
https://www.sporttechie.com/player-tracking-data-contract-negotiations/

Player Tracking Data Presents Touchy Subject For Contract Negotiations

Joe Lemire March 1, 2018

The Denver Broncos have been using Catapult's wearable technology since 2014, but running back C.J. Anderson said he only gained access to his data this past fall — and had a career-high 1,007 rushing yards, saying, "I believe I had a better year on the football field because of the data that I have."

While speaking on a technology panel at last week's Sloan Sports Analytics Conference, Anderson said that, as helpful as the tracking data was to him on the field, it might not be so beneficial when shared with team executives.

"Now if I walk into [general manager] John Elway's office and say, 'Hey Elway, A-B-C,' but he says, 'X-Y-Z,' I might not get paid as much as I would like or as much as I think I should be getting paid because he can use that data of the issues and the problems that I have. 'This is why I don't want to pay you,' or 'this is why I'm getting rid of you, this is why you're getting waived, this is why you're getting traded.'

"I think that will be the fear in our business will be the negotiation side of things. It's already cutthroat."

Even seemingly straightforward measurements, like a hockey player's skating speed, may have layers of complexity. As Don Fehr, the executive director of the National Hockey League Players Association, explained on a player advocacy panel, context matters.

"There's this big push to quantify everything without, as of yet, knowing what those quantities mean," he said. "So we can say you skated a little bit slower than you did three years ago, does that mean your play is better or worse? Are you taking time to figure out where you're going or can you not keep up any more?

"So there's a real danger in negotiations of having a lot of statistics that merely provide excuses for people to do what they want to do."

Therein remains the thorny nature of the proliferation of wearable data and how that information will be applied in professional sports. Some leagues have begun addressing the issue in their respective collective bargaining agreements, with voluntary participation a cornerstone of each policy, except the NFL that mandates players wearing the Zebra tracking chip in their shoulder pads during games. The NBA has banned the data from contract negotiations or player transactions. Major League Baseball prohibits the data from use in salary arbitration discussions. The NHL's CBA, finalized in 2013, does not explicitly address the issue, which will become more pressing given the league's stated desire to have league-wide tracking within two years. "Anything that can help grow the game," NHL commissioner Gary Bettman said.

The NBA is forming a committee to further explore the issue, but National Basketball Players Association executive director Michele Roberts was clear that player privacy was paramount in their conversations.

"We caution the players about how, if at all, they should be disclosing this information," she said. "Efficacy, validation — those things are important to us. We don't know if some of this stuff is, frankly, junk. And we certainly don't want it to have it be the source of or be used in contract negotiations. Maintaining control of the data has been our primary focus."

That echoes the refrain of NFL Players Inc. president Ahmad Nassar who, when speaking at SportTechie's State of the Industry event last month, said player ownership of data was the "central bedrock starting point" of his group's partnership conversation with Whoop.

Recently retired MLB pitcher Chris Capuano shared the prevailing opinion of his peers, when he explained, "I guess their fear is, 'How is this data going to be used against me?'" Baseball already has an external radar- and camera-based tracking system, Statcast, that measures players' movements on the field.

Generally, such external tracking information and other movement data is seen as less circumspect as the biometric wearables that glean heart rate and other such metrics.

"The way I look at it is two different buckets of wearable technology," said longtime NBA agent Jim Tanner, whose client list has included Tim Duncan, Grant Hill and Ray Allen. "The first is the movement tracking — acceleration, deceleration, load, things like that. That doesn't concern me as much as the biometric stuff where they're analyzing the oxygen in your blood or the recovery rates or things like that where, to me, that's very personal to the player."

Indeed, that is about the only consensus on the issue: players, agents and unions all believe the athletes should own and control their own data. What shape tracking technology takes in the years to come — whether it's personally worn or externally collected — will steer the direction of the conversation, along with the implications that can be definitively drawn.

"Obviously this is the new hot fad, and it's going to be out there," Fehr said. "Whether it'll be around in any meaningful fashion five or 10 years from now, I think, is anybody's guess."

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What Do You Need to Know as a Sport Scientist Besides Sport Science | Complementary Training

This should keep me busy for the next year or two. Mladen Jovanovic is a genius.

from complemntarytraining.com

http://complementarytraining.net/what-do-you-need-to-know-as-a-sport-scientist-besides-sport-science/

What Do You Need to Know as a Sport Scientist Besides Sport Science

There are new roles emerging in contemporary sports field: sport scientist and performance manager. To be honest I have no clue how to define them – sometimes they are just a way to break out of the normal terminology (see Semantic Stretch and Power of Association in Made to Stick book, which is by the way excellent) and appear fancy and important. But sometimes they represent genuine additional roles and obligations on top of strength and conditioning / physical preparation positions. I will focus on those.

Please note that these are only my opinions, so take them with grain of salt. I will cover some of those extra skills needed and provide good resources where to find quality knowledge.

1. Basic and Advanced Statistics

As a sport scientist you will mostly deal with descriptive statistics, although if you plan publishing you will need to make generalizations or inferences to a bigger population from a sample you have. In a club settings, coaches will not give a f#ck about p values and confidence intervals – they generally have no interest in making generalizations beyond the group of athletes they are working with. Also, they are not interested in 'averages', but rather in single individuals and outliers.

This makes most of the statistical methods useless (unless you are publishing in journals). This is also a positive thing, because you will use lot simpler approaches;  approaches that are understandable by both coaches and athletes.

Here are some of the topics you will need to know

Basic

1. Effect size statistics (e.g. Cohen's D)
2. Linear regression and model fitting and model diagnostics (e.g. Cook's distance etc)
3. Concept of Reliability (Typical Error) and Smallest Worthwhile Change (SWC) when assessing an individual and performance changes

Advanced*

1. Non-Linear regression and generalized linear models (e.g. logistic regression)
2. Time-series analysis
3. Clustering, Factor Analysis and Principal Components Analysis (PCA)
4. Machine Learning, Data Mining, Predictive Modeling

* You do not need to know these in detail – only be kinda familiar with them (especially the last one) so you can hire someone to implement those with the data you have. Just make sure to know what they are and what they give to you. Besides these are definitely not going to be understandable to athletes and coaches 🙂 

I am by no means expert on those methods and I am learning them myself as well.

The good sources of information are Statistics in Kinesiology by Vincent and Weir

A New View of Statistics by Will Hopkins

Coursera courses on Statistics, Data Analysis & Machine Learning
Excellent paper on Mathematical Modeling by David Clarke and Philip Skiba

2. Data Visualization

It is very important to be able to convey data and insight to decision makers. Effective graph and table design is then a must have skill. I really love the books by Stephen Few

3. Excel

Excel is lingua franca of sport scientists. Even the richest clubs still use Excel and Dropbox combo. Here are some of the great resources for you to consider if you want to learn Excel

And excellent Excel tricks for Sport by John Lythe

4. PowerPivot

Some say this is the best addition to Excel since, well, Excel. It is very powerful add-on to Excel. I made Annual Planner for Sports v2.0 using PowerPivot.

5. R/MATLAB/Python

It is important to know at least one of those programming languages. R is getting bigger and bigger in data analysis and it is completely FREE, with a LOT of 'extensions' and libraries (see the list below). Python is also completely free and is catching up with data analysis, visualization and statistics. MATLAB is awesome engineering tool (SIMULINK is great tool), but more and more scientists are switching to R/Python.

If you want to learn R here are the couple of great books to start with

As a R user you will definitely need to learn great packages developed Hadley Wickham, like ggplot2, reshape2, plyr and stringr.

Some other 'extensions' of R are:

Shiny (to make web apps)
Slidify (to make awesome interactive presentations/slides)
Knitr (for reproducible research and dynamics documents)
rCharts and googleVis (for interactive charts)

To see excellent presentation that uses all the above features click HERE and HERE.

6. LabView

I have very little experience with LabView, but it is a important to know how it works if you plan working with lab equipment or programming your own (e.g. force mats, pressure mats). It is visual programming environment similar to SIMULINK.

7. Knowledge of some Athlete Management Software (AMS) Platforms

It is important to have at least basic experience working with some of the following AMS platforms: SMARTABASE, EDGE10, Apollo, TrainingPeaks.

8. Managing People

This is pet-peeve of mine. As Carl Valle used to say: "It's not what you know it's what you can get your athletes to do".

I have put this last on the list, but it is actually the most important.

Here are some of the titles I found helpful and some that are recommended by smarter people than me. This is by no means extensive list – there are a lot more great resources out there.

Also make sure the check Dan Ariely free course on Coursera on irrational human behavior and two books by Ray McLean from Leading Teams.

 

We X-Rayed Some MLB Baseballs. Here’s What We Found. | FiveThirtyEight

Here's more from the previous blog post to support "The Juiced Ball Theory". For those science-y types who are ten-twenty years late to the party.

from fivethirtyeight.com
https://fivethirtyeight.com/features/juiced-baseballs/

We X-Rayed Some MLB Baseballs. Here's What We Found.By Rob Arthur and Tim Dix Mar. 1, 2018On 6,105 occasions last season, a major leaguer walked to the plate and hammered a baseball over the outfield wall. The 2017 season broke the home run record that was set in 2000 — the peak of the steroid era — when players hit 5,693 homers, and it built upon the remarkable 5,610 that were hit in 2016. It was a stunning display of power that played out in every MLB park almost every night. And with spring training underway in Florida and Arizona, MLB's power surge is showing no sign of letting up.But while we now know what caused the spike in home runs at the turn of the century — even if we didn't at the time — the reason for the most recent flurry of long balls remains an unsolved mystery. Any number of factors might have contributed to the home run surge, including bigger, stronger players or a new emphasis on hitting fly balls. But none of those possibilities looms larger than the ball itself.MLB and its commissioner, Rob Manfred, have repeatedly denied rumors that the ball has been altered in any way — or "juiced" — to generate more homers. But a large and growing body of research shows that, beginning in the middle of the 2015 season, the MLB baseball began to fly further. And new research commissioned by "ESPN Sport Science," a show that breaks down the science of sports,¹ suggests that MLB baseballs used after the 2015 All-Star Game were subtly but consistently different than older baseballs. The research, performed by the Keck School of Medicine at the University of Southern California and Kent State University's Department of Chemistry and Biochemistry, reveals changes in the density and chemical composition of the baseball's core — and provides our first glimpse inside the newer baseballs.Looking inside the balls and testing their chemical composition revealed that the cores of recent balls were somewhat less dense than the cores of balls used before the 2015 All-Star Game. The newer cores weigh about a half a gram less than the older ones, which might be enough to cause baseballs hit on a typical home run trajectory to fly about 6 inches farther. That alone is hardly enough to explain the home run surge of recent seasons, but when combined with previous research finding that baseballs began to change in other small ways starting around the same time, it suggests that a number of minor differences may have combined to contribute to the remarkable upswing in home run power we've witnessed since 2015.Asked about these findings, MLB noted that it had commissioned a group of scientists and statisticians to investigate any changes to the ball, and that the committee would issue a report on its research soon. According to Alan Nathan, one of the physicists on the commission, the task force found that all the characteristics that MLB regularly measures, including the weight, circumference, seam height and bounciness of the ball, were within ranges that meant variations in the baseballs were unlikely to significantly affect home run rates. MLB declined to provide the data supporting these assertions.Independent investigations by FiveThirtyEight, publications like The Ringer, and Nathan himself have shown differences in the characteristics of the ball and the way it performs. Research has shown that balls used in games after the 2015 All-Star Game were bouncier and less air resistant compared with baseballs from the 2014 season, when players hit a relatively modest 4,186 homers, the fewest since 1995. (Nathan noted that MLB does not regularly measure air resistance.) Taken together, these changes would result in a ball that would come off the bat at a higher speed and carry farther. While investigations have been able to show that the baseball behaves differently in recent years, no one had looked inside the ball to see if there was evidence of changes to the way the baseball is constructed.So far, these investigations have primarily looked at the exterior of the baseball.Broadly, MLB baseballs — which are produced by Rawlings in Costa Rica — are made of three components: an exterior shell of cowhide, a winding of several layers of yarn, and a core of rubber-coated cork, also known as a "pill."To analyze possible changes to the inside of the ball, particularly the core, "ESPN Sport Science" purchased one new ball from Rawlings and seven game-used baseballs from eBay, confirming their authenticity through MLB's authenticator program.²The eight baseballs we tested were split into two groups: an "old group" of four balls used in games played between August 2014 and May 2015, and a "new group" of three balls used in games played between August 2016 and July 2017, plus the brand-new ball. The aim was to see if the internal composition of the baseballs had changed in ways that would affect the ball's performance.³The balls were first analyzed by Dr. Meng Law, Dr. Jay Acharya and Darryl Hwang at the Keck School of Medicine at USC using a computerized tomography, or CT, scan. This test is typically used to look inside a human head or body, but in this case, it allowed Dr. Law's team to examine the interior of the baseballs without cracking them open and destroying them.Initial CT imaging showed that baseballs in the same group had a negligible variation in internal properties.When comparing the new and old groups, however, there was a clear difference in the density of the core.In an MLB baseball, the core consists of four parts: a cork pellet at the center, surrounded by a layer of black rubber held together by a rubber ring where the halves meet, all of which is then molded together in a layer of pink rubber.Dr. Law's team isolated the density difference to the outer (pink) layer of the core, which was, on average, about 40 percent less dense in the new group of balls.While other parts of the ball showed slight differences in density and volume, none were as noteworthy as the changes to the core.It's not just that the inside of the ball looks different — the chemical composition of the cores appears to have changed as well. After being tested at the Keck School, the same set of balls were sent to Kent State University. There, researchers at Soumitra Basu's lab in the Chemistry and Biochemistry department cut open the balls to examine the cores using a thermogravimetric analysis (TGA). This test essentially cooks a material to see which parts parts of it vaporize at which temperatures. Using that information, researchers can create a molecular profile of a given material.This test showed that the pink layer of the core in baseballs from the new group was, on average, composed of about 7 percent more polymer than the same area in baseballs from the old group. Additionally, an analysis with a scanning electron microscope showed that the same layer in the new balls contained, on average, 10 percent less silicon, relative to the amount of other ingredients in the pill. According to the Kent State researchers, these chemical changes produced a more porous, less dense layer of rubber — which explains the results seen in the CT scan at the Keck School.It may not seem obvious, but these slight changes in the chemical composition of the core could have an impact on how the balls played once they were sewn up and shipped to major league teams. Less dense cores could mean lighter baseballs. The cores of the new balls weighed, on average, about 0.5 grams less than the cores from the old group. This difference was statistically significant, which means it's highly unlikely that it was due to sampling error. The overall weight of the balls also dropped by an average of about a 0.5 grams between groups, but, unlike with the cores, this difference was not statistically significant.⁴Half a gram isn't much — it's only about the weight of a paperclip. A tiny change like this might add only about 6 inches to flight of a baseball hit on a typical home run trajectory, according to Nathan's calculations. But the timing of these changes to the weight and density of the core coincides with a much larger boost to the bounciness of the baseball. According to a previous analysis performed by The Ringer, that increase in bounciness alone would add around 0.6 mph to the speed of the ball as it leaves the bat and add roughly 3 feet to the travel distance of a fly ball — enough to make the difference between the warning track and the stands.On top of the fact that the balls became bouncier as the core itself changed, previous research at FiveThirtyEight showed that they also became less air resistant. The decrease in drag is probably a result of a smaller, slicker baseball with lower seams. The change in air resistance could add an additional 5 feet to the travel distance of a fly ball. Combine all these factors together — a lighter, more compact baseball with tighter seams and more bounce — and the ball could fly as much as 8.6 feet farther. According to Nathan's calculations, this would lead to a more than 25 percent increase in the number of home runs. Asked whether these changes in combination could have significantly affected the home run rate, MLB declined to comment.In actuality, home runs spiked by about 46 percent between 2014 and 2017, which means that the changes to the ball could account for more than half of the increase. The remainder could be reasonably chalked up to a philosophical shift among MLB hitters, who are likely swinging upward to maximize the number of balls they hit in the air and are not shy about the increase in strikeouts that may come with that approach.MLB Commissioner Rob Manfred has repeatedly denied that the baseball is juiced. On numerous occasions, he has said league testing found that baseballs continue to fall within the range that MLB designates as acceptable, and he recently said that MLB testing showed the balls to be fundamentally the same. But even if the baseballs still meet the league's manufacturing guidelines, their performance could change enough to double (or, theoretically, halve) the number of home runs hit in a year.In fact, in January of 2015, Rawlings filed a patent application for a manufacturing process that would allow it to produce softballs and non-MLB baseballs⁵ that were as bouncy as possible while still falling within the manufacturing specifications set by the league. This type of ball is constructed quite differently from MLB baseballs, so there's no indication that this patent means Rawlings is deliberately manipulating major league baseballs in this way, but it demonstrates that it's at least theoretically possible for balls to be "fundamentally the same" while also performing differently than they have in the past.Kathy Smith-Stephens, senior director of quality and compliance at Rawlings, said that no change had been made to the baseballs but that "natural variation" occurs in the manufacturing process. She noted that they "continuously tweak" — though later in the interview she asked that we say "continuously refine" — the manufacturing process in an effort to reduce variations, but said that Rawlings' internal testing had shown no difference in the ball's weight or bounciness.Evidence that the baseball is at least partially responsible for the last few years' spike in the home run rate mounted throughout the summer of 2017 and reached a peak during October's World Series. In those seven games, the Houston Astros and Los Angeles Dodgers smashed 24 homers, including eight in one game. In the wake of this power display, Manfred asked all 30 teams to start storing baseballs in a climate-controlled room and commissioned a task force of scientists and statisticians to investigate whether the ball was juiced in 2017. Our own research, combined with controlled tests from three separate academic laboratories, strongly suggests that the physical properties of the ball have changed. Taken together, all these studies give us a lot of evidence to suggest that today's baseballs differ in meaningful ways from those of a few years ago. In other words, there are many questions for Manfred's committee to address.Special thanks to Sean O'Rourke, Dr. Cynthia Bir and Nathan Beals for additional research assistance.

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Neuroscience Can Project On-Base Percentages Now | FanGraphs Baseball

Once this type of data can be incorporated into scouting and player development, there will be less draft mistakes and better hitters. 

Subject: Neuroscience Can Project On-Base Percentages Now | FanGraphs Baseball

from fangraphs.com

https://www.fangraphs.com/blogs/neuroscience-can-project-batting-lines-now/

Neuroscience Can Project On-Base Percentages Now

Eno Sarris

I have an early, hazy memory of Benito Santiago explaining to a reporter the approach that had led to his game-winning hit moments earlier. "I see the ball, I hit it hard," said Santiago in his deep accent. From which game, in what year, I can't remember. Also, it isn't really important: it's a line we've heard before. Nevertheless, it contains multitudes.

We know, for example, that major-league hitters have to see well to hit well. Recent research at Duke University has once again made explicit the link between eye sight, motor control, and baseball outcomes. This time, though, they've split out some of the skills involved, and it turns out that Santiago's deceptively simple description involves nuanced levels of neuromotor activity, each predictive of different aspects of a hitter's abilities. Will our developing knowledge about those different skills help us better sort young athletes, or better develop them? That part's to be determined.

A team of researchers spread across Duke ran baseball players from two full professional organizations through a battery of nine tests on Nike Sensory Stations to measure different aspects of a player's sensory motor abilities. After creating something similar to Major League Equivalency lines for each player, the researchers were able to test the effect of each of the scores against real-life baseball outcomes.

"If you have a 23-year-old, completely average outfielder, the model predicts that his on-base percentage in the major leagues would be .292," explains Kyle Burris, one of the researchers on the project. "The model would expect a similar player who scores one standard deviation higher on the perception span task to have an OBP of .300."

The high-level, easy takeaway from their study is that these skills, taken as a whole, are predictive of good plate discipline. There was no link to slugging percentage, though, so we're not quite yet predicting full batting lines from your neuromotor scores.

But if you drill down a bit into these new findings, you'll see that there is a great deal here to get excited about. Here's a profound image that shows how each subsection of the larger skill set was linked to baseball outcomes. Darker colors denote a stronger relationship between the skill and the baseball statistic.

A table of findings reprinted with permission from Kyle Burris, Kelly Vittetoe, Benjamin Ramger, Sunith Suresh, Surya T. Tokdar, Jerome P. Reiter & L. Gregory Appelbaum "Sensorimotor abilities predict on-field performance in professional baseball" in Scientific ReportsTake a look at the row labeled "perception span," in particular, and you find an interesting story. That task was linked to good on-base percentages and strikeout rates, but not necessarily good walk rates.

"It's kind of like a game of Simon," says Burris as he tries to explain the perception-span task, "but for a split second, it gives you shapes that appear in various aspects of your peripheral vision, and you have to determine was there a square there, or a pentagon there, and it flashed at you in a split second and you have to try and remember what the shape was."
When we asked players what they see when the ball is released, a good portion of the responses detailed how little is ultimately visible to the eye. And there's that study of cricket which suggests that cricket players get more from information they gather before the release of the ball than after. This finding fits right in: players who are good at noticing things on the periphery — like the way a forearm might look different on a breaking ball, or the way the body might drag on a changeup — are better at making contact.
Hidden within the other differences between the tasks and their links to outcomes is a similar story: both the ability to suss out quickly the difference between shapes seen both near and far, and also to capture a target quickly were both good for making contact. That makes sense.

But why would hand-eye coordination be better for player's walk rate than his strikeout rate?

Partly, this could be because players have to start their swing before they know if they want to swing — a requirement velocity puts upon them — and hand-eye coordination helps them to better stop that swing if the pitch is a ball.

Partly, this could be a result of the limited capacity for actually testing hand-eye coordination. The particular task linked to that number requires respondents to tap baseballs as they appear on a screen, testing how fast they can do so.
"I'm not sure that it actually goes and tests hand-eye coordination," admitted Burris, who is headed to Cleveland for a summer internship with the Indians. "There is a little bit of hand-eye coordination in that you have to see it and then immediately translate that to a motor response, but I'd say that that was almost response-time-esque."

If you look at the separate reaction-time outcomes, you'll see a similar link to walk rate, so maybe that's the key skill in taking walk. Reacting quicker.
Or there's another way to separate the skills. You could consider the first three tasks — visual clarity, contrast sensitivity, and depth perception — as "hardware." They're linked to outcomes, of course, because there's a decent part of the game that requires good eye sight. But they're the sort of thing with which you're born.

"There will never be a blind ballplayer," said co-author Gregory Appelbaum.
Those other six tasks, though? They represent the software of our neuromotor system. They represent our ability to take the visual information given to us and process it. Software is more malleable, subject to updates. Software can be changed for the better.

"There is evidence that these processes can be improved," agreed Appelbaum. "There have been demonstrations of neuroplasticity in these processes."
Appelbaum pointed to two interesting studies that pointed to the fact that our neuromotor system's software could be trained. A study from 2015 of which he was part showed that "significant learning was observed in tasks with high visuomotor control demands but not in tasks of visual sensitivity," for one.
A 2014 study at the University of California-Riverside found that actual baseball outcomes could be improved by using a "perceptual learning program." In that study, players reported improvements such as being able to see further, and having eyes that felt stronger and didn't tire as quickly.
Appelbaum is ready to find out what these visual training technologies will look like as we go forward. He's helping launch the Duke Vision Sports Center, a clinic and lab where researchers will use sensory stations, immersive reality, and more, in order to pursue this line of thinking.

When it comes to new stats coming out of Statcast, I've personally seen a change in how players assess the numbers. Early distaste has given away to curiosity, as more players — Yonder Alonso and Andrew Heaney, for example, in my own experience — now speak up at the end of interviews to ask me about launch angle, exit velocity, and how they can use that data to train and improve.

So, while the Boston Red Sox have long been using the link between neuromotor skills and baseball outcomes in their minor leagues in an effort to bring "neuroscouting" to their own organization, these new findings offer a different use for neuromotor study. Instead of sorting players, there's major potential to use these activities to develop players and get the most out of them.
There may never be a blind baseball player, sure. But that's just hardware. Let's see how we can make the most out of our favorite player's software.

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The Sixth Tool: Training Baseball Pitch Recognition - MIT Sloan Analytics Conference

 

An objective tool to measure pitch recognition skill before the fact is HUGE in terms of both scouting and developing players. When the intangible becomes tangible, that's a huge leap, IMO. 

Subject: The Sixth Tool: Training Baseball Pitch Recognition - MIT Sloan Analytics Conference

http://www.sloansportsconference.com/content/the-sixth-tool-training-baseball-pitch-recognition/

The skill of pitch recognition, measured with metrics such as 0-Swing%, Line Drive %, and BB/K ratio, is so important that it's become a virtual sixth tool in baseball. But it's generally considered to be more a talent than a learned skill … and therefore not trainable. This talk describes and demonstrates a new approach that adapts the video-occlusion method developed in sport science research laboratories in order to both test and train this elusive perceptual-cognitive skill. Running on a laptop computer, video-occlusion can be used to measure the pitch recognition ability of prospects, diagnose pitch recognition problems, and systematically develop pitch recognition skill in high-performance baseball batters.

To see presentation slides click here

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Hidden Gold on the Diamond? The Contribution of the Relative Age Effect to Talent Estimation Errors of High School Players in the June MLB Draft Robert Brustad | 2016 SABR Analytics Conference presentations

2016 SABR Analytics Conference presentations | Society for American Baseball Research:

3:30-4:30 p.m., Friday, March 11Presented by Baseball Info Solutions, RP5 and RP6 took place back-to-back in a single session.

RP5: Hidden Gold on the Diamond? The Contribution of the Relative Age Effect to Talent Estimation Errors of High School Players in the June MLB Draft
Robert Brustad

• Audio: Click here to listen to Robert Brustad's presentation (MP3; 34:05) 
• Slides: Click here to view slides from Robert Brustad's presentation (PPT)

Identifying and projecting talent of high school players is complicated by meaningful age differences that exist among players within the same draft class. The “relative age effect” (RAE) refers to the tendency for older athletes within any competitive age cohort to appear more “talented” than younger athletes when current performance differences can be partially attributed to additional maturation and accumulated practice favoring older players. This study examined relative age influences on the probability of draft selection and eventual return value of high school players with the assumptions that relatively older players a) are more likely to be selected but b) relatively younger players have a higher ceiling and provide greater potential return. All high school draft selections in the first twenty rounds of the 2005 through 2012 MLB June drafts were included. The first analysis included those players born within the 12-month age range corresponding with the age of “typical” high school senior year players. Chi-square frequency analysis revealed a significant difference that favored the selection of older players. Return value was then assessed through subsequent accumulated MLB WAR values of three age-on-draft day groups. Significant differences existed in return value favoring the youngest group of players (m draft day age = 17.97 yrs.) who outperformed the average (m age = 18.40 yrs.) and oldest players (m age = 18.79 yrs.) by 2.36 and 2.44 times, respectively. This advantage was also present for seven of the eight individual drafts. Maturational considerations involved in projecting talent will be further discussed.

Robert Brustad is Professor in the School of Sport and Exercise Science at the University of Northern Colorado and former Editor of the Journal of Sport & Exercise Psychology. His focus is on the design of talent identification and talent development systems in sport with primary interest devoted to the role of physical and psychological maturation and development on sport performance. He has consulted with various professional sport organizations and the United States Olympic Committee.

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