If you ask him how many MLB players he’s worked with, he’ll simply grin. A few have given testimonials to boost his website, a few have talked on his podcast, but most stay confidential. They have to. Their careers depend on it.
Kevin Wilson is known around the baseball world as one of the top hitting consultants in the country. He played professionally for seven seasons, bouncing between minor league affiliate to independent ball leagues. He’s won a gold medal in the IBAF World Cup in Taichung, Taiwan with Team USA in 2013. As noted in the Athletic, Clint Hurdle invited him to Pirates’ 2019 Spring Training. He works with college and professional teams, has access to almost every MLB clubhouse on the east coast, and could fill a dinner party with some of the biggest names in the game today.
I asked him what he does and said he brings clarity to hitters who are drowning in information. His voice is calm, soothing. His smile wraps around the sentence making it impossible for me not to do the same. Last summer he spent eight weekends straight at an airport. And so far in 2019, he’s never been busier.
The moment a dollar became attached to a baseball swing, there’s been a hitting coach huddled over a stat sheet trying to gain an advantage the naked-eye missed. This is not new. In 1858, a new “box score” was developed to gain insight on individual performances beyond the line score. Later came Allen Roth and the invention of the slugging percentage and on-base percentage for deeper offensive attribution. Then came Bill James, SABR, and advanced metrics continuing to give that hitting coach hunched over his desk with a pot of coffee brewing into the small hours of the night a chance to gain an advantage.
The idea of using data to influence a game or an offense has never changed, and yet, our obsession today with swing/hitting analytics has never been greater. Perhaps the problem isn’t data, but our relationship with it. Perhaps this is why it took me three weeks to schedule an interview with Kevin Wilson between flights.
“To me the role of a hitting coach is a person that could slide up next to somebody and get to know them first as a person,” Kevin explains the role of the hitting coach today, “the role should first be: mentor, father-figure, psychologist, swing-mechanic, list all of the above. In 2019, there are a lot of hitting coaches that are ‘swing’ coaches, coaches that lack communication skills, that don’t know how to express what they see or discover without data-dives.”
Kevin’s methods might seem unconventional in 2019. He goes for walks with players. He wants to hear about their kid’s soccer games. He asks about last time their mom came to the park. In our interview, he never once talks about ‘data ruining the game’ or argues its lack of importance. He studies the player, intrinsically, then analytically.
“We forget that sometimes, yes, there are mechanical issues. Hitting coaches need to understand how the player’s body moves first, then understand where they are mentally. Most of the time, you fix those two things, the mechanics will fall into place.”
It sounds too simple. According to Kevin, “Hitting is simple. It’s just not that easy.”
But in our case—in 2019—it’s not that simple. It’s actually a paradox.
In 2002, D. D. Woods, E. S. Patterson, E. M. Roth studied this relationship between data and our cognitive system. In this study, they found with each round of technological advances we have tremendously increased our ability to collect, transmit, and transform access to new data. Yet, our ability to interpret and extract meaning from artificial fields of data has expanded much more slowly, they argue, if at all. (Cognition, Technology & Work, 2002, Volume 4, Number 1, Page 2, D. D. Woods, E. S. Patterson, E. M. Roth)
Baseball, more specifically, hitting has gone through its latest round of advancement. With the social media boom acting as jet fuel, there’s never been a time in history where information was as readily available. But even Joe Madden, one of the earliest adaptors to the newest round of analytics, said this to the Athletic:
We’ve never had more transparency, access, and advancement in offensive analytics, and yet, hitters have never been more confused. This is what D. D. Woods, E. S. Patterson, E. M. Roth refer to as the data availability paradox. It’s paradoxical because every organization today recognizes access to deeper analytics is a benefit while that exact waterfall of data simultaneously dramatically challenges a player’s ability to make sense of it (D. D. Woods, E. S. Patterson, E. M. Roth, Page 4).
Our gut reaction to this is what spurs the Twitter blowouts about Old School vs. New School: the solution’s easy—stop using analytics. Reduce the available data and just play. But this approach mischaracterizes the importance of data and fails to bring necessary context when needed. In other terms: the information you’re willingly throwing away today could be the information the hitter needs tomorrow.
The other approach, which is just as ineffective, is to focus specifically on one subset of data without understanding the full context (D. D. Woods, E. S. Patterson, E. M. Roth, 24). This is the effect of breaking down swing mechanics, swing analytics, and swing optimization rather than understanding who the hitter is what they can actually provide during competition.
“There’s a lot of swing coaches out there,” Kevin Wilson explains, “but not many hitting coaches.”
It’s not our fault, though. There’s something magical about hitting a baseball. Something pure about hearing a wooden stick connecting with cowhide. A mystic that leaves us in awe of a towering home run, and starry-eyed each time a water-cooler is dumped over the head of the night’s walk-off hero. The sound ties memories to our hearts. There’s just something special about Mike Trout at the plate. We know it is, and yet we may not ever know why. It’s beautiful as it is elusive.
Hitting a baseball mystifies and cradles our hearts.
Yet, hitting a baseball is the only part of a sport that can drive our brains insane.
American Football: A game based on our primal instinct to acquire territory and destroy the enemy? American brain: give me more.
Basketball: Teamwork, speed, agility, precision. I live for it.
Soccer: Community, passion, chanting, singing, unity. Yes.
Even pitching satisfies our brain’s need for rationality: Throw harder; get more opportunity. Increase body input; get more output. Perfect.
But a hitter explaining to the press his three-home run night was a result of “feeling good at the plate” or even “felt nothing” can send hot coals down the spine of a right-brainer. Mike Trout explaining how he tries to “swing down on the ball”? He’s wrong. He must be wrong. Data shows us his launch angle was… **Twitter implodes**
So, we dive first into a data waterfall of algorithmic theory because it’s the only clue toward the optimum path to success. We deconstruct. We build correlations. We define Mike Trout. We become gurus.
It makes sense. It’s math.
And yet, the Phillies—arguably the team who dove the deepest into algorithmic hitting in 2019—fired their guru?
Information has given our brain a roadmap to success, and yet that roadmap is continually derailing our cognitive ability to actually get there. It’s a paradox that can freeze even the best players.
Let’s take a look at an example:
“The whole place just lit up. I mean, all the [alarm] lights came on. So instead of being able to tell you what went wrong, the lights were absolutely no help at all.”
Comment by one space controller in mission control after the Apollo 12 spacecraft was struck by lightning (Murray and Cox, 1989)
This is data overload. All of the data was available and properly functioning. However, the people executing on the data could no longer cognitively interpret importance. It’s like your email getting instantly hit with 10,000 emails at once.
This is our problem. We are sending 10,000 emails at once to all of our hitters and expecting them to respond to each one while simultaneously performing their job at maximum capacity.
So, how do we fix it?
The solution is based on what D. D. Woods, E. S. Patterson, E. M. Roth refer to as “context sensitivity” (Cognition, Technology & Work, 2002, Volume 4, Number 1, 14)
In response to similar issues of the Apollo example, computer-based solutions started to implement systems that would prioritize alarms. The information was given in context. It was presented to human beings in a format and timeliness that made sense to their particular situation. As D. D. Woods, E. S. Patterson, E. M. Roth said:
“There is a widespread myth that information is something in the world that does not depend on the point of view of the observers and that is “or is often” independent of the context in which it occurs. This is simply not the case. There are no facts of fixed significance. The available data are raw materials. A particular datum gains significance or meaning only from its relationship to the context in which it occurs or could occur including the perspective of the observers. As a result, informativeness is not a property of the data field alone, but is a relationship between the observers and the data field.” (D. D. Woods, E. S. Patterson, E. M. Roth, 15)
It’s a relationship between the data you provide and the way hitters can utilize it.
And the system in place between? A translator: a hitting coach.
Baseball today is not going through a data revolution—data has been developing and progressing just as the baseball has evolved from the Lemon Ball to our Super Ball—it has merely experienced its latest round of progression. But it is the first time where we believe the generations before us are not just obsolete, but incorrect, and only data can ‘fix’ baseball. Baseball’s post-modernism has placed theory above reality, and wisdom, experience, and feel have slowly crept into the Algorithm’s crosshairs while we pull the trigger.
Allan Roth may have said it best:
“I know perfectly well that baseball cannot be played one hundred percent according to figures, and that the human element is even more important. I realize that certain sets of figures on players and teams will change from time to time, but nevertheless, by a deep and systematic research into the detailed statistics which I have in mind, there is bound to come to light numerous facts which were previously unknown, and which would prove of great value.”
But for some reason, this has been lost. Systematic research can provide great value, only to the context by which baseball can be played. Perhaps this is why Kevin Wilson spends most of his summers in airports and walking with players.
A hitting coach is not a statistician. They are not just a swing coach. They’re also not a person who rejects data. They are the translation between raw data and human performance. Or as Kevin Wilson told us, “I need to know the person first, then meet them where they are.”
Understand the data. Provide context. Optimize for the player.
Some teams are starting to realize ‘the translator’ is more valuable than the ‘guru.’ Some are not. The Phillies just hired theirs. The Cardinals just fired theirs.
The pendulum is swinging back towards player optics even if it conflicts with what’s simulated in a vacuum. Unfortunately for our right brains, data within the game can only live behind a human touch and a human’s ability to translate that context to other humans on the field.
I asked Kevin what his final goals are. He said he hopes to help teams and players to the point where they don’t need him anymore. He can help organizations utilize R&D [Research & Development] sustainably while giving players a greater filter to be their own translator. Then he can retire and play golf.
Kevin is booked solid until October. College programs are already asking for November availability. Retirement may be a few decades away.
Cognition, Technology & Work, 2002, Volume 4, Number 1
D. D. Woods, E. S. Patterson, E. M. Roth
Roth to MacPhail, August 4, 1941, Roth papers.