Player Development Theory: Analytics vs. Competition

I recently sat down with a few of my college teammates to watch a movie. As most college nights go with nerds who also happen to be baseball players, we began discussing coaching theory and inevitably the data revolution that has changed baseball dramatically over the past couple of years. I began to realize that there seems to be a disconnect between “data” and “competing”. Many questions were raised, but the majority of the questions always landed on the same underlying concern: Just because the data says it’s good, doesn’t mean the player can do it in a game. I challenged myself to deep dive into ways that we can connect data to in-game competition.

I’ve been around many coaches and I’ve always had pride in being a player who buys in to a process and believes in what the mission of a team is in terms of practice process and team chemistry growth. I also have been unapologetically inquisitive and curious about the reasonings behind certain coaching decisions. I’ve played alongside a ton of people who are much better than me, and because of that I have always been intrigued by how players develop and how they can become better using tools that are provided. As I sat with three of my teammates, I began to realize that we can connect data and competitive nature in a training setting that will translate to a game. There IS a way to use the information, but it’s not as simple as “know what your Vertical Break is on a 4 seam and go throw the shit out of it on the mound.” When we view the development of players, we often view certain metrics that are easily comprehended but are very often results oriented. Common examples are Earned Run Average or Batting Average. At our core, we really believe these items are indicative of a talented baseball player who is performing well. We also tend to see basic statistics as the end result of hard work and a process. One of my teammates, who I should emphasize is one of our better pitchers and also an awesome teammate, argued from the perspective that a coaching staff can’t analyze player data and expect it to help, because at the end of the day a player must have a little bit of “f!@# you” in them in order to have success on the baseball diamond. I whole-heartedly agree, but I think he leaves out an important key in player development that often gets lost in metrical data. One statement that has been thrown in my direction and resonates with me is the cue “arm with information”. I believe that if players are given the opportunity to buy into metrics and growth without blindly chasing a number on the radar gun, they give themselves the ultimate chance to be great.

So how do we handle the guy who says, “I don’t want to know my data”? The answer in my eyes is simple, we don’t force them to understand their data, but we tell them to look at guys like DeGrom, Bauer, Scherzer, etc. who consistently view their own pitching repertoire or abilities through an analytical lens. Almost all, if not every elite big-league pitcher is completely aware of their own stuff and how it can improve: in short, elite players are willing to diverge from the path of “I’m great and I’m good because I throw gas (etc.) and I just get people out” to “I have the ability to compete at the highest level but in order to be the best in the world I cannot be comfortable with my stuff as it is.” We’re viewing a data revolution, and it’s not data thrown in someone’s face that makes the difference, it’s the interpretation and the implementation of data into every day baseball. I was lucky enough once to listen to Andy McKay from the Seattle Mariners org talk once about what his job looks like. He said one thing in particular that stuck out to me: every pitcher at the pro level is good enough to throw a ball to a spot, but data tells them if that pitch was thrown with 100% conviction. The days of a single radar gun telling us everything we can know about a pitcher are long gone, and those pitchers that light up a gun AND convict pitches that meet their highest metrical capabilities are the pitchers that will a.) last and b.) get better over time. So how do we gain buy in from these players? We show them examples of players similar to them that meet the same pitch by pitch standards and create a general understanding that if a pitch is thrown at its full capability, we will receive results similar on the pitch (disclosure: THIS DOES NOT MEAN WE COOKIE CUT OUR PLAYERS).

I think often about the velocity vs. command debate that baseball dads and Eric Sim love on Twitter. I believe the issue here isn’t velo vs. command; the issue is that we aren’t attacking both with full conviction. The nature of the beast is that the harder you throw a baseball, the harder it is to hit, but if it’s a ball it’s a ball at 95 mph or 83 mph. My answer to this dilemma is simple: be aggressive in our pursuit of useful data. When I began delving into the data on my own pitches, I threw a changeup perhaps once a game. I wanted a big loopy curveball to strike people out. I sucked, and nobody swung and missed. I dove into what the metrics on a changeup might look like, and began to recognize that I could meet the spin direction, efficiency, and movement patterns of elite changeups. The more I began to understand that spinning my changeup as close to 10:00 on the axis as I could gave me the ability to really gain depth, I noticed that I found myself spotting the pitch up much more frequently than ever before. Why? I had an epiphany when I realized that the exact specifications of a pitch gave me the ability to repeat the same mechanical process over and over until I gained the results I so badly craved: 9:45 on the spin axis, 100% efficiency, 2000 RPM’s. My end goal wasn’t to just throw a nasty changeup, it was to land it for a strike and get swing and miss on all of my pitches. I got to wondering if others would see it in the same light that I saw it: using data on my pitches gave me the challenge of repeating mechanics until I had complete mastery of the shape and location of a pitch. Why would we NOT apply this concept to any pitcher that is trying to hone his skills? Maybe there’s more to baseball training and data use than telling a pitcher to throw a pitch that is a strike. What if we challenged a pitcher to throw a fastball as hard as he could while maintaining the shape of the pitch that we know will lead to success? I believe whole-heartedly that arming pitchers with information solves the velocity crisis that many outdated baseball programs are afraid to use. An adaptation that combines feel, effort, and mechanics all can be derived from a super basic understanding of Rapsodo or Trackman data.

The recognition that data must be used in the development of players on any level has no negative side effects. We should be challenging players, because we know that as humans we become content with success. It’s basic human nature to avoid stress and growth when one thing has been working for an extended amount of time, but what if we began to question if there is more to being good than just being above average? I’ve mentioned earlier the likenesses of the DeGrom’s of the world who somehow manage to come back each time they throw as a better pitcher, but I don’t think there’s anything stopping any one player from using these same adaptation patterns in their own game. DeGrom didn’t wake up one morning and decide that he’d throw a 100 mph heater up in the zone. He decided that adding velocity and command up in the zone would play better and more effectively. These types of thoughts begin and end with data interpretation, and every pitcher has the ability to gain or lose strengths based solely on their willingness to use data to his own advantage.

Psychology and Ethics in Leadership major, Data Analytics minor at Hendrix College. Pursuing a career in Player Development