Reese Kaplan -- Using Those Math Class Skills After All

Let's try an experiment right now thinking about the process of running a baseball organization rather than the actual men who take the field and play the game.  

It's much easier for most of us to envision what a rotation containing Justin Verlander or Jacob deGrom would be like, or how Brandon Nimmo or his unnamed replacement would better help win ballgames.  What's much less analyzed and evaluated are the duties of the business folks who prepare the club for its upcoming roster construction.

We've all grown weary of the words "analytics", "metrics" and "benchmarks."  We all don't generate nearly as much interest hearing the Mets have expanded their numerical analysis staff as we would about the up and coming prospect who just bashed his way into the record books.  

The thing is that for all that scouts and other baseball professionals do in evaluating both younger talent and established veterans, there always was a whole lot of luck and non-scientific variables involved in how successful or not that person will be.

Not too many of us think about the math that's overtaken the game.  Yes, we hear terms that weren't in the baseball lexicon of our youth like spin rate, launch angle, WHIP, OPS, WAR and batted ball exit velocity.  

As Little Leaguers we were more concerned with taking pitches that weren't in the strike zone, hitting the ball "where they ain't" fooling the baserunners with our defensive acting skills and making the thrown baseball accurate regardless of whether it is from a fielder or a pitcher.  

When I was a child I envisioned some of my own metrics for the game that caused my fellow schoolchildren to laugh at my pseudo intellect and my obsession with the game itself.  

I remember arguing vividly about the level of pitcher Tom Seaver was by factoring in things that others had never before considered such as the number of runs scored when he pitched, the percentage of games his team won with and without him on the mound, the ballpark playing characteristics that made it easier to pitch in New York than it was in more extreme architecture like Fenway or Wrigley, and how all of these variables could be put into new stats that could help ascertain really who was the best of the best.  

Fast forward many years to the Oakland A's and how in the silly dramatization of the pivotal book Moneyball in which performance analytics were introduced to folks outside of the Major Leagues for likely the very first time.  

If you didn't read the book but did partake in the film, remember when Jonah Hill as Peter Brand teamed up with Brad Pitt as Billy Beane to discuss things like OBP as pivotal to creating the opportunity for runs to score.  

Up until then it was more looking at pure statistical totals for things like RBIs rather than how the batters actually got the opportunity to drive runs home.  

Now fast forward 20 years past that semi-fictitious analysis of the 2002 season (and please forget the Art Howe managerial stint that was later inflicted upon the Mets).  Instead, think about the lesser headlines about how the Mets are actively looking to improve the depth and breadth of their analytical personnel to help the club make more well informed decisions on personnel who will help the team win more frequently.  

While this analysis is certainly a valid and intellectually relevant way of contributing to the team's record in the standings, it's never 100% perfect.  Obviously there were hidden gem numbers behind the acquisitions for a reasonable price of Daniel Vogelbach and for an unreasonable one to bring in Darrin Ruf.  On paper they may have seemed like an aggregate platoon monster designated hitter, but the reality didn't work out as envisioned.  

So instead of abandoning this approach due to unforeseen performance problems, the front office doubled down to make this team and its ability to apply analytical theory greater and more proficient.  

Now no one came here to read about the factorials, standard deviations nor trends found by doing this seemingly dry poring over of the various available data, yet how do any other types of large businesses succeed in what they do when it takes more than low prices, convenient packaging or a clever TV commercial to entice customers to part with their money?  

The whole concept of artificial intelligence is often rooted in making decisions based upon mathematically valid equations rather than relying solely on a gut feeling for doing something.  

So, going into the next round of winter meetings, this exercise in baseball as a business is not just about figuring out who will sell more jerseys if signed, what players to trade who don't meet the new fangled performance metrics, and how to best utilize the coaching staff to help not just today but also for the future.  

It's less about finding the next Mike Trout as it is the supporting pieces around him that need to make it possible for the superstar to succeed.  

Notice that there are players in the Mets history who struggled in year one in uniform like Francisco Lindor, Carlos Beltran and others, but their talent helped them to adapt and persevere.  

The behind-the-scenes gurus are the ones trying to make that effort more plausible and faster by figuring out what it is that's needed for success on the field.  And that is going to take a lot more effort that choosing between an oft-injured player's demand for a multi-million dollar long term deal.