Friday, April 5, 2019

Analytics overkill is not a good thing

The return of Major League Baseball season means the same
thing to many people.


You hear terms like “the crack of the bat,” “roar of the crowd,”
“and it’s 1-2-3 strikes you’re out.” You also hear references to
hot dogs, peanuts, cracker jacks and for the adults, fermented
drinks that involve barley and hops. I could think of many other
colloquialisms but in the interest of space, I won’t.


Like any San Francisco Giants fan, I’m bracing for a rough
couple of years. Saying that is not meant to be a Debbie Downer
but I choose to deal in reality. Between what most people
consider “The Big Three” (football, basketball, baseball), football
is my favorite but I enjoy baseball and basketball for different
reasons. However, in my preference, I would pick baseball.


I have long enjoyed many things about baseball from in-game
strategy to gauging hitters’ counts versus pitchers’ counts and
many more. Lately, however, there has been a fly in the soup that
sometimes saps my enjoyment. It does not curtail my enjoyment
to the point where I will not watch or follow it but as a middle-aged
46-year man brings out the triggered old soul.


So what makes me triggered? Analytics overkill. To be fair, analytics
is not limited to baseball because football and basketball have found
plenty of ways to use them.


So what exactly are analytics? They are a collection of statistics that
when properly applied can provide a competitive advantage to a
team or individual. Through the collection and analyzation of these
data, sports analytics inform players, coaches and other staff in
order to facilitate decision making both during and prior to sporting
events. The term “sports analytics” was popularized in mainstream
sports culture following the release of the 2011 film, Moneyball, in
which Oakland Athletics General Manager Billy Beane (played by
Brad Pitt) relies heavily on the use of analytics to build a competitive
team on a minimal budget.
There are two key aspects of sports analytics - on-field and off-field
analytics. On-field analytics deals with improving the on-field
performance of teams and players. It digs deep into aspects such
as game tactics and player fitness. Off-field analytics deals with
the business side of sports. Off-field analytics focuses on helping a
sport organisation or body surface patterns and insights through
data that would help increase ticket and merchandise sales,
improve fan engagement, etc. Off-field analytics essentially uses
data to help rights holders take better decisions that would lead to
higher growth and increased profitability.
The emphasis on analytics has drawn a vast reaction in that there
are those who “swear by them” and those who “swear at them.”
The ones in the latter category refer to it as the “nerdification
of sports.”

If you think about, analytics have always been part of sports, they
just were not being called such. As long as I can remember,
baseball coaches have kept spray charts, calculated batting
averages versus right-handed and left-handed pitchers to name
a few. Football coaches have been keeping track of play charts
like rushing yardage to the left, middle and right. Basketball
coaches have long kept shot charts.

The problem is not necessarily the statistics themselves, it is the
overuse of the ones that either a) Have little to no relevance and
b) Are ones that the common man has no capacity to understand
because the people that come up with them have probably never
picked up a bat or glove in their life. Being good with numbers
might make you a good accountant but it does not make you an
expert on a sport.

I watch a baseball now and they show the launch angle and exit
velocity, how fast the outfielder is running to the ball and how fast
the base-runners are maneuvering in miles per hour. That’s a
little over the top for my taste not to mention the extraneous stats
that look more like calculus.

If I am a baseball coach, sure, I want to know if No. 2 hitter is
2-for-25 lifetime against my starting pitcher while the No. 3 hitter
is 10-for-25. Some information can be useful but too much is not.

Nowadays, it’s not uncommon for a team to take computer data and
fill out the day’s starting lineup and hand it to the manager as
opposed to the manager creating the lineup card. I’m not saying the
ones that come up with the analytics are not smart but the numbers
don’t translate to having a feel for the game.

Translation, more information does not mean better.


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