office managers are the only ones that rely on big data and analytics. The fans of the game
also use big data and analytics at their pleasure. The biggest example
will be the fans that participate in fantasy baseball. Each player is run
through big data and analytics to show their projected stats. This is what the fans
use to decide who to play and draft on their fantasy teams. Some say that MLB is
losing their younger crowds due to the fact the games are usually three hours
help fill the dull moments they show interest facts, and stats to entertain the
example, as the game goes on they may show a stat saying that in the last
twelve games Josh Harrison has batting .425 against left handers. A statistic that may
take a person awhile to calculate, is available to the announcer at the push of
big data and analytics to cover a large amount of statistics has given a chance
for a different statistic every pitch to keep the fans engaged in many ways.
thinking about any professional sport, especially baseball, which is America’s
sport, there must be money tied to it.
in every professional sport, especially baseball, money is a very big aspect
when it comes to size of a team. The size of a team like (New York Yankees)
that’s a large team and the (Oakland Athletics) that is a small team, their
organization in a market can make decent/corrupt decisions based on their
economic status (Academy,
For example, with small market
organization teams that don’t have money, they should spend it wisely unless
they want a better outcome for their team; whereas, a larger market
organization team doesn’t have to spend their money wisely due to the fact its
expendable (Lewis, 2013).
The process for spending money
during a Major League Baseball player draft, which occurs around June each
year. Within the draft, it has fifty rounds of selections which all thirty
teams eventually pick a player that is most valuable for their team and the
process goes on. When deciding on a player to be picking to be drafted, it is recommended
that the team manager, scouts manger, and a professional mentor for the team to
be there for the reason. Looking at players for draft day it known that if the higher
the draftee that more valuable that player will be for that team. According to Lewis
(2013) it is also a procedure to know when to pick a player early or wait for a
different round. In the selection process of the draft there are two main theories
Lewis (2013) narrow for the teams to make it and easier process and selection.
The main theory is
by and large considered the “old” scouting theory. Scouts wander out
and assess players everywhere throughout the nation. They do no give careful
consideration to insights, but instead construct choices in light of the five strengths:
speed, speed, arm quality, hitting capacity and mental strength (Lewis, 2003). Each
scout has/had experienced “scout school” and is given a flyer on what
ought to be searched for in specific parts of baseball, for example, arm
quality, handling, running, and the most essential hitting. For arm, quality
assessment, scouts are told to search for players showing a “liquid arm
activity and simple discharge” (Major League Baseball, 2001). Besides, arm quality assessment is led with the help of a
radar gun. In the taking care of arrangement, a player with a solid arm and
protective aptitudes can and do convey a player to the major leagues.
second theory is based on the Oakland A’s manager. Billy
Beane and was mentioned in a novel that illustrated by Michael Lewis entitled “Moneyball”.
Additionally, Beane had faith in the thought to choose college players who are skilled
on a higher level (college) in comparison to the secondary school
“phenom” who needs to be molded into a skilled player. Beane’s
speculation was made in perspective of made by a sabermetrician named Bill
James. “Sabermetrics is the numerical and measurable investigation of
baseball records” (Academy, 2015). James invested years endeavoring to
unravel numbers by means of the Bill James Baseball Abstract, which thus,
brought about a particular reasoning on hitters.
The Moneyball theory puts no notice on the body of the
competitor or the physical strategies that the competitor have (Lewis, 2013).
This theory represents the straightforwardness of baseball by making two
inquiries: Does this player get on base? plus, Can he hit? As indicated by
Lewis (2003), Billy Beane (inspiration of Moneyball) chose to base his drafting
of position players/hitters on specific measurements. The two measurements that Billy came up with was
to, include an on-base percentage (OBP) and slugging rate (Academy, 2015).
These two measurements he came up with is consolidated to frame another
measurement approached base on an addition on-base slugging (OPS). Another angle
that Beane’s approach was the absence of devotion on control (Lewis, 2013). In
this manner, Beane trusted that power could be created, yet persistence at the
plate and the thrive to get on base proved unable.
A topic that was argued in the movie is
if big data analytics would even work. Especially if even seemed ethical. Investing millions of
dollars in someone that you only know behind a computer screen is a slap in the
face to the hard work the scouts dedicate their lives to each season. Scouts base their
lifestyle on the road following players around the country to provide the best
team they can. Is
watching a player and knowing them face to face a thing of the past? Although
analytics have been able to spot forgotten key components. For example, a number
one draft pick or a wanted player may be someone flashy, with a lot of eyes on
analytics can find valuable players under the radar that can save the team
money and still produce wins.
Another impact that big data and analytics plays in
baseball is the way it affects the team economically. Baseball teams in the
MLB pay their players ungodly amounts of money sometimes. A perfect example is
Clayton Kershaw. In 2014 he signed a seven-year contract for $215,000,000. That is what they
believe he is worth to their program (Badenhausen, 2016). Analytics plays a big part in running a player through his statistics
and projections to evaluate if their superstar will be worth a long term
the other hand, if teams use big data analytics such as Billy Beane did, a team
is able to afford productive players at a lower cost. Some teams in the MLB
have more money to spend on players such as the Yankees, who have repeatedly
been a powerhouse due to the money they can spend. Teams with less money
are able to find valuable players than can afford and get them wins.