In path to follow to be successful. Using algorithms

            In
conclusion, big data is data collected over a large group of people. Analytics
is the ability to calculate, analyze, process, and evaluate that data.
So big data and analytics go hand in
hand. They
also go along with America’s pastime, baseball. Analytics have had such a huge
impact on the steps taken to add players to their team. As well as save them money, make
money, and field the best team to win a championship. Also, big data and analytics has an
impact on the fans. With
the amount of information available on the internet powering IS, each pitch is
full of information and statistics of that player. Though, teams can make and save
money by using analytical programs to know each player’s worth for better or
worse. Lastly,
this technology has given baseball teams a path to follow to be successful.
Using algorithms and projections to
win the game inside the game, the game of numbers. So, maybe players need to start
packing their calculators along with their baseball gear.

            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
contract. On
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.  

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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
him. Although
analytics can find valuable players under the radar that can save the team
money and still produce wins.

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.

The
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 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.

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.

When
thinking about any professional sport, especially baseball, which is America’s
sport, there must be money tied to it.
Virtually
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,
2015).
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).

Front
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
long.
To
help fill the dull moments they show interest facts, and stats to entertain the
audience.
For
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
a button.
Using
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.

This
does play a factor, but it is hard to argue the fact that analytics have indeed
increased the productivity of the game. The players that perform the best are
the players that the teams bring to compete, therefore creating a game full of
superstar athletes for fans to enjoy. This could be the reason baseball players
refer the big leagues as, the “Show”.

“Tigers head coach Steve Bieser was introduced to Dr.
Peter Fadde’s product during his tenure at Southeast Missouri State University
by hitting coach Dillon Lawson. The pair had embraced a “Moneyball”
mentality in other ways — using sabermetric measures like runs created and
weighted on-base average to build lineups — and their investment in
plate-approach paid off. Lawson was even hired away to serve as minor-league
hitting coach for the Houston Astros, who, for better or worse, might be the
most analytical organization in all of sports. The pair reunited prior to the
2017 season at Missouri, where they’re enjoying greater resources than they had
at Southeast Missouri State — and, in Lawson’s case, greater buy-in than he
experienced in the pros. “There’s fewer people at the college level to
convince that the numbers have value,” he said. “Regardless of what
organization you’re in and how data-driven they are, there’s still plenty of
people who are within that organization who aren’t completely sold on it.”The
numbers’ value is evident in the Tigers’ results. Missouri won 36 games in its
first year under Bieser and Lawson, the most for the program since 2008. Their
offensive gains were impressive and widespread. Compared to 2016, the Tigers
scored an additional run per game and upped their collective batting average
(15 points), on-base percentage (17 points), and slugging percentage (46
points), according to data from The Baseball Cube” (R.J. Anderson, 2017).

Although
this is pretty successful some believe it deviates from the game. For example, in the
movie “Trouble with the Curve” an old time scout does not believe that
analytics covers all aspects of a good ballplayer. He prefers to watch the
players in person rather than behind a screen. Over the years he has
been able to tell a talented hitter by the sound of the ball off the bat. He notices a hitch in
his swing that his statistics do not show on paper. In the end a number one
draft pick is a bust, because his hitch in his swing gives him trouble with the
curve. Below
is head coach Steve Bieser having conversation about analytics and how it’s
used to find players with in baseball.

On
the other hand, even with the extreme advantage big data analytics has provided
to baseball, some find it to over the top for the beloved game of baseball. For example, analytics
has become the standard when scouting college and phenomenal high school
players. Baseball
has been around for 171 years, over that time the game has changed in a couple
ways (Helyar,
2011, p. 1-10). The
game still consists of a ball, bat, and glove. Although the way teams
find players and ways to provide the best baseball players the world has to
offer has made leaps and bounds. One way to find the best player for your team
is called scouting. Scouting
is a player consisted to going to their games following them, studying them,
and really understanding what that player is about inside and out. After all, a team is a
band of brothers, and a team is called family and that family would like to
know who is joining into their family for the long run. Now, a MLB team can
look up a player’s statistics, run their numbers through a program, and see if
he is projected or ready to join their team.

 Offensively, to be successful, they calculated
to execute greater than seventy-five percent. Also, strike out less
than ten percent. A
key component is to score three or more runs per inning, record four or more
base hits, score seven or more runs, and steal three or more bases. Big data and analytics
have proven and projected if a team can play the game within the game at eighty
percent or better, the chances of victory are a given. This just goes to show
analytics can detect, and predict what the naked eye might be able to dissect.

Big
data is the collection of data over a vast amount of people. There give or take a
thousand rostered players in the MLB.
This
does not include each team’s minor league systems (farm teams), the thousands
of college and even high school teams that all thirty teams keep statistics on
to better their teams now and in the future. The guys behind the
scenes run statistics using information technology and programs to use pin
point accuracy to use numbers as a guide to put the best possible nine men on
the field to win your team a world series championship.        Baseball
is a game inside a game, both team has to play the inside game. Whichever team can play
the game within the game better than their opponent will be victorious. Coaches have joined
forces with mathematicians to develop a system to win the game inside the game
of numbers. There
is only one perfect man and they hung him on a cross, no one player can play
perfect. The
system thought up a program that finds successes if played at 80 percent level. For example, from a
pitching standpoint they are supposed to retire the lead-off man greater than
sixty-seven percent of the time. Aim for one or less hits per hitting. Strive to throw a first
pitch strike more than sixty-five percent of time.

Statistics
are held for each player, coach, and team in Major League Baseball (MLB).
These statistics have changed the game for the better in more ways than one. On the other hand, some
believe enhanced analytics of the game tend to veer baseball from its roots. A prime example of big
data analytics in baseball, is shown in the movie Moneyball. For teams be successful
they need to win, score more runs than the other team’s, etc. Seems like a
simple process, analytics have provided MLB teams draft the best prospects in
the country and tell the front office what they are worth. Numbers flood the game
such as batting averages, on base percentages, strikeouts, walks, and the list
goes on.

            Although, in this paper the topic is big data and
analytics in sports, the sport being discussed is baseball.
The
sport is riddled with big data analytics to the extent some fans could never
believe. On and off the field,
calculated homerun balls, a pitcher’s velocity to home plate, the stat line of
a superstar player over the span of the last ten games.
The
list goes on and has not even scratched the surface of how big data and
analytics affects America’s pastime. Baseball
has been called the game of numbers, analytics has proved this point in more
ways than one.

            Though, big
data, goes hand in hand with analytics, as well as Information Systems (IS) in
a way. Big
data can be described as, profoundly and astronomically immense data sets that
may be analyzed computationally to reveal patterns, trends, and sodalities,
especially relating to human comportment and interactions. Therefore, being the
perfect match, or missing puzzle piece that completes analytics. More organizations are storing, handling, and abstracting
value from data of all forms and sizes.
Systems that support big volumes of
both controlled and formless data will continue to elevate. The market will authorize platforms that benefit data
custodians oversee and protect astronomically immense data while empowering end
users to analyze that data. These systems will mature to function well inside
of enterprise Information Technology IT systems and standards. Some brief examples of big data in
analytics are: Public Sector Accommodations, Healthcare Contributions, Learning
Accommodations, Insurance Accommodations, Industrialized and Natural Resources,
Conveyance Services, Banking Sectors, and Fraud Detection (7
Examples of Big Data Use cases In Real Life, 2017).
All these examples show big data is numbers or patterns taking from or for a
larger group.

Big data and analytics play such a
keystone role in today’s society. In this paper, will discuss how big data and
analytics are being used in baseball. Also, at the same time I will show
ways on how big data and analytics can relate to information systems. For example, both of these elements
are connected with the world of the internet. Also both big data and analytics are
limited to the limitations of the internet. The internet has made many advances
over time and thus bringing advances, as well as advantages in today’s society.
This is also works simultaneously
with Information Systems (IS). Information Systems (IS) does go hand in hand in
more ways than one. For
example, analytics, analytics is a multidimensional field that utilizes calculations,
data, analytical modeling and appliance knowledge techniques to find
consequential patterns and knowledge in recorded data. Information Systems (IS)
also relies on statistics and meaningful patterns, to provide fast and accurate
data. In
fact, there are three types of
analytics; Descriptive, Predictive, and Perspective are the three types most
commonly found in Information Systems (IS).