In conclusion, big data is data collected over

            In
conclusion, big data is data collected over a large group of people. Analytics
is the ability to calculate, analyze, process, and test 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.

Conclusion
and final thoughts

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            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 test 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 can find valuable players than can
afford and get them wins.  

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.

Putting
your money where your mouth is

 

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 considering 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.

Pick your poison

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.

Fan Favorites

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”.

to pursue these major-league solutions”. (R.J. Anderson,
2017)

dugout, and even in the front office — it is a large part of
what drives colleges

within that organization who aren’t completely sold on
it.” on the field, in the

you’re in and how data-driven they are, there’s still plenty
of people who are

level to convince that the numbers have value”. “Regardless
of what organization

their investment in plate-approach paid off, there is fewer
people at the college

measures like runs created and weighted on-base average to
build lineups — and

“Moneyball” has been taking an approach in other ways —
using sabermetric

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 an example of how Moneyball has
been used over the years in way it cannot be explained since the movie came
out. R.J. Anderson (2017) a sports journalist from CBS sports wrote an article
on “how college baseball teams
are now embracing the big data approach”. R.J.
Anderson (2017) wrote this article based on Iowa and Missouri universities
that have programs that are mixing technology and analytics within baseball.
Below is a quote from his article about these universities teams mixing
technology and analytics together.

Old school v. new

On the other hand, even with the extreme advantage big
data and 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.

Baseball is a game inside a game, both team must 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.

A game inside a game

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.

Big
Data in Baseball

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.

Overview

Big data and
analytics play such a keystone role today. In this paper, I 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 elements relate to 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 today. 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 these three types of analytics are most commonly found in
Information Systems (IS).

Abstract