Inconclusion, big data is data collected over a large group of people. Analyticsis the ability to calculate, analyze, process, and evaluate that data.So big data and analytics go hand inhand. Theyalso go along with America’s pastime, baseball.
Analytics have had such a hugeimpact on the steps taken to add players to their team. As well as save them money, makemoney, and field the best team to win a championship. Also, big data and analytics has animpact on the fans. Withthe amount of information available on the internet powering IS, each pitch isfull of information and statistics of that player. Though, teams can make and savemoney by using analytical programs to know each player’s worth for better orworse. Lastly,this technology has given baseball teams a path to follow to be successful.Using algorithms and projections towin the game inside the game, the game of numbers. So, maybe players need to startpacking their calculators along with their baseball gear.
Another impact that big data and analytics plays inbaseball is the way it affects the team economically. Baseball teams in theMLB pay their players ungodly amounts of money sometimes. A perfect example isClayton Kershaw. In 2014 he signed a seven-year contract for $215,000,000. That is what theybelieve he is worth to their program (Badenhausen, 2016).
Analytics plays a big part in running a player through his statisticsand projections to evaluate if their superstar will be worth a long termcontract. Onthe other hand, if teams use big data analytics such as Billy Beane did, a teamis able to afford productive players at a lower cost. Some teams in the MLBhave more money to spend on players such as the Yankees, who have repeatedlybeen a powerhouse due to the money they can spend. Teams with less moneyare able to find valuable players than can afford and get them wins. A topic that was argued in the movie isif big data analytics would even work. Especially if even seemed ethical.
Investing millions ofdollars in someone that you only know behind a computer screen is a slap in theface to the hard work the scouts dedicate their lives to each season. Scouts base theirlifestyle on the road following players around the country to provide the bestteam they can. Iswatching a player and knowing them face to face a thing of the past? Althoughanalytics have been able to spot forgotten key components. For example, a numberone draft pick or a wanted player may be someone flashy, with a lot of eyes onhim. Althoughanalytics can find valuable players under the radar that can save the teammoney and still produce wins.
The Moneyball theory puts no notice on the body of thecompetitor or the physical strategies that the competitor have (Lewis, 2013).This theory represents the straightforwardness of baseball by making twoinquiries: Does this player get on base? plus, Can he hit? As indicated byLewis (2003), Billy Beane (inspiration of Moneyball) chose to base his draftingof position players/hitters on specific measurements. The two measurements that Billy came up with wasto, include an on-base percentage (OBP) and slugging rate (Academy, 2015).These two measurements he came up with is consolidated to frame anothermeasurement approached base on an addition on-base slugging (OPS). Another anglethat Beane’s approach was the absence of devotion on control (Lewis, 2013). Inthis manner, Beane trusted that power could be created, yet persistence at theplate and the thrive to get on base proved unable.
Thesecond theory is based on the Oakland A’s manager. BillyBeane 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 skilledon a higher level (college) in comparison to the secondary school”phenom” who needs to be molded into a skilled player. Beane’sspeculation was made in perspective of made by a sabermetrician named BillJames. “Sabermetrics is the numerical and measurable investigation ofbaseball records” (Academy, 2015). James invested years endeavoring tounravel numbers by means of the Bill James Baseball Abstract, which thus,brought about a particular reasoning on hitters.
The main theory isby and large considered the “old” scouting theory. Scouts wander outand assess players everywhere throughout the nation. They do no give carefulconsideration to insights, but instead construct choices in light of the five strengths:speed, speed, arm quality, hitting capacity and mental strength (Lewis, 2003). Eachscout has/had experienced “scout school” and is given a flyer on whatought to be searched for in specific parts of baseball, for example, armquality, handling, running, and the most essential hitting. For arm, qualityassessment, scouts are told to search for players showing a “liquid armactivity and simple discharge” (Major League Baseball, 2001).
Besides, arm quality assessment is led with the help of aradar gun. In the taking care of arrangement, a player with a solid arm andprotective aptitudes can and do convey a player to the major leagues.The process for spending moneyduring a Major League Baseball player draft, which occurs around June eachyear.
Within the draft, it has fifty rounds of selections which all thirtyteams eventually pick a player that is most valuable for their team and theprocess goes on. When deciding on a player to be picking to be drafted, it is recommendedthat the team manager, scouts manger, and a professional mentor for the team tobe there for the reason. Looking at players for draft day it known that if the higherthe 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 adifferent round. In the selection process of the draft there are two main theoriesLewis (2013) narrow for the teams to make it and easier process and selection. Whenthinking about any professional sport, especially baseball, which is America’ssport, there must be money tied to it.Virtuallyin every professional sport, especially baseball, money is a very big aspectwhen 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, theirorganization in a market can make decent/corrupt decisions based on theireconomic status (Academy,2015).For example, with small marketorganization teams that don’t have money, they should spend it wisely unlessthey want a better outcome for their team; whereas, a larger marketorganization team doesn’t have to spend their money wisely due to the fact itsexpendable (Lewis, 2013). Frontoffice managers are the only ones that rely on big data and analytics. The fans of the gamealso use big data and analytics at their pleasure. The biggest examplewill be the fans that participate in fantasy baseball. Each player is runthrough big data and analytics to show their projected stats.
This is what the fansuse to decide who to play and draft on their fantasy teams. Some say that MLB islosing their younger crowds due to the fact the games are usually three hourslong.Tohelp fill the dull moments they show interest facts, and stats to entertain theaudience.Forexample, as the game goes on they may show a stat saying that in the lasttwelve games Josh Harrison has batting .
425 against left handers. A statistic that maytake a person awhile to calculate, is available to the announcer at the push ofa button.Usingbig data and analytics to cover a large amount of statistics has given a chancefor a different statistic every pitch to keep the fans engaged in many ways. Thisdoes play a factor, but it is hard to argue the fact that analytics have indeedincreased the productivity of the game. The players that perform the best arethe players that the teams bring to compete, therefore creating a game full ofsuperstar athletes for fans to enjoy. This could be the reason baseball playersrefer 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 Universityby hitting coach Dillon Lawson. The pair had embraced a “Moneyball”mentality in other ways — using sabermetric measures like runs created andweighted on-base average to build lineups — and their investment inplate-approach paid off. Lawson was even hired away to serve as minor-leaguehitting coach for the Houston Astros, who, for better or worse, might be themost analytical organization in all of sports. The pair reunited prior to the2017 season at Missouri, where they’re enjoying greater resources than they hadat Southeast Missouri State — and, in Lawson’s case, greater buy-in than heexperienced in the pros. “There’s fewer people at the college level toconvince that the numbers have value,” he said. “Regardless of whatorganization you’re in and how data-driven they are, there’s still plenty ofpeople who are within that organization who aren’t completely sold on it.”Thenumbers’ value is evident in the Tigers’ results.
Missouri won 36 games in itsfirst year under Bieser and Lawson, the most for the program since 2008. Theiroffensive gains were impressive and widespread. Compared to 2016, the Tigersscored an additional run per game and upped their collective batting average(15 points), on-base percentage (17 points), and slugging percentage (46points), according to data from The Baseball Cube” (R.J. Anderson, 2017).Althoughthis is pretty successful some believe it deviates from the game. For example, in themovie “Trouble with the Curve” an old time scout does not believe thatanalytics covers all aspects of a good ballplayer. He prefers to watch theplayers in person rather than behind a screen.
Over the years he hasbeen able to tell a talented hitter by the sound of the ball off the bat. He notices a hitch inhis swing that his statistics do not show on paper. In the end a number onedraft pick is a bust, because his hitch in his swing gives him trouble with thecurve. Belowis head coach Steve Bieser having conversation about analytics and how it’sused to find players with in baseball.Onthe other hand, even with the extreme advantage big data analytics has providedto baseball, some find it to over the top for the beloved game of baseball. For example, analyticshas become the standard when scouting college and phenomenal high schoolplayers.
Baseballhas been around for 171 years, over that time the game has changed in a coupleways (Helyar,2011, p. 1-10). Thegame still consists of a ball, bat, and glove. Although the way teamsfind players and ways to provide the best baseball players the world has tooffer has made leaps and bounds. One way to find the best player for your teamis called scouting. Scoutingis 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 aband of brothers, and a team is called family and that family would like toknow who is joining into their family for the long run. Now, a MLB team canlook up a player’s statistics, run their numbers through a program, and see ifhe is projected or ready to join their team. Offensively, to be successful, they calculatedto execute greater than seventy-five percent. Also, strike out lessthan ten percent. Akey component is to score three or more runs per inning, record four or morebase hits, score seven or more runs, and steal three or more bases. Big data and analyticshave proven and projected if a team can play the game within the game at eightypercent or better, the chances of victory are a given. This just goes to showanalytics can detect, and predict what the naked eye might be able to dissect.
Bigdata is the collection of data over a vast amount of people. There give or take athousand rostered players in the MLB.Thisdoes not include each team’s minor league systems (farm teams), the thousandsof college and even high school teams that all thirty teams keep statistics onto better their teams now and in the future.
The guys behind thescenes run statistics using information technology and programs to use pinpoint accuracy to use numbers as a guide to put the best possible nine men onthe field to win your team a world series championship. Baseballis a game inside a game, both team has to play the inside game. Whichever team can playthe game within the game better than their opponent will be victorious. Coaches have joinedforces with mathematicians to develop a system to win the game inside the gameof numbers. Thereis only one perfect man and they hung him on a cross, no one player can playperfect. Thesystem thought up a program that finds successes if played at 80 percent level. For example, from apitching standpoint they are supposed to retire the lead-off man greater thansixty-seven percent of the time.
Aim for one or less hits per hitting. Strive to throw a firstpitch strike more than sixty-five percent of time.Statisticsare 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, somebelieve enhanced analytics of the game tend to veer baseball from its roots. A prime example of bigdata analytics in baseball, is shown in the movie Moneyball. For teams be successfulthey need to win, score more runs than the other team’s, etc.
Seems like asimple process, analytics have provided MLB teams draft the best prospects inthe country and tell the front office what they are worth. Numbers flood the gamesuch as batting averages, on base percentages, strikeouts, walks, and the listgoes on. Although, in this paper the topic is big data andanalytics in sports, the sport being discussed is baseball.
Thesport is riddled with big data analytics to the extent some fans could neverbelieve. On and off the field,calculated homerun balls, a pitcher’s velocity to home plate, the stat line ofa superstar player over the span of the last ten games.Thelist goes on and has not even scratched the surface of how big data andanalytics affects America’s pastime. Baseballhas been called the game of numbers, analytics has proved this point in moreways than one. Though, bigdata, goes hand in hand with analytics, as well as Information Systems (IS) ina way. Bigdata can be described as, profoundly and astronomically immense data sets thatmay be analyzed computationally to reveal patterns, trends, and sodalities,especially relating to human comportment and interactions. Therefore, being theperfect match, or missing puzzle piece that completes analytics. More organizations are storing, handling, and abstractingvalue from data of all forms and sizes.
Systems that support big volumes ofboth controlled and formless data will continue to elevate. The market will authorize platforms that benefit datacustodians oversee and protect astronomically immense data while empowering endusers to analyze that data. These systems will mature to function well insideof enterprise Information Technology IT systems and standards. Some brief examples of big data inanalytics are: Public Sector Accommodations, Healthcare Contributions, LearningAccommodations, Insurance Accommodations, Industrialized and Natural Resources,Conveyance Services, Banking Sectors, and Fraud Detection (7Examples of Big Data Use cases In Real Life, 2017).All these examples show big data is numbers or patterns taking from or for alarger group. Big data and analytics play such akeystone role in today’s society. In this paper, will discuss how big data andanalytics are being used in baseball. Also, at the same time I will showways on how big data and analytics can relate to information systems.
For example, both of these elementsare connected with the world of the internet. Also both big data and analytics arelimited to the limitations of the internet. The internet has made many advancesover time and thus bringing advances, as well as advantages in today’s society.This is also works simultaneouslywith Information Systems (IS). Information Systems (IS) does go hand in hand inmore ways than one. Forexample, analytics, analytics is a multidimensional field that utilizes calculations,data, analytical modeling and appliance knowledge techniques to findconsequential patterns and knowledge in recorded data. Information Systems (IS)also relies on statistics and meaningful patterns, to provide fast and accuratedata. Infact, there are three types ofanalytics; Descriptive, Predictive, and Perspective are the three types mostcommonly found in Information Systems (IS).