Big Data Baseball Big data and analytics are sweeping the business world, and the professional sports industry is no exception. Baseball, football, soccer, hockey, ten nis, and even sailboat racing are finding ways to analyze data about players and competing teams in order to improve performance. The use of analytics and big data has revolutionized the game of base ball as we know it, including defensive shifts, swing path changes, and how teams acquire and develop players. Given the huge disparities in Major League Baseball (MLB) team budgets, wealthier teams typi cally have the advantage in recruiting the best play ers. Michael Lewis's book Moneyball, published in 2003, describes how Oakland Athletics manager Billy Beane was able to turn the underdog A's into a win ning team by using big data analytics to guide deci sions about which players to recruit and cultivate. Rigorous statistical analysis had demonstrated that on-base percentage and slugging percentage were better indicators of offensive success (and cheaper to obtain on the open market) than more historically valued qualities such as speed and contact. These observations flew in the face of conventional base ba1l wisdom and the beliefs of many baseba1l talent scouts and coaches. Beane rebuilt the A's based on these findings, producing a consistently winning team for a number of years by using advanced ana lytics to gain insights into each player's value and contribution to team success that wealthier teams had overlooked. Big data is credited with helping the Boston Red Sox win the World Series in 2004 and the St. Louis Cardinals win in 2006 and 2011. 1b varying degrees, every Major League Baseball team today uses big data and deep analytics to support decisions about many aspects of the game. However, some teams, such as the Pittsburgh Pirates, Chicago Cubs, and Houston Astros, were slower to do so than others, and suffered lackluster performance until they em braced big data more fu1ly. Findings from big data analytics have changed the importance baseball teams attach to specific skills of players. Skills that previously could not be quantified are now receiving more attention, including fielding, base running, and stealing. Skill in fielding is espe cially valued today. For example, Mike Trout, center fielder for the Los Angeles Angels, is highly regarded by team owners because he's an exceptional fielder and base runner and an exceptiona1ly intelligent base ball player, even though he lacked stellar sta tistics in home runs. Today the biggest challenge is not whether to use big data in baseball but how to use it effectively. It is not always possible to inter pret the data and separate out what is "noise" and what is actually actionable information. The amount of data players and pitchers must deal with can be overwhelming-pitch usage, swing planes, spin rates, etc. When a player steps into the batter's box, every hitter is different in terms of how much information that person can absorb before getting bogged down in it. Some want to know what a pitcher will do in cer tain situations-what pitches the pitcher will use and how often that person uses them-while some want to just step in with a clear head and look for the ball. There's only so much data a person can use without dissecting too much and getting too distracted from the task at hand . Many baseball experts still believe that tradi tional methods of player evaluation, along with gut instinct, money, and luck, are still key ingredients for winning teams. For example, the San Francisco Giants use big data and statistics, but also base their player recruitment decisions on the opinions of scouts and coaches. According to Giants bench coach Ron Wotus, numbers rea1ly can't tell the whole story about the quality of the playeri so the Giants inte grate statistical data with scouting, coaching, and player experience, especially when dealing with op ponents outside the National League that the Giants do not see regularly. Being able to exploit an indi vidual player'S strengths comes more from knowing the player and his ability as opposed to the statistics, Wotus believes. Shortstops with good arms can play farther from home plate than normal at times, while fast runners can play closer to home plate than usual. There are nuances to defending the opposition that are not statistically related, but statistics help when you don't know players well enough to know what to expect from them.

230 Part Two Information Technology Infrastructure Sources.' www.statsperform.com, accessed January 25, 2020; www. "MLB Clubs Stay Focused on Future of Analytics, " www.mlb.com.ac mlb.com, accessed January 25, 2020; "Sports Analytics Market Size, cessed March 14, 2019; "Changing the Game: How Data Analytics Is Shares 2020 By 'Ibp Key Players: IBM, SAp, SAS, Thbleau Software, Upending Baseball," Knowledge @ Wharton, February 21, 2019; and "A Oracle, STATS, Prozone," Market Watch, January 30, 2020; "Nick San View from the Front Lines of Baseball's Data-Analytics Revolution," Miguel, 'San Francisco Giants: Analytics Are Not the Answer',"www. McKinsey Quarterly, July 2018. aroundthefoghorn.com, accessed February 4, 2020; Richard Justice, CASE STUDY QUESTIONS 1. How did information technology change the game 3. How much should baseball rely on big data and of baseball? Explain. analytics? Explain your answer. 2. How did information technology affect decision making at MLB teams? What kinds of decisions changed as the result of using big data? .. there are more than 100,000 airline flights each day. TWitter generates more than 12 terabytes of data daily. According to the International Data Center (IDC) technology research firm, data are more than doubling every two years, so the amount of data available to organizations is skyrocketing. Businesses are interested in big data because they can reveal more patterns and interesting relationships than smaller data sets, with the potential to provide new insights into customer behavior, weather patterns, financial market activity, or other phenomena. For example, Shutterstock, the global online image mar ketplace, stores 24 million images, adding 10,000 more each day. Th find ways to optimize the buying experience, Shutterstock analyzes its big data to find out where its website visitors place their cursors and how long they hover over an image before making a purchase. Big data is also finding many uses in the public sector, For example, city governments have been using big data to manage traffic flows and to fight crime. The Interactive Session on Management illustrates how Major League Baseball is using big data to improve player and team performance. However, to derive business value from these data, organizations need new technologies and tools capable of managing and analyzing nontraditional data along with their traditional enterprise data. They also need to know what ques tions to ask of the data and limitations of big data. Capturing, storing, and analyz ing big data can be expensive, and information from big data may not necessarily help decision makers. It's important to have a clear understanding of the problem big data will solve for the business. The chapter-ending case explores these issues. Business Intelligence Infrastructure Suppose you wanted concise, reliable information about current operations, trends, and changes across the entire company. If you worked in a large com pany, the data you need might have to be pieced together from separate sys tems, such as sales, manufacturing, and accounting, and even from external sources, such as demographic or competitor data. Increasingly, you might need to use big data. A contemporary infrastructure for business intelligence has an array of tools for obtaining useful information from all the different types of data used by businesses today, including semistructured and unstructured big data in vast quahtities. These capabilities include data warehouses and data marts, Hadoop, in-memory computing, and analytical platforms. Some of these capabilities are available as cloud services.