Big Data Baseball

Big data and analytics are sweeping the business world, and the professional sports industry is no exception. Baseball, football, soccer, hockey, tennis, 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 baseball 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 typically have the advantage in recruiting the best players. Michael Lewiss book Moneyball, published in 2003, describes how Oakland Athletics manager Billy Beane was able to turn the underdog As into a winning team by using big data analytics to guide decisions 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 baseball wisdom and the beliefs of many baseball talent scouts and coaches. Beane rebuilt the As based on these findings, producing a consistently winning team for a number of years by using advanced analytics to gain insights into each players 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. To 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 embraced big data more fully.

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 especially valued today. For example, Mike Trout, center fielder for the Los Angeles Angels, is highly regarded by team owners because hes an exceptional fielder and base runner and an exceptionally intelligent base ball player, even though he lacked stellar statistics 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 interpret 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 overwhelmingpitch usage, swing planes, spin rates, etc. When a player steps into the batters 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 certain situationswhat pitches the pitcher will use and how often that person uses themwhile some want to just step in with a clear head a d look for the ball. Theres 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 traditional 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 really cant tell the whole story about the quality of the player; so the Giants integrate statistical data with scouting, coaching, and player experience, especially when dealing with opponents outside the National League that the Giants do not see regularly. Being able to exploit an individual players 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 dont know players well enough to know what to expect from them.

Sources: www.statsperform.com, accessed January 25, 2020; www. mlb.com, accessed January 25, 2020; Sports Analytics Market Size, Shares 2020 By Top Key Players: IBM, SAP, SAS, Tableau Software, Oracle, STATS, Prozone, MarketWatch, January 30, 2020; Nick San Miguel, San Francisco Giants: Analytics Are Not the Answer,www. aroundthefoghorn.com, accessed February 4, 2020; Richard Justice, MLB Clubs Stay Focused on Future of Analytics, www.mlb.com, accessed March 14, 2019; Changing the Game: How Data Analytics Is Upending Baseball, Knowledge @ Wharton, February 21, 2019; and A View from the Front Lines of Baseballs Data-Analytics Revolution, McKinsey Quarterly, July 2018.

Case Study Questions PLEASE GIVE NEW ANSWERS NOT REUSED

1a. How did information technology change the game of baseball? Explain.

1b. How did information technology affect decision making at MLB teams? What kinds of decisions changed as the result of using big data?

1c. How much should baseball rely on big data and analytics? Explain your answer.