Statistics in Baseball. Ali Bachani Chris Gomsak Jeff Nitz

Transcription

1 Statistics in Baseball Ali Bachani Chris Gomsak Jeff Nitz EIS Mini-Paper Professor Adner October 7, 2013

2 2 Statistics in Baseball EIS Statistics in Baseball The Baseball Ecosystem... 3 An Unfair Game... 5 Early Attempts at Innovation... 6 Resistance to Innovation... 6 Billy Beane & the Oakland A s... 7 Ecosystem Barriers... 9 Co-innovation risk... 9 Adoption risk Ecosystem Threats Conclusion Sources... 14

3 3 Statistics in Baseball It's all about evaluating skills and putting a price on them. Thirty years ago, stockbrokers used to buy stock strictly by feel. Let's put it this way: Anyone in the game with a 401(k) has a choice. They can choose a fund manager who manages their retirement by gut instinct, or one who chooses by research and analysis. I know which way I'd choose. Billy Beane [Statistics in baseball is a] threat of not just the way of doing business, but in their minds it's threatening the game. But really what it's threatening is their livelihoods, it's threatening their jobs, it's threatening the way that they do things. And every time that happens, whether it's the government or a way of doing business or whatever it is, the people are holding the reins, have their hands on the switch. They go bat shit crazy. I mean, anybody who's not building a team right and rebuilding it using your model, they're dinosaurs. They'll be sitting on their ass on the sofa in October, watching the Boston Red Sox win the World Series. - Arliss Howard as John Henry in Moneyball The Baseball Ecosystem The baseball ecosystem, like many other sports, has a number of stakeholders including players, agents, coaches, fans, etc. (Exhibit 1). Exhibit 1: Baseball Ecosystem Scouts Fans Players General Manager MLB Team Media Agents Coaches Sponsors Owners

4 4 Statistics in Baseball The introduction of statistics in baseball dramatically reduced the influence of the scout on the baseball ecosystem. The scout traditionally evaluated a player, and there are two key inefficiencies in the pre-statistics ecosystem and evaluation methods: The 5 tools The supposedly core athletic characteristics that defined a good player. These were running speed, arm strength, hitting for average, hitting for power and fielding. Traditionally, these were judged subjectively by scouts, with several game observations. There was minimal correlation to actual performance in major league baseball. Other over-looked skills, such as walks, were not found on this evaluation. Appearance Though never publicly disclosed as a criterion, appearance was an issue both consciously and subconsciously. Consciously, scouts were judging players looks and style to ascertain whether the player would be good or bad for ticket sales, and whether or not they had potential star power. Kevin Youkilis is cited in Moneyball as the poster child of this phenomenon when the author quotes a scout asking Who wants to see that? that being Youkilis. Youkilis would go on to win two World Series with the Red Sox. Countless research outside the field of baseball has also shown that good looking people are generally more likely to be hired, promoted and given positive reviews. As a result, subjective evaluation by scouts affected player worth and distorted performance evaluation.

5 5 Statistics in Baseball An Unfair Game Major League Baseball is inherently an unfair game. Without a significant revenue sharing agreement or binding salary cap, teams with large markets, such as New York, Los Angeles, and Boston, are able to vastly outspend their small-market competitors. In 2000 for example, the New York Yankees had an opening day payroll of $93M, nearly triple the Oakland A's budget of $32M (Exhibit 2). This uneven playing field has been further tilted against small market teams by free agency (i.e. large market teams can buy away your best talent) and by heightened disparity in media distribution revenues (e.g. Yankee's YES network). Given that baseball is a highly unfair game, how can a small market team, such as the A's ever manage to compete? $M $100 $90 $80 $70 $60 $50 $40 $30 $20 $10 $- $93 Exhibit 2: 2000 Opening Day Payrolls by Team NY Yankees Los Angeles Atlanta Baltimore Arizona NY Mets Boston Cleveland Texas Tampa Bay St. Louis Colorado Chicago Cubs Seattle Detroit San Diego San Francisco Anaheim Houston Philadelphia Cincinnati Toronto Milwaukee Montreal Oakland Chicago White Sox Pittsburgh Kansas City Florida Minnesota $32

6 6 Statistics in Baseball Early Attempts at Innovation Bill James, an American baseball writer, historian, and statistician, was among the first pioneers to attempt to bring the use of advanced statistics to baseball, specifically to the area of player evaluations. His work, beginning in the 1970 s, was termed sabermetrics in reference to the Society for American Baseball Research (SABR). James scientifically analyzed and studied baseball, primarily through the use of statistical data, in an attempt to determine why teams won and lost, and what statistics and behaviors led to wins. He was a pioneer in his efforts to gather, analyze, and generate impactful insights from player and game statistics, and his work paved the way for future innovators that were able to take his ideas and methods and apply them successfully. Resistance to Innovation Unfortunately, James faced significant resistance from the baseball ecosystem in the 1970 s, 80 s, and early 90s, as all of the necessary pieces of the puzzle to take advanced analytics within the sport towards full adoption were not yet in place. The important stakeholders in the baseball ecosystem who would need to embrace, implement, and enhance his ideas, both those who managed baseball franchises as well as those who owned the statistics he needed, did not yet trust this new innovation. In fact, they were threatened by it. James search for baseball knowledge was constrained by raw statistics; all he had to work with at the time were box scores. The company that kept the score sheets for MLB, the Elias Sports Bureau ( ESB ), was unwilling to help James in granting access to information beyond what was published in the daily newspapers. The attitude of ESB was a mix of possessiveness and indifference. They withheld a number of statistics from James, and ultimately published a book through the Elias

7 7 Statistics in Baseball Sports Bureau to directly compete with his Abstracts work. ESB was unwilling to help James in his quest without guaranteeing their own financial rewards for sharing the available information, and as a key player in the value chain, their resistance to share this data with James resulted in a stagnation of his efforts to further drive the innovation of advanced statistics in the sport. Also a barrier was James place as an outsider to the baseball world. John Henry, who wanted to implement statistical analysis in his own franchise, spoke to this in the following quote: For a man who had never played professional baseball to impose upon even a pathetic major league franchise an entirely new day of doing things meant alienating the baseball insiders he employed: the manager, the scouts, the players. Billy Beane & the Oakland A s Billy Beane was the man who would ultimately change the game of baseball by implementing the innovation of advanced statistics in his management of the Oakland A s. A former major league player and first round draft pick, Beane started with the A s in their minor league system as an advanced scout, eventually working his way up to team GM in Beane was a baseball insider who had been part of the game and its ecosystem for many years as a player, but unique to any GM before him, Beane brought an open mind and a willingness to take risks and leverage the use of advanced analytics to evaluate players and identify under-valued talent. Having nearly attended Stanford instead of signing with the Mets as rookie, Beane had a unique mind that was ripe for the opportunity to try something different with the tools that were available, including the enhanced availability of valuable statistics as well as advances in computer technology to analyze data. He ultimately developed and implemented an analytical, evidence-based, saber-metric approach within the A s organization, using many of the ideas

8 8 Statistics in Baseball and tools pioneered by Bill James in earlier decades to evaluate players and make roster decisions on which players to acquire, start, and trade, and did so with tremendous success. While implementing his innovative approach, Beane faced resistance from his own organization s ecosystem. After many tense discussions with his own scouts, who feared being cut out of personnel decisions, some of his staff left the team. But his insistence on trying a new way despite this resistance paved the way for the A s success in the late 90 s and early 2000 s. Ultimately, the A s were the most cost-effective team in the 2000 s (Exhibit 3) winning more games at a lower cost than any other team. Michael Lewis documented the A s and Beane s innovative approach in his book Moneyball in 2003, which helped raise awareness of sabremetrics and paved the way for future adoption throughout the baseball ecosystem. Once the world became aware of one MLB franchise s successful use of these innovative methods, other teams, best exhibited by the Boston Red Sox, embraced the innovation of statistics in player evaluation. A massive improvement in adoption of advanced analytics and statistics resulted, both in baseball and beyond in other professional sports such as the NBA.

9 9 Statistics in Baseball Exhibit 3: 2002 Team Performance & ROI 140% ROI* 120% 100% 80% 60% 40% 20% 0% -20% -40% -60% A's Yankees Regular Season Wins * ROI calculated by comparing expected team payroll vs. actual team payroll. Expected team payroll defined as actual # of wins * league average $/win. As a formula: (Actual team wins * League Avg $/win) / Actual Payroll - 1. Take the A's for example, (103 wins * $834K / win league average) / $40M actual payroll - 1 = 117% ROI Ecosystem Barriers The success of statistics in baseball depended heavily on capabilities from outside the traditional ecosystem and the reduction of scout s power and influence. Co-innovation risk Data gathering When Bill James first wrote about sabermetrics, there was little to no consolidated data available for potential players. Even in 2002, when Billy Beane started to use statistical data for the A s, he had difficulty gathering the required information: he would often gather it through contacts, self-reported player sheets and such.

10 10 Statistics in Baseball Data processing Until the late 1990s, statistical software was expensive, difficult to use and rare. Equally, computers were not affordable and did not have the data processing capabilities required. Personnel skills In 2002, when statistical software was relatively cheaper and computer hardware had the required processing capabilities, baseball teams did not have the personnel that understood technological capabilities now available in the market and did not have the background in statistics that would allow Billy Beane to leap frog the competition despite his small payroll. Adoption risk All participants in the ecosystem were impacted by the introduction of statistics in baseball. The most prominent negative impact was on scouts (Exhibit 4). As discussed above, scouts were the main link between available players and the teams, which gave scouts enormous power over players and team management. A significant proportion of general managers had also previously worked as scouts and were aligned with existing talent evaluation methods. Statistical analysis fundamentally changed the way players were evaluated and reduced the need for scouts, relegating them to judging a far smaller set of characteristics that could not be judged statistically.

11 11 Statistics in Baseball Exhibit 4: Adoption Risk by Participant

12 12 Statistics in Baseball Ecosystem Threats To understand why the baseball ecosystem repelled statistical analysis for so long, we classified significant baseball innovations of the last 70 years. Starting with the integration of African American baseball players in the late 1940 s, we grouped each innovation by its effects on market links and competencies. Unique among baseball innovations, statistics and racial integration stood out as disruptive to both market links and competencies. Statistics in baseball revolutionized the fundamental market linkage with players, and drastically reduced the importance of scout-player relations and observations. At the same time, statistics greatly enhanced a GM s competence and ability to assess player performance, but only for GM s who were statistically-minded or open to change. For the majority of GM s who had been scouts or lawyers, this technology destroyed their traditional player assessment competencies. Exhibit 5: Innovations in Baseball

13 13 Statistics in Baseball Conclusion It would be fair to assume that statistical analysis in player evaluation is an easily reproduced, short term advantage in baseball management. Remarkably, payroll efficiency in 2013 (Exhibit 6) has remained largely unchanged since 2003 with the A s maintaining a significant advantage. Exactly why the A s have managed to stay ahead of the competition is unclear but it is likely that Beane and his staff have managed to further refine their formulas, statistics, and algorithms, constantly staying one step ahead of the competition. Exhibit 6: 2013 Team Performance & ROI 120% 100% A's 80% 60% ROI* 40% 20% 0% -20% -40% -60% Yankees Regular Season Wins

14 14 Statistics in Baseball Sources Lewis, Michael. Moneyball Baseballreference.com Various Wikipedia articles