Baseball Sabermetrics
Derek Lucente
Professor Allen
English Comp 1
December 16th, 2013
Sabermetrics Sabermetrics is the mathematical analysis of baseball records and data. Sabermetrics uses its own unique statistical categories to measure player performance instead of the more orthodox categories including: runs batted in (RBI), home runs (HR), and wins (W) for a pitcher. The theory behind sabermetrics produces data using complex formulas to determine the value of a player for the team as a whole rather than what his own individual end-of-season statistical line indicates. By far, baseball has both the longest seasons and the longest games, making it extremely hard to evaluate data from a single game at the beginning of a season and develop a prediction …show more content…
for game number one hundred sixty two at the end of the season. Hugely inflated contracts and multimillion dollar deals spawned the need for more in depth research to assure owners that their team would produce the intended return on investment. Sabermetrics sees flaws in the traditional statistics that ultimately only evaluate a single player rather than their impact and potential role on the team as a whole. With most major league teams using the same stale statistics since the conception of baseball in 1839, do Sabermetrics provide the foundation for assembling a championship baseball team in today’s game? Traditionally, major league baseball teams asses a player and create projections using the same strict offensive guidelines, with pitchers as an exception because of the specificity of their role.
A player was evaluated using averages from the following categories: hits, runs, runs-batted-in, homeruns, and stolen bases. While somewhat effective, these can be very linear at times considering the amount of variables involved within the game of baseball. If one looks at a player such as David Ortiz in 2003, his regular season stats alone would leave out much of what Ortiz did as a whole. While above average for a power hitter, his regular season batting average of .309 pales in comparison to the .760 average he achieved in the World Series. Statisticians began to realize that a blanket average of an entire baseball season was not enough to judge the worth of a player, thus the creation of more situational-based categories. Baseball began to look at a player for their strengths in particular situations rather than clumping all data to create a skewed average that does not reveal their usefulness in a specific role for the team. Sabermetrics takes these numbers and refines them as they pertain to filling a specific hole or a gap in a team’s …show more content…
needs. Before the term “sabermetrics” was coined by Bill James to honor the Society for American Baseball Research: SABR, many people utilized similar practices of collecting and analyzing baseball data. Managers such as Earl Weaver of the Baltimore Orioles used self-devised systems for deciding when to make changes to his roster during baseball games. Other teams such as the Brooklyn Dodgers hired statisticians such as Branch Rickey to evaluate player research based on complicated mathematics, rather than the simple averages used at the time. Rickey paved the way for men such as Billy Beane, Bill James, and Theo Epstien. While many people before Bill James devised ground breaking ways to win baseball games, he was the first man to give this practice a name making him the “father of sabermetrics” (Quote Conan, Neal: Interview with Bill James 2011 NPR). Author and statistician Bill James, the father and creator of sabermetric research, began his journey to fame and success in major league baseball in the mid to late seventies. Slowly growing in popularity, sabermetrics took its largest leap into the mainstream with the introduction of Bill James in 1977. James wrote his first book about baseball analysis categorizing statistics in ways never seen before, such as player performance on a month by month basis or stolen bases against a certain pitcher rather than just as a whole. In 1984, James created a statistical collection known as the “Project Score Sheet” in which volunteers contributed thousands of pieces of data from every single game of major league baseball ever played. James went on to write many volumes of “The Baseball Abstract” where he created his own complex formulas and categories in which to evaluate players such that he could place an exact value in terms of overall worth to any player on any team. James later went on to work as a senior consultant to Red Sox co-owner John Henry and worked closely with Theo Epstien in assembling the Red Sox teams of the mid 2000’s. A great example of his practice can be seen in the 2013 film “Moneyball” in which Oakland Athletics manager, Billy Beane, is seen using these techniques to make decisions that wouldn’t be made had these systems not existed. For instance, a player with a higher overall batting average may now be substituted with a player who had a lower overall batting average, but a higher one against the particular pitcher at hand. The Oakland Athletics shocked major league baseball and the country by winning twenty games in a row in 2002. The 2002 Athletics were faced with a common dilemma, replacing key players that were instrumental to the team’s success in the past. Jason Giambi, Johnny Damon, and Jason Isringhausen were invaluable to the team but would not be rejoining in April, leaving a huge hole for general manager Beane to fill. Billy Beane employed Bill James’ practices to scout players to fill these roles using his unconventional statistical categories. Beane had a gift for seeing value in a player where no one else did by considering thing such as on-base-percentage or defensive statistics that had been previously looked over. Instead of looking to replace these superstars by finding a player that matched each offensive category statistic for statistic, he attempted to simply match the numbers by combining statistics from a multitude of players while also taking into account the newer categories in sabermetrics. After controversially trading his best remaining player on the team for what was thought to be several inferior players, the Athletics began a successful run that would soon go down in history. The team went on to win twenty games in a row, setting a new franchise and American League record but would ultimately later go on to lose in the first round of the playoffs. This story is brilliantly and realistically portrayed in the film Moneyball directed by Bennet Miller, which is based on the 2004 book written by Michael Lewis. In November 2002, after the magical season portrayed in the film, the Red Sox attempted to hire Billy Beane as their general manager for the upcoming season. The Red Sox believed that Beane’s philosophies in unison with Bill James’s theories would guarantee them the ultimate long term success: a world championship. When Beane declined the offer, Theo Epstien was hired for the vacant general manager position and Beane went on to teach him everything he knew about sabermetrics in a 48-hour window before returning to Oakland. This would eventually pay off in a big way for the Boston Red Sox organization. Epstien brilliantly signed players such as David Ortiz, Bill Mueller, and Keith Foulke through incorporating sabermetric pratcices. The Red Sox went on to win the world championship in Epstein’s second year as general manager. The Red Sox would continue to use these methods to dominate major league baseball, still to this day winning their third World Series title in a decade after a brutal eighty-six year drought. Sabermetrics appeared to singlehandedly tackle the dreaded curse of the Bambino, cast on the team in 1919 by none other than the infamous Babe Ruth.
Undeniably, Sabermetrics developed new ways help determine who the better player is, Ken Griffey Jr.
or Frank Thomas, for a particular team. However, like any theoretical system, it cannot guarantee victory one hundred percent of the time. After all, not every team can win the World Series every year. Again, the 2002 Athletics serve as a perfect example, winning twenty games in a row only to meet their demise upon entering the post season. Like any system based on projections, unforeseen events hold the potential to destroy any and all predictions of performance such as injury, overall health, and performance enhancing drugs. All three of these categories make using mathematics to evaluate players an imperfect science. For instance, former Red Sox player Jacoby Ellsbury provided a top-tier on-base-percentage of .426 this past season, contributing to a World Series victory. However, this statistic did little to help the team during the 256 games he missed due to injury from 2010 to 2012, an unpredictable
variable.
Sabermetric research was a major breakthrough for the professional baseball scene and continues to evolve to this day, becoming more precise and fine-tuned with each new season. One could go as far as saying that this practice is essential to any organization that wishes to compete at the major league level and maintain a level playing field against its peers. Every one of the thirty major league teams were using Sabermetrics as of 2012, dwindling the competitive advantage that sabermetrics first provided. However, the system continues to be a great foundation and should be considered a top tier tool in assembling a championship baseball team. Sabermetric theory is a great way to measure player performance by projecting figures based on current performance. However, at the end of the day, no system can predict when a player will fall short of their previously projected numbers. Players must consistently perform on the field, and no statistical theory in the world can ensure this will happen. It would appear that winning baseball games comes down to a mixture of statistical analysis, skill, and luck.
Works Cited
Bradford, Rob. "Billy Beane Reflects on 10-year Anniversary of Almost Becoming a Red
Sox." Full Count RSS. WEEI, 16 Nov. 12. Web. 15 Dec. 2013
Conan, Neal. "The Man behind the 'Moneyball ' Sabermetrics." Http://www.npr.org. National
Public Radio, 26 Sept. 2011. Web. 04 Dec. 2013.
Corcoran, Cliff. "How Important Was Moneyball to the Success of the 2002 A 's?"SI.com. Sports
Illustrated, 22 Sept. 2011. Web. 15 Dec. 2013
Neyer, Rob. "Bill James and the Advent of Sabermetrics." Encyclopedia Britannica Online.
Encyclopedia Britannica, n.d. Web. 04 Dec. 2013.
"SABR." Society for American Baseball Research. Cecilia Tan, 1999. Web. 04 Dec. 2013.
Silverman, Jacob. "How Sabermetrics Works." HowStuffWorks. Matt Crenshaw, n.d. Web. 04
Dec. 2013.
Wikipedia contributors. "Sabermetrics." Wikipedia, The Free Encyclopedia. Wikipedia, The Free
Encyclopedia, 15 Nov. 2013. Web. 5 Dec. 2013.
Professor Allen
English Comp 1
December 16th, 2013
Sabermetrics Sabermetrics is the mathematical analysis of baseball records and data. Sabermetrics uses its own unique statistical categories to measure player performance instead of the more orthodox categories including: runs batted in (RBI), home runs (HR), and wins (W) for a pitcher. The theory behind sabermetrics produces data using complex formulas to determine the value of a player for the team as a whole rather than what his own individual end-of-season statistical line indicates. By far, baseball has both the longest seasons and the longest games, making it extremely hard to evaluate data from a single game at the beginning of a season and develop a prediction …show more content…
for game number one hundred sixty two at the end of the season. Hugely inflated contracts and multimillion dollar deals spawned the need for more in depth research to assure owners that their team would produce the intended return on investment. Sabermetrics sees flaws in the traditional statistics that ultimately only evaluate a single player rather than their impact and potential role on the team as a whole. With most major league teams using the same stale statistics since the conception of baseball in 1839, do Sabermetrics provide the foundation for assembling a championship baseball team in today’s game? Traditionally, major league baseball teams asses a player and create projections using the same strict offensive guidelines, with pitchers as an exception because of the specificity of their role.
A player was evaluated using averages from the following categories: hits, runs, runs-batted-in, homeruns, and stolen bases. While somewhat effective, these can be very linear at times considering the amount of variables involved within the game of baseball. If one looks at a player such as David Ortiz in 2003, his regular season stats alone would leave out much of what Ortiz did as a whole. While above average for a power hitter, his regular season batting average of .309 pales in comparison to the .760 average he achieved in the World Series. Statisticians began to realize that a blanket average of an entire baseball season was not enough to judge the worth of a player, thus the creation of more situational-based categories. Baseball began to look at a player for their strengths in particular situations rather than clumping all data to create a skewed average that does not reveal their usefulness in a specific role for the team. Sabermetrics takes these numbers and refines them as they pertain to filling a specific hole or a gap in a team’s …show more content…
needs. Before the term “sabermetrics” was coined by Bill James to honor the Society for American Baseball Research: SABR, many people utilized similar practices of collecting and analyzing baseball data. Managers such as Earl Weaver of the Baltimore Orioles used self-devised systems for deciding when to make changes to his roster during baseball games. Other teams such as the Brooklyn Dodgers hired statisticians such as Branch Rickey to evaluate player research based on complicated mathematics, rather than the simple averages used at the time. Rickey paved the way for men such as Billy Beane, Bill James, and Theo Epstien. While many people before Bill James devised ground breaking ways to win baseball games, he was the first man to give this practice a name making him the “father of sabermetrics” (Quote Conan, Neal: Interview with Bill James 2011 NPR). Author and statistician Bill James, the father and creator of sabermetric research, began his journey to fame and success in major league baseball in the mid to late seventies. Slowly growing in popularity, sabermetrics took its largest leap into the mainstream with the introduction of Bill James in 1977. James wrote his first book about baseball analysis categorizing statistics in ways never seen before, such as player performance on a month by month basis or stolen bases against a certain pitcher rather than just as a whole. In 1984, James created a statistical collection known as the “Project Score Sheet” in which volunteers contributed thousands of pieces of data from every single game of major league baseball ever played. James went on to write many volumes of “The Baseball Abstract” where he created his own complex formulas and categories in which to evaluate players such that he could place an exact value in terms of overall worth to any player on any team. James later went on to work as a senior consultant to Red Sox co-owner John Henry and worked closely with Theo Epstien in assembling the Red Sox teams of the mid 2000’s. A great example of his practice can be seen in the 2013 film “Moneyball” in which Oakland Athletics manager, Billy Beane, is seen using these techniques to make decisions that wouldn’t be made had these systems not existed. For instance, a player with a higher overall batting average may now be substituted with a player who had a lower overall batting average, but a higher one against the particular pitcher at hand. The Oakland Athletics shocked major league baseball and the country by winning twenty games in a row in 2002. The 2002 Athletics were faced with a common dilemma, replacing key players that were instrumental to the team’s success in the past. Jason Giambi, Johnny Damon, and Jason Isringhausen were invaluable to the team but would not be rejoining in April, leaving a huge hole for general manager Beane to fill. Billy Beane employed Bill James’ practices to scout players to fill these roles using his unconventional statistical categories. Beane had a gift for seeing value in a player where no one else did by considering thing such as on-base-percentage or defensive statistics that had been previously looked over. Instead of looking to replace these superstars by finding a player that matched each offensive category statistic for statistic, he attempted to simply match the numbers by combining statistics from a multitude of players while also taking into account the newer categories in sabermetrics. After controversially trading his best remaining player on the team for what was thought to be several inferior players, the Athletics began a successful run that would soon go down in history. The team went on to win twenty games in a row, setting a new franchise and American League record but would ultimately later go on to lose in the first round of the playoffs. This story is brilliantly and realistically portrayed in the film Moneyball directed by Bennet Miller, which is based on the 2004 book written by Michael Lewis. In November 2002, after the magical season portrayed in the film, the Red Sox attempted to hire Billy Beane as their general manager for the upcoming season. The Red Sox believed that Beane’s philosophies in unison with Bill James’s theories would guarantee them the ultimate long term success: a world championship. When Beane declined the offer, Theo Epstien was hired for the vacant general manager position and Beane went on to teach him everything he knew about sabermetrics in a 48-hour window before returning to Oakland. This would eventually pay off in a big way for the Boston Red Sox organization. Epstien brilliantly signed players such as David Ortiz, Bill Mueller, and Keith Foulke through incorporating sabermetric pratcices. The Red Sox went on to win the world championship in Epstein’s second year as general manager. The Red Sox would continue to use these methods to dominate major league baseball, still to this day winning their third World Series title in a decade after a brutal eighty-six year drought. Sabermetrics appeared to singlehandedly tackle the dreaded curse of the Bambino, cast on the team in 1919 by none other than the infamous Babe Ruth.
Undeniably, Sabermetrics developed new ways help determine who the better player is, Ken Griffey Jr.
or Frank Thomas, for a particular team. However, like any theoretical system, it cannot guarantee victory one hundred percent of the time. After all, not every team can win the World Series every year. Again, the 2002 Athletics serve as a perfect example, winning twenty games in a row only to meet their demise upon entering the post season. Like any system based on projections, unforeseen events hold the potential to destroy any and all predictions of performance such as injury, overall health, and performance enhancing drugs. All three of these categories make using mathematics to evaluate players an imperfect science. For instance, former Red Sox player Jacoby Ellsbury provided a top-tier on-base-percentage of .426 this past season, contributing to a World Series victory. However, this statistic did little to help the team during the 256 games he missed due to injury from 2010 to 2012, an unpredictable
variable.
Sabermetric research was a major breakthrough for the professional baseball scene and continues to evolve to this day, becoming more precise and fine-tuned with each new season. One could go as far as saying that this practice is essential to any organization that wishes to compete at the major league level and maintain a level playing field against its peers. Every one of the thirty major league teams were using Sabermetrics as of 2012, dwindling the competitive advantage that sabermetrics first provided. However, the system continues to be a great foundation and should be considered a top tier tool in assembling a championship baseball team. Sabermetric theory is a great way to measure player performance by projecting figures based on current performance. However, at the end of the day, no system can predict when a player will fall short of their previously projected numbers. Players must consistently perform on the field, and no statistical theory in the world can ensure this will happen. It would appear that winning baseball games comes down to a mixture of statistical analysis, skill, and luck.
Works Cited
Bradford, Rob. "Billy Beane Reflects on 10-year Anniversary of Almost Becoming a Red
Sox." Full Count RSS. WEEI, 16 Nov. 12. Web. 15 Dec. 2013
Conan, Neal. "The Man behind the 'Moneyball ' Sabermetrics." Http://www.npr.org. National
Public Radio, 26 Sept. 2011. Web. 04 Dec. 2013.
Corcoran, Cliff. "How Important Was Moneyball to the Success of the 2002 A 's?"SI.com. Sports
Illustrated, 22 Sept. 2011. Web. 15 Dec. 2013
Neyer, Rob. "Bill James and the Advent of Sabermetrics." Encyclopedia Britannica Online.
Encyclopedia Britannica, n.d. Web. 04 Dec. 2013.
"SABR." Society for American Baseball Research. Cecilia Tan, 1999. Web. 04 Dec. 2013.
Silverman, Jacob. "How Sabermetrics Works." HowStuffWorks. Matt Crenshaw, n.d. Web. 04
Dec. 2013.
Wikipedia contributors. "Sabermetrics." Wikipedia, The Free Encyclopedia. Wikipedia, The Free
Encyclopedia, 15 Nov. 2013. Web. 5 Dec. 2013.