Author: Gabriel B. Costa
Publisher: McFarland
ISBN: 0786454466
Category : Sports & Recreation
Languages : en
Pages : 241
Book Description
The past 30 years have seen an explosion in the number and variety of baseball books and articles. Following the lead of pioneers Bill James, John Thorn, and Pete Palmer, researchers have steadily challenged the ways we think about player and team performance--and along the way revised what we thought we knew of baseball history. This book by the authors of Understanding Sabermetrics (2008) goes beyond the explanation of new statistics to demonstrate their use in solving some of the more familiar problems of baseball research, such as how to compare players across generations; how to account for the effects of ballparks and rules changes; and how to measure the effectiveness of the sacrifice bunt or the range of the Gold Glove-winning shortstop. Instructors considering this book for use in a course may request an examination copy here.
Practicing Sabermetrics
Author: Gabriel B. Costa
Publisher: McFarland
ISBN: 0786454466
Category : Sports & Recreation
Languages : en
Pages : 241
Book Description
The past 30 years have seen an explosion in the number and variety of baseball books and articles. Following the lead of pioneers Bill James, John Thorn, and Pete Palmer, researchers have steadily challenged the ways we think about player and team performance--and along the way revised what we thought we knew of baseball history. This book by the authors of Understanding Sabermetrics (2008) goes beyond the explanation of new statistics to demonstrate their use in solving some of the more familiar problems of baseball research, such as how to compare players across generations; how to account for the effects of ballparks and rules changes; and how to measure the effectiveness of the sacrifice bunt or the range of the Gold Glove-winning shortstop. Instructors considering this book for use in a course may request an examination copy here.
Publisher: McFarland
ISBN: 0786454466
Category : Sports & Recreation
Languages : en
Pages : 241
Book Description
The past 30 years have seen an explosion in the number and variety of baseball books and articles. Following the lead of pioneers Bill James, John Thorn, and Pete Palmer, researchers have steadily challenged the ways we think about player and team performance--and along the way revised what we thought we knew of baseball history. This book by the authors of Understanding Sabermetrics (2008) goes beyond the explanation of new statistics to demonstrate their use in solving some of the more familiar problems of baseball research, such as how to compare players across generations; how to account for the effects of ballparks and rules changes; and how to measure the effectiveness of the sacrifice bunt or the range of the Gold Glove-winning shortstop. Instructors considering this book for use in a course may request an examination copy here.
Reasoning with Sabermetrics
Author: Gabriel B. Costa
Publisher: McFarland
ISBN: 0786492813
Category : Sports & Recreation
Languages : en
Pages : 217
Book Description
Sabermetrics, the specialized analysis of baseball through empirical evidence, provides an impartial perspective from which to explore the game. In this work, the third in a series, three mathematicians employ statistical science in an attempt to answer some of baseball's toughest questions. For instance, how good were the 1961 New York Yankees? How bad were the 1962 Mets? Which team was the best of the Deadball Era? They also strive to determine baseball's greatest player at various positions. Throughout, the objective evidence allows for debate devoid of emotion and personal biases, providing a fresh, balanced evaluation of these and many other challenging questions. Instructors considering this book for use in a course may request an examination copy here.
Publisher: McFarland
ISBN: 0786492813
Category : Sports & Recreation
Languages : en
Pages : 217
Book Description
Sabermetrics, the specialized analysis of baseball through empirical evidence, provides an impartial perspective from which to explore the game. In this work, the third in a series, three mathematicians employ statistical science in an attempt to answer some of baseball's toughest questions. For instance, how good were the 1961 New York Yankees? How bad were the 1962 Mets? Which team was the best of the Deadball Era? They also strive to determine baseball's greatest player at various positions. Throughout, the objective evidence allows for debate devoid of emotion and personal biases, providing a fresh, balanced evaluation of these and many other challenging questions. Instructors considering this book for use in a course may request an examination copy here.
Understanding Sabermetrics
Author: Gabriel B. Costa
Publisher: McFarland
ISBN: 1476667667
Category : Sports & Recreation
Languages : en
Pages : 221
Book Description
Interest in Sabermetrics has increased dramatically in recent years as the need to better compare baseball players has intensified among managers, agents and fans, and even other players. The authors explain how traditional measures--such as Earned Run Average, Slugging Percentage, and Fielding Percentage--along with new statistics--Wins Above Average, Fielding Independent Pitching, Wins Above Replacement, the Equivalence Coefficient and others--define the value of players. Actual player statistics are used in developing models, while examples and exercises are provided in each chapter. This book serves as a guide for both beginners and those who wish to be successful in fantasy leagues.
Publisher: McFarland
ISBN: 1476667667
Category : Sports & Recreation
Languages : en
Pages : 221
Book Description
Interest in Sabermetrics has increased dramatically in recent years as the need to better compare baseball players has intensified among managers, agents and fans, and even other players. The authors explain how traditional measures--such as Earned Run Average, Slugging Percentage, and Fielding Percentage--along with new statistics--Wins Above Average, Fielding Independent Pitching, Wins Above Replacement, the Equivalence Coefficient and others--define the value of players. Actual player statistics are used in developing models, while examples and exercises are provided in each chapter. This book serves as a guide for both beginners and those who wish to be successful in fantasy leagues.
Sabermetrics
Author: Gabriel B. Costa
Publisher: Academic Press
ISBN: 0128223464
Category : Mathematics
Languages : en
Pages : 219
Book Description
Sabermetrics: Baseball, Steroids, and How the Game has Changed Over the Past Two Generations offers an introduction to this increasing area of interest to statisticians, students of the game, and many others. Pairing a primer on the applied math with an overview of the origin of the field and its context within baseball today, the work provides an engaging resource for students and interested readers. It includes coverage of relevant baseball history, Bill James and SABR, broken records and steroids. Drawing on the author's experience teaching the subject at Seton Hall University since 1988, Sabermetrics also offers practice questions and solutions for class use. - Provides an accessible, brief introduction to the practice of sabermetrics - Approaches the topic in context with recent trends and issues in baseball - Includes questions and solutions for math practice
Publisher: Academic Press
ISBN: 0128223464
Category : Mathematics
Languages : en
Pages : 219
Book Description
Sabermetrics: Baseball, Steroids, and How the Game has Changed Over the Past Two Generations offers an introduction to this increasing area of interest to statisticians, students of the game, and many others. Pairing a primer on the applied math with an overview of the origin of the field and its context within baseball today, the work provides an engaging resource for students and interested readers. It includes coverage of relevant baseball history, Bill James and SABR, broken records and steroids. Drawing on the author's experience teaching the subject at Seton Hall University since 1988, Sabermetrics also offers practice questions and solutions for class use. - Provides an accessible, brief introduction to the practice of sabermetrics - Approaches the topic in context with recent trends and issues in baseball - Includes questions and solutions for math practice
Moneyball: The Art of Winning an Unfair Game
Author: Michael Lewis
Publisher: W. W. Norton & Company
ISBN: 0393066231
Category : Sports & Recreation
Languages : en
Pages : 337
Book Description
Michael Lewis’s instant classic may be “the most influential book on sports ever written” (People), but “you need know absolutely nothing about baseball to appreciate the wit, snap, economy and incisiveness of [Lewis’s] thoughts about it” (Janet Maslin, New York Times). One of GQ's 50 Best Books of Literary Journalism of the 21st Century Just before the 2002 season opens, the Oakland Athletics must relinquish its three most prominent (and expensive) players and is written off by just about everyone—but then comes roaring back to challenge the American League record for consecutive wins. How did one of the poorest teams in baseball win so many games? In a quest to discover the answer, Michael Lewis delivers not only “the single most influential baseball book ever” (Rob Neyer, Slate) but also what “may be the best book ever written on business” (Weekly Standard). Lewis first looks to all the logical places—the front offices of major league teams, the coaches, the minds of brilliant players—but discovers the real jackpot is a cache of numbers?numbers!?collected over the years by a strange brotherhood of amateur baseball enthusiasts: software engineers, statisticians, Wall Street analysts, lawyers, and physics professors. What these numbers prove is that the traditional yardsticks of success for players and teams are fatally flawed. Even the box score misleads us by ignoring the crucial importance of the humble base-on-balls. This information had been around for years, and nobody inside Major League Baseball paid it any mind. And then came Billy Beane, general manager of the Oakland Athletics. He paid attention to those numbers?with the second-lowest payroll in baseball at his disposal he had to?to conduct an astonishing experiment in finding and fielding a team that nobody else wanted. In a narrative full of fabulous characters and brilliant excursions into the unexpected, Michael Lewis shows us how and why the new baseball knowledge works. He also sets up a sly and hilarious morality tale: Big Money, like Goliath, is always supposed to win . . . how can we not cheer for David?
Publisher: W. W. Norton & Company
ISBN: 0393066231
Category : Sports & Recreation
Languages : en
Pages : 337
Book Description
Michael Lewis’s instant classic may be “the most influential book on sports ever written” (People), but “you need know absolutely nothing about baseball to appreciate the wit, snap, economy and incisiveness of [Lewis’s] thoughts about it” (Janet Maslin, New York Times). One of GQ's 50 Best Books of Literary Journalism of the 21st Century Just before the 2002 season opens, the Oakland Athletics must relinquish its three most prominent (and expensive) players and is written off by just about everyone—but then comes roaring back to challenge the American League record for consecutive wins. How did one of the poorest teams in baseball win so many games? In a quest to discover the answer, Michael Lewis delivers not only “the single most influential baseball book ever” (Rob Neyer, Slate) but also what “may be the best book ever written on business” (Weekly Standard). Lewis first looks to all the logical places—the front offices of major league teams, the coaches, the minds of brilliant players—but discovers the real jackpot is a cache of numbers?numbers!?collected over the years by a strange brotherhood of amateur baseball enthusiasts: software engineers, statisticians, Wall Street analysts, lawyers, and physics professors. What these numbers prove is that the traditional yardsticks of success for players and teams are fatally flawed. Even the box score misleads us by ignoring the crucial importance of the humble base-on-balls. This information had been around for years, and nobody inside Major League Baseball paid it any mind. And then came Billy Beane, general manager of the Oakland Athletics. He paid attention to those numbers?with the second-lowest payroll in baseball at his disposal he had to?to conduct an astonishing experiment in finding and fielding a team that nobody else wanted. In a narrative full of fabulous characters and brilliant excursions into the unexpected, Michael Lewis shows us how and why the new baseball knowledge works. He also sets up a sly and hilarious morality tale: Big Money, like Goliath, is always supposed to win . . . how can we not cheer for David?
Beyond Batting Average
Author: Lee Panas
Publisher: Lulu.com
ISBN: 0557312248
Category : Sports & Recreation
Languages : en
Pages : 153
Book Description
Over the past few decades, a multitude of advanced hitting, pitching, fielding and base running measures have been introduced to the baseball world. This comprehensive sabermetrics primer will introduce you to these new statistics with easy to understand explanations and examples. It will illustrate the evolution of statistics from simple traditional measures to the more complex metrics of today. You will learn how all the statistics are connected to winning and losing games, how to interpret them, and how to apply them to performance on the field. By the end of this book, you will be able to evaluate players and teams through statistics more thoroughly and accurately than you could before.
Publisher: Lulu.com
ISBN: 0557312248
Category : Sports & Recreation
Languages : en
Pages : 153
Book Description
Over the past few decades, a multitude of advanced hitting, pitching, fielding and base running measures have been introduced to the baseball world. This comprehensive sabermetrics primer will introduce you to these new statistics with easy to understand explanations and examples. It will illustrate the evolution of statistics from simple traditional measures to the more complex metrics of today. You will learn how all the statistics are connected to winning and losing games, how to interpret them, and how to apply them to performance on the field. By the end of this book, you will be able to evaluate players and teams through statistics more thoroughly and accurately than you could before.
Big Data Baseball
Author: Travis Sawchik
Publisher: Macmillan + ORM
ISBN: 1250063515
Category : Sports & Recreation
Languages : en
Pages : 235
Book Description
Big Data Baseball provides a behind-the-scenes look at how the Pittsburgh Pirates used big data strategies to end the longest losing streak in North American pro sports history. New York Times Bestseller After twenty consecutive losing seasons for the Pittsburgh Pirates, team morale was low, the club’s payroll ranked near the bottom of the sport, game attendance was down, and the city was becoming increasingly disenchanted with its team. Big Data Baseball is the story of how the 2013 Pirates, mired in the longest losing streak in North American pro sports history, adopted drastic big-data strategies to end the drought, make the playoffs, and turn around the franchise’s fortunes. Big Data Baseball is Moneyball for a new generation. Award-winning journalist Travis Sawchik takes you behind the scenes to expertly weave together the stories of the key figures who changed the way the Pirates played the game, revealing how a culture of collaboration and creativity flourished as whiz-kid analysts worked alongside graybeard coaches to revolutionize the sport and uncover groundbreaking insights for how to win more games without spending a dime. From pitch framing to on-field shifts, this entertaining and enlightening underdog story closely examines baseball’s burgeoning big data movement and demonstrates how the millions of data points which aren’t immediately visible to players and spectators, are the bit of magic that led the Pirates to finish the 2013 season in second place and brought an end to a twenty-year losing streak.
Publisher: Macmillan + ORM
ISBN: 1250063515
Category : Sports & Recreation
Languages : en
Pages : 235
Book Description
Big Data Baseball provides a behind-the-scenes look at how the Pittsburgh Pirates used big data strategies to end the longest losing streak in North American pro sports history. New York Times Bestseller After twenty consecutive losing seasons for the Pittsburgh Pirates, team morale was low, the club’s payroll ranked near the bottom of the sport, game attendance was down, and the city was becoming increasingly disenchanted with its team. Big Data Baseball is the story of how the 2013 Pirates, mired in the longest losing streak in North American pro sports history, adopted drastic big-data strategies to end the drought, make the playoffs, and turn around the franchise’s fortunes. Big Data Baseball is Moneyball for a new generation. Award-winning journalist Travis Sawchik takes you behind the scenes to expertly weave together the stories of the key figures who changed the way the Pirates played the game, revealing how a culture of collaboration and creativity flourished as whiz-kid analysts worked alongside graybeard coaches to revolutionize the sport and uncover groundbreaking insights for how to win more games without spending a dime. From pitch framing to on-field shifts, this entertaining and enlightening underdog story closely examines baseball’s burgeoning big data movement and demonstrates how the millions of data points which aren’t immediately visible to players and spectators, are the bit of magic that led the Pirates to finish the 2013 season in second place and brought an end to a twenty-year losing streak.
Teaching Statistics Using Baseball
Author: Jim Albert
Publisher: American Mathematical Society
ISBN: 1470469383
Category : Mathematics
Languages : en
Pages : 257
Book Description
Teaching Statistics Using Baseball is a collection of case studies and exercises applying statistical and probabilistic thinking to the game of baseball. Baseball is the most statistical of all sports since players are identified and evaluated by their corresponding hitting and pitching statistics. There is an active effort by people in the baseball community to learn more about baseball performance and strategy by the use of statistics. This book illustrates basic methods of data analysis and probability models by means of baseball statistics collected on players and teams. Students often have difficulty learning statistics ideas since they are explained using examples that are foreign to the students. The idea of the book is to describe statistical thinking in a context (that is, baseball) that will be familiar and interesting to students. The book is organized using a same structure as most introductory statistics texts. There are chapters on the analysis on a single batch of data, followed with chapters on comparing batches of data and relationships. There are chapters on probability models and on statistical inference. The book can be used as the framework for a one-semester introductory statistics class focused on baseball or sports. This type of class has been taught at Bowling Green State University. It may be very suitable for a statistics class for students with sports-related majors, such as sports management or sports medicine. Alternately, the book can be used as a resource for instructors who wish to infuse their present course in probability or statistics with applications from baseball. The second edition of Teaching Statistics follows the same structure as the first edition, where the case studies and exercises have been replaced by modern players and teams, and the new types of baseball data from the PitchFX system and fangraphs.com are incorporated into the text.
Publisher: American Mathematical Society
ISBN: 1470469383
Category : Mathematics
Languages : en
Pages : 257
Book Description
Teaching Statistics Using Baseball is a collection of case studies and exercises applying statistical and probabilistic thinking to the game of baseball. Baseball is the most statistical of all sports since players are identified and evaluated by their corresponding hitting and pitching statistics. There is an active effort by people in the baseball community to learn more about baseball performance and strategy by the use of statistics. This book illustrates basic methods of data analysis and probability models by means of baseball statistics collected on players and teams. Students often have difficulty learning statistics ideas since they are explained using examples that are foreign to the students. The idea of the book is to describe statistical thinking in a context (that is, baseball) that will be familiar and interesting to students. The book is organized using a same structure as most introductory statistics texts. There are chapters on the analysis on a single batch of data, followed with chapters on comparing batches of data and relationships. There are chapters on probability models and on statistical inference. The book can be used as the framework for a one-semester introductory statistics class focused on baseball or sports. This type of class has been taught at Bowling Green State University. It may be very suitable for a statistics class for students with sports-related majors, such as sports management or sports medicine. Alternately, the book can be used as a resource for instructors who wish to infuse their present course in probability or statistics with applications from baseball. The second edition of Teaching Statistics follows the same structure as the first edition, where the case studies and exercises have been replaced by modern players and teams, and the new types of baseball data from the PitchFX system and fangraphs.com are incorporated into the text.
Introduction to Data Science
Author: Rafael A. Irizarry
Publisher: CRC Press
ISBN: 1000708039
Category : Mathematics
Languages : en
Pages : 836
Book Description
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.
Publisher: CRC Press
ISBN: 1000708039
Category : Mathematics
Languages : en
Pages : 836
Book Description
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.
The Sabermetric Revolution
Author: Benjamin Baumer
Publisher: University of Pennsylvania Press
ISBN: 0812245725
Category : Sports & Recreation
Languages : en
Pages : 208
Book Description
The authors look at the history of statistical analysis in baseball, how it can best be used today and how its it must evolve for the future.
Publisher: University of Pennsylvania Press
ISBN: 0812245725
Category : Sports & Recreation
Languages : en
Pages : 208
Book Description
The authors look at the history of statistical analysis in baseball, how it can best be used today and how its it must evolve for the future.