Practicing Sabermetrics
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| Author | : Gabriel B. Costa |
| Publisher | : McFarland |
| Total Pages | : 241 |
| Release | : 2009-10-21 |
| Genre | : Sports & Recreation |
| ISBN | : 0786454466 |
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.
| Author | : Gabriel B. Costa |
| Publisher | : McFarland |
| Total Pages | : 221 |
| Release | : 2019-06-07 |
| Genre | : Sports & Recreation |
| ISBN | : 1476635021 |
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.
| Author | : Gabriel B. Costa |
| Publisher | : McFarland |
| Total Pages | : 217 |
| Release | : 2012-08-17 |
| Genre | : Sports & Recreation |
| ISBN | : 0786492813 |
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.
| Author | : Gabriel B. Costa |
| Publisher | : Academic Press |
| Total Pages | : 219 |
| Release | : 2021-10-27 |
| Genre | : Mathematics |
| ISBN | : 0128223464 |
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
| Author | : Jim Albert |
| Publisher | : American Mathematical Society |
| Total Pages | : 257 |
| Release | : 2022-02-04 |
| Genre | : Mathematics |
| ISBN | : 1470469383 |
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.
| Author | : Michael Lewis |
| Publisher | : W. W. Norton & Company |
| Total Pages | : 337 |
| Release | : 2004-03-17 |
| Genre | : Sports & Recreation |
| ISBN | : 0393066231 |
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?
| Author | : Benjamin Baumer |
| Publisher | : University of Pennsylvania Press |
| Total Pages | : 204 |
| Release | : 2014-01-16 |
| Genre | : Sports & Recreation |
| ISBN | : 0812209125 |
From the front office to the family room, sabermetrics has dramatically changed the way baseball players are assessed and valued by fans and managers alike. Rocketed to popularity by the 2003 bestseller Moneyball and the film of the same name, the use of sabermetrics to analyze player performance has appeared to be a David to the Goliath of systemically advantaged richer teams that could be toppled only by creative statistical analysis. The story has been so compelling that, over the past decade, team after team has integrated statistical analysis into its front office. But how accurately can crunching numbers quantify a player's ability? Do sabermetrics truly level the playing field for financially disadvantaged teams? How much of the baseball analytic trend is fad and how much fact? The Sabermetric Revolution sets the record straight on the role of analytics in baseball. Former Mets sabermetrician Benjamin Baumer and leading sports economist Andrew Zimbalist correct common misinterpretations and develop new methods to assess the effectiveness of sabermetrics on team performance. Tracing the growth of front office dependence on sabermetrics and the breadth of its use today, they explore how Major League Baseball and the field of sports analytics have changed since the 2002 season. Their conclusion is optimistic, but the authors also caution that sabermetric insights will be more difficult to come by in the future. The Sabermetric Revolution offers more than a fascinating case study of the use of statistics by general managers and front office executives: for fans and fantasy leagues, this book will provide an accessible primer on the real math behind moneyball as well as new insight into the changing business of baseball.
| Author | : Lee Panas |
| Publisher | : Lulu.com |
| Total Pages | : 153 |
| Release | : 2010 |
| Genre | : Sports & Recreation |
| ISBN | : 0557312248 |
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.
| Author | : Rafael A. Irizarry |
| Publisher | : CRC Press |
| Total Pages | : 836 |
| Release | : 2019-11-20 |
| Genre | : Mathematics |
| ISBN | : 1000708039 |
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.
| Author | : Max Marchi |
| Publisher | : CRC Press |
| Total Pages | : 302 |
| Release | : 2018-11-19 |
| Genre | : Mathematics |
| ISBN | : 1351107070 |
Analyzing Baseball Data with R Second Edition introduces R to sabermetricians, baseball enthusiasts, and students interested in exploring the richness of baseball data. It equips you with the necessary skills and software tools to perform all the analysis steps, from importing the data to transforming them into an appropriate format to visualizing the data via graphs to performing a statistical analysis. The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the ggplot2 graphics functions and employ a tidyverse-friendly workflow throughout. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, catcher framing, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and launch angles and exit velocities. All the datasets and R code used in the text are available online. New to the second edition are a systematic adoption of the tidyverse and incorporation of Statcast player tracking data (made available by Baseball Savant). All code from the first edition has been revised according to the principles of the tidyverse. Tidyverse packages, including dplyr, ggplot2, tidyr, purrr, and broom are emphasized throughout the book. Two entirely new chapters are made possible by the availability of Statcast data: one explores the notion of catcher framing ability, and the other uses launch angle and exit velocity to estimate the probability of a home run. Through the book’s various examples, you will learn about modern sabermetrics and how to conduct your own baseball analyses. Max Marchi is a Baseball Analytics Analyst for the Cleveland Indians. He was a regular contributor to The Hardball Times and Baseball Prospectus websites and previously consulted for other MLB clubs. Jim Albert is a Distinguished University Professor of statistics at Bowling Green State University. He has authored or coauthored several books including Curve Ball and Visualizing Baseball and was the editor of the Journal of Quantitative Analysis of Sports. Ben Baumer is an assistant professor of statistical & data sciences at Smith College. Previously a statistical analyst for the New York Mets, he is a co-author of The Sabermetric Revolution and Modern Data Science with R.
