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.

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.

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?

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.

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.

When Bill James published his original Historical Baseball Abstract in 1985, he produced an immediate classic, hailed by the Chicago Tribune as the “holy book of baseball.” Now, baseball's beloved “Sultan of Stats” (The Boston Globe) is back with a fully revised and updated edition for the new millennium. Like the original, The New Bill James Historical Baseball Abstract is really several books in one. The Game provides a century's worth of American baseball history, told one decade at a time, with energetic facts and figures about How, Where, and by Whom the game was played. In The Players, you'll find listings of the top 100 players at each position in the major leagues, along with James's signature stats-based ratings method called “Win Shares,” a way of quantifying individual performance and calculating the offensive and defensive contributions of catchers, pitchers, infielders, and outfielders. And there's more: the Reference section covers Win Shares for each season and each player, and even offers a Win Share team comparison. A must-have for baseball fans and historians alike, The New Bill James Historical Baseball Abstract is as essential, entertaining, and enlightening as the sport itself.

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

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.

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.

Baseball "by The Book."