Broken up into sections (pitching, fielding, hitting), this authoritative yet fun and easy guide will help readers young and old fully understand and comprehend the statistics that are the present and future of our national pastime. We all know what a .300 hitter looks like. The same with a 20-game winner. Those numbers are ingrained in our brains. But do they mean as much as we think? Do we feel the same way when we hear a batter has a .390 wOBA? How about a pitcher with a 1.2 WHIP? These statistics are the future of modern baseball, and no fan should be in the dark about how these metrics apply to the game. In the last twenty years, an avalanche of analytics has taken over the way the game is played, managed, and assessed, but the statistics that drive the sport (metrics like wRC+, FIP, and WAR, just to name a few) read like alphabet soup to a large number of fans who still think batting average, RBIs, and wins are the best barometers for baseball players. In A Fan’s Guide to Baseball Analytics, MLB.com reporter and columnist Anthony Castrovince has taken on the role as explainer to help such fans understand why the old stats don’t always add up. Readers will also learn where these modern stats came from, what they convey, and how to use them to evaluate players of the present, past, and future. For instance, what if we told you that when Joe DiMaggio had his famous 56-game hitting streak in 1941, helping him win the AL MVP, that there was, perhaps, someone more deserving? In fact, the great Ted Williams actually had a higher fWAR, bWAR, wRC+, OPS, OPS+, ISO, RC . . . well, you get the picture. So, streak or no streak, Williams should have been league MVP. An introductory course on sabermetrics, A Fan’s Guide to Baseball Analytics is an easily digestible resource that readers can keep turning back to when they see a modern metric referenced in today’s baseball coverage.

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.

Baseball Hacks isn't your typical baseball book--it's a book about how to watch, research, and understand baseball. It's an instruction manual for the free baseball databases. It's a cookbook for baseball research. Every part of this book is designed to teach baseball fans how to do something. In short, it's a how-to book--one that will increase your enjoyment and knowledge of the game. So much of the way baseball is played today hinges upon interpreting statistical data. Players are acquired based on their performance in statistical categories that ownership deems most important. Managers make in-game decisions based not on instincts, but on probability - how a particular batter might fare against left-handedpitching, for instance. The goal of this unique book is to show fans all the baseball-related stuff that they can do for free (or close to free). Just as open source projects have made great software freely available, collaborative projects such as Retrosheet and Baseball DataBank have made great data freely available. You can use these data sources to research your favorite players, win your fantasy league, or appreciate the game of baseball even more than you do now. Baseball Hacks shows how easy it is to get data, process it, and use it to truly understand baseball. The book lists a number of sources for current and historical baseball data, and explains how to load it into a database for analysis. It then introduces several powerful statistical tools for understanding data and forecasting results. For the uninitiated baseball fan, author Joseph Adler walks readers through the core statistical categories for hitters (batting average, on-base percentage, etc.), pitchers (earned run average, strikeout-to-walk ratio, etc.), and fielders (putouts, errors, etc.). He then extrapolates upon these numbers to examine more advanced data groups like career averages, team stats, season-by-season comparisons, and more. Whether you're a mathematician, scientist, or season-ticket holder to your favorite team, Baseball Hacks is sure to have something for you. Advance praise for Baseball Hacks: "Baseball Hacks is the best book ever written for understanding and practicing baseball analytics. A must-read for baseball professionals and enthusiasts alike." -- Ari Kaplan, database consultant to the Montreal Expos, San Diego Padres, and Baltimore Orioles "The game was born in the 19th century, but the passion for its analysis continues to grow into the 21st. In Baseball Hacks, Joe Adler not only demonstrates thatthe latest data-mining technologies have useful application to the study of baseball statistics, he also teaches the reader how to do the analysis himself, arming the dedicated baseball fan with tools to take his understanding of the game to a higher level." -- Mark E. Johnson, Ph.D., Founder, SportMetrika, Inc. and Baseball Analyst for the 2004 St. Louis Cardinals

Data and analytics have the potential to provide sports organizations with a competitive advantage both on and off the field. Yet even as the use of analytics in sports has become commonplace, teams regularly find themselves making big investments without significant payoff. This book is a practical, nontechnical guide to incorporating sports data into decision making, giving leaders the knowledge they need to maximize their organization’s investment in analytics. Benjamin C. Alamar—a leading expert who has built high-performing analytics groups—surveys the current state of the use of data in sports, including both specifics around the tools and how to deploy them most effectively. Sports Analytics offers a clear, easily digestible overview of data management, statistical models, and information systems and a detailed understanding of their vast possibilities. It walks readers through the essentials of understanding the value of different types of data and strategies for building and managing an analytics team. Throughout, Alamar illustrates the value of analytics with real-world examples and case studies from both the sports and business sides. Sports Analytics has guided a range of sports professionals to success since its original publication in 2013. This second edition adds examples and strategies that focus on using data on the business side of a sports organization, provides concrete strategies for incorporating different types of data into decision making, and updates all discussions for the rapid technological developments of the last decade.

Zack Hample's bestselling, smart, and funny fan’s guide to baseball explains the ins and outs of pitching, hitting, running, and fielding, while offering insider trivia and anecdotes that will appeal to anyone—whether you're a major league couch potato, life-long season ticket-holder, or a beginner. • What is the difference between a slider and a curveball? • At which stadium did “The Wave” first make an appearance? • Which positions are never played by lefties? • Why do some players urinate on their hands? Combining the narrative voice and attitude of Michael Lewis with the compulsive brilliance of Schott’s Miscellany, Watching Baseball Smarter will increase your understanding and enjoyment of the sport—no matter what your level of expertise. Featuring a glossary of baseball slang, an appendix of important baseball stats, and an appendix of uniform numbers.

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.

With every passing season, statistical analysis is playing an ever-increasing role in how hockey is played and covered. Knowledge of the underlying numbers can help fans stretch their enjoyment of the game. Acting as an invaluable supplement to traditional analysis, Stat Shot: A Fan’s Guide to Hockey Analytics can be used to test the validity of conventional wisdom and to gain insight into what teams are doing behind the scenes — or maybe what they should be doing! Inspired by Bill James’s Baseball Abstract, Rob Vollman has written a timeless reference of the mainstream applications and limitations of hockey analytics. With over 300 pages of fresh analysis, it includes a guide to the basics, how to place stats into context, how to translate data from one league to another, the most comprehensive glossary of hockey statistics, and more. Whether A Fan’s Guide to Hockey Analytics is used as a primer for today’s new statistics, as a reference for leading edge research and hard-to-find statistical data, or read for its passionate and engaging storytelling, it belongs on every serious fan’s bookshelf. A Fan’s Guide to Hockey Analytics makes advanced stats simple, practical, and fun.

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.

Predictably Irrational meets Moneyball in ESPN veteran writer and statistical analyst Keith Law’s iconoclastic look at the numbers game of baseball, proving why some of the most trusted stats are surprisingly wrong, explaining what numbers actually work, and exploring what the rise of Big Data means for the future of the sport. For decades, statistics such as batting average, saves recorded, and pitching won-lost records have been used to measure individual players’ and teams’ potential and success. But in the past fifteen years, a revolutionary new standard of measurement—sabermetrics—has been embraced by front offices in Major League Baseball and among fantasy baseball enthusiasts. But while sabermetrics is recognized as being smarter and more accurate, traditionalists, including journalists, fans, and managers, stubbornly believe that the "old" way—a combination of outdated numbers and "gut" instinct—is still the best way. Baseball, they argue, should be run by people, not by numbers.? In this informative and provocative book, teh renowned ESPN analyst and senior baseball writer demolishes a century’s worth of accepted wisdom, making the definitive case against the long-established view. Armed with concrete examples from different eras of baseball history, logic, a little math, and lively commentary, he shows how the allegiance to these numbers—dating back to the beginning of the professional game—is firmly rooted not in accuracy or success, but in baseball’s irrational adherence to tradition. While Law gores sacred cows, from clutch performers to RBIs to the infamous save rule, he also demystifies sabermetrics, explaining what these "new" numbers really are and why they’re vital. He also considers the game’s future, examining how teams are using Data—from PhDs to sophisticated statistical databases—to build future rosters; changes that will transform baseball and all of professional sports.

Baseball "by The Book."