In the numbers-obsessed sport of baseball, statistics don't merely record what players, managers, and owners have done. Properly understood, they can tell us how the teams we root for could employ better strategies, put more effective players on the field, and win more games. The revolution in baseball statistics that began in the 1970s is a controversial subject that professionals and fans alike argue over without end. Despite this fundamental change in the way we watch and understand the sport, no one has written the book that reveals, across every area of strategy and management, how the best practitioners of statistical analysis in baseball-people like Bill James, Billy Beane, and Theo Epstein-think about numbers and the game. Baseball Between the Numbers is that book. In separate chapters covering every aspect of the game, from hitting, pitching, and fielding to roster construction and the scouting and drafting of players, the experts at Baseball Prospectus examine the subtle, hidden aspects of the game, bring them out into the open, and show us how our favorite teams could win more games. This is a book that every fan, every follower of sports radio, every fantasy player, every coach, and every player, at every level, can learn from and enjoy.
How Wall Street Strategies Took a Major League Baseball Team from Worst to First First
Author: Jonah Keri
Category: Sports & Recreation
What happens when three financial industry whiz kids and certified baseball nuts take over an ailing major league franchise and implement the same strategies that fueled their success on Wall Street? In the case of the 2008 Tampa Bay Rays, an American League championship happens—the culmination of one of the greatest turnarounds in baseball history. In The Extra 2%, financial journalist and sportswriter Jonah Keri chronicles the remarkable story of one team’s Cinderella journey from divisional doormat to World Series contender. When former Goldman Sachs colleagues Stuart Sternberg and Matthew Silverman assumed control of the Tampa Bay Devil Rays in 2005, it looked as if they were buying the baseball equivalent of a penny stock. But the incoming regime came armed with a master plan: to leverage their skill at trading, valuation, and management to build a model twenty-first-century franchise that could compete with their bigger, stronger, richer rivals—and prevail. Together with “boy genius” general manager Andrew Friedman, the new Rays owners jettisoned the old ways of doing things, substituting their own innovative ideas about employee development, marketing and public relations, and personnel management. They exorcized the “devil” from the team’s nickname, developed metrics that let them take advantage of undervalued aspects of the game, like defense, and hired a forward-thinking field manager as dedicated to unconventional strategy as they were. By quantifying the game’s intangibles—that extra 2% that separates a winning organization from a losing one—they were able to deliver to Tampa Bay something that Billy Beane’s “Moneyball” had never brought to Oakland: an American League pennant. A book about what happens when you apply your business skills to your life’s passion, The Extra 2% is an informative and entertaining case study for any organization that wants to go from worst to first.
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.
Sabermetrics is taking baseball by storm. Whether you like it or not, it's difficult to watch a game without being inundated by stats. Are the numbers really as important as the Sabermetricians claim? How did we get to this point? Do we need to learn baseball al over again? Broken Baseball Numbers will answer these questions and many more. In the end, you'll have a totally new perspective on America's pastime.
The most ubiquitous feature of Harlem life between the world wars was the game of "numbers." Thousands of wagers, usually of a dime or less, would be placed on a daily number derived from U.S. bank statistics. The rewards of "hitting the number," a 600-to-1 payoff, tempted the ordinary men and women of the Black Metropolis with the chimera of the good life. This book tells the story of this illegal form of gambling and the central role it played in the lives of African Americans who flooded into Harlem in the wake of World War I. For a dozen years the "numbers game" was one of America's rare black-owned businesses, turning over tens of millions of dollars every year. The most successful "bankers" were known as Black Kings and Queens, and they lived royally. Yet the very success of "bankers" like Stephanie St. Clair and Casper Holstein attracted Dutch Schultz, Lucky Luciano, and organized crime to the game. By the late 1930s, most of the profits were being siphoned out of Harlem.
Written by a renowned multidisciplinary team of expert shoulder surgeons, athletic trainers, and physical therapists, this winning reference delivers the most comprehensive and up-to-date information on the evaluation, treatment, rehabilitation, and prevention of shoulder injuries in throwing and other overhead athletes. Included is critical information on shoulder anatomy and biomechanics, clinical examination, imaging, resistance training and core strengthening, and specific exercises for the overhead shoulder... plus state-of-the-art techniques for treatment and rehabilitation of each type of injury, including a separate section for pediatric overhead athletes. All physicians, coaches, trainers, strength and conditioning specialists, and therapists who care for overhead athletes at all levels of participation are sure to find this an indispensable resource. Book jacket.
Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more
Business Problems and Solutions with R, Revised and Expanded Edition
Author: Thomas W. Miller
Publisher: FT Press
To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more
The retiring of a number to honor a player likely began with the New York Yankees. The Yankees were not the first team to experiment with numbers on uniforms to identify players, but they were the first to wear numbers permanently and retired Lou Gehrig's number 4 in 1939. This book covers retired numbers in baseball's major and minor leagues. In the major league section of the book, a player's name is followed by his retired number, the name of the team that retired it, the year that it was retired, the player's primary position, and the teams he was affiliated with during his playing career. The author then presents a brief summary of the player's career and lists any major awards or honors he won. Retiring numbers in the minor leagues is a bit different; a player who excels in the minors isn't usually with a team for long because he is promoted to the majors. In the minor league section, a player's name is followed by a brief summary of his significance. After both the major and minor league sections, readers will find team-by-team and numerical lists of honored players.
"One of the more momentous books of the decade."—The New York Times Book Review Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of the website FiveThirtyEight. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future. In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball to global pandemics, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science. Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise. With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read.
This guide to all things Baltimore Oriole covers the team's history as one of the American League's eight charter franchises, including the incredible legacy of Cal Ripken, Jr., memories from Memorial Stadium, and how singing "Thank God I'm a Country Boy" during the seventh-inning stretch has become a fan-favorite tradition. Author Dan Connolly has collected every essential piece of Orioles knowledge and trivia, as well as must-do activities, providing an entertaining and enlightening read for any Oriole fan.
The Hype, Hokum, and Humbug of America's Favorite Pastime
Author: Bob Newhardt Carroll
Category: Sports & Recreation
Explores the myths and legends of baseball that have shaped the history of the sport, discussing who really invented baseball, ten Hall of Fame "mistakes," and five great things that never happened in a major league game
Publisher: The Mathematical Association of America
This book illustrates basic methods of data analysis and probability models by means of baseball statistics collected on players and teams. The idea of the book is to describe statistical thinking in a context that will be familiar and interesting to students. 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.
Through extensive interviews and archival research, Joe Clark has uncovered the engaging details of Australian baseball’s unique, and often turbulent, 125-year history, and for the first time the dynamic story of Australian baseball is told. Initially accepted only grudgingly in the late nineteenth century as an off-season substitute for cricket, baseball in Australia steadily rose in prominence. Starting with neighborhood games played between improvised teams, the sport grew to include state and national leagues and a spirited international competition. Both the shortcomings and the triumphs of Australian baseball are revealed in A History of Australian Baseball: Time and Game, from an ill-fated late-nineteenth-century baseball tour of America and the political firestorm surrounding the formation of the Australian Baseball League in the 1990s, to the amazing defeat of the powerhouse Cuban team in the Intercontinental Cup of 1999.
Why has the national pastime fallen behind other so-called major sports? Is the trend reversible? This book addresses these important questions by identifying the most unique, persistent and substantial issues that have impaired and most likely restricted Major League Baseballs development and potential as a professional sport.
Most baseball fans, players and even team executives assume that the National Pastime's infatuation with statistics is simply a byproduct of the information age, a phenomenon that blossomed only after the arrival of Bill James and computers in the 1980s. They couldn't be more wrong. In this unprecedented new book, Alan Schwarz - whom bestselling Moneyball author Michael Lewis calls "one of today's best baseball journalists" - provides the first-ever history of baseball statistics, showing how baseball and its numbers have been inseparable ever since the pastime's birth in 1845. He tells the history of this obsession through the lives of the people who felt it most: Henry Chadwick, the 19th-century writer who invented the first box score and harped endlessly about which statistics mattered and which did not; Allan Roth, Branch Rickey's right-hand numbers man with the late-1940s Brooklyn Dodgers; Earnshaw Cook, a scientist and Manhattan Project veteran who retired to pursue inventing the perfect baseball statistic; John Dewan, a former Strat-O-Matic maven who built STATS Inc. into a multimillion-dollar powerhouse for statistics over the Internet; and dozens more. Almost every baseball fan for 150 years has been drawn to the game by its statistics, whether through newspaper box scores, the backs of Topps baseball cards, The Baseball Encyclopedia, or fantasy leagues. Today's most ardent stat scientists, known as "sabermetricians," spend hundreds of hours coming up with new ways to capture the game in numbers, and engage in holy wars over which statistics are best. Some of these men - and women -- are even being hired by major league teams to bring an understanding of statistics to a sport that for so long shunned it. Taken together, Schwarz paints a history not just of baseball statistics, but of the soul of the sport itself. The Numbers Game will be an invaluable part of any fan's library and go down as one of the sport's classic books.
A thorough guide to the upcoming baseball season provides detailed profiles of major league players, teams, and prospects, along with information on statistics for the past five seasons and projections for the 2007 baseball season. Original. 100,000 first printing.