Sabermetrics, the search for objective knowledge about baseball through statistical analysis, has taken over the national pastime. The authors argue that this approach began as a useful corrective but has come to harm baseball. The book demonstrates that the so-called moneyball approach, based on sabermetrics, offers only limited guidance for assembling a team, managing games, and evaluating player performance. Equally important, the obsession with statistics and vision of the game as wholly predictable obscure baseball's spectacular improvisational quality. It is the game's unquantifiable and relentless capacity to surprise--the source of wonder so central to its greatest stories and personalities--that informs any real appreciation of baseball.

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.

“Winner of the 2018 CASEY Award for Best Baseball Book of the Year.” The former ESPN columnist and analytics pioneer dramatically recreates an action-packed 2017 game between the Oakland A’s and eventual World Series Champion Houston Astros to reveal the myriad ways in which Major League Baseball has changed over the last few decades. On September 8, 2017, the Oakland A’s faced off against the Houston Astros in a game that would signal the passing of the Moneyball mantle. Though this was only one regular season game, the match-up of these two teams demonstrated how Major League Baseball has changed since the early days of Athletics general manager Billy Beane and the publication of Michael Lewis’ classic book. Over the past twenty years, power and analytics have taken over the game, driving carefully calibrated teams like the Astros to victory. Seemingly every pitcher now throws mid-90s heat and studiously compares their mechanics against the ideal. Every batter in the lineup can crack homers and knows their launch angles. Teams are relying on unorthodox strategies, including using power-losing—purposely tanking a few seasons to get the best players in the draft. As he chronicles each inning and the unfolding drama as these two teams continually trade the lead—culminating in a 9-8 Oakland victory in the bottom of the ninth—Neyer considers the players and managers, the front office machinations, the role of sabermetrics, and the current thinking about what it takes to build a great team, to answer the most pressing questions fans have about the sport today.

From Aerial Surveying over London & the UK in a noisy twin engine plane to flying small single and twin engine planes from the warmth of the Caribbean, up the East Coast of America, across the Atlantic Ocean, over the Greenland Ice Cap towards the North Pole and back down to Fairoaks Airport near London. A light-hearted look at flying.

Propounding his "small ball theory" of sports literature, George Plimpton proposed that "the smaller the ball, the more formidable the literature." Of course he had the relatively small baseball in mind, because its literature is formidable--vast and varied, instructive, often wildly entertaining, and occasionally brilliant. From this bewildering array of baseball books, Ron Kaplan has chosen 501 of the best, making it easier for fans to find just the books to suit them (or to know what they're missing). From biography, history, fiction, and instruction to books about ballparks, business, and rules, anyone who loves to read about baseball will find in this book a companionable guide, far more fun than a reference work has any right to be.

How to stay in charge in a world populated by algorithms that beat us in chess, find us romantic partners, and tell us to “turn right in 500 yards.” Doomsday prophets of technology predict that robots will take over the world, leaving humans behind in the dust. Tech industry boosters think replacing people with software might make the world a better place—while tech industry critics warn darkly about surveillance capitalism. Despite their differing views of the future, they all agree: machines will soon do everything better than humans. In How to Stay Smart in a Smart World, Gerd Gigerenzer shows why that’s not true, and tells us how we can stay in charge in a world populated by algorithms. Machines powered by artificial intelligence are good at some things (playing chess), but not others (life-and-death decisions, or anything involving uncertainty). Gigerenzer explains why algorithms often fail at finding us romantic partners (love is not chess), why self-driving cars fall prey to the Russian Tank Fallacy, and how judges and police rely increasingly on nontransparent “black box” algorithms to predict whether a criminal defendant will reoffend or show up in court. He invokes Black Mirror, considers the privacy paradox (people want privacy, but give their data away), and explains that social media get us hooked by programming intermittent reinforcement in the form of the “like” button. We shouldn’t trust smart technology unconditionally, Gigerenzer tells us, but we shouldn’t fear it unthinkingly, either.

This book examines the influence of key films on public understanding of big data and the algorithmic systems that structure our digitally mediated lives. From star-powered blockbusters to civic-minded documentaries positioned to facilitate weighty debates about artificial intelligence, these texts frame our discourse and mediate our relationship to technology. Above all, they impact society’s abilities to regulate AI and navigate big tech’s political and economic maneuvers to achieve market dominance and regulatory capture. Foregrounding data politics with close readings of key films like Moneyball, Minority Report, The Social Dilemma, and Coded Bias, Gerald Sim reveals compelling ways in which films and tech industry–adjacent media define apprehension of AI. With the mid-2010s techlash in danger of fizzling out, Screening Big Data explores the relationship between this resistance and cultural infrastructure while highlighting the urgent need to refocus attention onto how technocentric media occupy the public imagination. This book will interest students and scholars of film and media studies, digital culture, critical data studies, and technopolitics.