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A look at baseball data from a statistical modeling perspective! There is a fascination among baseball fans and the media to collect data on every imaginable event during a baseball game and this book addresses a number of questions that are of interest to many baseball fans. These include how to rate players, predict the outcome of a game or the attainment of an achievement, making sense of situational data, and deciding the most valuable players in the World Series. Aimed at a general audience, the text does not assume any prior background in probability or statistics, although a knowledge of high school abgebra will be helpful.In its formative years, from the 1970s through the 1990s, sabermetrics was p- marily an amateur undertaking. Publications were aimed at a relatively small audience of baseball fans. To be sure, this ever-growing group of aficionados brought a lot of sophistication to baseball analysis, and were constantly looking for statistical insights beyond the listings of the top ten batters found in popular newspapers and magazines. But their influence on the baseball profession was very limited. A few consultants like Craig Wright developed temporary relati- ships with various teams, but none were able to stay long enough to create a p- manent sabermetrician staff position. (See Rob Neyers November 11, 2002, arti- 1 cle on ESPN. com. ) All of this changed, however, in 2002 with the hiring of Bill James by the Boston Red Sox. With that move, we have seen the admittance of the foremost proponent of sabermetrics into the top echelon of professional ba- ball management. The art and science of careful statistical analysis, it now seems, had made it into the big leagues. Since the publication of the first edition of Curve Ball in 2001, we have been overwhelmed by the positive responses from readers and critics. Were pleased with the reception, of course, but we dont want to rest on our laurels. Like a pitcher refining his repertoire, weve revised, expal#}
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