Search results for: big-data-baseball-math-miracles-and-the-end-of-a-20-year-losing-streak

Big Data Baseball

Author : Travis Sawchik
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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. Pittsburghers joked their town was the city of champions...and the Pirates. 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. 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 small-market Pirates played the game. For manager Clint Hurdle and the front office staff to save their jobs, they could not rely on a free agent spending spree, instead they had to improve the sum of their parts and find hidden value. They had to change. From Hurdle shedding his old-school ways to work closely with Neal Huntington, the forward-thinking data-driven GM and his team of talented analysts; to pitchers like A. J. Burnett and Gerrit Cole changing what and where they threw; to Russell Martin, the undervalued catcher whose expert use of the nearly-invisible skill of pitch framing helped the team's pitchers turn more balls into strikes; to Clint Barmes, a solid shortstop and one of the early adopters of the unconventional on-field shift which forced the entire infield to realign into positions they never stood in before. Under Hurdle's leadership, a culture of collaboration and creativity flourished as he successfully blended whiz kid analysts with graybeard coaches—a kind of symbiotic teamwork which was unique to the sport. Big Data Baseball is Moneyball on steroids. It is an entertaining and enlightening underdog story that uses the 2013 Pirates season as the perfect lens to examine the sport's burgeoning big-data movement. With the help of data-tracking systems like PitchF/X and TrackMan, the Pirates collected millions of data points on every pitch and ball in play to create a tome of color-coded reports that revealed groundbreaking insights for how to win more games without spending a dime. In the process, they discovered that most batters struggled to hit two-seam fastballs, that an aggressive defensive shift on the field could turn more batted balls into outs, and that a catcher's most valuable skill was hidden. All these data points which aren't immediately visible to players and spectators, are the bit of magic that led the Pirates to spin straw in to gold, finish the 2013 season in second place, end a twenty-year losing streak.

Big Data in Cognitive Science

Author : Michael N. Jones
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While laboratory research is the backbone of collecting experimental data in cognitive science, a rapidly increasing amount of research is now capitalizing on large-scale and real-world digital data. Each piece of data is a trace of human behavior and offers us a potential clue to understanding basic cognitive principles. However, we have to be able to put the pieces together in a reasonable way, which necessitates both advances in our theoretical models and development of new methodological techniques. The primary goal of this volume is to present cutting-edge examples of mining large-scale and naturalistic data to discover important principles of cognition and evaluate theories that would not be possible without such a scale. This book also has a mission to stimulate cognitive scientists to consider new ways to harness big data in order to enhance our understanding of fundamental cognitive processes. Finally, this book aims to warn of the potential pitfalls of using, or being over-reliant on, big data and to show how big data can work alongside traditional, rigorously gathered experimental data rather than simply supersede it. In sum, this groundbreaking volume presents cognitive scientists and those in related fields with an exciting, detailed, stimulating, and realistic introduction to big data – and to show how it may greatly advance our understanding of the principles of human memory, perception, categorization, decision-making, language, problem-solving, and representation.

SABR 50 at 50

Author : Bill Nowlin
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SABR 50 at 50 celebrates and highlights the Society for American Baseball Research’s wide-ranging contributions to baseball history. Established in 1971 in Cooperstown, New York, SABR has sought to foster and disseminate the research of baseball—with groundbreaking work from statisticians, historians, and independent researchers—and has published dozens of articles with far-reaching and long-lasting impact on the game. Among its current membership are many Major and Minor League Baseball officials, broadcasters, and writers as well as numerous former players. The diversity of SABR members’ interests is reflected in this fiftieth-anniversary volume—from baseball and the arts to statistical analysis to the Deadball Era to women in baseball. SABR 50 at 50 includes the most important and influential research published by members across a multitude of topics, including the sabermetric work of Dick Cramer, Pete Palmer, and Bill James, along with Jerry Malloy on the Negro Leagues, Keith Olbermann on why the shortstop position is number 6, John Thorn and Jules Tygiel on the untold story behind Jackie Robinson’s signing with the Dodgers, and Gai Berlage on the Colorado Silver Bullets women’s team in the 1990s. To provide history and context, each notable research article is accompanied by a short introduction. As SABR celebrates fifty years this collection gathers the organization’s most notable research and baseball history for the serious baseball reader.

Markets against Modernity

Author : Ryan H. Murphy
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In Markets Against Modernity, economist Ryan Murphy documents a clear continuity between the systematic errors people make in their personal lives and the gaps between public opinion and informed opinion. These errors cluster around specific divergences between how the modern world’s institutions function—including global markets, pluralistic democracy, and even science itself—and how evolution trained our brains to understand the nature of economic relationships, social relationships, and humanity’s relationship to the physical world. Murphy calls these systematic divergences Ecological Irrationality. Exploring them leads him to even more prickly questions—and to conclusions that may challenge the beliefs of those who understand that, for instance, modern vaccines are safe and effective. Do we actually want a less cohesive society? Is doing a task yourself financially prudent? And if we recognize an expert consensus, is there even a way to implement it and achieve the desired effects?

Python for Programmers

Author : Paul J. Deitel
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The professional programmer’s Deitel® guide to Python® with introductory artificial intelligence case studies Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today’s most compelling, leading-edge computing technologies and programming in Python–one of the world’s most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details. In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you’ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you’ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop®, Spark™ and NoSQL databases, the Internet of Things and more. You’ll also work directly or indirectly with cloud-based services, including Twitter, Google Translate™, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub and more. Features 500+ hands-on, real-world, live-code examples from snippets to case studies IPython + code in Jupyter® Notebooks Library-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code Rich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptions Procedural, functional-style and object-oriented programming Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames Static, dynamic and interactive visualizations Data experiences with real-world datasets and data sources Intro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression AI, big data and cloud data science case studies: NLP, data mining Twitter®, IBM® Watson™, machine learning, deep learning, computer vision, Hadoop®, Spark™, NoSQL, IoT Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras and more Accompanying code examples are available here: http://ptgmedia.pearsoncmg.com/imprint_downloads/informit/bookreg/9780135224335/9780135224335_examples.zip. Register your product for convenient access to downloads, updates, and/or corrections as they become available. See inside book for more information.