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This book inscribes itself in the growing context of sports analytics and the combination of data science with sports medicine, which deeply impact the world of sports. Today, ever-more data are being collected and lead to ground-breaking and game-changing new insights. This book provides a solid understanding of these new trends in sports and offers the interested reader with a starting point from which to dive into this blossoming field.
This book presents the field of sports statistics to two very distinct target audiences, namely academicians, in order to raise their interest in this growing field, and, on the other hand, sports fans, who, even without advanced mathematical knowledge, will be able to understand the data analysis and gain new insights into their favourite sports. The book thus offers a unique perspective on this attractive topic by combining sports analytics, data visualisation and advanced statistical procedures to extract new findings from sports data such as improved rankings or prediction methods. Bringing together insights from football, tennis, basketball, track and field, and baseball, the book will appeal to aficionados of any sport, and, thanks to its cutting-edge data analysis tools, will provide the reader with completely new insights into their favourite sport in an engaging and user-friendly way.
In Scorecasting, University of Chicago behavioral economist Tobias Moskowitz teams up with veteran Sports Illustrated writer L. Jon Wertheim to overturn some of the most cherished truisms of sports, and reveal the hidden forces that shape how basketball, baseball, football, and hockey games are played, won and lost. Drawing from Moskowitz's original research, as well as studies from fellow economists such as bestselling author Richard Thaler, the authors look at: the influence home-field advantage has on the outcomes of games in all sports and why it exists; the surprising truth about the universally accepted axiom that defense wins championships; the subtle biases that umpires exhibit in calling balls and strikes in key situations; the unintended consequences of referees' tendencies in every sport to "swallow the whistle," and more. Among the insights that Scorecasting reveals: • Why Tiger Woods is prone to the same mistake in high-pressure putting situations that you and I are • Why professional teams routinely overvalue draft picks • The myth of momentum or the "hot hand" in sports, and why so many fans, coaches, and broadcasters fervently subscribe to it • Why NFL coaches rarely go for a first down on fourth-down situations--even when their reluctance to do so reduces their chances of winning. In an engaging narrative that takes us from the putting greens of Augusta to the grid iron of a small parochial high school in Arkansas, Scorecasting will forever change how you view the game, whatever your favorite sport might be.
Evaluating statistical procedures through decision and game theory, as first proposed by Neyman and Pearson and extended by Wald, is the goal of this problem-oriented text in mathematical statistics. First-year graduate students in statistics and other students with a background in statistical theory and advanced calculus will find a rigorous, thorough presentation of statistical decision theory treated as a special case of game theory. The work of Borel, von Neumann, and Morgenstern in game theory, of prime importance to decision theory, is covered in its relevant aspects: reduction of games to normal forms, the minimax theorem, and the utility theorem. With this introduction, Blackwell and Professor Girshick look at: Values and Optimal Strategies in Games; General Structure of Statistical Games; Utility and Principles of Choice; Classes of Optimal Strategies; Fixed Sample-Size Games with Finite Ω and with Finite A; Sufficient Statistics and the Invariance Principle; Sequential Games; Bayes and Minimax Sequential Procedures; Estimation; and Comparison of Experiments. A few topics not directly applicable to statistics, such as perfect information theory, are also discussed. Prerequisites for full understanding of the procedures in this book include knowledge of elementary analysis, and some familiarity with matrices, determinants, and linear dependence. For purposes of formal development, only discrete distributions are used, though continuous distributions are employed as illustrations. The number and variety of problems presented will be welcomed by all students, computer experts, and others using statistics and game theory. This comprehensive and sophisticated introduction remains one of the strongest and most useful approaches to a field which today touches areas as diverse as gambling and particle physics.
Statistics for Sport and Exercise Studies guides the student through the full research process, from selecting the most appropriate statistical procedure, to analysing data, to the presentation of results, illustrating every key step in the process with clear examples, case-studies and data taken from real sport and exercise settings. Every chapter includes a range of features designed to help the student grasp the underlying concepts and relate each statistical procedure to their own research project, including definitions of key terms, practical exercises, worked examples and clear summaries. The book also offers an in-depth and practical guide to using SPSS in sport and exercise research, the most commonly used data analysis software in sport and exercise departments. In addition, a companion website includes more than 100 downloadable data sets and work sheets for use in or out of the classroom, full solutions to exercises contained in the book, plus over 1,300 PowerPoint slides for use by tutors and lecturers. Statistics for Sport and Exercise Studies is a complete, user-friendly introduction to the use of statistical tests, techniques and procedures in sport, exercise and related subjects. Visit the companion website at: www.routledge.com/cw/odonoghue
The New York Times bestseller – with a new afterword about early specialization in youth sports – from the author of Range: Why Generalists Triumph in a Specialized World. The debate is as old as physical competition. Are stars like Usain Bolt, Michael Phelps, and Serena Williams genetic freaks put on Earth to dominate their respective sports? Or are they simply normal people who overcame their biological limits through sheer force of will and obsessive training? In this controversial and engaging exploration of athletic success and the so-called 10,000-hour rule, David Epstein tackles the great nature vs. nurture debate and traces how far science has come in solving it. Through on-the-ground reporting from below the equator and above the Arctic Circle, revealing conversations with leading scientists and Olympic champions, and interviews with athletes who have rare genetic mutations or physical traits, Epstein forces us to rethink the very nature of athleticism.
This is an open access book. The 5th International Scientific Meeting on Public Health and Sports 2023 was organized by the Faculty of Sport Sciences, Universitas Negeri Malang. The 5th ISMoPHS 2023 is a conference that aims to bring together academics, scholars, researchers, and educators from various countries to exchange and share their knowledge, research results, and experiences on all aspects of public health and sports science. The theme of the 5th ISMoPHS is “Shifting Power and Equity in Global Health”.
Journey "inside the numbers" for an exceptional set of statistical tools and rules that can help explain the winning, or losing, ways of a basketball team. Basketball on Paper doesn't diagram plays or explain how players get in shape, but instead demonstrates how to interpret player and team performance. Dean Oliver highlights general strategies for teams when they're winning or losing and what aspects should be the focus in either situation. He describes and quantifies the jobs of team leaders and role players, then discusses the interactions between players and how to achieve the best fit. Oliver conceptualizes the meaning of teamwork and how to quantify the value of different types of players working together. He examines historically successful NBA teams and identifies what made them so successful: individual talent, a system of putting players together, or good coaching. Oliver then uses these statistical tools and case studies to evaluate the best players in history, such as Magic Johnson, Wilt Chamberlain, Bill Russell, and Charles Barkley and how they contributed to their teams' success. He does the same for some of the NBA's "oddball" players-Manute Bol, Muggsy Bogues, and Dennis Rodman and for the WNBA's top players. Basketball on Paper is unique in its incorporation of business and analytical concepts within the context of basketball to measure the value of players in a cooperative setting. Whether you're looking for strategies or new ideas to throw out while watching the ballgame at a sports bar, Dean Oliver'sBasketball on Paper will give you amazing new insights into teamwork, coaching, and success.
NSCA's Essentials of Sport Science provides the most contemporary and comprehensive overview of the field of sport science and the role of the sport scientist. It is a primary preparation resource for the Certified Performance and Sport Scientist (CPSS) certification exam.
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data