They were a ragtag team of girls who wanted to play soccer, and no one took them seriously. Their male coach expected little from his "ladies, " and their mediocre performance convinced them he was right.Then a kind of miracle happened. Emiria Salzmann, Thunder's new coach, a top player herself, knew what it took to win--discipline, relentless drills, thigh-burning sprints, and an inspired passing game. The girls hated it, but their coach never let up. Tough and determined, she showed them what it felt like to be winners--and they loved it. As the momentum grew with a string of victories, the girls thrived on the competition, believing they had the right stuff to become champions.They were right! With spirits soaring, Thunder won its league on the last day of the season and headed for the state cup, emerging not just as powerful athletes but as strong, confident, emotionally healthy human beings--champions in the game of soccer, and in the game of life.

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    In A Coach’s Life, he looks back on the great games, teams, players, strategies, and rivalries that defined his career and, in a new final chapter, discusses his retirement from the game. The fundamentals of good basketball are the fundamentals of character—passion, discipline, focus, selflessness, and responsibility—and superlative mentor and coach Dean Smith imparts them all with equal authority.

    The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your dataWonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author

     What does it take to drop ten strokes from your golf score? What part of Tiger Woods’ game makes him a winner? Traditional golf stats can't answer these questions. Broadie, a professor at Columbia Business School, helped the PGA Tour develop its cutting-edge strokes gained putting stat. In this eye-opening new book, Broadie uses analytics from the financial world to uncover the secrets of the game of golf. He crunches mountains of data to show both professional and amateur golfers how to make better decisions on the course.  This eagerly awaited resource is for any player who wants to understand the pros, improve golf skills, and make every shot count.

    It's not the star.It's not money. It's not a strategy.It's something else entirely.Several years ago, Sam Walker set out to answer one of the most hotly debated questions in sports: What are the greatest teams of all time? He devised a formula, then applied it to thousands of teams from leagues all over the world, from the NBA to the English Premier League to Olympic field hockey. When he was done, he had a list of the sixteen most dominant teams in history. At that point, he became obsessed with another, more complicated question: What did these freak teams have in common?As Walker dug into their stories, a pattern emerged: Each team had the same type of captain--a singular leader with an unconventional skill set who drove it to achieve sustained, historic greatness.Fueled by a lifetime of sports spectating, twenty years of reporting, and a decade of painstaking research, The Captain Class tells the surprising story of what makes teams exceptional. Drawing on original interviews with athletes from two dozen countries, as well as general managers, coaches, executives, and others skilled at building teams, Walker identifies the seven core qualities of this Captain Class--from extreme doggedness and emotional control to a knack for nonverbal communication to tactical aggression and the courage to stand apart.Told through riveting accounts of some of the most pressure-soaked moments in sports history--from Bill Russell's legendary "Coleman Play" in the 1957 NBA Finals to Barcelona's "Figo Game" against Real Madrid in 2000--The Captain Class doesn't just bring these events to life; it presents a fresh, counterintuitive take on leadership that can be applied to a wide spectrum of competitive disciplines.The men and women who make up the Captain Class were never the most skilled athletes, nor were they gifted orators or paragons of sportsmanship. They were often role players who were allergic to the spotlight. In short, the seven attributes they shared challenge your assumptions of what inspired leadership looks like.

    Most of the time this math works quietly behind the scenes . . . until it doesn't. All sorts of seemingly innocuous mathematical mistakes can have significant consequences.Math is easy to ignore until a misplaced decimal point upends the stock market, a unit conversion error causes a plane to crash, or someone divides by zero and stalls a battleship in the middle of the ocean.Exploring and explaining a litany of glitches, near misses, and mathematical mishaps involving the internet, big data, elections, street signs, lotteries, the Roman Empire, and an Olympic team, Matt Parker uncovers the bizarre ways math trips us up, and what this reveals about its essential place in our world. Getting it wrong has never been more fun.

    Never again will you order the Poisson Distribution in a French restaurant!This updated version features all new material.

    Gaining access to personal letters, albums and scrapbooks, plus spending hours with family members among some 300 interviews, has allowed the authors to craft the definitive biography of Pistol Pete Maravich, who lived a life of triumph and tragedy.

    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 scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.

    It is designed as a reference for students in the physical sciences and engineering.

    In keeping with his reputation for writing about big ideas in an illuminating and graceful way, he shows how sports respond to deep human needs; describes the ways in which baseball, football and basketball became national institutions and how they reached their present forms; and covers the evolution of rules, the rise and fall of the most successful teams, and the historical significance of the most famous and influential figures such as Babe Ruth, Vince Lombardi, and Michael Jordan. Whether he is writing about baseball as the agrarian game, football as similar to warfare, basketball as the embodiment of post-industrial society, or the moral havoc created by baseball's designated hitter rule, Mandelbaum applies the full force of his learning and wit to subjects about which so many Americans care passionately: the games they played in their youth and continue to follow as adults. By offering a fresh and unconventional perspective on these games, The Meaning of Sports makes for fascinating and rewarding reading both for fans and newcomers.

     Two hot topics team up in The Baseball Economist, and the result is a refreshing, clear- eyed survey of a playing field that has changed radically in recent years. Utilizing the latest economic methods and statistical analysis, writer, economics professor, and popular blogger J. C. Bradbury dissects burning baseball topics with his original Sabernomic perspective, such as: • Did steroids have nothing to do with the recent home run records? Incredibly, Bradbury's research, reviewed by Stanford economists, reveals steroids had little statistical significance. • Is the big-city versus small-city competition really lopsided? Bradbury shows why the Marlins and Indians are likely to dominate big-city franchises in the coming years. • Which players are ridiculously overvalued? Bradbury lists all players by team with their revenue value to the team listed in dollars—including a dishonor role of those players with negative values. • Is major league baseball a monopoly that can't govern itself? Bradbury sets out what rules the owners really need to play by, and what the players' union should be doing. • Does it help to lobby for balls and strikes? How would Babe Ruth perform in today's game? And who killed all the left-handed catchers, anyway? The Baseball Economist knows. Providing far more than a mere collection of numbers, Bradbury shines the light of his economic thinking on baseball, exposing the power of tradeoffs, competition, and incentives. Statistics alone aren't enough anymore. Fans, fantasy buffs, and players, as well as coaches at all levels who want to grasp what is really happening on the field today and in the coming years, will use and enjoy Bradbury's brilliant new understanding of the national pastime.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.