Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player's shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable for students, technicians, coaches, data analysts and applied researchers. Features: One of the first books to provide statistical and data mining methods for the growing field of analytics in basketball Presents tools for modelling graphs and figures to visualize the data Includes real world case studies and examples, such as estimations of scoring probability using the Golden State Warriors as a test case Provides the source code and data so readers can do their own analyses on NBA teams and players

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.

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. This up-to-the-minute reference will help you master all three facets of sports analytics — and use it to win! Sports Analytics and Data Science is the most accessible and practical guide to sports analytics for everyone who cares about winning and everyone who is interested in data science. You’ll discover how successful sports analytics blends business and sports savvy, modern information technology, and sophisticated modeling techniques. You’ll master the discipline through realistic sports vignettes and intuitive data visualizations–not complex math. Every chapter focuses on one key sports analytics application. Miller guides you through assessing players and teams, predicting scores and making game-day decisions, crafting brands and marketing messages, increasing revenue and profitability, and much more. Step by step, you’ll learn how analysts transform raw data and analytical models into wins: both on the field and in any sports business.

Basketball Analytics is a must-read for any sports analytics enthusiast or student of the game of basketball. Authors Stephen Shea, Ph.D. (Professor of Mathematics) and Christopher Baker (Software Engineer) utilize their unique skill-set to introduce original metrics for analyzing player performance, team style and team construction in the NBA. While demonstrating an awareness of the industry's best ideas, the authors present original, objective and efficient strategies for understanding how teams win. New player performance statistics include Offensive Efficiency (OE), Efficient Offensive Production (EOP), Defensive Stops Gained (DSG), and Approximate Value (AV). OE reflects a player's ability to make the most fundamental offensive decisions. EOP adjusts a player's points and assists based on his efficiency. DSG gives a complete measure of a player's defensive contributions, without relying on individual player statistics like blocks and steals. AV is a measure of total player performance that rivals any publicly available statistic. Basketball Analytics introduces groundbreaking metrics on player involvement in the offense. Point, Rebound and Assist Balance aggregate player usage in these critical statistics. New studies on the NBA show whether teams should strive for balance or unbalance. An NBA draft pick value study determines the average value of each pick and the likelihood of landing a star or role player with each draft position. The results of this study are used to discuss topics including the biggest draft blunders and steals, the draft success of each NBA team, and the quality of each draft class dating back to 1977. This valuable understanding of the NBA Draft creates a foundation for discussing various approaches to team development and construction. Additionally, the authors discuss redefining the positions on the court, unpredictability in the game, data visualization, and applications of spatial tracking technology. There are many intensely debated questions surrounding the NBA today. Who are the most valuable players, and how do they compare to past greats? Which players have the greatest impact on their team's defense? Should Kobe Bryant be concerned with getting his teammates involved in the offense? How do offenses differ in the clutch, and which players thrive in these situations? How difficult is it for a team to rebuild through the draft? Basketball Analytics introduces new statistics and new concepts to explore these questions and more.

Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an MBA player’s shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable for students, technicians, coaches, data analysts and applied researchers. Features: · One of the first books to provide statistical and data mining methods for the growing field of analytics in basketball. · Presents tools for modelling graphs and figures to visualize the data. · Includes real world case studies and examples, such as estimations of scoring probability using the Golden State Warriors as a test case. · Provides the source code and data so readers can do their own analyses on NBA teams and players.

A quick and easy read to help you take your understanding of basketball to the next level. Basketball is one of the most popular sports in the world, yet most fans don't know much about the details of the game. Stats vs Analytics will provide an introduction to the world of analytics and provide insight into the story of the game that goes beyond the boxscore. Without overcomplicating the issues Basketball Stats vs Analytics will give you insight into the metrics of the modern game. Highly competitive teams in leagues all over the world rely on analytics to scout opponents and find talent. Get a brief introduction to why some players are highly valued, even though the standard boxscores may not show big numbers, learn what is the relationship between stats and analytics, gain more insight into the value of the players you like to watch and see how coaches and GMs use analytics to help them win. Learn the basics about: True Shooting Percentage (TS%) Effective Shooting Percentage (EFG%) Points Per Possession (PPP) Player Efficiency Rating (PER) Usage Rate or Percentage (USG%) Offensive Rating (ORtg) Offensive and Defensive Rebound Rate (ORB% and DRB%) Assist and Turnover Rate (AST% and TOV%) Steal and Block Rate Points Per 100 Possessions Offensive and Defensive Efficiency Basketball Stats vs Analytics: A Quick and Easy Beginners Guide to Basketball Analytics is a must read for anyone hoping to share in basketball conversations with astute friends and collegues. Or a great read for people new to the game that want to understand a little bit more of why things happen and what makes a team or player great. Scroll up and click on the "buy now" button to raise your understanding of the game of basketball!

A typical NBA game can yield approximately 2,800 statistical events in thirty-two different categories. In Numbers Don’t Lie Yago Colás started with a simple question: How did basketball analytics get from counting one stat, the final score, to counting thousands? He discovered that what we call “basketball”—rules, equipment, fundamental skills, techniques, tactics, strategies—has changed dramatically since its invention and today encompasses many different forms of play, from backyards and rec leagues to the NBA Finals. Numbers Don’t Lie explores the power of data to tell stories about ourselves and the world around us. As advanced statistical methods and big-data technologies transform sports, we now have the power to count more things in greater detail than ever before. These numbers tell us about the past, present, and future that shape how basketball is played on the floor, decisions are made in front offices, and the sport is marketed and consumed. But what is the relationship between counting and what counts, between quantification and value? In Numbers Don’t Lie Colás offers a three-part history of counting in basketball. First, he recounts how big-data basketball emerged in the past twenty years, examines its current practices, and analyzes how it presents itself to the public. Colás then situates big data within the deeper social, cultural, and conceptual history of counting in basketball and beyond and proposes alternative frameworks of value with which we may take fuller stock of the impact of statistics on the sport. Ultimately, Colás challenges the putative objectivity of both quantification and academic writing by interweaving through this history a series of personal vignettes of life at the intersection of basketball, counting, and what counts.