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| data science in sports: Sports Analytics and Data Science Thomas W. Miller, 2015-11-18 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. |
| data science in sports: Sports Analytics Benjamin C. Alamar, 2024-05-28 Data and analytics have the potential to provide sports organizations with a competitive advantage both on and off the field. Yet even as the use of analytics in sports has become commonplace, teams regularly find themselves making big investments without significant payoff. This book is a practical, nontechnical guide to incorporating sports data into decision making, giving leaders the knowledge they need to maximize their organization’s investment in analytics. Benjamin C. Alamar—a leading expert who has built high-performing analytics groups—surveys the current state of the use of data in sports, including both specifics around the tools and how to deploy them most effectively. Sports Analytics offers a clear, easily digestible overview of data management, statistical models, and information systems and a detailed understanding of their vast possibilities. It walks readers through the essentials of understanding the value of different types of data and strategies for building and managing an analytics team. Throughout, Alamar illustrates the value of analytics with real-world examples and case studies from both the sports and business sides. Sports Analytics has guided a range of sports professionals to success since its original publication in 2013. This second edition adds examples and strategies that focus on using data on the business side of a sports organization, provides concrete strategies for incorporating different types of data into decision making, and updates all discussions for the rapid technological developments of the last decade. |
| data science in sports: Basketball Data Science Paola Zuccolotto, Marica Manisera, 2020-01-03 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 |
| data science in sports: Data Analysis and Research for Sport and Exercise Science Craig Williams, Chris Wragg, 2004-08-02 Data Analysis and Research for Sport and Exercise Science is tailored to suit undergraduate sports and exercise science students seeking a clear understanding of data and statistics to support their scientific research. The text is divided into three main areas: Research and Design, Data Analysis and the Interpretation of Findings. Topics covered in the book include: * introduction to the scientific research method * the literature review * developing your research question and experimental design * using statistical analysis to interpret results * presentation of your data * discussing your results and drawing conclusions. Both authors have supervised many student dissertations and have an excellent understanding of the concerns and pitfalls facing those new to this field. |
| data science in sports: Data Analysis in Sport Peter O'Donoghue, Lucy Holmes, 2014-10-24 Making sense of sports performance data can be a challenging task but is nevertheless an essential part of performance analysis investigations. Focusing on techniques used in the analysis of sport performance, this book introduces the fundamental principles of data analysis, explores the most important tools used in data analysis, and offers guidance on the presentation of results. The book covers key topics such as: The purpose of data analysis, from statistical analysis to algorithmic processing Commercial packages for performance and data analysis, including Focus, Sportscode, Dartfish, Prozone, Excel, SPSS and Matlab Effective use of statistical procedures in sport performance analysis Analysing data from manual notation systems, player tracking systems and computerized match analysis systems Creating visually appealing ‘dashboard’ interfaces for presenting data Assessing reliability. The book includes worked examples from real sport, offering clear guidance to the reader and bringing the subject to life. This book is invaluable reading for any student, researcher or analyst working in sport performance or undertaking a sport-related research project or methods course |
| data science in sports: Sports Data Mining Robert P. Schumaker, Osama K. Solieman, Hsinchun Chen, 2010-09-10 Data mining is the process of extracting hidden patterns from data, and it’s commonly used in business, bioinformatics, counter-terrorism, and, increasingly, in professional sports. First popularized in Michael Lewis’ best-selling Moneyball: The Art of Winning An Unfair Game, it is has become an intrinsic part of all professional sports the world over, from baseball to cricket to soccer. While an industry has developed based on statistical analysis services for any given sport, or even for betting behavior analysis on these sports, no research-level book has considered the subject in any detail until now. Sports Data Mining brings together in one place the state of the art as it concerns an international array of sports: baseball, football, basketball, soccer, greyhound racing are all covered, and the authors (including Hsinchun Chen, one of the most esteemed and well-known experts in data mining in the world) present the latest research, developments, software available, and applications for each sport. They even examine the hidden patterns in gaming and wagering, along with the most common systems for wager analysis. |
| data science in sports: Analytic Methods in Sports Thomas A. Severini, 2020-04-15 One of the greatest changes in the sports world in the past 20 years has been the use of mathematical methods to analyze performances, recognize trends and patterns, and predict results. Analytic Methods in Sports: Using Mathematics and Statistics to Understand Data from Baseball, Football, Basketball, and Other Sports, Second Edition provides a concise yet thorough introduction to the analytic and statistical methods that are useful in studying sports. The book gives you all the tools necessary to answer key questions in sports analysis. It explains how to apply the methods to sports data and interpret the results, demonstrating that the analysis of sports data is often different from standard statistical analyses. The book integrates a large number of motivating sports examples throughout and offers guidance on computation and suggestions for further reading in each chapter. Features Covers numerous statistical procedures for analyzing data based on sports results Presents fundamental methods for describing and summarizing data Describes aspects of probability theory and basic statistical concepts that are necessary to understand and deal with the randomness inherent in sports data Explains the statistical reasoning underlying the methods Illustrates the methods using real data drawn from a wide variety of sports Offers many of the datasets on the author’s website, enabling you to replicate the analyses or conduct related analyses New to the Second Edition R code included for all calculations A new chapter discussing several more advanced methods, such as binary response models, random effects, multilevel models, spline methods, and principal components analysis, and more Exercises added to the end of each chapter, to enable use for courses and self-study |
| data science in sports: Sports Performance Measurement and Analytics Lorena Martin, 2016-02-03 A PRACTICAL, REAL-WORLD GUIDE TO ANALYTICS FOR THE 5 MAJOR SPORTS: FOOTBALL, BASKETBALL, BASEBALL, SOCCER, AND TENNIS GAIN A COMPETITIVE EDGE! This is the first real-world guide to building and using analytical models for measuring and assessing performance in the five major sports: football, basketball, baseball, soccer, and tennis. Unlike books that focus strictly on theory, this book brings together sports measurement and statistical analyses, demonstrating how to examine differences across sports as well as between player positions. This book will provide you with the tools for cutting-edge approaches you can extend to the sport of your choice. Expert Northwestern University data scientist, UC San Diego researcher, and competitive athlete, Lorena Martin shows how to use measures and apply statistical models to evaluate players, reduce injuries, and improve sports performance. You’ll learn how to leverage a deep understanding of each sport’s principles, rules, attributes, measures, and performance outcomes. Sports Performance Measurement and Analytics will be an indispensable resource for anyone who wants to bring analytical rigor to athletic competition: students, professors, analysts, fans, physiologists, coaches, managers, and sports executives alike. All data sets, extensive code, and additional examples are available for download at http://www.ftpress.com/martin/ What are the qualities a person must have to become a world-class athlete? This question and many more can be answered through research, measurement, statistics, and analytics. This book gives athletes, trainers, coaches, and managers a better understanding of measurement and analytics as they relate to sports performance. To develop accurate measures, we need to know what we want to measure and why. There is great power in accurate measures and statistics. Research findings can show us how to prevent injuries, evaluate strengths and weaknesses, improve team cohesion, and optimize sports performance. This book serves many readers. People involved with sports will gain an appreciation for performance measures and analytics. People involved with analytics will gain new insights into quantified values representing physical, physiological, and psychological components of sports performance. And students eager to learn about sports analytics will have a practical introduction to the field. This is a thorough introduction to performance measurement and analytics for five of the world’s leading sports. The only book of its kind, it offers a complete overview of the most important concepts, rules, measurements, and statistics for each sport, while demonstrating applications of real-world analytics. You’ll find practical, state-of-the-art guidance on predicting future outcomes, evaluating an athlete’s market value, and more. |
| data science in sports: Data Analytics in Professional Soccer Daniel Link, 2018-02-16 Daniel Link explores how data analytics can be used for studying performance in soccer. Based on spatiotemporal data from the German Bundesliga, the six individual studies in this book present innovative mathematical approaches for game analysis and player assessment. The findings can support coaches and analysts to improve performance of their athletes and inspire other researchers to advance the research field of sports analytics. |
| data science in sports: NSCA's Essentials of Sport Science NSCA -National Strength & Conditioning Association, Duncan French, Lorena Torres Ronda, 2021-02-19 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. |
| data science in sports: Data Analytics in Football Daniel Memmert, Dominik Raabe, 2018-05-30 Data Analytics in Football provides students, researchers and coaches with a firm grounding in the principles of modern performance analysis. It offers an insight into the use of positional data, exploring how it can be collected, modelled, analysed and interpreted. Introducing cutting-edge methods, the book challenges long-held assumptions and encourages a new way of thinking about football analysis. Based on data collected from the German Bundesliga and the UEFA Champions League, the book seeks to define the role of positional data in football match analysis by exploring topics such as: What is positional data analysis and how did it emerge from conventional match analysis? How can positional data be collected and which technologies can be used? What are the benefits of a data-driven approach to decision making in football? What Key Performance Indicators based on positional data should be used? How can traditional match analysis be complemented by using positional data and advanced KPIs? How can these new methods evolve in the future? Accessibly written, packed full of examples from elite football and supplemented with expert interviews (Ralf Rangnick, Urs Siegenthaler and others), Data Analytics in Football is a thought-provoking, rigorously evidence-based guide to the use of data analytics in football performance analysis. As such, it is a vital resource for any student, researcher or coach interested in performance analysis and skill acquisition, or anyone interested in football more generally. |
| data science in sports: Data Science Ivo D. Dinov, Milen Velchev Velev, 2021-12-06 The amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the problems of time. The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public. |
| data science in sports: Sport Business Analytics C. Keith Harrison, Scott Bukstein, 2016-11-18 Developing and implementing a systematic analytics strategy can result in a sustainable competitive advantage within the sport business industry. This timely and relevant book provides practical strategies to collect data and then convert that data into meaningful, value-added information and actionable insights. Its primary objective is to help sport business organizations utilize data-driven decision-making to generate optimal revenue from such areas as ticket sales and corporate partnerships. To that end, the book includes in-depth case studies from such leading sports organizations as the Orlando Magic, Tampa Bay Buccaneers, Duke University, and the Aspire Group. The core purpose of sport business analytics is to convert raw data into information that enables sport business professionals to make strategic business decisions that result in improved company financial performance and a measurable and sustainable competitive advantage. Readers will learn about the role of big data and analytics in: Ticket pricing Season ticket member retention Fan engagement Sponsorship valuation Customer relationship management Digital marketing Market research Data visualization. This book examines changes in the ticketing marketplace and spotlights innovative ticketing strategies used in various sport organizations. It shows how to engage fans with social media and digital analytics, presents techniques to analyze engagement and marketing strategies, and explains how to utilize analytics to leverage fan engagement to enhance revenue for sport organizations. Filled with insightful case studies, this book benefits both sports business professionals and students. The concluding chapter on teaching sport analytics further enhances its value to academics. |
| data science in sports: Sprawlball Kirk Patrick Goldsberry, 2019 Beautifully illustrated and sharply written, SprawlBall is both a celebration and a critique of the 3-point shot. If you want to understand how the modern NBA came to be, you'll need to read this book. --Nate Silver, editor of fivethirtyeight.com From the leading expert in the exploding field of basketball analytics, a stunning infographic decoding of the modern NBA: who shoots where, and how. The field of basketball analytics has leaped to overdrive thanks to Kirk Goldsberry, whose visual maps of players, teams, and positions have helped teams understand who really is the most valuable player at any position. SprawlBall combines stunning visuals, in-depth analysis, fun, behind-the-scenes stories and gee-whiz facts to chart a modern revolution. From the introduction of the 3-point line to today, the game has changed drastically . . . Now, players like Steph Curry and Draymond Green are leading the charge. In chapters like The Geography of the NBA, The Interior Minister (Lebron James), The Evolution of Steph Curry, and The Investor (James Harden), Goldsberry explains why today's on-court product--with its emphasis on shooting, passing, and spacing--has never been prettier or more democratic. And it's never been more popular. For fans of Bill Simmons and FreeDarko, SprawlBall is a bold new vision of the game, presenting an innovative, cutting-edge look at the sport based on the latest research, as well as a visual and infographic feast for fans. |
| data science in sports: Sport Analytics Gil Fried, Ceyda Mumcu, 2016-11-10 The increasing availability of data has transformed the way sports are played, promoted and managed. This is the first textbook to explain how the big data revolution is having a profound influence across the sport industry, demonstrating how sport managers and business professionals can use analytical techniques to improve their professional practice. While other sports analytics books have focused on player performance data, this book shows how analytics can be applied to every functional area of sport business, from marketing and event management to finance and legal services. Drawing on research that spans the entire sport industry, it explains how data is influencing the most important decisions, from ticket sales and human resources to risk management and facility operations. Each chapter contains real world examples, industry profiles and extended case studies which are complimented by a companion website full of useful learning resources. Sport Analytics: A data-driven approach to sport business and management is an essential text for all sport management students and an invaluable reference for any sport management professional involved in operational research. |
| data science in sports: The Science of Success: What Researchers Know that You Should Know Paula J. Caproni, 2016-12-08 Short description. |
| data science in sports: Computer Science in Sport Arnold Baca, 2014-10-03 Computers are a fundamentally important tool in sport science research, sports performance analysis and, increasingly, in coaching and education programmes in sport. This book defines the field of ‘sport informatics’, explaining how computer science can be used to solve sport-related problems, in both research and applied aspects. Beginning with a clear explanation of the functional principles of hardware and software, the book examines the key functional areas in which computer science is employed in sport, including: knowledge discovery and database development data acquisition, including devices for measuring performance data motion tracking and analysis systems modelling and simulation match analysis systems e-learning and multimedia in sports education Bridging the gap between theory and practice, this book is important reading for any student, researcher or practitioner working in sport science, sport performance analysis, research methods in sport, applied computer science or informatics. |
| data science in sports: Artificial Intelligence in Sport Performance Analysis Duarte Araújo, Micael Couceiro, Ludovic Seifert, Hugo Sarmento, Keith Davids, 2021-04-20 To understand the dynamic patterns of behaviours and interactions between athletes that characterize successful performance in different sports is an important challenge for all sport practitioners. This book guides the reader in understanding how an ecological dynamics framework for use of artificial intelligence (AI) can be implemented to interpret sport performance and the design of practice contexts. By examining how AI methodologies are utilized in team games, such as football, as well as in individual sports, such as golf and climbing, this book provides a better understanding of the kinematic and physiological indicators that might better capture athletic performance by looking at the current state-of-the-art AI approaches. Artificial Intelligence in Sport Performance Analysis provides an all-encompassing perspective in an innovative approach that signals practical applications for both academics and practitioners in the fields of coaching, sports analysis, and sport science, as well as related subjects such as engineering, computer and data science, and statistics. |
| data science in sports: Basketball on Paper Dean Oliver, 2020-02-15 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. |
| data science in sports: Science Meets Sports Christophe Ley, Yves Dominicy, 2020-11 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. |
| data science in sports: Statistics for Sports and Exercise Science John Newell, Tom Aitchison, Stanley Grant, 2014-12-05 Statistics in Sport and Exercise Science assumes no prior knowledge of statistics and uses real-life case studies to introduce the importance of statistics in sport and exercise science. Statistical tests and techniques are described here in a friendly and easy-to-understand manner, giving you the confidence to analyses data and complete your own statistical studies. |
| data science in sports: Data Science Without Makeup Mikhail Zhilkin, 2021-11-01 - The book shows you what 'data science' actually is and focuses uniquely on how to minimize the negatives of (bad) data science - It discusses the actual place of data science in a variety of companies, and what that means for the process of data science - It provides ‘how to’ advice to both individuals and managers - It takes a critical approach to data science and provides widely-relatable examples |
| data science in sports: Analyzing Baseball Data with R, Second Edition Max Marchi, Jim Albert, Benjamin S. Baumer, 2018-11-19 Analyzing Baseball Data with R Second Edition introduces R to sabermetricians, baseball enthusiasts, and students interested in exploring the richness of baseball data. It equips you with the necessary skills and software tools to perform all the analysis steps, from importing the data to transforming them into an appropriate format to visualizing the data via graphs to performing a statistical analysis. The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the ggplot2 graphics functions and employ a tidyverse-friendly workflow throughout. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, catcher framing, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and launch angles and exit velocities. All the datasets and R code used in the text are available online. New to the second edition are a systematic adoption of the tidyverse and incorporation of Statcast player tracking data (made available by Baseball Savant). All code from the first edition has been revised according to the principles of the tidyverse. Tidyverse packages, including dplyr, ggplot2, tidyr, purrr, and broom are emphasized throughout the book. Two entirely new chapters are made possible by the availability of Statcast data: one explores the notion of catcher framing ability, and the other uses launch angle and exit velocity to estimate the probability of a home run. Through the book’s various examples, you will learn about modern sabermetrics and how to conduct your own baseball analyses. Max Marchi is a Baseball Analytics Analyst for the Cleveland Indians. He was a regular contributor to The Hardball Times and Baseball Prospectus websites and previously consulted for other MLB clubs. Jim Albert is a Distinguished University Professor of statistics at Bowling Green State University. He has authored or coauthored several books including Curve Ball and Visualizing Baseball and was the editor of the Journal of Quantitative Analysis of Sports. Ben Baumer is an assistant professor of statistical & data sciences at Smith College. Previously a statistical analyst for the New York Mets, he is a co-author of The Sabermetric Revolution and Modern Data Science with R. |
| data science in sports: Sports Analytics Leonard C Maclean, William T Ziemba, 2021-12-22 This book is a collection of applications of analytic techniques to a number of popular sports including baseball, basketball, hockey, Jai Alai, NFL football and horseracing. We focus on both the statistics of the sporting events and betting strategies on the events. The subject is fascinating as there are many twists and subtle complicated decisions.Sports analytics applies mathematical and statistical methods to important questions in the structure and performance of sporting activities using the same basic methods and approaches as data analysts in other disciplines.Sports games and events are a fruitful area for study and to evaluate betting strategies as there is extensive data and mean reversion. With prices changing continuously, risk arbitrage bets can be made. Moreover, little errors, like a penalty to a player or an error in a call by a referee, can change the score of a game and corresponding betting prices. The collection and analysis of in-game data can inform players, coaches and staff on effective decision making during sporting events.Novel features of the book include: an analysis of who were the greatest baseball batters; analyses of the players most important to team success (and they are not necessarily the best players) in basketball, NFL football and hockey; a tutorial on risk arbitrage and its applications to NFL football and NBA basketball; a discussion of many ad hoc decision rules by coaches and players and what was really optimal; in the racing section we discuss breeding, the analysis of various bets like the Rainbow and ordinary Pick 6, a discussion and betting on the most important races and a visit to the Breeders' Cup with Ed Thorp to demonstrate the place and show system in action. |
| data science in sports: Statistical Sports Models in Excel Andrew Mack, 2019-07-09 What does it take to create a winning sports betting model?Many people are interested in learning how to make smarter sports betting wagers. Not many of those same people are excited at the prospect of learning applied statistics to better inform their choices. You can't entirely blame them. Statistics has a well-earned reputation for being somewhat inaccessible by non-academics. It is a field filled with heavy terminology, confusing formulaic notation and concepts not fully relevant to the beginning sports bettor. To make matters worse, nobody in possession of a positive edge model is all that keen to show you how it works. As a result of this, sports modelling successfully requires a toolbox that you largely have to assemble for yourself. This book is my attempt to correct that, as I pull back the curtain on Excel spreadsheet models and techniques I developed in my first few years of sports betting. It is designed to give you an accessible crash course on modelling that will get you up to speed on the key relevant principles of statistics with a minimum amount of heavy technical jargon. You'll learn the most effective concepts in an easy-to-understand way and greatly speed up your learning curve in the process. Best of all, I'll walk you through sports betting models for many major sports: NFL, NBA, NHL, AFL and the English Premier League. You'll even be able to download these spreadsheet models and work your way through them while following this book.We'll be doing all of this in one of the most user friendly programs available: Excel. This program might not be the expert's first choice of platform for modelling work, but you'll be surprised just how much you can do with a spreadsheet or two.If you're ready to take your sports modelling to the next level - open this book, fire up Excel, and let's get to work. See you on the inside. |
| data science in sports: Mathletics Wayne L. Winston, 2012-03-18 How math can be used to improve performance and predict outcomes in professional sports Mathletics is a remarkably entertaining book that shows readers how to use simple mathematics to analyze a range of statistical and probability-related questions in professional baseball, basketball, and football, and in sports gambling. How does professional baseball evaluate hitters? Is a singles hitter like Wade Boggs more valuable than a power hitter like David Ortiz? Should NFL teams pass or run more often on first downs? Could professional basketball have used statistics to expose the crooked referee Tim Donaghy? Does money buy performance in professional sports? In Mathletics, Wayne Winston describes the mathematical methods that top coaches and managers use to evaluate players and improve team performance, and gives math enthusiasts the practical tools they need to enhance their understanding and enjoyment of their favorite sports—and maybe even gain the outside edge to winning bets. Mathletics blends fun math problems with sports stories of actual games, teams, and players, along with personal anecdotes from Winston's work as a sports consultant. Winston uses easy-to-read tables and illustrations to illuminate the techniques and ideas he presents, and all the necessary math concepts—such as arithmetic, basic statistics and probability, and Monte Carlo simulations—are fully explained in the examples. After reading Mathletics, you will understand why baseball teams should almost never bunt, why football overtime systems are unfair, why points, rebounds, and assists aren't enough to determine who's the NBA's best player—and much, much more. In a new epilogue, Winston discusses the stats and numerical analysis behind some recent sporting events, such as how the Dallas Mavericks used analytics to become the 2011 NBA champions. |
| data science in sports: Handbook of Statistical Methods and Analyses in Sports Jim Albert, Mark E. Glickman, Tim B. Swartz, Ruud H. Koning, 2017-02-03 This handbook will provide both overviews of statistical methods in sports and in-depth treatment of critical problems and challenges confronting statistical research in sports. The material in the handbook will be organized by major sport (baseball, football, hockey, basketball, and soccer) followed by a section on other sports and general statistical design and analysis issues that are common to all sports. This handbook has the potential to become the standard reference for obtaining the necessary background to conduct serious statistical analyses for sports applications and to appreciate scholarly work in this expanding area. |
| data science in sports: Data Science in Chemistry Thorsten Gressling, 2020-11-23 The ever-growing wealth of information has led to the emergence of a fourth paradigm of science. This new field of activity – data science – includes computer science, mathematics and a given specialist domain. This book focuses on chemistry, explaining how to use data science for deep insights and take chemical research and engineering to the next level. It covers modern aspects like Big Data, Artificial Intelligence and Quantum computing. |
| data science in sports: Building Data Science Teams DJ Patil, 2011-09-15 As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success. Topics include: What it means to be data driven. The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team. |
| data science in sports: Storytelling with Data Cole Nussbaumer Knaflic, 2015-10-09 Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it! |
| data science in sports: Critical Issues in Global Sport Management Nico Schulenkorf, Stephen Frawley, 2016-10-04 The social, cultural and economic significance of sport has never been more evident than it is today. Adopting a critical management perspective, this book examines the most important themes and challenges in global sport management. From match-fixing, doping, bribery and corruption to corporate social responsibility, governance, and new media, it helps students, researchers and practitioners to understand the changing face of the global sport industry. Written by leading international sport management experts, Critical Issues in Global Sport Management includes twenty chapters and real-life case studies from around the world. It examines contemporary governance and management issues as well as the ethical challenges faced by the global sport industry, including questions of integrity and accountability in recent drug scandals that have been widely reported and debated. This book deals with such questions and many more, highlighting the fact that the global sport system is in urgent need of new and innovative solutions to these ongoing problems. Based on cutting-edge research from the US, UK, Australia, Europe and beyond, this book will add depth and currency to any course in sport management, sport business, sport development, or sport events. |
| data science in sports: Marketing Data Science Thomas W. Miller, 2015-05-02 Now, a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance. |
| data science in sports: Digitalization, Digital Transformation and Sustainability in the Global Economy Tim A. Herberger, Jörg J. Dötsch, 2021-07-27 This book highlights the opportunities and risks of digitalization and digital transformation for our global economy at both the micro and macro level. Experts from various fields, presenting both scientific and practice-oriented perspectives, identify and critically analyse areas of tension and development potential in connection with new business models and sustainability efforts in our society. It is divided into four parts, the first of which highlights new technological advances in areas such as blockchain, cryptocurrencies and fintechs, and discusses the challenges they pose for public regulation. The second part illustrates digitalization’s effects on and potential advantages for public welfare, focusing on key areas such as education, health and smart cities. The third part focuses on challenges for corporate and public management, particularly for leadership and Corporate Social Responsibility, while the fourth part discusses new dimensions for analysis based on big data. The contributions gathered here are partly an outcome of the International Conference on Digitalization, Digital Transformation and Sustainability held in Budapest in October 2020 and generously supported by the Hanns Seidel Foundation. |
| data science in sports: Sports Math Roland B. Minton, 2016-11-03 Can you really keep your eye on the ball? How is massive data collection changing sports? Sports science courses are growing in popularity. The author’s course at Roanoke College is a mix of physics, physiology, mathematics, and statistics. Many students of both genders find it exciting to think about sports. Sports problems are easy to create and state, even for students who do not live sports 24/7. Sports are part of their culture and knowledge base, and the opportunity to be an expert on some area of sports is invigorating. This should be the primary reason for the growth of mathematics of sports courses: the topic provides intrinsic motivation for students to do their best work. From the Author: The topics covered in Sports Science and Sports Analytics courses vary widely. To use a golfing analogy, writing a book like this is like hitting a drive at a driving range; there are many directions you can go without going out of bounds. At the driving range, I pick out a small target to focus on, and that is what I have done here. I have chosen a sample of topics I find very interesting. Ideally, users of this book will have enough to choose from to suit whichever version of a sports course is being run. The book is very appealing to teach from as well as to learn from. Students seem to have a growing interest in ways to apply traditionally different areas to solve problems. This, coupled with an enthusiasm for sports, makes Dr. Minton’s book appealing to me.—Kevin Hutson, Furman University |
| data science in sports: Big Data MBA Bill Schmarzo, 2015-12-11 Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions. |
| data science in sports: Mathletics Wayne L. Winston, Scott Nestler, Konstantinos Pelechrinis, 2022-02-22 How to use math to improve performance and predict outcomes in professional sports Mathletics reveals the mathematical methods top coaches and managers use to evaluate players and improve team performance, and gives math enthusiasts the practical skills they need to enhance their understanding and enjoyment of their favorite sports—and maybe even gain the outside edge to winning bets. This second edition features new data, new players and teams, and new chapters on soccer, e-sports, golf, volleyball, gambling Calcuttas, analysis of camera data, Bayesian inference, ridge regression, and other statistical techniques. After reading Mathletics, you will understand why baseball teams should almost never bunt; why football overtime systems are unfair; why points, rebounds, and assists aren’t enough to determine who’s the NBA’s best player; and more. |
| data science in sports: The MVP Machine Ben Lindbergh, Travis Sawchik, 2019-06-04 Move over, Moneyball -- this New York Times bestseller examines major league baseball's next cutting-edge revolution: the high-tech quest to build better players. As bestselling authors Ben Lindbergh and Travis Sawchik reveal in The MVP Machine, the Moneyball era is over. Fifteen years after Michael Lewis brought the Oakland Athletics' groundbreaking team-building strategies to light, every front office takes a data-driven approach to evaluating players, and the league's smarter teams no longer have a huge advantage in valuing past performance. Lindbergh and Sawchik's behind-the-scenes reporting reveals: How undersized afterthoughts José Altuve and Mookie Betts became big sluggers and MVPs How polarizing pitcher Trevor Bauer made himself a Cy Young contender How new analytical tools have overturned traditional pitching and hitting techniques How a wave of young talent is making MLB both better than ever and arguably worse to watch Instead of out-drafting, out-signing, and out-trading their rivals, baseball's best minds have turned to out-developing opponents, gaining greater edges than ever by perfecting prospects and eking extra runs out of older athletes who were once written off. Lindbergh and Sawchik take us inside the transformation of former fringe hitters into home-run kings, show how washed-up pitchers have emerged as aces, and document how coaching and scouting are being turned upside down. The MVP Machine charts the future of a sport and offers a lesson that goes beyond baseball: Success stems not from focusing on finished products, but from making the most of untapped potential. |
| data science in sports: From Big Data to Big Profits Russell Walker, 2015-07-01 Technological advancements in computing have changed how data is leveraged by businesses to develop, grow, and innovate. In recent years, leading analytical companies have begun to realize the value in their vast holdings of customer data and have found ways to leverage this untapped potential. Now, more firms are following suit and looking to monetize Big Data for big profits. Such changes will have implications for both businesses and consumers in the coming years. In From Big Data to Big Profits, Russell Walker investigates the use of Big Data to stimulate innovations in operational effectiveness and business growth. Walker examines the nature of Big Data and how businesses can use it to create new monetization opportunities. Using case studies of Apple, Netflix, Google, LinkedIn, Zillow, Amazon, and other leaders in the use of Big Data, Walker explores how digital platforms such as mobile apps and social networks are changing the nature of customer interactions and the way Big Data is created and used by companies. Such changes, as Walker points out, will require careful consideration of legal and unspoken business practices as they affect consumer privacy. Companies looking to develop a Big Data strategy will find great value in the SIGMA framework, which he has developed to assess companies for Big Data readiness and provide direction on the steps necessary to get the most from Big Data. Rigorous and meticulous, From Big Data to Big Profits is a valuable resource for students, researchers, and professionals with an interest in Big Data, digital platforms, and analytics |
| data science in sports: Pay as You Play Paul Tomkins, Graeme Riley, 2013-07-15 'These Turbulent Times' gathers together the best articles to appear on the award-winning Tomkins Times website since its inception in 2009, covering all aspects of Liverpool FC's fortunes in that time. The analysis encompasses a wide range of styles and approaches, including straight opinion pieces; historical reporting on major events; in-depth tactical reviews; professional data analysis on a number of statistical issues; legal issues courtesy of a prominent football lawyer; and exclusive interviews with key personnel at the club. It includes many pieces that have only previously appeared behind the site's paywall. |
| data science in sports: Football Analytics with Python & R Eric A. Eager, Richard A. Erickson, 2023-08-15 Baseball is not the only sport to use moneyball. American football fans, teams, and gamblers are increasingly using data to gain an edge against the competition. Professional and college teams use data to help select players and identify team needs. Fans use data to guide fantasy team picks and strategies. Sports bettors and fantasy football players are using data to help inform decision making. This concise book provides a clear introduction to using statistical models to analyze football data. Whether your goal is to produce a winning team, dominate your fantasy football league, qualify for an entry-level football analyst position, or simply learn R and Python using fun example cases, this book is your starting place. You'll learn how to: Apply basic statistical concepts to football datasets Describe football data with quantitative methods Create efficient workflows that offer reproducible results Use data science skills such as web scraping, manipulating data, and plotting data Implement statistical models for football data Link data summaries and model outputs to create reports or presentations using tools such as R Markdown and R Shiny And more |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
