Teaching Statistics Using Baseball is a collection of case studies and exercises applying statistical and probabilistic thinking to the game of baseball. Baseball is the most statistical of all sports since players are identified and evaluated by their corresponding hitting and pitching statistics. There is an active effort by people in the baseball community to learn more about baseball performance and strategy by the use of statistics. This book illustrates basic methods of data analysis and probability models by means of baseball statistics collected on players and teams. Students often have difficulty learning statistics ideas since they are explained using examples that are foreign to the students. The idea of the book is to describe statistical thinking in a context (that is, baseball) that will be familiar and interesting to students. The book is organized using a same structure as most introductory statistics texts. There are chapters on the analysis on a single batch of data, followed with chapters on comparing batches of data and relationships. There are chapters on probability models and on statistical inference. The book can be used as the framework for a one-semester introductory statistics class focused on baseball or sports. This type of class has been taught at Bowling Green State University. It may be very suitable for a statistics class for students with sports-related majors, such as sports management or sports medicine. Alternately, the book can be used as a resource for instructors who wish to infuse their present course in probability or statistics with applications from baseball. The second edition of Teaching Statistics follows the same structure as the first edition, where the case studies and exercises have been replaced by modern players and teams, and the new types of baseball data from the PitchFX system and fangraphs.com are incorporated into the text.
Publisher: The Mathematical Association of America
This book illustrates basic methods of data analysis and probability models by means of baseball statistics collected on players and teams. The idea of the book is to describe statistical thinking in a context that will be familiar and interesting to students. The second edition of Teaching Statistics follows the same structure as the first edition, where the case studies and exercises have been replaced by modern players and teams, and the new types of baseball data from the PitchFX system and fangraphs.com are incorporated into the text.
A Guide to Teaching Statistics: Innovations and BestPractices addresses the critical aspects of teaching statisticsto undergraduate students, acting as an invaluable tool for bothnovice and seasoned teachers of statistics. Guidance on textbook selection, syllabus construction, andcourse outline Classroom exercises, computer applications, and Internetresources designed to promote active learning Tips for incorporating real data into course content Recommendations on integrating ethics and diversity topics intostatistics education Strategies to assess student's statistical literacy, thinking,and reasoning skills Additional material online at ahref="http://www.teachstats.org/"www.teachstats.org/a
Sport and statistics collide in this collection of articles (from American Statistical Association publications) on using statistics to analyze sport. Most of the articles will be accessible to readers with a general knowledge of statistics. New material from the editors and other notable contributors introduces each section of the book.
This book provides an overview of biomedical applications in sports, including reviews of the current state-of-the art methodologies and research areas. Basic principles with specific case studies from different types of sports as well as suggested student activities and homework problems are included. Equipment design and manufacturing, quantitative evaluation methods, and sports medicine are given special focus. Biomechanical Principles and Applications in Sports can be used as a textbook in a sports technology or sports engineering program, and is also ideal for graduate students and researchers in biomedical engineering, physics, and sports physiology. It can also serve as a useful reference for professional athletes and coaches interested in gaining a deeper understanding of biomechanics and exercise physiology to improve athletic performance.
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.
Mathematics in Games, Sports, and Gambling: The Games People Play, Second Edition demonstrates how discrete probability, statistics, and elementary discrete mathematics are used in games, sports, and gambling situations. With emphasis on mathematical thinking and problem solving, the text draws on numerous examples, questions, and problems to explain the application of mathematical theory to various real-life games. This updated edition of a widely adopted textbook considers a number of popular games and diversions that are mathematically based or can be studied from a mathematical perspective. Requiring only high school algebra, the book is suitable for use as a textbook in seminars, general education courses, or as a supplement in introductory probability courses. New in this Edition: Many new exercises, including basic skills exercises More answers in the back of the book Expanded summary exercises, including writing exercises More detailed examples, especially in the early chapters An expansion of the discrete adjustment technique for binomial approximation problems New sections on chessboard puzzles that encourage students to develop graph theory ideas New review material on relations and functions Exercises are included in each section to help students understand the various concepts. The text covers permutations in the two-deck matching game so derangements can be counted. It introduces graphs to find matches when looking at extensions of the five-card trick and studies lexicographic orderings and ideas of encoding for card tricks. The text also explores linear and weighted equations in the section on the NFL passer rating formula and presents graphing to show how data can be compared or displayed. For each topic, the author includes exercises based on real games and actual sports data.
With its flexible capabilities and open-source platform, R has become a major tool for analyzing detailed, high-quality baseball data. Analyzing Baseball Data with R provides an introduction to R for sabermetricians, baseball enthusiasts, and students interested in exploring the rich sources of baseball data. It equips readers with the necessary skills and software tools to perform all of the analysis steps, from gathering the datasets and entering them in a convenient 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 traditional graphics functions in the base package and introduce more sophisticated graphical displays available through the lattice and ggplot2 packages. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and fielding measures. Each chapter contains exercises that encourage readers to perform their own analyses using R. All of the datasets and R code used in the text are available online. This book helps readers answer questions about baseball teams, players, and strategy using large, publically available datasets. It offers detailed instructions on downloading the datasets and putting them into formats that simplify data exploration and analysis. Through the book’s various examples, readers will learn about modern sabermetrics and be able to conduct their own baseball analyses.
While the term benchmarking is commonplace nowadays in institutional research and higher education, less common, is a solid understanding of what it really means and how it has been, and can be, used effectively. This volume begins by defining benchmarking as “a strategic and structured approach whereby an organization compares aspects of its processes and/or outcomes to those of another organization or set of organizations to identify opportunities for improvement.” Building on this definition, the chapters provide a brief history of the evolution and emergence of benchmarking in general and in higher education in particular. The authors apply benchmarking to: Enrollment management and student success Institutional effectiveness The potential economic impact of higher education institutions on their host communities. They look at the use of national external survey data in institutional benchmarking and selection of peer institutions, introduce multivariate statistical methodologies for guiding that selection, and consider a novel application of baseball sabermetric methods. The volume offers a solid starting point for those new to benchmarking in higher education and provides examples of current best practices and prospective new directions. This is the 156th volume of this Jossey-Bass series. Always timely and comprehensive, New Directions for Institutional Research provides planners and administrators in all types of academic institutions with guidelines in such areas as resource coordination, information analysis, program evaluation, and institutional management.
Since the first athletic events found a fan base, sports and statistics have always maintained a tight and at times mythical relationship. As a way to relay the telling of a game's drama and attest to the prodigious powers of the heroes involved, those reporting on the games tallied up the numbers that they believe best described the action and best defined the winning edge. However, they may not have always counted the right numbers. Many of our hallowed beliefs about sports statistics have long been fraught with misnomers. Whether it concerns Scottish football or American baseball, the most revered statistics often have little to do with any winning edge. Covering an international collection of sports, Statistical Thinking in Sports provides an accessible survey of current research in statistics and sports, written by experts from a variety of arenas. Rather than rely on casual observation, they apply the rigorous tools of statistics to re-examine many of those concepts that have gone from belief to fact, based mostly on the repetition of their claims. Leaving assumption behind, these researchers take on a host of tough questions- Is a tennis player only as good as his or her first serve? Is there such a thing as home field advantage? Do concerns over a decline in soccer's competitive balance have any merit? What of momentum-is its staying power any greater than yesterday's win? And what of pressure performers? Are there such creatures or ultimately, does every performer fall back to his or her established normative? Investigating a wide range of international team and individual sports, the book considers the ability to make predictions, define trends, and measure any number of influences. It is full of interesting and useful examples for those teaching introductory statistics. Although the articles are aimed at general readers, the serious researcher in sports statistics will also find the articles of value and highly useful as starting points for further research.
In A Mathematician at the Ballpark, professor Ken Ross reveals the math behind the stats. This lively and accessible book shows baseball fans how to harness the power of made predictions and better understand the game. Using real-world examples from historical and modern-day teams, Ross shows: • Why on-base and slugging percentages are more important than batting averages • How professional odds makers predict the length of a seven-game series • How to use mathematics to make smarter bets A Mathematician at the Ballpark is the perfect guide to the science of probability for the stats-obsessed baseball fans—and, with a detailed new appendix on fantasy baseball, an essential tool for anyone involved in a fantasy league.