A look at baseball data from a statistical modeling perspective! There is a fascination among baseball fans and the media to collect data on every imaginable event during a baseball game and this book addresses a number of questions that are of interest to many baseball fans. These include how to rate players, predict the outcome of a game or the attainment of an achievement, making sense of situational data, and deciding the most valuable players in the World Series. Aimed at a general audience, the text does not assume any prior background in probability or statistics, although a knowledge of high school abgebra will be helpful.

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.

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 t

Visualizing Baseball provides a visual exploration of the game of baseball. Graphical displays are used to show how measures of performance, at the team level and the individual level, have changed over the history of baseball. Graphs of career trajectories are helpful for understanding the rise and fall of individual performances of hitters and pitchers over time. One can measure the contribution of plays by the notion of runs expectancy. Graphs of runs expectancy are useful for understanding the importance of the game situation defined by the runners on base and number of outs. Also the runs measure can be used to quantify hitter and pitch counts and the win probabilities can be used to define the exciting plays during a baseball game. Special graphs are used to describe pitch data from the PitchFX system and batted ball data from the Statcast system. One can explore patterns of streaky performance and clutch play by the use of graphs, and special plots are used to predict final season batting averages based on data from the middle of the season. This book was written for several types of readers. Many baseball fans should be interested in the topics of the chapters, especially those who are interested in learning more about the quantitative side of baseball. Many statistical ideas are illustrated and so the graphs and accompanying insights can help in promoting statistical literacy at many levels. From a practitioner's perspective, the chapters offer many illustrations of the use of a modern graphics system and R scripts are available on an accompanying website to reproduce and potentially improve the graphs in this book.

R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data. The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data and this book is intended to be a useful resource for learning how to implement these procedures in R.

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 t

"... a smart and energetic collection of essays on baseball statistics. Curve Ball doesn't play misty-eyed homage to baseball's traditions and conventional wisdoms.... This is great stuff.... Curve Ball makes clear how pleasurable [stats] can be, and arguably how important, to view the great American game with real precision." -- The Wall Street Journal "Rating: 4.5 out of 5. Must own!" -- Baseballnotebook.com "In [Curve Ball] Albert & Bennett explain the game in ways the conventional press - even titans such as Bill James - cannot." -- Baseball America "[The book] illustrates how statistical reasoning can be useful in teasing out the role of chance from performance in baseball to better assess ability.... Curve Ball represents another advance in the genre of baseball and statistics books." -- Journal of the American Statistical Association There is a fascination among baseball fans and the media to collect data on every imaginable event during a baseball game and to use these data to try to understand characteristics of the game. But patterns in baseball data are difficult to detect due to the inherent chance variation that is present. This book addresses a number of questions that are of interest to many baseball fans - including how to rate players, predict the outcome of a game or the attainment of an attainment, make sense of situational data, and decide the most valuable players in the World Series. Curve Ball is directed to a general audience and does not assume that the reader has any prior background in probability or statistics, although knowledge of high school algebra will be helpful.

Visualizing Baseball provides a visual exploration of the game of baseball. Graphical displays are used to show how measures of performance, at the team level and the individual level, have changed over the history of baseball. Graphs of career trajectories are helpful for understanding the rise and fall of individual performances of hitters and pitchers over time. One can measure the contribution of plays by the notion of runs expectancy. Graphs of runs expectancy are useful for understanding the importance of the game situation defined by the runners on base and number of outs. Also the runs measure can be used to quantify hitter and pitch counts and the win probabilities can be used to define the exciting plays during a baseball game. Special graphs are used to describe pitch data from the PitchFX system and batted ball data from the Statcast system. One can explore patterns of streaky performance and clutch play by the use of graphs, and special plots are used to predict final season batting averages based on data from the middle of the season. This book was written for several types of readers. Many baseball fans should be interested in the topics of the chapters, especially those who are interested in learning more about the quantitative side of baseball. Many statistical ideas are illustrated and so the graphs and accompanying insights can help in promoting statistical literacy at many levels. From a practitioner's perspective, the chapters offer many illustrations of the use of a modern graphics system and R scripts are available on an accompanying website to reproduce and potentially improve the graphs in this book.

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.

There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books, andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to 't very complex models that cannot be 't by alternative frequentist methods. To 't Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN)

A collection of a wide range of research papers on applications of statistics to various sports from journals published by the American Statistical Association and the Royal Statistical Society from 2000 through 2004. The anthology is divided into eight sections (Baseball, Cricket, Football, Golf, Olympics/Track & Field, Soccer, Other Sports, and Miscellaneous), each comprising several research articles on applications of statistics to the corresponding sport written by leading researchers, and each featuring an original introduction written by a leading researcher on the application of statistics to the corresponding sport. The anthology includes research papers at all levels of statistical sophistication that utilize a wide variety of statistical methods, and should therefore be of great interest to the statistically inclined sports fan as well as instructors and students of statistics who are looking for examples of interesting applications of statistics to problems in sports.

Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors' research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.