Bøger af Jim Albert

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.

The book will be of interest to basefall fans who want to learn some sabermetrics, and also people who know sabermetrics but would like to use R in their data exploration. Many students do not work on baseball data because the datasets are very large. By learning R through our book, they will be encouraged to do more baseball research on their own.

A collection of graphs will be used to explore the game of baseball. Graphical displays will be used to show how measures of batting and pitching performance have changed over time, to explore the career trajectories of players, to understand the importance of the pitch count, and to see the patterns of speed, movement, and location of different types of pitches. Each chapter would consist of a series of graphs together with paragraphs of description and explanation so the reader fully understands the variables, any statistical methods, and the message communicated by the graphic.

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.

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.