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.

It is widely, and wrongly, assumed that books are never so valuable as when they lie unopened before us, waiting to be read. Good books bear multiple readings, and not merely because our memories fail us; the desire to repeat a good reading experience can be its own powerful motivation. And for bibliophiles, books can also be works of art, physical objects with an aesthetic value all their own. This guide for the book-loving baseball fan is written by one of the most knowledgeable collectors in the country, author and editor Mike Shannon. Beginning with a history of baseball books and collecting, it also identifies the most sought-after titles and explains how to find them, what to pay, and how to maintain their condition.

The Ultimate Review Guide for the CRT, RRT, and CSE Exams! Continuous Up-to-date NBRC Examination Guidelines and Correlations on Companion Website Comprehensive Respiratory Therapy Exam Preparation Guide, Second Edition is a comprehensive study guide for respiratory therapy students and graduates of accredited respiratory therapy education programs who are seeking to take the Certified Respiratory Therapist (CRT) or Registered Respiratory Therapist (RRT) credentialing exams from the National Board for Respiratory Care (NBRC). Comprehensive Respiratory Therapy Exam Preparation Guide, Second Edition is reflective of the current CRT, RRT, and CSE exam matrix and authored by experts who take the credentialing exam annually, so you can be confident that the content and format of this guide is current! Important Notice: The digital edition of this book is missing some of the images or content found in the physical edition.

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.

George Gmelch describes what life is like for professional baseball players, describing their everyday routines and traditions.

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.