Analyzing Baseball Data with R Chapman Hall CRC The R Series 1st Edition by Max Marchi, Jim Albert ISBN 1466570229 9781466570221

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Product details:
ISBN 10: 1466570229
ISBN 13: 9781466570221
Author: Max Marchi, 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.

Analyzing Baseball Data with R Chapman Hall CRC The R Series 1st Edition Table of contents:

• Chapter 1: Introduction to R

  • Basics of R Programming

  • Setting Up R Environment

  • Data Structures in R

• Chapter 2: Baseball Data Sources

  • Understanding Baseball Data

  • Importing and Cleaning Data

  • Overview of Data Sources

• Chapter 3: Descriptive Statistics

  • Basic Statistical Measures

  • Visualizing Data

  • Summary Functions in R

• Chapter 4: Graphics with ggplot2

  • Introduction to ggplot2

  • Creating Basic Plots

  • Customizing Graphs

• Chapter 5: Measuring Performance

  • Batting Statistics

  • Pitching Statistics

  • Fielding Statistics

• Chapter 6: Linear Models

  • Introduction to Linear Regression

  • Applying Linear Models to Baseball Data

  • Interpreting Results

• Chapter 7: Simulation

  • Simulating Baseball Scenarios

  • Monte Carlo Simulations

  • Using Simulations for Predictions

• Chapter 8: Decision Making

  • Decision Analysis in Baseball

  • Optimizing Strategies

  • Game Theory Applications

• Chapter 9: Advanced Statistical Models

  • Generalized Linear Models

  • Mixed Models

  • Bayesian Methods

• Chapter 10: Case Studies

  • In-Depth Analysis of Specific Players

  • Team Performance Analysis

  • Historical Comparisons

• Chapter 11: Network Analysis

  • Analyzing Connections Between Players

  • Social Network Metrics

  • Visualization of Networks

• Chapter 12: Machine Learning

  • Introduction to Machine Learning

  • Supervised and Unsupervised Learning

  • Applications in Baseball

• Chapter 13: Predictive Modeling

  • Building Predictive Models

  • Evaluating Model Performance

  • Predicting Future Performance

• Chapter 14: Shiny Applications

  • Creating Interactive Web Applications

  • Using Shiny with Baseball Data

  • Deploying Shiny Apps

• Appendices

  • Glossary of Terms

  • Additional Resources

  • R Code Examples

• References

  • Comprehensive Bibliography

• Index

  • Detailed Index of Topics and Terms

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