Analyzing Baseball Data with R Third 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.
Analyzing Baseball Data with R Third 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.
Jim Albert is a Distinguished University Professor of Statistics at Bowling Green State University. He has authored or co-authored several books including Curve Ball and Visualizing Baseball and was the editor of the Journal of Quantitative Analysis of Sports. He received the Significant Contributor to Statistics in Sports award in 2003 from the Section of Statistics in Sports of the American Statistical Association. Ben Baumer is a Professor of Statistical and 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. He has received the Waller Education Award from the ASA Section on Statistics and Data Science Education, the Significant Contributor Award from the ASA Section on Statistics in Sports, and the Contemporary Baseball Analysis Award from the Society for American Baseball Research. 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.
Inhaltsangabe
Foreword Preface 1. The Baseball Datasets 2. Introduction to R 3. Graphics 4. The Relation Between Runs and Wins 5. Value of Plays Using Run Expectancy 6. Balls and Strikes Effects 7. Catcher Framing 8. Career Trajectories 9. Simulation 10. Exploring Streaky Performances 11. Using a Database to Compute Park Factors 12. Working with Large Data 13. Home Run Hitting 14. Making a Scientific Presentation using Quarto 15. Using Shiny for Baseball Applications Appendices A. Retrosheet Files Reference B. Historical Notes on PITCHf/x Data C. Statcast Data Reference References Indices Subject index R index
Foreword Preface 1. The Baseball Datasets 2. Introduction to R 3. Graphics 4. The Relation Between Runs and Wins 5. Value of Plays Using Run Expectancy 6. Balls and Strikes Effects 7. Catcher Framing 8. Career Trajectories 9. Simulation 10. Exploring Streaky Performances 11. Using a Database to Compute Park Factors 12. Working with Large Data 13. Home Run Hitting 14. Making a Scientific Presentation using Quarto 15. Using Shiny for Baseball Applications Appendices A. Retrosheet Files Reference B. Historical Notes on PITCHf/x Data C. Statcast Data Reference References Indices Subject index R index
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