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商品描述
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.
商品描述(中文翻譯)
《使用 R 分析棒球數據（第二版）》向棒球數據分析師、棒球愛好者及有興趣探索棒球數據豐富性的學生介紹 R 語言。它提供了執行所有分析步驟所需的技能和軟體工具，從導入數據到將其轉換為適當格式，再到通過圖形可視化數據，最後進行統計分析。
作者首先介紹了公開可用的棒球數據集概覽，以及 R 語言的數據結構類型和探索性數據管理能力的簡要介紹。他們還涵蓋了 ggplot2 圖形函數，並在整本書中採用 tidyverse 友好的工作流程。書中大部分內容通過流行的棒球數據分析主題來展示 R 的使用，包括畢氏公式、得分預期、捕手框架、職業生涯軌跡、比賽和賽季的模擬、球員的連勝行為模式，以及發射角度和出球速度。書中使用的所有數據集和 R 代碼均可在線獲得。
第二版的新內容包括系統性地採用 tidyverse 以及納入 Statcast 球員追蹤數據（由 Baseball Savant 提供）。第一版的所有代碼已根據 tidyverse 的原則進行修訂。整本書強調了 tidyverse 套件，包括 dplyr、ggplot2、tidyr、purrr 和 broom。由於 Statcast 數據的可用性，兩個全新的章節得以實現：一個探討捕手框架能力的概念，另一個使用發射角度和出球速度來估算全壘打的概率。通過書中的各種範例，您將學習現代棒球數據分析及如何進行自己的棒球分析。
Max Marchi 是克里夫蘭印地安人的棒球分析分析師。他曾是 The Hardball Times 和 Baseball Prospectus 網站的定期貢獻者，並曾為其他 MLB 球隊提供諮詢。
Jim Albert 是波林格林州立大學的傑出大學教授，專攻統計學。他著有或合著多本書籍，包括 Curve Ball 和 Visualizing Baseball，並曾擔任 Journal of Quantitative Analysis of Sports 的編輯。
Ben Baumer 是史密斯學院的統計與數據科學助理教授。曾擔任紐約大都會的統計分析師，他是 The Sabermetric Revolution 和 Modern Data Science with R 的合著者。
