Teaching Statistics Using Baseball / Edition 1

Teaching Statistics Using Baseball is a collection of case studies and exercises applying statistical and probabilistic thinking to the game of baseball. Baseball is the most statistical of all sports, since players are identified and evaluated by their corresponding hitting and pitching statistics. There is an active effort by people in the baseball community to… See more details below

Overview

Teaching Statistics Using Baseball is a collection of case studies and exercises applying statistical and probabilistic thinking to the game of baseball. Baseball is the most statistical of all sports, since players are identified and evaluated by their corresponding hitting and pitching statistics. There is an active effort by people in the baseball community to learn more about baseball performance and strategy by the use of statistics. This book illustrates basic methods of data analysis and probability models by means of baseball statistics collected on players and teams. Students often have difficulty learning statistics ideas since they are explained using examples that are foreign to the students. The idea of the book is to describe statistical thinking in a context (that is, baseball) that will be familiar and interesting to students.

The book is organized using a same structure as most introductory statistics texts. There are chapters on the analysis on a single batch of data, followed with chapters on comparing batches of data and relationships. There are chapters on probability models and on statistical inference. The book can be used as the framework for a one-semester introductory statistics class focused on baseball or sports. This type of class has been taught at Bowling Green State University. It may be very suitable for a statistics class for students with sports-related majors, such as sports management or sports medicine. Alternately, the book can be used as a resource for instructors who wish to infuse their present course in probability or statistics with applications from baseball.

Product Details

ISBN-13:
9780883857274
Publisher:
Mathematical Association of America
Publication date:
10/01/2003
Series:
Classroom Resource Materials Ser.
Edition description:
New Edition
Pages:
304
Product dimensions:
7.00(w) x 9.80(h) x 0.80(d)

Related Subjects

Preface
1. An Introduction to Baseball Statistics

2. Exploring a Single Batch of Baseball Data
Case Study 2-1: Looking at Teams' Offensive Statistics
Case Study 2-2: A Tribute to Cal Ripken
Case Study 2-3: A Tribute to Roger Clemens
Case Study 2-4: Analyzing Baseball Attendance
Case Study 2-5: Manager Statistics: the Use of Sacrifice Bunts

3. Comparing Batches and Standardization
Case Study 3-1: Barry Bonds and Junior Griffey
Case Study 3-2: Robin Roberts and Whitey Ford
Case Study 3-3: Home Runs- A Comparison of 1927,1961, 1998, and 2001
Case Study 3-4: Slugging Percentages Are Normal
Case Study 3-5: Great Batting Averages

4. Relationships Between Measurement Variables
Case Study 4-1: Relationships in team Offensive Statistics
Case Study 4-2: Runs and Offensive Statistics
Case Study 4-3: Most Valuable Hitting Statistics
Case Study 4-4: Creating a New Measure of Offensive Performance Using Multiple Regression
Case Study 4-5: How Important is a Run?
Case Study 4-6: Baseball Players Regress to the Mean
Case Study 4-7: The 2000 Dinger Drop-Off

5. Introduction to Probability Using Tabletop Games
Case Study 5-1: What is Barry Bonds's Home Run Probability?
Case Study 5-2: Big League Baseball
Case Study 5-3: All Star Baseball
Case Study 5-4: Strat-O-Matic Baseball

6. Probability Distributions and Baseball
Case Study 6-1: The Binomial Distribution and Hits Per Game
Case Study 6-2: Modeling Runs Scored: Getting on Base
Case Study 6-3: Modeling Runs Scored: Advancing he Runners to Home

7. Introduction to Statistical Inference
Case Study 7-1: Ability and Performance
Case Study 7-2: Simulating a Batter's Performance if His Ability is Known
Case Study 7-3: Learning about a Batter's Ability
Case Study 7-4: Interval Estimates for Ability
Case Study 7-5: Comparing Wade Boggs and Tony Gwynn

8. Topics in Statistical Inference
Case Study 8-1: Situational Hitting Statistics for Todd Helton
Case Study 8-2: Observed Situational Effects for Many Players
Case Study 8-3: Modeling Batting Averages for Many Players
Case Study 8-4: Models for Situational Effects
Case Study 8-5: Is John Olerud Streaky?
Case Study 8-6: A Streaky Die

9. Modeling Baseball Using a Markov Chain
Case Study 9-1: Introduction to a Markov Chain
Case Study 9-2: A Half-Inning of Baseball as a Markov Chain
Case Study 9-3: Useful Markov Chain Calculations
Case Study 9-4: The Value of Different On-base Events
A. An Introduction to Baseball
B. Datasets Used in the Book and Acquiring Baseball Data over the Internet
References
Index