A Baseball Statistics Class

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# A Baseball Statistics Class - PowerPoint PPT Presentation

A Baseball Statistics Class. Jim Albert Department of Mathematics and Statistics Bowling Green State University albert@bgnet.bgsu.edu Supported by the National Science Foundation. Outline. Describe the intro stats class at BGSU Why focus a class on sports? Examples of Data analysis

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A Baseball Statistics Class

Jim Albert

Department of Mathematics and Statistics

Bowling Green State University

albert@bgnet.bgsu.edu

Supported by the National Science Foundation

Outline
• Describe the intro stats class at BGSU
• Why focus a class on sports?
• Examples of Data analysis
• Examples of Probability
• Examples of Inference
MATH 115 – Introduction to Statistics
• Satisfies math elective for students in College of Arts and Sciences
• Required by students in health college
• Students have range of math skills
• Goal of course is statistical literacy – how does one draw conclusions from data
• Book is at the level of Moore, Basic Practice of Statistics
Class is hard to teach
• No one wants to take stats.
• Easy to focus on number crunching rather than concepts.
• Students have little interest in the topics and datasets discussed.
• How to make the class more relevant to everyday life?
Statistics can made more interesting if we capitalize on “good” datasets
• Come in raw form
• Are authentic
• Are intrinsically interesting
• Are topical or controversial
• Offer substantial learning
• Lend itself to a variety of statistical analyses
Why base a stats course on baseball?
• Great American game
• Statistics are a integral part of baseball, used to rate players and teams.
• Players are known by their statistics (60, 56, 1.12)
• Relatively easy to model using probability.
MATH 115 b
• Special section of MATH 115 with a baseball emphasis
• I’ve taught it several times, most recently this summer.
• Text: Albert, Teaching Statistics Using Baseball, Mathematical Association of America.
Getting started with data analysis
• Looked at Bernie Williams’ baseball card.
• Started with a question “Was Bernie a big home run hitter?”
• Used graphs to answer the question.
Great home run hitters
• Watched part of Ken Burn’s documentary about Babe Ruth.
• Explored the slugging percentages of Babe.
• Interesting to plot SLG against his AGE(his career trajectory)
• Notice a familiar pattern.
• Interesting outlier (the bellyache heard around the world)
Do all players show a similar trajectory?
• Look at Barry Bonds’ slugging percentages over time.
• Shows unusual pattern towards the end of his career.
Baseball shapes
• Counts of things, like home run counts tend to be right-skewed.
• Derived baseball stats tend to be symmetric.
The Babe, Roger, and Barry
• Watched part of the movie “61*”
• Compared the home run rates of players in 1921, 1961, 2001
• Which outlierwas mostnotable?
The Second Best Baseball Player from BGSU?
• Orel Herscheiser was the best.
• Who was the 2nd best: Grant Jackson and Roger McDowell ? (Grant’s niece was in my class.)
• Compared their strikeout rates.
• Jackson was the better strikeout pitcher.
Fitting lines to scatterplots
• Used spaghetti to fit a line to (Home run, Slugging Percentage) for Mike Piazza’s data (note the Italian connection).
• Talked about the best batting measure. Is batting average or OBP better in predicting runs scored per game?
Regression effect
• Suppose your favorite team has a crummy season last season.
• I predict they will do better this season.
• The regression effect.
• Illustrate by looking at the number of wins of teams for two consecutive seasons.
Field of Dreams
• Watched part of the movie.
• Looked at the statistics of Shoeless Joe Jackson and Moonlight Graham.
• Who was better: Ty Cobb or Shoeless Jackson?
• Can you predict Jackson’s triple count for a season if you know his double count?
Introducing probability
• Played a simple dice game Big League Baseball.
• A single die controls the pitch (ball or strike).
• Two dice control the “in play” outcome.
• Simple enough you can talk about probabilities of various events (like a hit).
All-Star Baseball
• Spinner game where each spinner controls the hitting outcome for a single player.
• Student had a project where they constructed a spinner for a player given his career hitting statistics.
• Played a spinner game in class.
The spinner game motivates inference
• There is a distinction between a player's ability and his performance. An ability is an intrinsic quality of a player, say his batting talent, that we really don't know exactly. We do observe a player's performance, say his batting average for a particular season.
• The objective of Statistics is to learn about a player's ability on the basis of his performance.
Suppose a player’s true on-base percentage average is .4
• Use a 10-sided die to simulate the performance of a player in 20 plate appearances.
• Big distinction between his ability and his on-base performance in these games.
Do we observe chance variation in baseball?
• Watched part of “Angels in the Outfield”.
• Went to a Toledo Mud Hens game. Students were asked to look for lucky things that happened in the game (such as a groundball that found the right location for a hit)
• Are baseball players really streaky?
• Are situational statistics in baseball meaningful?(this is how players perform in different situations like Home/Away, in different months, against different pitchers, etc.)
Arguments against teaching this type of course

I’ll describe five objections

“All students aren’t interested in baseball”
• At BGSU, easy to fill one section with students who like baseball
• Don’t need to be a baseball fan, just willing to learn some baseball and statistics.
• Baseball is a serious business for players, managers and owners.
• Need a proper interpretation of statistics to be a successful baseball team.
• Controversy about the use of statistics – similar to the mistrust of statistics in the public area.
“The course appeals mainly to one gender”
• Course does tend to attract more men.
• But the course only requires a willingness to learn.“I don’t know any baseball, but my brothers played sports, and I was learning to learn.”
• Use baseball as the medium where students learn statistical concepts, such as learning about an ability (a parameter).
• Once the concept is learned, it is relatively easy to expose students to other examples outside baseball.
• Only topic that didn’t receive much attention was collecting data through sample surveys and designed experiments.
• But could include these topics within context of baseball.
Was the course successful?
• Fun for both instructor and the students.
• Enthusiasm of the instructor about the material had a positive impact on learning.
• Baseball is a great context for learning many statistics concepts.
• Students could make sense of the statistical conclusions.
Moral of this experiment
• Should explore alternative methods of teaching statistics.
• In particular, explore ways of engaging students through interesting applications so they can make more sense of statistical thinking.
Some references
• “A Baseball Statistics Class”, Journal of Statistics Education

http://www.amstat.org/publications/jse/v10n2/albert.html

• I created a blog of my recent class.http://bstats.blogspot.com/