Table Of ContentSports Analytics in Practice with R
Sports Analytics in Practice with R
Ted Kwartler
Add Affiliation
Maynard, MA
Robert Baker
Haymarket
[Give City], VA
This edition first published 2022
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v
Contents
Preface vii
Author Biography ix
Foreword xii
1 Introduction to R 1
2 Data Visualization: Best Practices 25
3 Geospatial Data: Understanding Changing Baseball Player Behavior 55
4 Evaluating Players for the Football Draft 91
5 Logistic Regression: Explaining Basketball Wins and Losses with Coefficients 133
6 Gauging Fan Sentiment in Cricket 155
7 Gambling Optimization 191
8 Exploratory Data Analysis: Searching Data for Opponent Insights 227
Index 253
vii
Preface
Sports is one of the few places where the data and outcomes are well known. Unlike
medicine which requires significant subject-matter expertise or business where the data
is proprietary in most cases, sports knowledge is relatively accessible, and the data and
outcomes are public. As a result, sports analytics serves as a great entry point for many
aspiring data scientists and analytics professionals. For the novice, this book demon-
strates the many facets and uses of countless techniques applicable outside of sports. It
should have more than enough topics and examples to aid learning for general practice.
For the avid R programmer and sports fan, the book likely has some new functions and
techniques which may be less well known. These readers will delight in improving and
expanding the demonstrated methods once the core concepts are understood. Finally, for
those already in the sports analytics world the techniques and individual chapter topics
can serve as a reference and starting point in their professional analysis. For instance,
much of the use cases in the chapters can be adjusted to specific sports or updated by
more recent underlying data.
This book has been a long journey in the making. Originally the book’s scope was
centered on individualized chapters demonstrating analytical techniques within a
sports context. The goal is that a reader inherits various tools that act as a foundation for
analysis to build upon and add complexity with subsequent analyses as the reader’s
technical acumen and sports interests grow. Each chapter is meant to be a standalone
reference as the reader explores and learns. This also frees up the reader to focus on top-
ics of interest. For example, a reader may not want to learn about natural language
processing so could skip that chapter altogether to focus on another subject such as
optimizing a fantasy football lineup. The book’s undertaking grew in complexity due to
a personal commitment to demonstrate concepts on diverse data sets including
Paralympic athletes, female soccer and basketball, and less US-centric popular sports
including cricket in addition to the more typically demonstrated sports analyses of
men’s football, baseball, and basketball. My goal is to make the subject accessible and
relevant to many in the analytics field despite this effort slowing the book’s creation.
Keep in mind a chapter’s concepts can be applied to many sports domains. For example,
the text analysis applied to cricket fan forum posts can easily be applied to men’s basket-
ball fan tweets or forum posts. Each chapter’s takeaway is meant to be a broadly useful
tool, not a brittle or narrowly focused analysis. Additionally, the book was delayed due
to the pandemic’s effect on the sports-world. Admittedly the shortened seasons, canceled
viii Preface
games, and other changes that created outlier statistics pales in comparison to the
pandemic’s hardship and humanistic impact outside of sports. Despite these challenges,
the book’s end result was worth the delay. The final product covers many diverse con-
cepts, and data, encouraging analytics professionals to enjoy the intersection of sports
and analysis.
The book’s supporting website is www.rstatsbook.com. The site contains data and scripts
along with any code revisions necessary as functions and packages change. Redundantly,
data is shared via git repository at www.github.com/kwartler/Practical_Sports_Analytics.
ix
Author Biography
Ted Kwartler
Adjunct Professor, Harvard University
Ted Kwartler is the VP, Trusted AI at DataRobot.
At DataRobot, Ted sets product strategy for explain-
able and ethical uses of data technology in the com-
pany’s application. Ted brings unique insights and
experience utilizing data, business acumen, and
ethics to his current and previous positions at
Liberty Mutual Insurance and Amazon. In addition
to having four DataCamp courses, he teaches grad-
uate courses at the Harvard Extension School and is
the author of Text Mining in Practice with R.
