Ted Kwartler - Sports Analytics in Practice with R

• 3D charts

• a
• assist - soccer statistic

• b
• bar chart best practices
• baseball player data loading
• baseball player name search
• baseball statistics ERA
• baseball tracking
• bigrams
• binary class model key performance indicators (KPI)
• binary classification
• box plot

• c
• chart comparisons
• chart data relevancy
• chart focus
• chart usage bar
• chart usage pie
• chart usage - proportional bar
• chart usage scatter
• chart usage - stacked bar
• combine (see
• confusion matrix
• corpora, see
• corpus

• d
• document term matrix
• DST - football position

• e
• explanatory modeling

• f
• fantasy sports
• football draft
• football lineup
• full join

• g
• generalized linear model
• ggpot facet grid
• glm
• goal - soccer statistic

• i
• IDE integrated development environment
• IF Else statement
• IF statement
• inner join
• installing package
• interactive plots

• k
• K-Means clustering
• K-Mediod clustering
• Kappa
• kernel density plot
• KNN binary classification
• KNN multi-class
• KNN regression
• KNN, K-Nearest Neighbors

• l
• left join
• lexicon
• library
• linear optimization
• linear programing
• loading libraries
• log-odds
• logistic regression
• logloss
• loops - FOR

• m
• MAC College Teams
• make.names cleaning
• markov chain

• n
• natural language processing (NLP) see
• natural log, e
• network graph 181-
• ngram
• Nightingale Rose Diagram
• NLP
• NLP workflow

• o
• optimization

• p
• pitch types
• pivot data
• plot - baseball diamond
• plot - basketball court
• plot - football field
• polarity
• predictive modeling

• q
• QB - football position

• r
• R software
• R-class character
• R-class factor
• R-class integer
• R-class list
• R-class logical
• R-class numeric
• R-Studio
• radar chart
• RB - football position

• s
• sample, explore, modify, model, asses
• scatter plot quadrants
• SEMMA
• sentiment analysis
• sequence matrix
• shot - soccer statistic
• spherical K-Means
• stopwords
• strike zone hit location
• strike zone pitch location
• string
• string manipulation
• summary
• supervised learning
• syntactic parsing

• t
• tabulation
• TE - football position
• theme_tufte
• timeline chart
• token
• tokenization
• Tufte

• u
• unsupervised learning

• v
• visualization

• w
• waterfall chart
• webscraping baseball statistics
• word cloud
• WR - football position

• Learn about R as a programming language
• Define Integrated Development Environment
• Define objects
• Learn the assignment operator
• Define functions
• Executing a loop
• Learn logical operators
• Learn about R data types
• Learn about object classes
• Indexing data objects
• Extending R functionality with packages
• Writing a custom function
• Create a scatter plot with sports data
• Create a heatmap with sports data

R is an open-source, freely available programming language used throughout this book. R is a powerful and longstanding programming language developed more than 20 years ago. It is a derivative of the S programming language for statistics originating in the mid-1990s developed by AT&T and Lucent Technologies. Unlike other programming languages, R is optimized specifically for statistics including but not limited to simulation, machine learning, visualizations, and traditional statistical modeling (linear regression) as well as tests. Due to the open-source nature of R, many developers, academics, and enthusiasts have contributed to its development for their specific needs. As a result, the language is extensible meaning it can be easily used for various purposes. For example, through R markdown, simple websites and presentations can be created. In another use case, R can be used for traditional linear modeling or machine learning and can draw upon various data types for analysis including audio files, digital images, text, numeric, and various other data files and types. Thus, it is widely used and nonspecialized other than to say R is an analysis language. This differs from other languages which specialize in web development like Ruby or python which has extended its functionality to building applications not just analysis.

In this textbook, the R language is applied specifically to sports contexts. Of course, the code in this book can be used to extend your understanding of sports analytics. It may give you insights to a particular sport or analytical aspect within the sport itself such as what statistics should be focused on to win a basketball game. However, learning the code in this book can also help open up a world of analytical capabilities beyond sports. One of the benefits of learning statistics, programming, and various analysis methods with sports data is that the data is widely available and outcomes are known. This means that your analysis, models, and visualizations can be applied, and you can review the outcomes as you expand upon what is covered in this book. This differs from other programming and statistical examples which may resort to boring, synthetic data to illustrate an analytical result. Using sports data is realistic and can be future oriented, making the learning more challenging yet engaging. Modeling the survivors of the Titanic pales in comparison since you cannot change the historical outcome or save future cruise ship mates. Thus, modeling which team will win a match or which player is a good draft pick is a superior learning experience.

If you are new to programming dont be intimidated. R is a forgiving language in that things like spacing an indentation are ignored. Further, the R community is well supported and a simple online search of any error message usually finds an answer quickly on any number of sites.

To begin your R and sports analytics journey, please download the base-R distribution for your operating system. The Comprehensive R Archive Network, CRAN, is the home of the official R distribution as well as officially supported packages (more on that in a bit). The site to download base-R is https://cran.r-project.org.