Pitch Location Charts with PITCHf/x and ggplot

Name: Pitch Location Charts with PITCHf/x and ggplot
Rating: 4.8 (107 reviews)

Visually analyze each at-bat of a baseball game.

Created byCharles Redmond

Last updated 7/2015

English

What you'll learn

scrape PITCHf/x data into an R session
plot pitch locations with ggplot
visualize pitch type and speed with ggplot
subset vectors in R
work with color in ggplot
facet in ggplot
label with geom_text
work with seq, lapply, unlist, and unique in R
save plots as png's
write for loops in R
work with factors in R

Course content

6 sections • 37 lectures • 2h 11m total length

Introduction1:01
In this video, I state the goals of the course.
PITCHf/x Data4:14
After viewing this lecture, you will know where to access PITCHf/x data and understand a bit about its structure.
Scraping the Data4:50
After viewing this lecture, you will be able to scrape the data for a single game from the PITCHf/x website and access the data frames in which the data is stored.
Joining the atbat and pitch Data Frames4:46
After viewing this lecture, you will be able to join the atbat and pitch data frames.
Choosing Columns from the atbat Data Frame4:39
In this lecture, we select what we need from the atbat data frame.
Choosing Columns from the pitch Data Frame2:31
In this lecture, we select what we need from the pitch data frame.

The Strike-Zone1:50
In this lecture, I give the coordinates of the strike-zone.
Drawing the Strike-Zone4:24
After viewing this lecture, you will be able to work with geom_path to draw a strike-zone with ggplot.
Vizualizing the Pitch Locations2:26
After viewing this lecture, you will be able to plot all of the pitches thrown by Max Scherzer as points.
Visualizing Pitch Speed3:42
After viewing this lecture, you will be able to use the ggplot size parameter within the aesthetics to visualize the speed of each pitch.
Visualizing Pitch Type3:17
After viewing this lecture, you will be able to use the ggplot color parameter within the aesthetics to visualize the type of each pitch.
Subsetting R vectors and "which"4:27
After viewing this lecture, you will be able to subset vectors and use the "which" function in R.
Better Pitch Descriptions4:37
After viewing this lecture, you will be able to use the "which" function to generate a more descriptive pitch type column.

Hue3:59
After viewing this lecture, you will be able to set the hue of points in ggplot.
Chromaticity (saturation)1:49
After viewing this lecture, you will be able to set the chromaticity, or saturation, of points in ggplot.
Luminance (brightness)1:08
After viewing this lecture, you will be able to set the luminance, or brightness, of points in ggplot.
Palettes1:51
After viewing this lecture, you will be able to use the RColorBrewer package to pick a color palette for displaying points in ggplot.
Manual Color Specification3:04
After viewing this lecture, you will be able to manually specify your own color palette for displaying points in ggplot.
Colors and Factors5:02
After viewing this lecture, you will understand factors and you will be able to work with them.

Faceting with One Variable5:06
After viewing this lecture, you will be able to facet in ggplot, and you will also be able to work with geom_text.
Stand on the Correct Side4:10
In this lecture, we do more problem solving with the "which" function.
Facet Order through Factors1:32
In this lecture, we do more problem solving with factors.
Faceting by At-Bat5:34
In this lecture, we do more problem solving with faceting and geom_text.

Specifying Batter and Inning5:41
In this lecture, we separate out a single at-bat for visualization. We also learn how to work with the paste function and how to set limits for the axes.
Labeling Pitches4:30
In this lecture, we do more problem solving with geom_text, and we learn about the vjust parameter.
Replacing "In Play"5:27
In this lecture, we do more problem solving with the "which" function.
Counting the Number of Pitches in an At-Bat3:47
In this lecture, we review grouping and counting with dplyr.
lapply and seq2:01
After viewing this lecture, you will be able to use the seq and lapply functions in R.
unlist1:49
After viewing this lecture, you will be able to work with the unlist function.
Visualizing Pitch Enumeration2:23
In this lecture, we finally enumerate the pitches within each at-bat.

Saving a Single Plot2:36
After viewing this lecture, you will be able to save a plot from ggplot to a png.
For Loop Part I3:38
In this lecture, we learn how to write a for loop. We also learn how to work with the unique function.
For Loop Part II3:41
In this lecture, we make the necessary changes to our code withing the for loop.
Difficulties and Problems!2:41
In this lecture, we discover two additional problems we must solve before our final product is satisfactory.
Solving the Color Problem4:12
In this lecture, we learn how to work with the names function to solve our color problem.
Solving the Speed Problem Part I4:59
In this lecture, I discuss the mappings of intervals to intervals, a technique that will help us solve our speed problem.
Solving the Speed Problem Part II and Wrap-Up3:58
In this lecture, we implement the solution described in the last lecture and wrap-up the course.

Requirements

Students will need to have R and RStudio installed on their own computers.

Description

In this course, we make use of PITCHf/x data to create pitch location charts for a given baseball game. We break the game out into each at-bat and visualize the location, type, and speed of each pitch, the order in which the pitches were thrown, and the outcome of the at-bat.

In order to accomplish this, we will be taking a deep dive into ggplot. We will learn much about how to work with color, how to use aesthetics, and how to facet. We will also gain additional R skills, such as how to subset a vector and how to work with factors.

One should be able to complete the course, at a relaxed pace, in about three weeks. It is best if students already have a little bit of a background in R, dplyr, and ggplot, but it is not completely necessary.

Who this course is for:

This course is for students interested in learning how to create pitch location charts and how to wrangle data from PITCHf/x.
It would be best for each student to have a bit of a background in R, dplyr, and ggplot, but it is not completely necessary.

Pitch Location Charts with PITCHf/x and ggplot

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Course content

Extracting PITCHf/x Data6 lectures • 22min

Multi-Batter Visualizations7 lectures • 25min

Working with Color6 lectures • 17min

Faceting4 lectures • 16min

At-Bat by At-Bat7 lectures • 26min

Slide-Show7 lectures • 26min

Requirements

Description

Who this course is for: