Chapter 5 Plots
Jim Albert
June 8, 2017
library(dplyr)
library(ggplot2)
library(stringr)The Data
Using the pitchRX package, I downloaded all of the pitch data for all games in the 2016 season. From this large dataset, I collected the data for 2044 pitches thrown by Clayton Kershaw.
Here I read in the pitchFX data and show a few lines.
library(readr)
CK <- read_csv("https://bayesball.github.io/VB/data/kershaw2016.csv")
head(CK)## # A tibble: 6 x 15
## pitch_type px pz des num
## <chr> <dbl> <dbl> <chr> <int>
## 1 FF 0.089 2.750 Called Strike 7
## 2 FF 0.083 2.721 Swinging Strike 7
## 3 FF -2.651 2.690 Ball 7
## 4 CU -0.644 -0.231 Ball 7
## 5 FF 0.642 4.521 Ball 7
## 6 FF -1.410 2.327 Swinging Strike 7
## # ... with 10 more variables: gameday_link <chr>, start_speed <dbl>,
## # spin_dir <dbl>, spin_rate <dbl>, pfx_x <dbl>, pfx_z <dbl>, type <chr>,
## # pitcher_name <chr>, event <chr>, stand <chr>Here are the variables in the data frame CK.
- pitch_type - type of pitch thrown
- px - horizontal location in zone
- pz - vertical location in zone
- des - outcome of pitch
- start_speed - speed of pitch as it leaves the pitcher’s hand
- event - outcome of the plate appearance
- stand
Load several packages.
library(ggplot2)
library(dplyr)Pitch Types Thrown
To get an understanding of what pitch types are thrown, we construct a dotplot of the frequencies of the pitch types (variable pitch_type).
S_CK <- filter(summarize(group_by(CK, pitch_type),
N=n()),
pitch_type %in% c("SL", "FF", "CU", "CH"))
ggplot(S_CK, aes(pitch_type, N)) +
geom_point(size=3, color="blue") +
coord_flip() +
ggtitle("Frequencies of Pitch Type of Clayton Kershaw") +
theme(plot.title = element_text(size = 14,
hjust = 0.5))
Pitch Speeds
These different pitch types are thrown at different speeds. The following display is a boxplot of the speeds (varialbe start_speed) of the four types of pitches thrown by Kershaw.
ggplot(filter(CK, pitch_type %in%
c("SL", "FF", "CU", "CH")),
aes(pitch_type, start_speed)) +
geom_boxplot() + coord_flip() +
ggtitle("Pitch Speeds") +
theme(plot.title = element_text(size = 14,
hjust = 0.5)) +
ylim(70, 100)
Pitch Breaks
These pitch types are also distinguished by their movement or break. The variables pfx_x and pfx_z give the horizontal and vertical break amounts. (The perspective is from the catcher behind the plate.) The following graph shows the movements for each type of pitch.
CK <- filter(CK, pitch_type %in% c("CU",
"FF", "SL"))
ggplot(CK,
aes(pfx_x, pfx_z, shape=pitch_type)) +
geom_point(color="blue", size=2, alpha=0.5) +
ggtitle("Pitch Breaks") +
theme(plot.title = element_text(size = 14,
hjust = 0.5)) +
xlab("Horizontal Break") + ylab("Vertical Break")
Pitch Locations
The variables px and pz give the horizontal and vertical locations of the pitch viewed from the catcher’s perspective. The zone for an average hitter is added to the plots so we can see which pitches are inside and outside of the zone.
topKzone <- 3.5
botKzone <- 1.6
inKzone <- -0.85
outKzone <- 0.85
kZone <- data.frame(
x=c(inKzone, inKzone, outKzone, outKzone, inKzone),
y=c(botKzone, topKzone, topKzone, botKzone, botKzone)
)
ggplot(CK) +
geom_point(data= filter(CK, pitch_type=="CU"),
aes(px, pz), shape=1) +
geom_point(data= filter(CK, pitch_type=="FF"),
aes(px, pz), shape=2) +
geom_point(data= filter(CK, pitch_type=="SL"),
aes(px, pz), shape=3) +
geom_path(aes(x, y), data=kZone, lwd=1, col="blue") +
facet_wrap(~ pitch_type, ncol=2) +
xlim(-2, 2) + ylim(-0.5, 5) +
theme(strip.text = element_text(size = rel(1.5),
hjust=0.5,
color = "black")) +
ggtitle("Pitch Locations") +
theme(plot.title = element_text(size = 14,
hjust = 0.5))
Two-dimensional contour plots (from fitting a two-dimensional density estimate) are helpful for visualizing the locations of the different types of pitches.
ggplot(CK) +
geom_density_2d(aes(px, pz), color="black") +
geom_path(aes(x, y), data=kZone, lwd=1, col="blue") +
facet_wrap(~ pitch_type, ncol=2) +
xlim(-2, 2) + ylim(-0.5, 5) +
theme(strip.text = element_text(size = rel(1.5),
hjust=0.5,
color = "black")) +
ggtitle("Pitch Locations") +
theme(plot.title = element_text(size = 14,
hjust = 0.5))
Pitch Outcomes
What are the outcomes of these different types of pitches? We use the variable des which gives a description of the pitch outcome.
SO <- summarize(group_by(CK, pitch_type, des), N=n())
SO <- mutate(SO,
Outcome=ifelse(str_detect(des, "Foul") == TRUE, "Foul",
ifelse(str_detect(des, "Swing") == TRUE |
des == "Missed Bunt", "Swing and Miss",
ifelse(str_detect(des, "Ball") == TRUE, "Ball",
ifelse(str_detect(des, "In play") == TRUE, "In play",
des)))))
SOS <- summarize(group_by(SO, pitch_type, Outcome),
F=sum(N))
SOS1 <- summarize(group_by(SO, pitch_type),
Total=sum(N))
inner_join(SOS, SOS1) %>%
mutate(Percentage = 100 * F / Total) -> SOS
ggplot(SOS,
aes(Outcome, Percentage)) +
geom_point(size=3, color="blue") +
coord_flip() + facet_wrap(~ pitch_type, ncol=1) +
theme(strip.text = element_text(size = rel(1.5),
hjust=0.5,
color = "black"))
Outcome of a Swing
What if the batter swings at the pitch? We focus on the frequencies of the three outcomes “Foul”, “In play”, and “Miss” for each pitch type.
CK <- mutate(CK,
Foul = str_detect(des, "Foul"),
InPlay = str_detect(des, "In play"),
Miss = str_detect(des, "Swing"),
Swing = Foul | InPlay | Miss)
CK_swing <- filter(CK, Swing == TRUE)
ggplot(CK_swing, aes(px, pz, color=Miss)) +
geom_point(alpha=0.75) +
facet_wrap(~ pitch_type, ncol=2) +
geom_path(aes(x, y), data=kZone, lwd=1, col="black") +
facet_wrap(~ pitch_type, ncol=2) +
xlim(-2, 2) + ylim(-0.5, 5) +
scale_colour_manual(values = c("gray60", "blue")) +
theme(strip.text = element_text(size = rel(1.5),
hjust=0.5,
color = "black"))