knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(readxl)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
library(knitr)
# Load the data
Upper626 <- read_excel("C:/Users/Franco Castagliuolo/OneDrive - Bentley University/Neers 24/Pitchers/Upper 626/Upper 626.xlsx")
# Filter the data for the pitcher Charlie Kutz
Charlie_Kutz_data <- Upper626 %>%
filter(Pitcher == "Kutz, Charlie")
# Create a detailed table for each pitch
detailed_pitch_table <- Charlie_Kutz_data %>%
select(AutoPitchType, RelSpeed, SpinRate, SpinAxis, HorzBreak, InducedVertBreak, PitchCall, RelHeight, RelSide, Extension) %>%
rename(
ReleaseSpeed = RelSpeed,
Tilt = SpinAxis,
HorizontalBreak = HorzBreak,
InducedVerticalBreak = InducedVertBreak,
ReleaseHeight = RelHeight,
ReleaseSide = RelSide
) %>%
mutate(
ReleaseSpeed = round(ReleaseSpeed, 2),
SpinRate = round(SpinRate, 2),
Tilt = round(Tilt, 2),
HorizontalBreak = round(HorizontalBreak, 2),
InducedVerticalBreak = round(InducedVerticalBreak, 2),
ReleaseHeight = round(ReleaseHeight, 2),
ReleaseSide = round(ReleaseSide, 2),
Extension = round(Extension, 2),
ClockTilt = round((Tilt / 30) %% 12, 1) # Interpret Tilt as clock face
)
# Display the detailed table
knitr::kable(detailed_pitch_table, caption = "Detailed Pitch Table for Charlie Kutz")
Detailed Pitch Table for Charlie Kutz
| Changeup |
88.66 |
2496.26 |
92.14 |
-20.18 |
1.95 |
StrikeCalled |
5.63 |
-1.90 |
5.57 |
3.1 |
| Sinker |
89.19 |
2371.82 |
90.19 |
-17.33 |
1.30 |
BallCalled |
5.30 |
-1.99 |
5.59 |
3.0 |
| Sinker |
88.44 |
2343.85 |
118.75 |
-17.64 |
11.03 |
InPlay |
5.59 |
-1.84 |
5.45 |
4.0 |
| Sinker |
87.90 |
2434.41 |
108.47 |
-16.64 |
6.68 |
StrikeCalled |
5.45 |
-2.00 |
5.35 |
3.6 |
| Sinker |
88.76 |
2433.30 |
102.69 |
-17.92 |
5.20 |
BallCalled |
5.29 |
-1.90 |
6.06 |
3.4 |
| Sinker |
88.37 |
2419.57 |
111.00 |
-15.68 |
7.23 |
InPlay |
5.47 |
-1.95 |
5.45 |
3.7 |
| Sinker |
89.73 |
2438.12 |
106.66 |
-18.71 |
6.81 |
StrikeCalled |
5.56 |
-1.94 |
5.07 |
3.6 |
| Sinker |
90.64 |
2363.55 |
120.88 |
-16.78 |
11.28 |
BallCalled |
5.45 |
-1.81 |
5.36 |
4.0 |
| NA |
80.12 |
2579.68 |
NA |
NA |
NA |
BallCalled |
5.60 |
-1.94 |
4.96 |
NA |
| Four-Seam |
87.91 |
2506.32 |
130.44 |
-11.95 |
11.31 |
BallCalled |
5.61 |
-1.91 |
5.25 |
4.3 |
| Sinker |
87.39 |
2246.32 |
104.29 |
-9.65 |
3.60 |
StrikeCalled |
5.58 |
-1.72 |
5.56 |
3.5 |
| NA |
82.06 |
2511.94 |
NA |
NA |
NA |
BallCalled |
5.74 |
-1.90 |
5.07 |
NA |
| NA |
87.99 |
2292.68 |
NA |
NA |
NA |
HitByPitch |
5.86 |
-1.89 |
5.41 |
NA |
| Changeup |
86.81 |
2276.26 |
116.37 |
-12.14 |
7.26 |
BallCalled |
5.59 |
-1.61 |
5.49 |
3.9 |
| Sinker |
88.32 |
2450.80 |
127.45 |
-12.32 |
10.58 |
BallCalled |
5.60 |
-1.81 |
5.41 |
4.2 |
| Sinker |
87.69 |
2409.80 |
108.95 |
-12.16 |
5.38 |
BallCalled |
5.53 |
-1.68 |
5.52 |
3.6 |
| Sinker |
88.31 |
2436.13 |
117.13 |
-13.11 |
7.84 |
StrikeCalled |
5.60 |
-1.68 |
5.58 |
3.9 |
| Changeup |
87.54 |
2357.86 |
113.98 |
-15.37 |
8.07 |
InPlay |
5.58 |
-1.73 |
5.42 |
3.8 |
# Calculate the total number of pitches
total_pitches <- nrow(detailed_pitch_table)
# Create a summary table
pitch_summary <- detailed_pitch_table %>%
group_by(AutoPitchType) %>%
summarise(
TotalPitches = n(),
Usage = sprintf("%.2f%%", n() / total_pitches * 100),
Balls = sum(PitchCall == "BallCalled"),
Strikes = sum(PitchCall != "BallCalled"), # Count everything not a ball as a strike
BallPercentage = sprintf('%.2f%%', Balls / TotalPitches * 100),
StrikePercentage = sprintf('%.2f%%', Strikes / TotalPitches * 100),
AvgVelocity = round(mean(ReleaseSpeed, na.rm = TRUE), 2),
AvgSpinRate = round(mean(SpinRate, na.rm = TRUE), 2),
AvgInducedVertBreak = round(mean(InducedVerticalBreak, na.rm = TRUE), 2),
AvgHorzBreak = round(mean(HorizontalBreak, na.rm = TRUE), 2),
AvgTilt = round(mean(Tilt, na.rm = TRUE), 2),
AvgClockTilt = round(mean(ClockTilt, na.rm = TRUE), 1), # Clock face interpretation
AvgReleaseHeight = round(mean(ReleaseHeight, na.rm = TRUE), 2),
AvgReleaseSide = round(mean(ReleaseSide, na.rm = TRUE), 2),
AvgExtension = round(mean(Extension, na.rm = TRUE), 2)
) %>%
select(AutoPitchType, Usage, everything())
# Display the total number of pitches
cat("Total number of pitches thrown: ", total_pitches, "\n")
## Total number of pitches thrown: 18
# Display the summary table
knitr::kable(pitch_summary, caption = "Summary Pitch Table for Charlie Kutz")
Summary Pitch Table for Charlie Kutz
| Changeup |
16.67% |
3 |
1 |
2 |
33.33% |
66.67% |
87.67 |
2376.79 |
5.76 |
-15.90 |
107.50 |
3.6 |
5.60 |
-1.75 |
5.49 |
| Four-Seam |
5.56% |
1 |
1 |
0 |
100.00% |
0.00% |
87.91 |
2506.32 |
11.31 |
-11.95 |
130.44 |
4.3 |
5.61 |
-1.91 |
5.25 |
| Sinker |
61.11% |
11 |
5 |
6 |
45.45% |
54.55% |
88.61 |
2395.24 |
6.99 |
-15.27 |
110.59 |
3.7 |
5.49 |
-1.85 |
5.49 |
| NA |
16.67% |
3 |
2 |
1 |
66.67% |
33.33% |
83.39 |
2461.43 |
NaN |
NaN |
NaN |
NaN |
5.73 |
-1.91 |
5.15 |
# Calculate maximum fastball velocity
max_fb_velocity <- detailed_pitch_table %>%
filter(AutoPitchType %in% c("Four-Seam", "Two-Seam", "Sinker", "Cutter")) %>%
summarise(MaxFBVelocity = max(ReleaseSpeed, na.rm = TRUE)) %>%
pull(MaxFBVelocity)
# Display the maximum fastball velocity
cat("Charlie Kutz maximum FB velocity: ", max_fb_velocity, "mph\n")
## Charlie Kutz maximum FB velocity: 90.64 mph
# Prepare data for plotting pitch locations
pitch_location_data <- Charlie_Kutz_data %>%
select(AutoPitchType, PlateLocHeight, PlateLocSide, PitchCall) %>%
rename(
PitchHeight = PlateLocHeight,
PitchSide = PlateLocSide
) %>%
mutate(
SwingTake = ifelse(PitchCall %in% c("StrikeSwinging", "FoulBallNonSwinging", "FoulBallFieldable", "FoulBallNotFieldable", "InPlay"), "Swing", "Take"),
Chase = ifelse(SwingTake == "Swing" & (PitchSide < -0.75 | PitchSide > 0.75 | PitchHeight < 1.5 | PitchHeight > 3.5), "Chase", "Non-Chase")
)
# Create the scatter plot with specified strike zone boxes
ggplot(pitch_location_data, aes(x = PitchSide, y = PitchHeight, color = SwingTake, shape = Chase)) +
geom_point(size = 3) + # Increase point size
geom_rect(aes(xmin = -0.5, xmax = 0.5, ymin = 1.75, ymax = 3.25), fill = NA, color = "red", linetype = "solid", size = 1) + # Red box
geom_rect(aes(xmin = -0.75, xmax = 0.75, ymin = 1.5, ymax = 3.5), fill = NA, color = "black", linetype = "solid", size = 1) + # Black box
geom_rect(aes(xmin = -1.25, xmax = 1.25, ymin = 1.25, ymax = 3.75), fill = NA, color = "gray", linetype = "solid", size = 1) + # Gray box
scale_x_continuous(limits = c(-2, 2)) +
scale_y_continuous(limits = c(0, 5)) +
coord_fixed(ratio = 1) + # Adjust ratio to shrink vertical distance
labs(title = "Pitch Locations for Charlie Kutz",
x = "Horizontal Location (feet)",
y = "Vertical Location (feet)",
color = "Swing/Take",
shape = "Chase") +
facet_wrap(~ AutoPitchType) + # Create individual graphs for each pitch type
theme_minimal() +
theme(
legend.position = "right",
panel.grid.major = element_line(color = "grey80"),
panel.grid.minor = element_line(color = "grey90"),
axis.text = element_text(color = "black"),
axis.title = element_text(color = "black"),
plot.title = element_text(color = "black"),
legend.background = element_rect(fill = "white", color = NA),
legend.key = element_rect(fill = "white", color = NA),
legend.text = element_text(color = "black"),
legend.title = element_text(color = "black")
)
# Create the scatter plot for horizontal and vertical breaks
ggplot(detailed_pitch_table, aes(x = HorizontalBreak, y = InducedVerticalBreak, color = AutoPitchType)) +
geom_point(size = 3) + # Increase point size
labs(title = "Pitch Movement for Charlie Kutz",
x = "Horizontal Break (inches)",
y = "Induced Vertical Break (inches)",
color = "Pitch Type") +
theme_minimal() +
theme(
legend.position = "right",
panel.grid.major = element_line(color = "grey80"),
panel.grid.minor = element_line(color = "grey90"),
axis.text = element_text(color = "black"),
axis.title = element_text(color = "black"),
plot.title = element_text(color = "black"),
legend.background = element_rect(fill = "white", color = NA),
legend.key = element_rect(fill = "white", color = NA),
legend.text = element_text(color = "black"),
legend.title = element_text(color = "black")
)
