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
Sanford625 <- read_excel("C:/Users/Franco Castagliuolo/OneDrive - Bentley University/Neers 24/Pitchers/Sanford 625/Sanford 625.xlsx")
# Filter the data for the pitcher Nate Isler
Nate_Isler_data <- Sanford625 %>%
filter(Pitcher == "Isler, Nate")
# Create a detailed table for each pitch
detailed_pitch_table <- Nate_Isler_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 Nate Isler")
Detailed Pitch Table for Nate Isler
| Four-Seam |
86.78 |
2106.05 |
197.79 |
6.79 |
22.51 |
BallCalled |
6.61 |
0.71 |
5.70 |
6.6 |
| Four-Seam |
87.11 |
2080.47 |
192.09 |
4.13 |
20.60 |
StrikeCalled |
6.50 |
0.68 |
5.79 |
6.4 |
| Four-Seam |
85.69 |
2107.52 |
195.64 |
5.07 |
19.46 |
FoulBallNotFieldable |
6.51 |
0.68 |
5.68 |
6.5 |
| Four-Seam |
88.96 |
2236.54 |
213.16 |
11.66 |
19.18 |
StrikeCalled |
6.56 |
0.62 |
5.79 |
7.1 |
| Curveball |
72.85 |
2348.58 |
43.23 |
-12.52 |
-11.46 |
BallCalled |
6.36 |
0.83 |
5.28 |
1.4 |
| Four-Seam |
87.29 |
2177.27 |
210.53 |
11.04 |
20.07 |
StrikeCalled |
6.48 |
0.77 |
5.93 |
7.0 |
| Four-Seam |
87.22 |
2195.28 |
209.47 |
9.74 |
18.61 |
StrikeCalled |
6.60 |
0.61 |
5.64 |
7.0 |
| Curveball |
76.75 |
2500.57 |
39.08 |
-11.83 |
-12.88 |
BallCalled |
6.47 |
0.57 |
5.39 |
1.3 |
| Curveball |
72.92 |
2351.11 |
33.53 |
-12.64 |
-17.14 |
BallCalled |
6.40 |
0.72 |
5.47 |
1.1 |
| Four-Seam |
86.69 |
2094.97 |
209.74 |
10.36 |
19.46 |
InPlay |
6.52 |
0.75 |
5.81 |
7.0 |
| Four-Seam |
86.64 |
2069.45 |
212.62 |
11.68 |
19.64 |
BallCalled |
6.48 |
0.77 |
5.91 |
7.1 |
| Four-Seam |
86.55 |
2208.47 |
197.93 |
5.92 |
19.65 |
InPlay |
6.52 |
0.78 |
5.73 |
6.6 |
# 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: 12
# Display the summary table
knitr::kable(pitch_summary, caption = "Summary Pitch Table for Nate Isler")
Summary Pitch Table for Nate Isler
| Curveball |
25.00% |
3 |
3 |
0 |
100.00% |
0.00% |
74.17 |
2400.09 |
-13.83 |
-12.33 |
38.61 |
1.3 |
6.41 |
0.71 |
5.38 |
| Four-Seam |
75.00% |
9 |
2 |
7 |
22.22% |
77.78% |
86.99 |
2141.78 |
19.91 |
8.49 |
204.33 |
6.8 |
6.53 |
0.71 |
5.78 |
# 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("Nate Isler maximum FB velocity: ", max_fb_velocity, "mph\n")
## Nate Isler maximum FB velocity: 88.96 mph
# Prepare data for plotting pitch locations
pitch_location_data <- Nate_Isler_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 Nate Isler",
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 Nate Isler",
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")
)
