Assignment 2: Pitching Statistics

For assignment two, we worked on understanding basic pitching metrics and using learning about how to filter specific data, program functions, and create scatter plots. For pitching metrics, we showed how to calculate innings pitched, earned run average, walks plus hits per inning pitched, and strikeout/walks.

When working with R, we plotted win percentage verse home runs allowed, earned runs average, and walks plus hits per inning pitcher as 3 different scatterplots.

Lastly, we also created algorithms to be able to calculate when a ball was in a strike zone and the percentage of strikes that a pitcher threw.

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library(readr)

library(tidyverse)
library(ggpubr)

TPD2000_2019 <- read_csv("MATH 494/Teams Pitching Data 2000 - 2019.csv")

#Part A1: Calculating Win Percent
TPD2000_2019$WinPerc <- round(TPD2000_2019$W / TPD2000_2019$G, 3)

#Part A2: Calculating WHIP
IP <- TPD2000_2019$IPouts / 3
BBHBPH <- TPD2000_2019$BBA + TPD2000_2019$HA
TPD2000_2019$WHIP <- round(BBHBPH / IP, 3)

#Part B1: Creating Scatter plots
wp_v_HRA<- ggplot(TPD2000_2019, aes(HRA, WinPerc)) +
geom_point(color="purple") +
geom_smooth(method = "lm", se=FALSE, color="#FF99FF") +
labs(title="Win Percentage vs Home Runs Allowed",

x="Home Runs Allowed", y="Winning Percentage") +
stat_cor(aes(label=..r.label..),label.x=90, label.y=0.8) +
stat_cor(aes(label=..rr.label..), label.x=90, label.y=0.745)

wp_v_ERA<- ggplot(TPD2000_2019, aes(ERA, WinPerc)) +
geom_point(color="blue") +
geom_smooth(method = "lm", se=FALSE, color="#FF99FF") +
labs(title="Win Percentage vs Team ERA",

x="Team ERA", y="Winning Percentage") +
stat_cor(aes(label=..r.label..),label.x=5, label.y=0.8) +
stat_cor(aes(label=..rr.label..), label.x=5, label.y=0.745)

wp_v_WHIP<- ggplot(TPD2000_2019, aes(WHIP, WinPerc)) +
geom_point(color="green") +
geom_smooth(method = "lm", se=FALSE, color="#FF99FF") +
labs(title="Win Percentage vs WHIP",

x="WHIP", y="Winning Percentage") +
stat_cor(aes(label=..r.label..),label.x=1.15, label.y=0.8) +
stat_cor(aes(label=..rr.label..), label.x=1.15, label.y=0.745)

ggarrange(wp_v_HRA, wp_v_ERA, wp_v_WHIP, ncol=2, nrow=2)

library(readxl)

library(tidyverse)
library(gt)
library(RColorBrewer)
library(dplyr)

selectPitchers <- subset(ODUBBFP23_24,
Pitcher %in% c('Bashara','Finn','Pond','Sulpizio'))

strikePitcher <- select(selectPitchers, Pitcher, PlateLocHeight, PlateLocSide)

strikeData <- data.frame(
Pitcher = c('Bashara','Finn','Pond','Sulpizio'),
Count_Pitcher = c(0,0,0,0),
StrikePer = c(0,0,0,0),
NumStrikes = c(0,0,0,0))

strikeData$Count_Pitcher <- table(strikePitcher$Pitcher)

strikePitcher <- filter(strikePitcher, !is.na(PlateLocSide))
strikePitcher$strikeCheck <- between(strikePitcher$PlateLocSide, -1, 1) &
between(strikePitcher$PlateLocHeight, 1.211,3.667)

strikePitcher |> group_by(Pitcher)|>
summarise(total = sum(strikeCheck)) -> totSt
totSt$total -> strikeData$NumStrikes

strikeData$StrikePer <- with(strikeData, round((NumStrikes/Count_Pitcher)*100, 0))

gt(strikeData) |>
tab_header(title = "Strike Data")|>
cols_label(Count_Pitcher='Total Pitches',
StrikePer='Strike %',
NumStrikes='# of Strikes')

ggplot(strikeData)+
geom_bar(aes(x = Pitcher, y = StrikePer,
fill = c('Bashara','Finn','Pond','Sulpizio')),
stat="identity")+
ggtitle("Strike % of select ODU Baseball Pitchers")+
scale_fill_brewer(palette = 'Set2')

Assignment 2 Code and Results

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