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FultzPhillyVsMagic.md

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#Stats were pulled from Basketball Reference.com

Magic vs. Philly: Comparing Fultz using Game Score

image

Import Packages

library(rvest) #version 0.3.6

library(dplyr)

library(hablar) #Used to convert data types

library(ggplot2) #graphing

library(stringr) #spliting columns

Import Data: Fultz Stats

Fultz 2020 - 2021 Season

Fultz2021 <- "https://www.basketball-reference.com/players/f/fultzma01/gamelog/2021/"

read_html(Fultz2021)

#After importing the html site, transform to data table

url <- Fultz2021

pageobj <- read_html(url, as.data.frame=T, stringsAsFactors = TRUE)

#Here, we indicate that this is the table we want to extract.

pageobj %>%
html_nodes("table") %>% .[[8]] %>% html_table(fill=T) -> Fultz2021

#Removes Rows with subheadings

Fultz2021 <- Fultz2021[!(Fultz2021$GS=="Did Not Play" | Fultz2021$GS=="Did Not Dress" | Fultz2021$GS=="GS" | Fultz2021$GS == "Inactive"),]

#Change Column Names

names(Fultz2021)[names(Fultz2021) == "Rk"] <- "TeamGame"

names(Fultz2021)[names(Fultz2021) == "G"] <- "PlayerGame"

names(Fultz2021)[names(Fultz2021) == "Tm"] <- "Team"

colnames(Fultz2021)[6] <-c("Location")

names(Fultz2021)[names(Fultz2021) == "Opp"] <- "Opponent"

colnames(Fultz2021)[8] <-c("WinLoss")

names(Fultz2021)[names(Fultz2021) == "GS"] <- "GameStarted"

names(Fultz2021)[names(Fultz2021) == "MP"] <- "MinsPlayed"

Fill in the Location Data for the game

Fultz2021$Location <- ifelse(Fultz2021$Location == "@", "Away", "Home")

Split MinsPlayed Column into two columns

Index <- str_split_fixed(Fultz2021$MinsPlayed, ":", 2) #Create County Index Matrix

Fultz2021 <- cbind(Fultz2021, Index) #Add CountyIndex Matrix to CountyIncome DataFrame

names(Fultz2021)[names(Fultz2021) == "1"] <- "Minutes"

names(Fultz2021)[names(Fultz2021) == "2"] <- "Seconds"

Split WinsLoss Column into two columns

Index <- str_split_fixed(Fultz2021$WinLoss, " ", 2) #Create County Index Matrix

Fultz2021 <- cbind(Fultz2021, Index) #Add CountyIndex Matrix to CountyIncome DataFrame

Fultz2021<- Fultz2021 %>% select(-WinLoss) #remove columns

names(Fultz2021)[names(Fultz2021) == "1"] <- "WinLoss"

Fultz2021<- Fultz2021 %>% select(-"2")

Update the Data Types
library(hablar)

colnames(Fultz2021)

Fultz2021 <- Fultz2021 %>% convert( int("TeamGame", "PlayerGame", "FG", "FGA", "3P", "3PA", "FT", "FTA", "ORB", "DRB", "TRB", "AST", "STL", "BLK", "TOV", "PF", "PTS", "+/-" ), num("FG%", "3P%", "FT%", "FG%", "FT%", "3P%", "GmSc"), dte("Date"), fct("Team", "Location", "Opponent", "WinLoss", "GameStarted"))

Create Data Dictionary: Fultz 2021 Season

#Create a vector that counts how many columns are in the data frame ColNumber <- (seq_len(ncol(Fultz2021))) #Turn the vector into its own data frame with the first column called ColNumber DataDictionary <- as.data.frame(ColNumber) #Add a column based on the vector of colnames DataDictionary$VariableName <- (colnames(Fultz2021)) #Add the variable type into the data frame as a new column called "type" DataDictionary$DataType <- (sapply(Fultz2021, class)) View(DataDictionary)

Graph: Fultz 2021 Season

ggplot(Fultz2021, aes(x = Minutes, y = GmSc)) + geom_point() + ggtitle("Fultz 2020-2021") + xlab("Minutes Played") + ylab("Game Score") + theme_classic() + theme(axis.text.x = element_blank(), axis.ticks = element_blank(), axis.line = element_line(linetype = "blank"))

Fultz 2019-2020 Season

Fultz2020 <- "https://www.basketball-reference.com/players/f/fultzma01/gamelog/2020"

read_html(Fultz2020)

After importing the html site, transform to data table

url <- Fultz2020

pageobj <- read_html(url, as.data.frame=T, stringsAsFactors = TRUE)

Here, we indicate that this is the table we want to extract.

pageobj %>%
html_nodes("table") %>% .[[8]] %>% html_table(fill=T) -> Fultz2020

Removes Rows with subheadings

Fultz2020 <- Fultz2020[!(Fultz2020$GS=="Did Not Play" | Fultz2020$GS=="Did Not Dress" | Fultz2020$GS=="GS" | Fultz2020$GS == "Inactive"),]

Change Column Names

names(Fultz2020)[names(Fultz2020) == "Rk"] <- "TeamGame"

names(Fultz2020)[names(Fultz2020) == "G"] <- "PlayerGame"

names(Fultz2020)[names(Fultz2020) == "Tm"] <- "Team"

colnames(Fultz2020)[6] <-c("Location")

names(Fultz2020)[names(Fultz2020) == "Opp"] <- "Opponent"

colnames(Fultz2020)[8] <-c("WinLoss")

names(Fultz2020)[names(Fultz2020) == "GS"] <- "GameStarted"

names(Fultz2020)[names(Fultz2020) == "MP"] <- "MinsPlayed"

Fill in the Location Data for the game

Fultz2020$Location <- ifelse(Fultz2020$Location == "@", "Away", "Home")

Split MinsPlayed Column into two columns

Index <- str_split_fixed(Fultz2020$MinsPlayed, ":", 2) #Create County Index Matrix

Fultz2020 <- cbind(Fultz2020, Index) #Add CountyIndex Matrix to CountyIncome DataFrame

names(Fultz2020)[names(Fultz2020) == "1"] <- "Minutes"

names(Fultz2020)[names(Fultz2020) == "2"] <- "Seconds"

Split WinsLoss Column into two columns

Index <- str_split_fixed(Fultz2020$WinLoss, " ", 2) #Create County Index Matrix Fultz2020 <- cbind(Fultz2020, Index) #Add CountyIndex Matrix to CountyIncome DataFrame Fultz2020<- Fultz2020 %>% select(-WinLoss) #remove columns names(Fultz2020)[names(Fultz2020) == "1"] <- "WinLoss" Fultz2020<- Fultz2020 %>% select(-"2")

Update the Data Types

library(hablar)

colnames(Fultz2020) Fultz2020 <- Fultz2020 %>% convert( int("TeamGame", "PlayerGame", "FG", "FGA", "3P", "3PA", "FT", "FTA", "ORB", "DRB", "TRB", "AST", "STL", "BLK", "TOV", "PF", "PTS", "+/-" ), num("FG%", "3P%", "FT%", "FG%", "FT%", "3P%", "GmSc"), dte("Date"), fct("Team", "Location", "Opponent", "WinLoss", "GameStarted"))

Create Data Dictionary

#Create a vector that counts how many columns are in the data frame ColNumber <- (seq_len(ncol(Fultz2020))) #Turn the vector into its own data frame with the first column called ColNumber DataDictionary <- as.data.frame(ColNumber) #Add a column based on the vector of colnames DataDictionary$VariableName <- (colnames(Fultz2020)) #Add the variable type into the data frame as a new column called "type" DataDictionary$DataType <- (sapply(Fultz2020, class)) View(DataDictionary)

ggplot(Fultz2020, aes(x = Minutes, y = GmSc)) + geom_jitter() + ggtitle("Fultz 2019-2020") + xlab("Minutes Played") + ylab("Game Score") + theme_classic() + theme(axis.text.x = element_blank(), axis.ticks = element_blank(), axis.line = element_line(linetype = "blank"))

Fultz 2018-2019 Season

Fultz2019 <- "https://www.basketball-reference.com/players/f/fultzma01/gamelog/2019" read_html(Fultz2019)

After importing the html site, transform to data table

url <- Fultz2019 pageobj <- read_html(url, as.data.frame=T, stringsAsFactors = TRUE) #Here, we indicate that this is the table we want to extract. pageobj %>%
html_nodes("table") %>% .[[8]] %>% html_table(fill=T) -> Fultz2019

Removes Rows with subheadings

Fultz2019 <- Fultz2019[!(Fultz2019$GS=="Did Not Play" | Fultz2019$GS=="Did Not Dress" | Fultz2019$GS=="GS" | Fultz2019$GS == "Inactive"),]

Change Column Names

names(Fultz2019)[names(Fultz2019) == "Rk"] <- "TeamGame" names(Fultz2019)[names(Fultz2019) == "G"] <- "PlayerGame" names(Fultz2019)[names(Fultz2019) == "Tm"] <- "Team" colnames(Fultz2019)[6] <-c("Location") names(Fultz2019)[names(Fultz2019) == "Opp"] <- "Opponent" colnames(Fultz2019)[8] <-c("WinLoss") names(Fultz2019)[names(Fultz2019) == "GS"] <- "GameStarted" names(Fultz2019)[names(Fultz2019) == "MP"] <- "MinsPlayed"

Fill in the Location Data for the game

Fultz2019$Location <- ifelse(Fultz2019$Location == "@", "Away", "Home")

Split MinsPlayed Column into two columns

Index <- str_split_fixed(Fultz2019$MinsPlayed, ":", 2) #Create County Index Matrix Fultz2019 <- cbind(Fultz2019, Index) #Add CountyIndex Matrix to CountyIncome DataFrame names(Fultz2019)[names(Fultz2019) == "1"] <- "Minutes" names(Fultz2019)[names(Fultz2019) == "2"] <- "Seconds"

Split WinsLoss Column into two columns

Index <- str_split_fixed(Fultz2019$WinLoss, " ", 2) #Create County Index Matrix Fultz2019 <- cbind(Fultz2019, Index) #Add CountyIndex Matrix to CountyIncome DataFrame Fultz2019<- Fultz2019 %>% select(-WinLoss) #remove columns names(Fultz2019)[names(Fultz2019) == "1"] <- "WinLoss" Fultz2019<- Fultz2019 %>% select(-"2")

Update the Data Types

#library(hablar) colnames(Fultz2019) Fultz2019 <- Fultz2019 %>% convert( int("TeamGame", "PlayerGame", "FG", "FGA", "3P", "3PA", "FT", "FTA", "ORB", "DRB", "TRB", "AST", "STL", "BLK", "TOV", "PF", "PTS", "+/-" ), num("FG%", "3P%", "FT%", "FG%", "FT%", "3P%", "GmSc"), dte("Date"), fct("Team", "Location", "Opponent", "WinLoss", "GameStarted"))

Create Data Dictionary

#Create a vector that counts how many columns are in the data frame ColNumber <- (seq_len(ncol(Fultz2019))) #Turn the vector into its own data frame with the first column called ColNumber DataDictionary <- as.data.frame(ColNumber) #Add a column based on the vector of colnames DataDictionary$VariableName <- (colnames(Fultz2019)) #Add the variable type into the data frame as a new column called "type" DataDictionary$DataType <- (sapply(Fultz2019, class)) View(DataDictionary)

ggplot(Fultz2019, aes(x = Minutes, y = GmSc)) + geom_jitter() + ggtitle("Fultz 2019-2020") + xlab("Minutes Played") + ylab("Game Score") + theme_classic() + theme(axis.text.x = element_blank(), axis.ticks = element_blank(), axis.line = element_line(linetype = "blank"))

Fultz 2017-2018 Season

Fultz2018 <- "https://www.basketball-reference.com/players/f/fultzma01/gamelog/2018" read_html(Fultz2018)

After importing the html site, transform to data table

url <- Fultz2018 pageobj <- read_html(url, as.data.frame=T, stringsAsFactors = TRUE) #Here, we indicate that this is the table we want to extract. pageobj %>%
html_nodes("table") %>% .[[8]] %>% html_table(fill=T) -> Fultz2018

Removes Rows with subheadings

Fultz2018 <- Fultz2018[!(Fultz2018$GS=="Did Not Play" | Fultz2018$GS=="Did Not Dress" | Fultz2018$GS=="GS" | Fultz2018$GS == "Inactive"),]

Change Column Names

names(Fultz2018)[names(Fultz2018) == "Rk"] <- "TeamGame" names(Fultz2018)[names(Fultz2018) == "G"] <- "PlayerGame" names(Fultz2018)[names(Fultz2018) == "Tm"] <- "Team" colnames(Fultz2018)[6] <-c("Location") names(Fultz2018)[names(Fultz2018) == "Opp"] <- "Opponent" colnames(Fultz2018)[8] <-c("WinLoss") names(Fultz2018)[names(Fultz2018) == "GS"] <- "GameStarted" names(Fultz2018)[names(Fultz2018) == "MP"] <- "MinsPlayed"

Fill in the Location Data for the game

Fultz2018$Location <- ifelse(Fultz2018$Location == "@", "Away", "Home")

Split MinsPlayed Column into two columns

Index <- str_split_fixed(Fultz2018$MinsPlayed, ":", 2) #Create County Index Matrix Fultz2018 <- cbind(Fultz2018, Index) #Add CountyIndex Matrix to CountyIncome DataFrame names(Fultz2018)[names(Fultz2018) == "1"] <- "Minutes" names(Fultz2018)[names(Fultz2018) == "2"] <- "Seconds"

Split WinsLoss Column into two columns

Index <- str_split_fixed(Fultz2018$WinLoss, " ", 2) #Create County Index Matrix Fultz2018 <- cbind(Fultz2018, Index) #Add CountyIndex Matrix to CountyIncome DataFrame Fultz2018<- Fultz2018 %>% select(-WinLoss) #remove columns names(Fultz2018)[names(Fultz2018) == "1"] <- "WinLoss" Fultz2018<- Fultz2018 %>% select(-"2")

Update the Data Types

#library(hablar) colnames(Fultz2018) Fultz2018 <- Fultz2018 %>% convert( int("TeamGame", "PlayerGame", "FG", "FGA", "3P", "3PA", "FT", "FTA", "ORB", "DRB", "TRB", "AST", "STL", "BLK", "TOV", "PF", "PTS", "+/-" ), num("FG%", "3P%", "FT%", "FG%", "FT%", "3P%", "GmSc"), dte("Date"), fct("Team", "Location", "Opponent", "WinLoss", "GameStarted"))

Create Data Dictionary

#Create a vector that counts how many columns are in the data frame ColNumber <- (seq_len(ncol(Fultz2018))) #Turn the vector into its own data frame with the first column called ColNumber DataDictionary <- as.data.frame(ColNumber) #Add a column based on the vector of colnames DataDictionary$VariableName <- (colnames(Fultz2018)) #Add the variable type into the data frame as a new column called "type" DataDictionary$DataType <- (sapply(Fultz2018, class)) View(DataDictionary)

ggplot(Fultz2018, aes(x = Minutes, y = GmSc)) + geom_jitter() + ggtitle("Fultz 2019-2020") + xlab("Minutes Played") + ylab("Game Score") + theme_classic() + theme(axis.text.x = element_blank(), axis.ticks = element_blank(), axis.line = element_line(linetype = "blank"))

Fults Career Game Stats

Fultz <- rbind(Fultz2018, Fultz2019, Fultz2020, Fultz2021) Fultz$PlayerGame <- seq.int(nrow(Fultz)) Fultz$Player <- "Fultz"

ggplot(Fultz, aes(x = PlayerGame, y = GmSc, color = Team)) + geom_point() + geom_smooth(se= FALSE) + ggtitle("Fultz Career Game Score") + xlab("Time") + ylab("Game Score") + theme_classic() + theme(axis.text.x = element_blank(), axis.ticks = element_blank(), axis.line = element_line(linetype = "blank"))

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