Game 7 2016 NBA Finals

Files

Game 7 2016 NBA Finals.Rmd: .Rmd file which contains the code I used to create this project.

Game 7 2016 NBA Finals.twb: Tableau file containing the dashboard created.

Game 7 2016 NBA Finals.xlsx: Cleaned data after web scraping.

Screenshot_of_Tableau_Dashboard.png: Screenshot of the dashboard created.

Introduction

The 2016 NBA Finals was arguably one of the most iconic NBA Finals series’ in the history of the NBA, with LeBron James, Kyrie Irving and the rest of the Cleveland Cavaliers squad pulling off a very unlikely upset against Stephen Curry’s Golden State Warriors.

After leaving the Cleveland Cavaliers in 2010 to join the Miami Heat - where he won 2 championships - James made a promise to come back to his hometown team and win them a championship. After falling short 4-2 in 2015, the Cavaliers looked to avenge their loss against a Warriors team who held the best regular season record in NBA history (73-9).

Led by Stephen Curry, Klay Thompson and Draymond Green, the Warriors led the 2016 NBA Finals 3-1 - 1 win away from reaching a consecutive championship. No team NBA history has ever come down from a 3-1 deficit to win the NBA Finals. Earlier in the playoffs, the Warriors were the 10th team in NBA history to overcome a 3-1 deficit against the Oklahoma City Thunder.

After a back-and-forth game, the Cleveland Cavaliers made history and went on to win Game 7, breaking the 52-year drought for any Cleveland major sports team to attain a championship.

Using the box score from ESPN, I am looking to create meaningful visualisations regarding players individual performances to summarise this game. This will be done through Tableau, after obtaining and cleaning the data in R.

Methodology: Web Scraping

Web scraping is the process of extracting data from a website, specifically the underlying HTML code used to create the website. This is important since not all times data will be available in a convenient format like a .csv or .xlsx file, and instead will be posted on the internet for people to view. This provides access to a substantially larger range of data, allowing us to perform more meaningful analyses and visualisations.

Data

Preparation

#Loading packages
library(tidyverse)
library(rvest)
library(knitr)
library(writexl)

#Creating function to scrape data from any website
scraper <- function(url, selector, output, all_nodes = TRUE) {
    #Loading the web page content
    content <- read_html(url)

    #Getting one or all nodes
    if(all_nodes == TRUE){
        data <- content %>%
            html_nodes(selector)
        }
    else{
        data <- content %>%
            html_node(selector)
        }
    #Outputting text, table, attributes, or attribute

    if(output == "text"){
        data <- data %>%
            html_text()
    } else if(output == "table"){
        data <- data %>%
            html_table()
    } else if(output == "attrs"){
        data <- data %>%
            html_attrs()
    } else{
        data <- data %>%
            html_attr(output)
        }
        #Final output
    data
}

#Creating function to convert columns into desired numeric format
convert_to_numeric <- function(column){
  if(any(str_detect(column, "\\d-"))){
    column <- str_replace(column, "-", "/")
    column <- sapply(column, function(x) eval(parse(text = x)))
  } else if(any(str_detect(column, "[[+-]]"))){
    column <- sapply(column, function(x) eval(parse(text = x)))
  } else if(any(str_detect(column, "%"))){
    column <- str_replace(column, "%", "/100")
    column <- sapply(column, function(x) eval(parse(text = x)))
  } else {
    column <- sapply(column, as.numeric)
  }
}

Scraping the data

#Scraping the data using the function we created
box_score <- scraper(url = "https://www.espn.com.au/nba/boxscore?gameId=400878160",
                     selector = "#gamepackage-boxscore-module .desktop",
                     output = "table")

Cleaning the Cavaliers data

#Cleveland

#Extracting box score for Cleveland
cleveland <- tibble(box_score[1]) %>%
  #Important to unnest since we had a list
  unnest() %>%
  #Changing column of players to be labelled to Player
  rename(Player = Starters) %>%
  #Removing unnecessary column
  filter(Player != "Bench") %>%
  #Since position with the name of the player, need to extract it
  mutate(Position = str_match(Player, "[[A-Z]]+$"),
         #When table was scraped, the name was also duplicated, so needed to remove duplcate
         Player = str_sub(str_match(Player, "\\w\\.\\s[[A-Z]][[A-Za-z]]+"), 1, -2))

#Separating the players into starters, bench, DNP as well as a table for overall team stats, and a table for all players that played after cleaning

#Starters
cleveland_starters <- cleveland[1:5, ]
#Converting columns into desired numeric format
cleveland_starters[2:15] <- map(cleveland_starters[2:15], convert_to_numeric) %>%
  as.tibble() 
#Replacing any NaN values with 0
cleveland_starters[is.na(cleveland_starters)] = 0
#Adding a column so we can indicate which team player is from
cleveland_starters <- cleveland_starters %>%
  mutate(Team = "Cleveland")
  
#Bench
cleveland_bench <- cleveland[6:8, ] 
#Converting columns into desired numeric format
cleveland_bench[2:15] <- map(cleveland_bench[2:15], convert_to_numeric) %>%
  as.tibble() 
#Replacing any NaN values with 0
cleveland_bench[is.na(cleveland_bench)] = 0
#Adding a column so we can indicate which team player is from
cleveland_bench <- cleveland_bench %>%
  mutate(Team = "Cleveland")

#DNP
cleveland_dnp <- cleveland[9:13, ] %>%
  #Adding a column so we can indicate which team player is from
  mutate(Team = "Cleveland")

#All players that played
cleveland_all_players_played <- bind_rows(cleveland_starters, cleveland_bench) %>%
  #Adding a column so we can indicate which team player is from
  mutate(Team = "Cleveland")

#Team
cleveland_team <- cleveland[14:15, ] %>%
  #Changing Player column to contain team name
  rename(Team = Player) %>%
  #Since our percentages are in the second row, had to move them first into first row so everything is in one row
  mutate(`FG PCT` = FG[2],
         `3PT PCT` = `3PT`[2],
         `FT PCT` = FT[2],
         #Separating FG, 3PT and FT Column to have separate columns for makes and attempts
         `FG MADE` = str_match(FG, "\\d+"),
         `FG ATT` = str_match(FG, "\\d+$"),
         `3PT MADE` = str_match(`3PT`, "\\d+"),
         `3PT ATT` = str_match(`3PT`, "\\d+$"),
         `FT MADE` = str_match(FT, "\\d+"),
         `FT ATT` = str_match(FT, "\\d+$"),
         #Adding team name
         Team = "Cleveland") %>%
  #Selecting needed columns and ordering them in a logical format
  select(Team, `FG MADE`, `FG ATT`, `FG PCT`, `3PT MADE`, `3PT ATT`, `3PT PCT`, `FT MADE`, `FT ATT`, `FT PCT`, `OREB`, `DREB`, `REB`, `AST`, `STL`, `BLK`, `TO`, `PF`, `PTS`) %>%
  #Dropping second row which does not contain any useful information after cleaning since everything is now on one row
  drop_na()

#Converting columns into desired numeric format
cleveland_team[2:19] <- map(cleveland_team[2:19], convert_to_numeric)

Cleaning the Warriors data

#Golden State Warriors

#Extracting box score for Golden State Warriors
gsw <- tibble(box_score[2]) %>%
  #Important to unnest since we had a list
  unnest() %>%
  #Changing column of players to be labelled to Player
  rename(Player = Starters) %>%
  #Removing unnecessary column
  filter(Player != "Bench") %>%
  #Since position with the name of the player, need to extract it
  mutate(Position = str_match(Player, "[[A-Z]]+$"),
         #When table was scraped, the name was also duplicated, so needed to remove duplcate
         Player = str_sub(str_match(Player, "\\w\\.\\s[[A-Z]][[A-Za-z]]+"), 1, -2))
 
#Separating the players into starters, bench, DNP as well as a table for overall team stats, and a table for all players that played after cleaning

#Starters
gsw_starters <- gsw[1:5, ]
#Converting columns into desired numeric format
gsw_starters[2:15] <- map(gsw_starters[2:15], convert_to_numeric) %>%
  as.tibble() 
#Replacing any NaN values with 0
gsw_starters[is.na(gsw_starters)] = 0
#Adding a column so we can indicate which team player is from
gsw_starters <- gsw_starters %>%
  mutate(Team = "GSW")

#Bench
gsw_bench <- gsw[6:10, ]
#Converting columns into desired numeric format
gsw_bench[2:15] <- map(gsw_bench[2:15], convert_to_numeric) %>%
  as.tibble() 
#Replacing any NaN values with 0
gsw_bench[is.na(gsw_bench)] = 0
#Adding a column so we can indicate which team player is from
gsw_bench <- gsw_bench %>%
  mutate(Team = "GSW")

#DNP
gsw_dnp <- gsw[11:13, ] %>%
  #Adding a column so we can indicate which team player is from
  mutate(Team = "GSW")

#All players that played
gsw_all_players_played <- bind_rows(gsw_starters, gsw_bench) %>%
  #Adding a column so we can indicate which team player is from
  mutate(Team = "GSW")

#Team
gsw_team <- gsw[14:15, ] %>%
  #Changing Player column to contain team name
  rename(Team = Player) %>%
  #Since our percentages are in the second row, had to move them first into first row so everything is in one row
  mutate(`FG PCT` = FG[2],
         `3PT PCT` = `3PT`[2],
         `FT PCT` = FT[2],
         #Separating FG, 3PT and FT Column to have separate columns for makes and attempts
         `FG MADE` = str_match(FG, "\\d+"),
         `FG ATT` = str_match(FG, "\\d+$"),
         `3PT MADE` = str_match(`3PT`, "\\d+"),
         `3PT ATT` = str_match(`3PT`, "\\d+$"),
         `FT MADE` = str_match(FT, "\\d+"),
         `FT ATT` = str_match(FT, "\\d+$"),
         #Adding team name
         Team = "GSW") %>%
  #Selecting needed columns and ordering them in a logical format
  select(Team, `FG MADE`, `FG ATT`, `FG PCT`, `3PT MADE`, `3PT ATT`, `3PT PCT`, `FT MADE`, `FT ATT`, `FT PCT`, `OREB`, `DREB`, `REB`, `AST`, `STL`, `BLK`, `TO`, `PF`, `PTS`) %>%
  #Dropping second row which does not contain any useful information after cleaning since everything is now on one row
  drop_na()

#Converting columns into desired numeric format
gsw_team[2:19] <- map(gsw_team[2:19], convert_to_numeric)

Team total stats

#Combining both teams total stats
team_stats <- bind_rows(cleveland_team, gsw_team)

Combined tables

#Combining both teams starters

all_starters <- bind_rows(cleveland_starters, gsw_starters)

#Combining both teams bench players

all_bench <- bind_rows(cleveland_bench, gsw_bench)

#Combining all players that played

all_played <- bind_rows(cleveland_all_players_played, gsw_all_players_played)

Final look at all tables created

Cleveland

cleveland_starters %>%
  kable()

Player	MIN	FG	3PT	FT	OREB	DREB	REB	AST	STL	BLK	TO	PF	+/-	PTS	Position	Team
K. Love	30	0.3333333	0.00	0.75	4	10	14	3	2	0	1	2	19	9	PF	Cleveland
L. James	47	0.3750000	0.20	0.80	1	10	11	11	2	3	5	1	4	27	SF	Cleveland
T. Thompson	32	1.0000000	0.00	0.75	0	3	3	0	0	2	0	4	2	9	C	Cleveland
K. Irving	43	0.4347826	0.40	1.00	3	3	6	1	1	1	2	3	10	26	PG	Cleveland
J. Smith	39	0.3846154	0.25	0.00	0	4	4	2	1	0	2	3	7	12	G	Cleveland

cleveland_bench %>%
  kable()

Player	MIN	FG	3PT	FT	OREB	DREB	REB	STL	TO	PF	+/-	PTS	Position	Team
R. Jefferson	26	0.2500000	0.0000000	0	1	8	9	1	0	1	-8	2	F	Cleveland
M. Williams	5	0.3333333	0.0000000	0	0	0	0	0	1	1	-5	2	PG	Cleveland
I. Shumpert	19	0.3333333	0.3333333	1	0	1	1	0	0	0	-9	6	G	Cleveland

cleveland_dnp %>%
  kable()

Player	MIN	FG	3PT	FT	OREB	DREB	REB	AST	STL	BLK	TO	PF	+/-	PTS	Position	Team
C. Frye	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	F	Cleveland
T. Mozgov	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	C	Cleveland
M. Dellavedova	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	PG	Cleveland
D. Jones	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	SG	Cleveland
J. Jones	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	SG	Cleveland

cleveland_all_players_played %>%
  kable()

Player	MIN	FG	3PT	FT	OREB	DREB	REB	AST	STL	BLK	TO	PF	+/-	PTS	Position	Team
K. Love	30	0.3333333	0.0000000	0.75	4	10	14	3	2	0	1	2	19	9	PF	Cleveland
L. James	47	0.3750000	0.2000000	0.80	1	10	11	11	2	3	5	1	4	27	SF	Cleveland
T. Thompson	32	1.0000000	0.0000000	0.75	0	3	3	0	0	2	0	4	2	9	C	Cleveland
K. Irving	43	0.4347826	0.4000000	1.00	3	3	6	1	1	1	2	3	10	26	PG	Cleveland
J. Smith	39	0.3846154	0.2500000	0.00	0	4	4	2	1	0	2	3	7	12	G	Cleveland
R. Jefferson	26	0.2500000	0.0000000	0.00	1	8	9	0	1	0	0	1	-8	2	F	Cleveland
M. Williams	5	0.3333333	0.0000000	0.00	0	0	0	0	0	0	1	1	-5	2	PG	Cleveland
I. Shumpert	19	0.3333333	0.3333333	1.00	0	1	1	0	0	0	0	0	-9	6	G	Cleveland

cleveland_team %>%
  kable()

Team	FG MADE	FG ATT	FG PCT	3PT MADE	3PT ATT	3PT PCT	FT MADE	FT ATT	FT PCT	OREB	DREB	REB	AST	STL	BLK	TO	PF	PTS
Cleveland	33	82	0.402	6	25	0.24	21	25	0.84	9	39	48	17	7	6	11	15	93

Golden State Warriors

gsw_starters %>%
  kable()

Player	MIN	FG	3PT	FT	OREB	DREB	REB	AST	STL	BLK	TO	PF	+/-	PTS	Position	Team
D. Green	47	0.7333333	0.7500000	1	1	14	15	9	2	0	2	3	-1	32	PF	GSW
H. Barnes	29	0.3000000	0.5000000	1	0	2	2	1	1	0	0	4	-6	10	SF	GSW
F. Ezeli	11	0.0000000	0.0000000	0	0	1	1	1	0	0	0	2	-9	0	C	GSW
S. Curry	39	0.3157895	0.2857143	1	0	5	5	2	1	1	4	4	-3	17	PG	GSW
K. Thompson	42	0.3529412	0.2000000	0	1	1	2	2	1	0	3	2	-11	14	SG	GSW

gsw_bench %>%
  kable()

Player	MIN	FG	3PT	FT	OREB	DREB	REB	AST	STL	BLK	TO	PF	+/-	PTS	Position	Team
A. Iguodala	38	0.3333333	0	0.0	2	7	9	4	2	2	0	3	3	4	F	GSW
M. Speights	5	0.0000000	0	0.0	2	2	4	0	0	2	0	0	3	0	F	GSW
A. Varejao	8	0.0000000	0	0.5	0	0	0	1	0	0	0	3	-9	1	C	GSW
L. Barbosa	4	0.5000000	1	0.0	0	0	0	0	0	0	0	1	5	3	G	GSW
S. Livingston	16	0.4285714	0	1.0	1	0	1	2	0	0	1	1	8	8	G	GSW

gsw_dnp %>%
  kable()

Player	MIN	FG	3PT	FT	OREB	DREB	REB	AST	STL	BLK	TO	PF	+/-	PTS	Position	Team
B. Rush	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	F	GSW
J. McAdoo	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	SF	GSW
I. Clark	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	DNP-COACH’S DECISION	G	GSW

gsw_all_players_played %>%
  kable()

Player	MIN	FG	3PT	FT	OREB	DREB	REB	AST	STL	BLK	TO	PF	+/-	PTS	Position	Team
D. Green	47	0.7333333	0.7500000	1.0	1	14	15	9	2	0	2	3	-1	32	PF	GSW
H. Barnes	29	0.3000000	0.5000000	1.0	0	2	2	1	1	0	0	4	-6	10	SF	GSW
F. Ezeli	11	0.0000000	0.0000000	0.0	0	1	1	1	0	0	0	2	-9	0	C	GSW
S. Curry	39	0.3157895	0.2857143	1.0	0	5	5	2	1	1	4	4	-3	17	PG	GSW
K. Thompson	42	0.3529412	0.2000000	0.0	1	1	2	2	1	0	3	2	-11	14	SG	GSW
A. Iguodala	38	0.3333333	0.0000000	0.0	2	7	9	4	2	2	0	3	3	4	F	GSW
M. Speights	5	0.0000000	0.0000000	0.0	2	2	4	0	0	2	0	0	3	0	F	GSW
A. Varejao	8	0.0000000	0.0000000	0.5	0	0	0	1	0	0	0	3	-9	1	C	GSW
L. Barbosa	4	0.5000000	1.0000000	0.0	0	0	0	0	0	0	0	1	5	3	G	GSW
S. Livingston	16	0.4285714	0.0000000	1.0	1	0	1	2	0	0	1	1	8	8	G	GSW

gsw_team %>%
  kable()

Team	FG MADE	FG ATT	FG PCT	3PT MADE	3PT ATT	3PT PCT	FT MADE	FT ATT	FT PCT	OREB	DREB	REB	AST	STL	BLK	TO	PF	PTS
GSW	32	83	0.386	15	41	0.366	10	13	0.769	7	32	39	22	7	5	10	23	89

Team Totals

team_stats %>%
  kable()

Team	FG MADE	FG ATT	FG PCT	3PT MADE	3PT ATT	3PT PCT	FT MADE	FT ATT	FT PCT	OREB	DREB	REB	AST	STL	BLK	TO	PF	PTS
Cleveland	33	82	0.402	6	25	0.240	21	25	0.840	9	39	48	17	7	6	11	15	93
GSW	32	83	0.386	15	41	0.366	10	13	0.769	7	32	39	22	7	5	10	23	89

all_starters %>%
  kable()

Player	MIN	FG	3PT	FT	OREB	DREB	REB	AST	STL	BLK	TO	PF	+/-	PTS	Position	Team
K. Love	30	0.3333333	0.0000000	0.75	4	10	14	3	2	0	1	2	19	9	PF	Cleveland
L. James	47	0.3750000	0.2000000	0.80	1	10	11	11	2	3	5	1	4	27	SF	Cleveland
T. Thompson	32	1.0000000	0.0000000	0.75	0	3	3	0	0	2	0	4	2	9	C	Cleveland
K. Irving	43	0.4347826	0.4000000	1.00	3	3	6	1	1	1	2	3	10	26	PG	Cleveland
J. Smith	39	0.3846154	0.2500000	0.00	0	4	4	2	1	0	2	3	7	12	G	Cleveland
D. Green	47	0.7333333	0.7500000	1.00	1	14	15	9	2	0	2	3	-1	32	PF	GSW
H. Barnes	29	0.3000000	0.5000000	1.00	0	2	2	1	1	0	0	4	-6	10	SF	GSW
F. Ezeli	11	0.0000000	0.0000000	0.00	0	1	1	1	0	0	0	2	-9	0	C	GSW
S. Curry	39	0.3157895	0.2857143	1.00	0	5	5	2	1	1	4	4	-3	17	PG	GSW
K. Thompson	42	0.3529412	0.2000000	0.00	1	1	2	2	1	0	3	2	-11	14	SG	GSW

#Combining both teams bench players

all_bench %>% 
  kable()

Player	MIN	FG	3PT	FT	OREB	DREB	REB	AST	STL	BLK	TO	PF	+/-	PTS	Position	Team
R. Jefferson	26	0.2500000	0.0000000	0.0	1	8	9	0	1	0	0	1	-8	2	F	Cleveland
M. Williams	5	0.3333333	0.0000000	0.0	0	0	0	0	0	0	1	1	-5	2	PG	Cleveland
I. Shumpert	19	0.3333333	0.3333333	1.0	0	1	1	0	0	0	0	0	-9	6	G	Cleveland
A. Iguodala	38	0.3333333	0.0000000	0.0	2	7	9	4	2	2	0	3	3	4	F	GSW
M. Speights	5	0.0000000	0.0000000	0.0	2	2	4	0	0	2	0	0	3	0	F	GSW
A. Varejao	8	0.0000000	0.0000000	0.5	0	0	0	1	0	0	0	3	-9	1	C	GSW
L. Barbosa	4	0.5000000	1.0000000	0.0	0	0	0	0	0	0	0	1	5	3	G	GSW
S. Livingston	16	0.4285714	0.0000000	1.0	1	0	1	2	0	0	1	1	8	8	G	GSW

#Combining all players that played

all_played %>%
  kable()

Player	MIN	FG	3PT	FT	OREB	DREB	REB	AST	STL	BLK	TO	PF	+/-	PTS	Position	Team
K. Love	30	0.3333333	0.0000000	0.75	4	10	14	3	2	0	1	2	19	9	PF	Cleveland
L. James	47	0.3750000	0.2000000	0.80	1	10	11	11	2	3	5	1	4	27	SF	Cleveland
T. Thompson	32	1.0000000	0.0000000	0.75	0	3	3	0	0	2	0	4	2	9	C	Cleveland
K. Irving	43	0.4347826	0.4000000	1.00	3	3	6	1	1	1	2	3	10	26	PG	Cleveland
J. Smith	39	0.3846154	0.2500000	0.00	0	4	4	2	1	0	2	3	7	12	G	Cleveland
R. Jefferson	26	0.2500000	0.0000000	0.00	1	8	9	0	1	0	0	1	-8	2	F	Cleveland
M. Williams	5	0.3333333	0.0000000	0.00	0	0	0	0	0	0	1	1	-5	2	PG	Cleveland
I. Shumpert	19	0.3333333	0.3333333	1.00	0	1	1	0	0	0	0	0	-9	6	G	Cleveland
D. Green	47	0.7333333	0.7500000	1.00	1	14	15	9	2	0	2	3	-1	32	PF	GSW
H. Barnes	29	0.3000000	0.5000000	1.00	0	2	2	1	1	0	0	4	-6	10	SF	GSW
F. Ezeli	11	0.0000000	0.0000000	0.00	0	1	1	1	0	0	0	2	-9	0	C	GSW
S. Curry	39	0.3157895	0.2857143	1.00	0	5	5	2	1	1	4	4	-3	17	PG	GSW
K. Thompson	42	0.3529412	0.2000000	0.00	1	1	2	2	1	0	3	2	-11	14	SG	GSW
A. Iguodala	38	0.3333333	0.0000000	0.00	2	7	9	4	2	2	0	3	3	4	F	GSW
M. Speights	5	0.0000000	0.0000000	0.00	2	2	4	0	0	2	0	0	3	0	F	GSW
A. Varejao	8	0.0000000	0.0000000	0.50	0	0	0	1	0	0	0	3	-9	1	C	GSW
L. Barbosa	4	0.5000000	1.0000000	0.00	0	0	0	0	0	0	0	1	5	3	G	GSW
S. Livingston	16	0.4285714	0.0000000	1.00	1	0	1	2	0	0	1	1	8	8	G	GSW

Creating Excel File

This file will be imported in Tableau to create a dashboard

#Creating a list of all of our tables created
sheets <- list("Cleveland Starters" = cleveland_starters, 
               "Cleveland Bench" = cleveland_bench,
               "Cleveland DNP" = cleveland_dnp,
               "Cleveland All Players that Played" = cleveland_all_players_played,
               "Cleveland Team Stats" = cleveland_team,
               "GSW Starters" = gsw_starters, 
               "GSW Bench" = gsw_bench,
               "GSW DNP" = gsw_dnp,
               "GSW All Players that Played" = gsw_all_players_played,
               "GSW Team Stats" = gsw_team,
               "Overall Team Stats" = team_stats,
               "All Starters" = all_starters,
               "All Bench" = all_bench,
               "All Players that Played" = all_played)

#Creating an excel file with all of our sheets
write_xlsx(sheets, path = "Game 7 2016 NBA Finals", col_names = TRUE)

Tableau

After creating my excel spreadsheet, I exported it into Tableau to create a dashboard highlighting some of the important stats for the game. I created my own boxscore, highlighting the player that attained the highest in each statistics, along with multiple bar charts to clearly show the comparison between players on different teams, as well as overall team statistics.

Conclusion

Web scraping is an extremely important skill to know to perform meaningful analysis. Combining this with data cleaning skills, web scraping gives us the ability to access much more data that may not be compiled in a clean .csv or .xlsx file ready for analysis, ultimately allowing us to delve deeper into any topic.

Name		Name	Last commit message	Last commit date
Latest commit History 13 Commits
.gitattributes		.gitattributes
Game 7 2016 NBA Finals.Rmd		Game 7 2016 NBA Finals.Rmd
Game 7 2016 NBA Finals.twb		Game 7 2016 NBA Finals.twb
Game 7 2016 NBA Finals.xlsx		Game 7 2016 NBA Finals.xlsx
README.md		README.md
Screenshot_of_Tableau_Dashboard.png		Screenshot_of_Tableau_Dashboard.png

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Game 7 2016 NBA Finals

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Introduction

Methodology: Web Scraping

Data

Preparation

Scraping the data

Cleaning the Cavaliers data

Cleaning the Warriors data

Team total stats

Combined tables

Final look at all tables created

Cleveland

Golden State Warriors

Team Totals

Creating Excel File

Tableau

Conclusion

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brendanoct/game-7-2016-nba-finals

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Game 7 2016 NBA Finals

Files

Introduction

Methodology: Web Scraping

Data

Preparation

Scraping the data

Cleaning the Cavaliers data

Cleaning the Warriors data

Team total stats

Combined tables

Final look at all tables created

Cleveland

Golden State Warriors

Team Totals

Creating Excel File

Tableau

Conclusion

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