Chirag Agrawal ccagrawal

## prime_players.csv

          
            Player
            Position
            Team
            Prime BPM
            Projected MPG

            
              Stephen Curry
              PG
              GSW
              12.4
              30.4

            
              Draymond Green
              PF
              GSW
              6.4
              30.8

            
              Andrew Bogut
              C
              GSW
              4.4
              23.7

            
              Andre Iguodala
              SG
              GSW
              4.4
              32.0

            
              Leandro Barbosa
              SG
              GSW
              2.9
              29.4

            
              Klay Thompson
              SG
              GSW
              2.7
              28.7

            
              Harrison Barnes
              SF
              GSW
              1.1
              25.4

            
              Festus Ezeli
              C
              GSW
              0.9
              16.0

            
              Brandon Rush
              SG
              GSW
              0.7
              23.7

## players.csv

          
            Player
            Season

            
              Allen Iverson
              2006

            
              Amar'e Stoudemire
              2005

            
              Anthony Davis
              2016

            
              Blake Griffin
              2016

            
              Carmelo Anthony
              2007

            
              Charles Barkley
              1988

            
              Chris Mullin
              1989

            
              Chris Webber
              2001

            
              Clyde Drexler
              1989

## nba_hca_by_team.R
library(sportsTools)
library(dplyr)
library(ggplot2)

start.year <- 2001
end.year <- 2015

all.games <- data.frame()

for (year in start.year:end.year) {

## lsn_scraper.R
options(stringsAsFactors = FALSE)

ScrapeLSN <- function(school, cycle) {

  base.url <- 'http://SCHOOL.lawschoolnumbers.com/stats/CYCLE'

  url <- gsub('SCHOOL', school, base.url)
  url <- gsub('CYCLE', cycle, url)

  src <- readLines(url)

## nba_hca.R
library(sportsTools)
library(ggplot2)
library(grid)

# All years since NBA-ABA Merger
years <- 1977:2016

# Create empty data frame to hold results from each year
df <- data.frame(matrix(nrow = length(years), ncol = 4))
colnames(df) <- c('year', 'mean', 'sd', 'n')

## game_variability.R
library(ggplot2)
library(moments)
library(sportsTools)
library(dplyr)

clean.lines <- function(data) {
  data <- data[-which(is.na(data$home.line)), ]
  data$expected <- -data$home.line
  data$actual <- data$home.score - data$away.score
  data$residual <- data$actual - data$expected

## nba_homecourt.R
# Quantifies homecourt advantage in each regular season

library(RCurl)
library(XML)

# Get Basketball Reference regular season schedule with home margin of victory for each game
GetSchedule <- function(year) {

  url <- paste("http://www.basketball-reference.com/leagues/NBA_", year, "_games.html", sep = "")
  tables <- readHTMLTable(url)

## Youtube_IMDb_Rater.R
library(rjson)
library(reshape)

user <- 'TheLaughFactory'

videos <- data.frame(matrix(data = 0, nrow = 1000, ncol = 8))
colnames(videos) <- c('id', 'link', 'title', 'date', 'duration', 'view.count', 'avg.rating', 'num.raters')

found.all <- FALSE
index <- 1

## FitBit.R
library(httr)
library(rjson)

key <- '<edited>'
secret <- '<edited>'
tokenURL <- 'http://api.fitbit.com/oauth/request_token'
accessTokenURL <- 'http://api.fitbit.com/oauth/access_token'
authorizeURL <- 'http://www.fitbit.com/oauth/authorize'

fbr <- oauth_app('MyHome', key, secret)

## Wordpress_MCMC.R
# For each day, find the appropriate Poisson Parameter and use it to calculate the likelihood of the actual view count
# Likelihood = P(D|H)

Calc.Likelihood <- function(changes, poissons, stats, periods) {
  likelihood <- 0
  for (day in 1:nrow(stats)) {
    proceed <- FALSE
    period <- 1
    while (!proceed) {
      if (day < changes[period]) {
Player	Position	Team	Prime BPM	Projected MPG
Stephen Curry	PG	GSW	12.4	30.4
Draymond Green	PF	GSW	6.4	30.8
Andrew Bogut	C	GSW	4.4	23.7
Andre Iguodala	SG	GSW	4.4	32.0
Leandro Barbosa	SG	GSW	2.9	29.4
Klay Thompson	SG	GSW	2.7	28.7
Harrison Barnes	SF	GSW	1.1	25.4
Festus Ezeli	C	GSW	0.9	16.0
Brandon Rush	SG	GSW	0.7	23.7
	Player	Season
	Allen Iverson	2006
	Amar'e Stoudemire	2005
	Anthony Davis	2016
	Blake Griffin	2016
	Carmelo Anthony	2007
	Charles Barkley	1988
	Chris Mullin	1989
	Chris Webber	2001
	Clyde Drexler	1989
	library(sportsTools)
	library(dplyr)
	library(ggplot2)

	start.year <- 2001
	end.year <- 2015

	all.games <- data.frame()

	for (year in start.year:end.year) {
	options(stringsAsFactors = FALSE)

	ScrapeLSN <- function(school, cycle) {

	base.url <- 'http://SCHOOL.lawschoolnumbers.com/stats/CYCLE'

	url <- gsub('SCHOOL', school, base.url)
	url <- gsub('CYCLE', cycle, url)

	src <- readLines(url)
	library(sportsTools)
	library(ggplot2)
	library(grid)

	# All years since NBA-ABA Merger
	years <- 1977:2016

	# Create empty data frame to hold results from each year
	df <- data.frame(matrix(nrow = length(years), ncol = 4))
	colnames(df) <- c('year', 'mean', 'sd', 'n')
	library(ggplot2)
	library(moments)
	library(sportsTools)
	library(dplyr)

	clean.lines <- function(data) {
	data <- data[-which(is.na(data$home.line)), ]
	data$expected <- -data$home.line
	data$actual <- data$home.score - data$away.score
	data$residual <- data$actual - data$expected
	# Quantifies homecourt advantage in each regular season

	library(RCurl)
	library(XML)

	# Get Basketball Reference regular season schedule with home margin of victory for each game
	GetSchedule <- function(year) {

	url <- paste("http://www.basketball-reference.com/leagues/NBA_", year, "_games.html", sep = "")
	tables <- readHTMLTable(url)
	library(rjson)
	library(reshape)

	user <- 'TheLaughFactory'

	videos <- data.frame(matrix(data = 0, nrow = 1000, ncol = 8))
	colnames(videos) <- c('id', 'link', 'title', 'date', 'duration', 'view.count', 'avg.rating', 'num.raters')

	found.all <- FALSE
	index <- 1
	library(httr)
	library(rjson)

	key <- '<edited>'
	secret <- '<edited>'
	tokenURL <- 'http://api.fitbit.com/oauth/request_token'
	accessTokenURL <- 'http://api.fitbit.com/oauth/access_token'
	authorizeURL <- 'http://www.fitbit.com/oauth/authorize'

	fbr <- oauth_app('MyHome', key, secret)
	# For each day, find the appropriate Poisson Parameter and use it to calculate the likelihood of the actual view count
	# Likelihood = P(D\|H)

	Calc.Likelihood <- function(changes, poissons, stats, periods) {
	likelihood <- 0
	for (day in 1:nrow(stats)) {
	proceed <- FALSE
	period <- 1
	while (!proceed) {
	if (day < changes[period]) {