boooeee

## team_ranking_example.R
library(tidyverse)

n_games <- 100

# Define the teams
teams <- c("rock", "paper", "scissors")
# simulated home advantage #
home_adv<-2

# Generate the data frame

## volleyball_win_probability.R
library(Matrix)
library(dplyr)
library(ggplot2)

# set points to win - 25 for sets 1-4, 15 for set 5 #
ptw<-15
# set point probabilities #
srp<-0.42 # probability of winning a point when serving #
sop<-0.58 # probability of winning a point when receiving serve (sideout) #
# create tibble of game states

## iphone_rundata.R
rm(list = ls(all = TRUE))

library(XML)
library(tidyverse)
library(lubridate)
library(scales)
library(ggthemes)
library(ggridges)
library(rpart)

## elam_ending_simulation.R
# Code for creating an Elam Ending simulation model #
rm(list = ls(all = TRUE))

library(dplyr)
library(tidyr)

loc<-"" # enter file path for exporting model output #

# point probabilities for each possession : 0-1-2-3-4 points #
tp<-c(0.505,0.031,0.325,0.137,0.002) # team with possession #

## animated_facet_wrap.R
# this code creates animated shooting windup charts for multiple players on a single chart #
rm(list = ls(all = TRUE))

library(dplyr)
library(ggplot2)
library(gganimate)
library(readr)

# enter location to output animated gifs #
locout<-""

## open_defended_animated.R
# this code creates animated shooting windup charts for a selected player #
rm(list = ls(all = TRUE))

library(dplyr)
library(ggplot2)
library(gganimate)
library(readr)

# enter location to output animated gifs #
locout<-""

## hexbank.R
# this code generates hexbin heat maps for bank shots, overlaid over a backboard #

# dbdr is a data frame with the following columns (sourced from 2013-2016 tracking data):
# bx (x-coordinate of backboard strike)
# by (y-coordinate of backboard strike)
ggplot(dbdr,aes(x=by,y=bz)) +
  theme_bw() +
  coord_fixed() +
  geom_hex(binwidth=c(0.25,0.25),aes(fill = stat((count)))) +
  scale_fill_gradient(low="lightblue1",high="darkblue") +

## playerbank.R
# this code generates bank shot scatterplots for different players via facet_wrap

# dbsel is a data frame with the following columns (sourced from 2013-2016 tracking data):
# nm (player name)
# bx (x-coordinate of backboard strike)
# by (y-coordinate of backboard strike)

ggplot(dbsel,aes(x=by,y=bz)) +
  theme_bw() +
  coord_fixed() +  # so the backboard is shown to scale #
	library(tidyverse)

	n_games <- 100

	# Define the teams
	teams <- c("rock", "paper", "scissors")
	# simulated home advantage #
	home_adv<-2

	# Generate the data frame
	library(Matrix)
	library(dplyr)
	library(ggplot2)

	# set points to win - 25 for sets 1-4, 15 for set 5 #
	ptw<-15
	# set point probabilities #
	srp<-0.42 # probability of winning a point when serving #
	sop<-0.58 # probability of winning a point when receiving serve (sideout) #
	# create tibble of game states
	rm(list = ls(all = TRUE))

	library(XML)
	library(tidyverse)
	library(lubridate)
	library(scales)
	library(ggthemes)
	library(ggridges)
	library(rpart)
	# Code for creating an Elam Ending simulation model #
	rm(list = ls(all = TRUE))

	library(dplyr)
	library(tidyr)

	loc<-"" # enter file path for exporting model output #

	# point probabilities for each possession : 0-1-2-3-4 points #
	tp<-c(0.505,0.031,0.325,0.137,0.002) # team with possession #
	# this code creates animated shooting windup charts for multiple players on a single chart #
	rm(list = ls(all = TRUE))

	library(dplyr)
	library(ggplot2)
	library(gganimate)
	library(readr)

	# enter location to output animated gifs #
	locout<-""
	# this code generates hexbin heat maps for bank shots, overlaid over a backboard #

	# dbdr is a data frame with the following columns (sourced from 2013-2016 tracking data):
	# bx (x-coordinate of backboard strike)
	# by (y-coordinate of backboard strike)
	ggplot(dbdr,aes(x=by,y=bz)) +
	theme_bw() +
	coord_fixed() +
	geom_hex(binwidth=c(0.25,0.25),aes(fill = stat((count)))) +
	scale_fill_gradient(low="lightblue1",high="darkblue") +
	# this code generates bank shot scatterplots for different players via facet_wrap

	# dbsel is a data frame with the following columns (sourced from 2013-2016 tracking data):
	# nm (player name)
	# bx (x-coordinate of backboard strike)
	# by (y-coordinate of backboard strike)

	ggplot(dbsel,aes(x=by,y=bz)) +
	theme_bw() +
	coord_fixed() + # so the backboard is shown to scale #