boooeee’s gists

boooeee / coding_trends.R

Created April 19, 2025 23:49

R code base analysis

	# this code analyzes all of the R scripts in a specified folder and then creates charts summarizing your coding trends over time #
	# Load necessary libraries
	library(tidyverse)
	library(fs)
	library(ggplot2)
	library(lubridate)
	library(dplyr)
	library(scales)

	# Specify your folder path - replace with your actual path

boooeee / team_ranking_example.R

Last active March 3, 2024 18:00

This code derives a schedule/home field adjusted team ranking for a randomly generated set of games. The randomly generated games can be replaced with actual teams and outcomes from the sport of our choice.

	library(tidyverse)

	n_games <- 100

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

	# Generate the data frame

boooeee / volleyball_win_probability.R

Created October 5, 2022 17:11

Code for calculating the probability of winning a set in volleyball based on score and probabilities of winning a point

	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

boooeee / iphone_rundata.R

Last active June 29, 2021 05:25

Code for analyzing and charting your iPhone run data

	rm(list = ls(all = TRUE))

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

boooeee / elam_ending_simulation.R

Created March 7, 2021 18:28

Code for simulating the outcome of the Elam Ending

	# 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 #

boooeee / animated_facet_wrap.R

Last active February 24, 2021 04:08

plot animated shooting windups for multiple players on a single chart

	# 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<-""

boooeee / open_defended_animated.R

Last active January 31, 2021 05:41

code for creating animated ggplot charts of a player's shooting windup - split by open vs. defended

	# 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<-""

boooeee / hexbank.R

Last active August 21, 2020 03:17

hexbin bank shots heat maps using ggplot

	# 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") +

boooeee / playerbank.R

Created August 18, 2020 04:39

NBA Player Bank Shot Scatter Plot using ggplot

	# 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 #