load("c:/Statistics/nba_pbp_14_24.RData")
View(all_pbp_data)
##################
### Homework 1 ###
##################
### Task 1 ###
# Read in the all_nba_pbp_data.csv file. It is big, so it might take some time!
# After you read in the data, select 5 players that you
# want to use for subsetting the data. You can either make
# one object or 5 different objects. It is completely up
# to you!
# You can remove the large object by using the rm(object_name) function.
### Task 2 ###
# For each of the 5 players, find their mean/sd distance.
# Which of the players has the longest mean distance?
# Which player has the tightest sd?
### Task 3 ###
# What is each player's total probability of making a shot?
# What is each player's probability of making a 2-point shot?
# What is each player's probability of making a 3-point shot?
### Task 4 ###
# What is each players expected shot value for 2-point field goals?
# What is each players expected shot value for 3-point field goals?
### Task 5 ###
# Give me 3 ideas for your final project.
### Task 6 ###
# Compile this document into an html file. File > Compile Report...
# You might be asked to install some packages, so definitely do that.
# Submit the html file on Canvas!
#Task 1
Tim_Duncan = subset(all_pbp_data, all_pbp_data$shooter == "Tim Duncan")
View(Tim_Duncan)
Ray_Allen = subset(all_pbp_data, all_pbp_data$shooter == "Ray Allen")
View (Ray_Allen)
Mo_Williams = subset(all_pbp_data, all_pbp_data$shooter == "Mo Williams")
View(Mo_Williams)
Kevin_Garnett = subset(all_pbp_data, all_pbp_data$shooter == "Kevin Garnett")
View(Kevin_Garnett)
Vince_Carter = subset(all_pbp_data, all_pbp_data$shooter == "Vince Carter")
View(Vince_Carter)
#Task 2
mean(all_pbp_data$distance[all_pbp_data$shooter == "Tim Duncan"], na.rm = TRUE)
## [1] 4.243767
mean(all_pbp_data$distance[all_pbp_data$shooter == "Ray Allen"], na.rm = TRUE)
## [1] 12.29412
mean(all_pbp_data$distance[all_pbp_data$shooter == "Mo Williams"], na.rm = TRUE)
## [1] 15.54348
mean(all_pbp_data$distance[all_pbp_data$shooter == "Kevin Garnett"], na.rm = TRUE)
## [1] 10.0922
mean(all_pbp_data$distance[all_pbp_data$shooter == "Vince Carter"], na.rm = TRUE)
## [1] 17.0766
# vince carter had the longest mean distance
sd(all_pbp_data$distance[all_pbp_data$shooter == "Tim Duncan"], na.rm = TRUE)
## [1] 6.031986
sd(all_pbp_data$distance[all_pbp_data$shooter == "Ray Allen"], na.rm = TRUE)
## [1] 11.97792
sd(all_pbp_data$distance[all_pbp_data$shooter == "Mo Williams"], na.rm = TRUE)
## [1] 9.023759
sd(all_pbp_data$distance[all_pbp_data$shooter == "Kevin Garnett"], na.rm = TRUE)
## [1] 8.979262
sd(all_pbp_data$distance[all_pbp_data$shooter == "Vince Carter"], na.rm = TRUE)
## [1] 11.16997
# Tim Duncan had the tightest standard deviation on distance
#Task 3
prop.table(table(Tim_Duncan$shooter, Tim_Duncan$result), margin = 1)
##
## made missed
## Tim Duncan 0.94736842 0.05263158
prop.table(table(Ray_Allen$shooter, Ray_Allen$result), margin = 1)
##
## made
## Ray Allen 1
prop.table(table(Mo_Williams$shooter, Mo_Williams$result), margin = 1)
##
## made missed
## Mo Williams 0.97826087 0.02173913
prop.table(table(Kevin_Garnett$shooter, Kevin_Garnett$result), margin = 1)
##
## made missed
## Kevin Garnett 0.96453901 0.03546099
prop.table(table(Vince_Carter$shooter, Vince_Carter$result), margin = 1)
##
## made missed
## Vince Carter 0.5584958 0.4415042
prop.table(table(all_pbp_data$shooter == "Tim Duncan"| all_pbp_data$shooter == "Tim Duncan ", all_pbp_data$result), margin = 1)
##
## made missed
## FALSE 0.4547610 0.5452390
## TRUE 0.5066667 0.4933333
prop.table(table(all_pbp_data$shooter == "Ray Allen" | all_pbp_data$shooter == "Ray Allen ", all_pbp_data$result), margin = 1)
##
## made missed
## FALSE 0.4547980 0.5452020
## TRUE 0.4146341 0.5853659
prop.table(table(all_pbp_data$shooter == "Mo Williams" | all_pbp_data$shooter == "Mo Williams ", all_pbp_data$result), margin = 1)
##
## made missed
## FALSE 0.4548093 0.5451907
## TRUE 0.4302103 0.5697897
Overall_Shooting_Duncan = subset(all_pbp_data
, all_pbp_data$shooter == "Tim Duncan"|
all_pbp_data$shooter == "Tim Duncan ")
Overall_Shooting_Allen = subset(all_pbp_data
, all_pbp_data$shooter == "Ray Allen")
Overall_Shooting_Williams = subset(all_pbp_data
, all_pbp_data$shooter == "Mo Williams")
Overall_Shooting_Garnett = subset(all_pbp_data
, all_pbp_data$shooter == "Kevin Garnett")
Overall_Shooting_Carter = subset(all_pbp_data
, all_pbp_data$shooter == "Vince Carter")
prop.table(table(Overall_Shooting_Duncan$result))
##
## made missed
## 0.5066667 0.4933333
#Overall Tim Duncan made 50.67% of his shots
prop.table(table(Overall_Shooting_Allen$result))
##
## made
## 1
#Overall Ray Allen made 100% of his shots
prop.table(table(Overall_Shooting_Williams$result))
##
## made missed
## 0.97826087 0.02173913
#Overall Mo Williams made 97.83% of his shots
prop.table(table(Overall_Shooting_Garnett$result))
##
## made missed
## 0.96453901 0.03546099
#Overall Kevin Garnett made 96.45% of his shots
prop.table(table(Overall_Shooting_Carter$result))
##
## made missed
## 0.5584958 0.4415042
#Overall Vince Carter made 55.85% of his shots
prop.table(table(Overall_Shooting_Duncan$result
, Overall_Shooting_Duncan$three),margin =2)
##
## FALSE TRUE
## made 0.5089286 0.0000000
## missed 0.4910714 1.0000000
# in this data, Tim Duncan made 50.89% of his 2 point shots
#in this data, Tim Duncan made 0% of his 3 point shots
prop.table(table(Overall_Shooting_Allen$result
, Overall_Shooting_Allen$three), margin =2)
##
## FALSE TRUE
## made 1 1
#in this data, Ray Allen made 100% of his 2 point shots
#in this data, Ray Allen made 100% of his 3 point shots
prop.table(table(Overall_Shooting_Williams$result
, Overall_Shooting_Williams$three), margin =2)
##
## FALSE TRUE
## made 0.97058824 1.00000000
## missed 0.02941176 0.00000000
#in this data, Mo Williams made 97.06% of his 2 point shots
#in this data, Mo Williams made 100% of his 3 point shots
prop.table(table(Overall_Shooting_Garnett$result
, Overall_Shooting_Garnett$three), margin =2)
##
## FALSE
## made 0.96453901
## missed 0.03546099
#in this data, Kevin Garnett made 96.45% of his 2 point shots
#in this data, Kevin garnett never attempted a 3 point shot
prop.table(table(Overall_Shooting_Carter$result
, Overall_Shooting_Carter$three), margin= 2)
##
## FALSE TRUE
## made 0.6452703 0.4976303
## missed 0.3547297 0.5023697
#in this data, Vince carter made 64.53% of his 2 point shots
#in this data, Vince carter made 49.76% of his 3 point shots
### Task 4 ###
Tim_Duncan2 = 2*.5089
#Tim Duncan's expected value of a 2 point shot is 1.0178 pts
Tim_Duncan3 = 3*1.0
#Tim Duncan's expected value of a 3 point shot is 3 pts
Ray_Allen2 = 2*1.0
#Ray Allen's expected value of a 2 point shot is 2 pts
Ray_Allen3 = 3*1.0
#Ray Allen's expected value of a 3 point shot is 3 pts
Mo_Williams2 = 2*.9706
#Mo Williams' expected value of a 2 point shot is 1.9412 pts
Mo_Williams3 = 3*1.0
#Mo Williams' expected value of a 3 point shot is 3 pts
Kevin_Garnett2 = 2*.9645
#Kevin Garnett's expected value of a 2 point shot is 1.929 pts
Kevin_Garnett3 = 3*0
#Kevin Garnett's expected value of a 3 point shot is 0 pts (Unknown since he didn't attempt one in this data set)
Vince_Carter2 = 2*.6453
#Vince Carter's expected value of a 2 point shot is 1.2906 pts
Vince_Carter3 = 3*.4976
#Vince Carter's expected value of a 3 point shot is 1.4928 pts
### Task 5 ###
#What is the expected value of a successfully converted 4th down attempt in relation to field position?
#At what point on the field is the expected value of a successful 4th down attempt great enough to risk?
#What is the expected value of a 2 point attempt from the 1 or 2 for both a passing and rushing play, and is it greater than the expected value of a standard pat try?