Getting Started with R: Concepts in sports analytics

#Homeruns so far
HR_Before<-c(11,13,12)
Wanted_Homeruns<-20
n_seasons<-4
#20=(11+13+12+x/4)
#80-36=X
x_4<-n_seasons*Wanted_Homeruns-sum(HR_Before)
x_4

[1] 44

Robert_HRs<-c(11,13,12,44)
mean(Robert_HRs)

[1] 20

sd(Robert_HRs)

[1] 16.02082

max(Robert_HRs)

[1] 44

min(Robert_HRs)

[1] 11

summary(Robert_HRs)

   Min. 1st Qu.  Median    Mean 
  11.00   11.75   12.50   20.00 
3rd Qu.    Max. 
  20.75   44.00 

Question 1

This is the sixth season of outfielder Juan Soto in the majors. If during the first five seasons he received 79, 108,41,145, and 135 walks, how many does he need on this season for his overall number of walks per season to be at least 100?

Soto_walks<-c(79,108,41,145,135)
wanted_walks<-100
number_of_seasons<-6
#needed walks for season six 
walks_6<-number_of_seasons*wanted_walks-sum(Soto_walks)
walks_6

[1] 92

Soto would need at least 92 walks for him to have an average of 100 walks per season.

The average salary of 10 baseball players is 72,000 dollars a week and the average salary of 4 soccer players is 84,000. Find the mean salary of all 14 professional players.

n_1<-10
n_2<-4
y_1<-72000
y_2<-84000
#mean salaay overall
salary_ave<-(n_1*y_1+n_2*y_2)/(n_1+n_2)
salary_ave

[1] 75428.57

bp_1<-7
fp_1<-9
w_1<-102000
w_2<-91000
#Mean salary overall

salary_ave2<-(bp_1*w_1+fp_1*w_2)/(bp_1+fp_1)
salary_ave2

[1] 95812.5

Overall salary for both baseball player and soccer player is 95812.50

getwd()

[1] "/cloud/project"

contract_length<-read.csv("allcontracts.csv",header = TRUE, sep =",")
contract_years<-contract_length$years

contract_mean<-mean(contract_years)
contract_mean<-round(contract_mean, digits = 1)#to round 
contract_mean

[1] 3.5

#Median 
contracts_median<-median(contract_years)
contracts_median

[1] 3

#Find the number of observations
contracts_n<-length(contract_years)
#Find the standard deviation 
contracts_sd<-sd(contract_years)

contracts_w1sd<-sum((contract_years-contract_mean)/contracts_sd<1)/contracts_n
#percentage of oberervations within one sd from the mean 
contracts_w1sd<-round(contracts_w1sd, digits = 1)
contracts_w1sd

[1] 0.8

#Difference from emirical 
contracts_w1sd-0.68

[1] 0.12

contracts_w2sd<-sum((contract_years-contract_mean)/contracts_sd<2)/contracts_n
contracts_w2sd

[1] 1

#Difference from emirical 
contracts_w2sd-0.95

[1] 0.05

# Within 3sd 
contracts_w3sd<-sum((contract_years-contract_mean)/contracts_sd<3)/contracts_n
contracts_w3sd

[1] 1

#Difference from empirical 
contracts_w3sd-.9973

[1] 0.0027

Create a Histogram

hist(contract_years,xlab = "Years Left in Contract",col = "green",border = "red", xlim = c(1,6), ylim = c(1,200),
   breaks = 3)

boxplot(contract_years,main="Years left in contract",ylab = "Years")

boxplot(contract_years,main="Years left in contract",ylab = "Years",col = "lightblue",border = "blue",horizontal = FALSE)

Question 3

doubles<-read.table("doubles_hit.csv",header = TRUE,sep = ",")
doubles_hit<-doubles$doubles_hit

doubles_hit_mean<-mean(doubles_hit)
doubles_hit_median<-median(doubles_hit)
doubles_hit_mean

[1] 23.55

doubles_hit_median

[1] 23.5

doubles_hit_n<-length(doubles_hit)
doubles_hit_sd<-sd(doubles_hit)

doubles_hit_w1sd<-sum((doubles_hit-doubles_hit_mean)/doubles_hit_sd<1)/doubles_hit_n
doubles_hit_w1sd

[1] 0.79

#Difference from empirical 
doubles_hit_w1sd-.68

[1] 0.11

doubles_hit_w2sd<-sum((doubles_hit-doubles_hit_mean)/doubles_hit_sd<2)/doubles_hit_n
doubles_hit_w2sd

[1] 1

#Difference from emirical
doubles_hit_w2sd-.95

[1] 0.05

doubles_hit_w3sd<-sum((doubles_hit-doubles_hit_mean)/doubles_hit_sd<3)/doubles_hit_n
doubles_hit_w3sd

[1] 1

#Difference from emirical
doubles_hit_w3sd-.9973

[1] 0.0027

**Histogram

hist(doubles_hit,xlab = "Number of Doubles",col = "blue",border = "lightblue",xlim = c(0,60),ylim = c(0,30),breaks = 7)

boxplot(doubles_hit,main="Boxplot of Doubles Hit by Player",ylab="Doubles",col = "blue",border = "lightblue")