Getting started in 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 3rd Qu.    Max. 
  11.00   11.75   12.50   20.00   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_seasons<-6
#Needed Walks on season 6 
walks_6<-number_seasons*wanted_walks-sum(Soto_Walks)
walks_6

[1] 92

Case Scenario 2

n_1<-10
n_2<-4
y_1<-72000
y_2<-84000
#Mean Salary 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<-10200
w_2<-91000
#Mean Salary Overall 
w_salary_ave<-(bp_1*w_1+fp_1*w_2)/(bp_1+fp_1)
w_salary_ave

[1] 55650

getwd()

[1] "/cloud/project"

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

contracts_mean<-mean(contract_years)
contracts_mean<-round(contracts_mean,digits = 1)
contracts_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_wsld<-sum((contract_years-contracts_mean)/contracts_sd<1/contracts_n)
#Percentage of observations within one sd from the mean 
contracts_wsld

[1] 252

#Difference from empirical
contracts_wsld-0.68

[1] 251.32

Create a Histogram

summary(cars)

     speed           dist       
 Min.   : 4.0   Min.   :  2.00  
 1st Qu.:12.0   1st Qu.: 26.00  
 Median :15.0   Median : 36.00  
 Mean   :15.4   Mean   : 42.98  
 3rd Qu.:19.0   3rd Qu.: 56.00  
 Max.   :25.0   Max.   :120.00  

hist(cars$dist)

Create a Histogram

hist(contract_years,xlab = "Years Left in Contract",col = "green",border = "red",
     xlim = c(0,8),ylim = c(0,250),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_mean

[1] 23.55

doubles_hit_median<-median(doubles_hit)
doubles_hit_median

[1] 23.5

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

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

[1] 1

#Difference from empirical 
doubles_hit_wsld-0.68

[1] 0.32

Histogram

hist(doubles_hit,xlab = "Number of Doubles", col = "blue",border = "lightblue",
     xlim = c(0,60),ylim = c(0,50),breaks = 5)

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