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 3rd Qu.    Max. 
  11.00   11.75   12.50   20.00   20.75   44.00 

Question 1

This is the six season of outfielder Juan Soto in MLB. 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 walks?

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

MLB Outfielder Juan Soto will need an at least 92 walks in order to average 100 walks for his six seasons tenure.

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<-102000
w_2<-91000
#Mean Salary Overall
weekly_salary_ave<-(bp_1*w_1+fp_1*w_2)/(bp_1+fp_1)
weekly_salary_ave

[1] 95812.5

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_w1sd<-sum((contract_years-contracts_mean)/contracts_sd<1)/contracts_n
#Percentage of observations within one sd from the mean
contracts_w1sd

[1] 0.842

#Difference from Empirical Rule
contracts_w1sd-0.68

[1] 0.162

#Within 2 Standard Deviation
contracts_w2sd<-sum((contract_years-contracts_mean)/contracts_sd<2)/contracts_n
contracts_w2sd

[1] 1

#Difference from Empirical Rule
contracts_w2sd-0.95

[1] 0.05

#Within 3 Standard Deviation
contracts_w3sd<-sum((contract_years-contracts_mean)/contracts_sd<3)/contracts_n
contracts_w3sd

[1] 1

#Difference from Empirical Rule
contracts_w3sd-0.9973

[1] 0.0027

Create a Histogram

hist(contract_years, xlab = "Years Left in Contract", col = "green", border = "red", xlim = c(1,6), ylim = c(0,400), breaks = 5)

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

Question 3

doubles<-read.csv("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<-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 Rule
doubles_hit_w1sd-0.68

[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 Empirical Rule
doubles_hit_w2sd-0.95

[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 Empirical Rule
doubles_hit_w3sd-0.9973

[1] 0.0027

Histogram

hist(doubles_hit, xlab = "Number of Doubles", col = "yellow", border = "black", xlim = c(0, 60), ylim = c(0,30), breaks = 6)

According to this histogram, the highest frequency of doubles hit by player is between 10 to 30.

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

According to this box plot visualization, players hit between 10-40 doubles hit that season. The thick horizontal line inside the box represents the median, which appears to be around 20-25 doubles hit.