Introduction to R for Sports Ananlytics, Part 2

Case-scenario 1 This is the fourth season of outfielder Luis Robert with the Chicago White Socks. If during the first three seasons he hit 11, 13, and 12 home runs, how many does he need on this season for his overall average to be at least 20?

# Home-runs so far
HR_before <- c(11, 13, 12)
# Average Number of Home-runs per season wanted
wanted_HR <- 20
# Number of seasons
n_seasons <- 4
# Needed Home-runs on season 4
x_4 <- n_seasons*wanted_HR - sum(HR_before)
# Minimum number of Home-runs needed by Robert
x_4

## [1] 44

# Robert's performance
Robert_HRs <- c(11, 13, 12,44)
# Find mean
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

fivenum(Robert_HRs)#Tukey's Five number summary

## [1] 11.0 11.5 12.5 28.5 44.0

hist(Robert_HRs)

soto_Walks_Before <- c(79,108,41,145,135)

`Case-scenario 2
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.


``` r
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

Question 2 The average salary of 7 basketball players is 102,000 dollars a week and the average salary of 9 NFL players is 91,000. Find the mean salary of all 16 professional players.

n_3<-7
y_3<-10200
n_4<-9
y_4<-91000
salary_ave_2<-(n_3*y_3+n_4*y_4)/(n_3+n_4)
salary_ave_2

## [1] 55650

Case-scenario 3 The frequency distribution below lists the number of active players in the Barclays Premier League and the time left in their contract.

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

# Mean 
contracts_mean  <- mean(contract_years)
contracts_mean

## [1] 3.458918

# Median
contracts_median <- median(contract_years)
contracts_median

## [1] 3

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

contracts_w1sd <- sum((contract_years - contracts_mean)/contracts_sd < 1)/ contracts_n
# Percentage of observation within one standard deviation of the mean
contracts_w1sd

## [1] 0.8416834

## Difference from empirical 
contracts_w1sd - 0.68

## [1] 0.1616834

contracts_w3sd <- sum((contract_years - contracts_mean)/ contracts_sd < 3)/contracts_n
contracts_w3sd

## [1] 1

## Difference from empirical 
contracts_w3sd - 0.9973

## [1] 0.0027

# Create histogram
hist(contract_years,xlab = "Years Left in Contract",col = "green",border = "red", xlim = c(0,8), ylim = c(0,225),breaks = 5)