library(data.table)
library(ggplot2)
library(dplyr)

read in data

pga<- fread("/Users/claire/Desktop/PGA_2022_money.csv", header=T, data.table=F)
liv<- fread("/Users/claire/Desktop/LIV_2022_money.csv", header=T, data.table=F)

i’m interested in seeing how total money from the 2021-2022 season differs between the PGA and LIV tours. we know LIV has a much larger pot of money, but it seems like the winners per tournament have a much steeper drop off (for example only those in the top 3 earned over 1 million, and those below the top 15 earned less than or equal to $250,000). but still. even those who WD’d earned money on LIV, compared to all those who miss the cut or withdraw in the PGA who earn 0.

from the tournament on may 28 2023:

tourn<- fread("/Users/claire/Desktop/Tnat_LIV.csv", header=T, data.table=F)
tourn$ord<- row_number(tourn)
ggplot(tourn, aes(x = ord, y=money))+
  geom_bar(stat="identity", fill="darkgreen")+
  labs(title="Money earned for LIV tournament on May 28 2023",x="Finish", y = "Money earned")+
  theme_minimal() +
  geom_label(label=tourn$name, nudge_x = 0.5, nudge_y = 0.5, 
    check_overlap = T, size=4)

plotting the distribution of money earned last season on the pga (black line = tour average):

pga$ord<- row_number(pga)
ggplot(pga, aes(x = name, y=money))+
  geom_bar(stat="identity", fill="darkgreen")+
  labs(title="Money earned on PGA tour for 2022 season. Wins overlayed in boxes. \nYellow line = tour average",x="Name", y = "Money earned")+
  theme_minimal() +
  geom_hline(yintercept=mean(pga$money), color="goldenrod1", size=2)+
  geom_label(label=pga$wins, nudge_x = 0.5, nudge_y = 0.5, 
    check_overlap = T, size=4)+
  theme(legend.position = "bottom", axis.text.x = element_text(angle=60, size=3, hjust = 1))

plotting the distribution of money earned last season on the LIV tour (black line = tour average):

here, I’m actually subtracting out the end of year bonuses just to see who earns what from tournament play in LIV, compared to tournament play on the PGA tour.

liv$money2<- ifelse(!is.na(liv$bonus), liv$money-liv$bonus, liv$money)
liv$ord<- row_number(liv)
ggplot(liv, aes(x = name, y=money2))+
  geom_bar(stat="identity", fill="darkgreen")+
  labs(title="Money earned on LIV tour for 2022 season. \nRed line = LIV average, yellow line = PGA average",x="Name", y = "Money earned")+
  theme_minimal() +
  geom_hline(yintercept=mean(liv$money2), color="red", size=2)+
  geom_hline(yintercept=mean(pga$money), color="goldenrod1", size=1.5)+
  theme(legend.position = "bottom", axis.text.x = element_text(angle=60, size=7, hjust = 1))

what overall place on the PGA tour would you have to finish to make the LIV average?

everyone in this plot made above the LIV tour average money. almost all of them won at least 1 tournament.

the lowest someone finished on the PGA tour to still make above the LIV tour average was 36th place.

pga_liv<- pga %>% filter(money>mean(liv$money2))
range(pga_liv$place)
## [1]  1 36
ggplot(pga_liv, aes(x = name, y=money))+
  geom_bar(stat="identity", fill="darkgreen")+
  labs(title="Money earned on PGA tour for 2022 season. Wins overlayed in boxes. \nYellow line = tour average",x="Name", y = "Money earned")+
  theme_minimal() +
  geom_hline(yintercept=mean(pga$money), color="goldenrod1", size=2)+
  geom_label(label=pga_liv$wins, nudge_y = 0.5, 
    check_overlap = T, size=4)+
  theme(legend.position = "bottom", axis.text.x = element_text(angle=60, size=7, hjust = 1))