In wiscostret/fplscrapR: Easy compiling of Fantasy Premier League data in R

Suppose we are interested in replicating 'Who Got The Assist?'s famous 'Talisman Theory' analysis for the 18/19 season, looking at the 'key men' for Premier League teams, players who score the largest proportion of team points. Specifically, selected non-appearance points that can be attributed to the individual player rather than the team as a whole.

With fplscrapR, we can do this analysis very easily.

First, we fetch the player stats using get_player_info:

library(fplscrapR)

df <- get_player_info(season=18)

Next we manipulate the data frame to represent our needs in several steps, using dplyr:

library(dplyr)

df2 <- 
  df %>% 
  mutate( # creating the 'non-appearance points' variable based on goals, assists, saves, penalty saves, and bonus points, with the exact scoring diferent for each position (element_type)
    napts = case_when(
      element_type == 1 ~ round(goals_scored * 6 + assists * 3 + saves/3 + penalties_saved*6 + bonus,0),
      element_type == 2 ~ goals_scored * 6 + assists * 3 + bonus,
      element_type == 3 ~ goals_scored * 5 + assists * 3 + bonus,
      element_type == 4 ~ goals_scored * 4 + assists * 3 + bonus)) %>% 
  group_by(team) %>% 
  mutate(teamtotal = sum(napts)) %>% # summarising team totals
  mutate(naptsprop = round(napts/teamtotal,2)) %>% # calculating the player proportion of the team total (and rounding) 
  top_n(n=1) # selecting the top player from each team

Finally, we create a nice-looking plot of our data, using ggplot2:

library(ggplot2)

df2 %>% 
  ggplot() + 
    geom_col(aes(x=reorder(second_name,naptsprop),y=naptsprop)) +
    theme_bw() +
    coord_flip() +
    labs(title="Who Got The Assists?'s Talisman Theory", x="Player",y="Proportion of team's non-appearance points scored",caption=paste("Data from fplscrapR | ",Sys.Date(),sep=""))