vignettes/talismantheory.Rmd
talismantheory.Rmd
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)