Packages Needed

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(DT)
## Warning: package 'DT' was built under R version 4.4.3

Read CSV File

ED_Stats <- read.csv("ED_Rankings.csv") %>%
  arrange(rank) %>%
  mutate(pos_rank_bef = row_number())
datatable(ED_Stats)

Get Mean Values

mean(ED_Stats$pass_grade)
## [1] 81.96316
mean(ED_Stats$run_grade)
## [1] 76.64474
mean(ED_Stats$pass_win)
## [1] 16.94211
mean(ED_Stats$run_stop)
## [1] 6.836842
mean(ED_Stats$prp)
## [1] 9.197368

Create Values Function

get_ed_values <- function(input_df) {
  df_ed_copy <- input_df %>% mutate(
    pass_grade = round(pmax(pmin((pass_grade-70) / 2, 10), 0), 2),  # 70-90, mean 80
    run_grade = round(pmax(pmin((run_grade-65) / 2, 10), 0), 2), # 65-85, mean 75
    pass_win = round(pmax(pmin((pass_win-10), 10), 0), 2), # 10-20, mean 15
    run_stop = round(pmax(pmin((run_stop-4)*2, 10), 0), 2), # 4-9, mean 6.5
    prp = round(pmax(pmin((prp-6)*1.67, 10), 0), 2), # 6-12, mean 9
    
  )
  
  return(df_ed_copy)
}

Create Final Dataset

new_stats_ed <- get_ed_values(ED_Stats) %>% 
  mutate(total = rowSums(select(., -player, -adp, -rank, -pos_rank_bef, -team))) %>% 
  arrange(-total) %>%
  mutate(pos = "ED", pos_rank_aft = row_number(), pos_rank_diff = pos_rank_bef-pos_rank_aft) %>%
  select(player, pass_grade, run_grade, pass_win, run_stop, prp, total, rank, pos, team, pos_rank_bef, pos_rank_aft, pos_rank_diff)
datatable(new_stats_ed)

Get Total Value and Rank

get the dataset that only contains the total and the ranks

ed_stats_total <- new_stats_ed %>%
  select(player, total, rank, pos, team, pos_rank_bef, pos_rank_aft, pos_rank_diff)
datatable(ed_stats_total)

Download Final Rankings

write.csv(ed_stats_total, "ed_stats_total.csv", row.names = FALSE)