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

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

Get Mean Values

mean(DL_Stats$pass_grade)
## [1] 71.08056
mean(DL_Stats$run_grade)
## [1] 76.72222
mean(DL_Stats$pass_win)
## [1] 9.622222
mean(DL_Stats$run_stop)
## [1] 7.175
mean(DL_Stats$prp)
## [1] 5.241667

Create Values Function

get_dl_values <- function(input_df) {
  df_dl_copy <- input_df %>% mutate(
    pass_grade = round(pmax(pmin((pass_grade-60) / 2, 10), 0), 2),  # 60-80, mean 70
    run_grade = round(pmax(pmin((run_grade-60) / 3, 10), 0), 2), # 60-90, mean 75
    pass_win = round(pmax(pmin((pass_win-4)*1.25, 10), 0), 2), # 4-12, mean 8
    run_stop = round(pmax(pmin((run_stop-4)*1.67, 10), 0), 2), # 4-10, mean 7
    prp = round(pmax(pmin((prp-3)*2.5, 10), 0), 2), # 3-7, mean 5
  )
  return(df_dl_copy)
}

Create Final Dataset

new_stats_dl <- get_dl_values(DL_Stats) %>% 
  mutate(total = rowSums(select(., -player, -adp, -rank, -pos_rank_bef, -team))) %>% 
  arrange(-total) %>%
  mutate(pos = "DL", 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_dl)

Get Total Value and Rank

get the dataset that only contains the total and the ranks

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

Download Final Rankings

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