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

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

Get Mean Values

mean(CB_Stats$man_grade)
## [1] 67.32414
mean(CB_Stats$zone_grade)
## [1] 75.21379
mean(CB_Stats$comp)
## [1] 53.64138
mean(CB_Stats$passer_rating)
## [1] 67.54828
mean(CB_Stats$missp)
## [1] 11.12069

Create Values Function

get_cb_values <- function(input_df) {
  df_cb_copy <- input_df %>% mutate(
    man_grade = round(pmax(pmin((man_grade-50) / 4, 10), 0), 2),  # 50-90, mean 70
    zone_grade = round(pmax(pmin((zone_grade-50) / 4, 10), 0), 2), # 50-90, mean 70
    comp = round(pmax(pmin(((100-comp)-25)/4, 10), 0), 2), # 75-35, mean 55
    passer_rating = round(pmax(pmin(((200-passer_rating)-100)/7, 10), 0), 2), # 100-30, mean 70
    missp = round(pmax(pmin(((100-missp)-80)/1.5, 10), 0), 2), # 20-5, mean 12.5
  )
  return(df_cb_copy)
}

Create Final Dataset

new_stats_cb <- get_cb_values(CB_Stats) %>% 
  mutate(total = rowSums(select(., -player, -adp, -rank, -pos_rank_bef, -team))) %>% 
  arrange(-total) %>%
  mutate(pos = "CB", pos_rank_aft = row_number(), pos_rank_diff = pos_rank_bef-pos_rank_aft) %>%
  select(player, man_grade, zone_grade, comp, passer_rating, missp, total, rank, pos, team, pos_rank_bef, pos_rank_aft, pos_rank_diff)
datatable(new_stats_cb)

Get Total Value and Rank

get the dataset that only contains the total and the ranks

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

Download Final Rankings

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