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

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

Get Mean Values

mean(TE_Stats$rec_grade)
## [1] 71.50952
mean(TE_Stats$rb_grade)
## [1] 58.57619
mean(TE_Stats$yprr)
## [1] 1.73619
mean(TE_Stats$drop)
## [1] 5.980952
mean(TE_Stats$ctc)
## [1] 44.01905

Create Values Function

get_te_values <- function(input_df) {
  df_copy <- input_df %>% mutate(
    rec_grade = round(pmax(pmin((rec_grade-50) / 4, 10), 0), 2),  # 50-90, mean 70
    rb_grade = round(pmax(pmin((rb_grade-40) / 3, 10), 0), 2), # 40-70, mean 55
    yprr = round(pmax(pmin((yprr-1) * 6.67, 10), 0), 2), # 1-2.5, mean 1.75
    drop = round(pmax(pmin(((100-drop)-90) * 1.33, 10), 0), 2), # 10-2.5, mean 6.25
    ctc = round(pmax(pmin((ctc-25) / 4, 10), 0), 2), # 25-65, mean 45
  )
  return(df_copy)
}

Create Final Dataset

new_stats_te <- get_te_values(TE_Stats) %>% 
  mutate(total = rowSums(select(., -player, -adp, -rank, -pos_rank_bef, -team))) %>% 
  arrange(-total) %>%
  mutate(pos = "TE", pos_rank_aft = row_number(), pos_rank_diff = pos_rank_bef-pos_rank_aft) %>%
  select(player, rec_grade, rb_grade, yprr, drop, ctc, total, rank, pos, team, pos_rank_bef, pos_rank_aft, pos_rank_diff)
datatable(new_stats_te)

Get Total Value and Rank

get the dataset that only contains the total and the ranks

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

Download Final Rankings

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