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

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

Get Mean Values

mean(OT_Stats$rb_grade)
## [1] 73.6375
mean(OT_Stats$pb_grade)
## [1] 80.24375
mean(OT_Stats$zone_grade)
## [1] 75.5
mean(OT_Stats$gap_grade)
## [1] 65.44688
mean(OT_Stats$pbe)
## [1] 96.6625

Create Values Function

get_ot_values <- function(input_df) {
  df_ot_copy <- input_df %>% mutate(
    rb_grade = round(pmax(pmin((rb_grade-65) / 2, 10), 0), 2),  # 65-85, mean 75
    pb_grade = round(pmax(pmin((pb_grade-65) / 2, 10), 0), 2), # 65-85, mean 75
    zone_grade = round(pmax(pmin((zone_grade-65) / 2, 10), 0), 2), # 65-85, mean 75
    gap_grade = round(pmax(pmin((gap_grade-55) / 2, 10), 0), 2), # 55-75, mean 65
    pbe = round(pmax(pmin((pbe-93) * 1.67, 10), 0), 2), # 93-99, mean 96
    
  )
  
  return(df_ot_copy)
}

Create Final Dataset

new_stats_ot <- get_ot_values(OT_Stats) %>% 
  mutate(total = rowSums(select(., -player, -adp, -rank, -pos_rank_bef, -team))) %>% 
  arrange(-total) %>%
  mutate(pos = "OT", pos_rank_aft = row_number(), pos_rank_diff = pos_rank_bef-pos_rank_aft) %>%
  select(player, rb_grade, pb_grade, zone_grade, gap_grade, pbe, total, rank, pos, team, pos_rank_bef, pos_rank_aft, pos_rank_diff)
datatable(new_stats_ot)

Get Total Value and Rank

get the dataset that only contains the total and the ranks

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

Download Final Rankings

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