6  IOL Rankings

6.1 Packages Needed

library(tidyverse)
library(DT)

6.2 Load IOL Players

iols <- read.csv("data/players.csv") %>% filter(position == "G" | position == "C")

6.3 Load Necessary CSV Files

blocking <- read.csv("data/offense_blocking.csv") %>% 
  select(player_id, grades_pass_block, grades_run_block, pbe) # pb grade, rb grade, pbe
run_blocking <- read.csv("data/ol/offense_run_blocking.csv") %>% 
  select(player_id, zone_grades_run_block, gap_grades_run_block) # zone grade, gap grade

6.4 Combine Datasets

iol_values <- iols %>% 
  left_join(blocking, by = c("id" = "player_id")) %>% 
  drop_na()
iol_values <- iol_values %>% 
  left_join(run_blocking, by = c("id" = "player_id")) %>% 
  drop_na()
iol_values <- iol_values %>% 
  rename(pb_grade = grades_pass_block, rb_grade = grades_run_block, zone_grade = zone_grades_run_block, gap_grade = gap_grades_run_block) %>%
  select(-id)

6.5 Get Mean Values (for testing)

mean(iol_values$pb_grade)

[1] 76.06596

mean(iol_values$rb_grade)

[1] 68.56383

mean(iol_values$pbe)

[1] 98.54043

mean(iol_values$zone_grade)

[1] 69.59149

mean(iol_values$gap_grade)

[1] 63.51064

6.6 Create Rating Function

get_iol_ratings <- function(input_df) {
  df_iol_copy <- input_df %>% mutate(
    pb_grade = round(pmax(pmin((pb_grade-65) / 2, 10), 0), 2), # 65-85, mean 75
    rb_grade = round(pmax(pmin((rb_grade-65) / 2, 10), 0), 2),  # 65-85, mean 75
    pbe = round(pmax(pmin((pbe-95) * 2.5, 10), 0), 2), # 95-99, mean 97
    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
  )
  
  return(df_iol_copy)
}

6.7 Create Final Dataset

iol_ratings <- get_iol_ratings(iol_values) %>% 
  mutate(total = rowSums(select(.,-name, -position, -team, -rank))) %>% 
  arrange(-total) #%>%
  #mutate(pos_rank_aft = row_number())

6.8 Display Ratings

datatable(iol_ratings)