4  TE Rankings

4.1 Packages Needed

library(tidyverse)
library(cfbfastR)
library(DT)

4.2 Load Play-by-Play Data

pbp_2025 <- load_cfb_pbp(seasons = 2025) # mean epa/target

4.3 Load TE Players

tes <- read.csv("data/players.csv") %>% filter(position == "TE")

4.4 Load Necessary CSV Files

receiving <- read.csv("data/receiving_summary.csv") %>% 
  select(player_id, grades_pass_route, yprr, drop_rate, contested_catch_rate) # rec grade, yprr, drop%, contested catch %
blocking <- read.csv("data/offense_blocking.csv") %>% 
  select(player_id, grades_run_block, grades_pass_block) # rb grade, pb grade

4.5 Combine Datasets and Pull Names

te_values <- tes %>% 
  left_join(receiving, by = c("id" = "player_id")) %>% 
  drop_na()
te_values <- te_values %>% 
  left_join(blocking, by = c("id" = "player_id")) %>% 
  drop_na()
te_values <- te_values %>% 
  rename(rec_grade = grades_pass_route, cc_rate = contested_catch_rate, pb_grade = grades_pass_block, rb_grade = grades_run_block)
te_names <- te_values %>% pull(name)

4.6 Calculate Mean EPA and Combine

receiver_epa <- pbp_2025 %>%
  filter(pass == 1) %>%
  group_by(receiver_player_name) %>%
  summarize(mean_epa = round(mean(EPA, na.rm = TRUE), 3)) %>%
  filter(!is.na(receiver_player_name), receiver_player_name %in% te_names) %>%
  rename(name = receiver_player_name) %>%
  arrange(-mean_epa)

te_values <- left_join(te_values, receiver_epa, by = "name") %>% select(-id)

4.7 Get Mean Values (for testing)

mean(te_values$rec_grade)

[1] 68.24595

mean(te_values$yprr)

[1] 1.577838

mean(te_values$drop_rate)

[1] 5.691892

mean(te_values$cc_rate)

[1] 48.31622

mean(te_values$rb_grade)

[1] 56.71351

mean(te_values$pb_grade)

[1] 66.61351

mean(te_values$mean_epa)

[1] 0.4502973

4.8 Create Rating Function

get_te_ratings <- function(input_df) {
  df_te_copy <- input_df %>% mutate(
    rec_grade = round(pmax(pmin((rec_grade-50) / 4, 10), 0), 2), # 50-90, mean 70
    yprr = round(pmax(pmin((yprr-1) * 6.67, 10), 0), 2), # 1-2.5, mean 1.75
    rb_grade = round(pmax(pmin((rb_grade-40) / 3, 10), 0), 2), # 40-70, mean 55
    pb_grade = round(pmax(pmin((pb_grade-55) / 4, 5), 0), 2), # 55-75, mean 65, worth 5
    drop_rate = round(pmax(pmin(((100-drop_rate)-90) / 1.5, 5), 0), 2), # 10-2.5, mean 6.25, worth 5
    cc_rate = round(pmax(pmin((cc_rate-35) / 6, 5), 0), 2), # 35-65, mean 50, worth 5
    mean_epa = round(pmax(pmin((mean_epa) * 5, 5), 0), 2), # 0-1, mean .5, worth 5
  )
  
  return(df_te_copy)
}

4.9 Create Final Dataset

te_ratings <- get_te_ratings(te_values) %>% 
  mutate(total = rowSums(select(.,-name, -position, -team, -rank))) %>% 
  arrange(-total) #%>%
  #mutate(pos_rank_aft = row_number())

4.10 Display Ratings

datatable(te_ratings)