1  QB Rankings

1.1 Packages Needed

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

1.2 Load Play-by-Play Data

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

1.3 Load QB Players

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

1.4 Load Necessary CSV Files

passing <- read.csv("data/qb/passing_summary.csv") %>% 
  select(player, grades_pass, qb_rating, twp_rate, btt_rate) # pass grade, qb rating, tw%, btt%

1.5 Combine Datasets and Pull Names

qb_values <- qbs %>% 
  left_join(passing, by = c("name" = "player")) %>% 
  drop_na()
qb_names <- qb_values %>% pull(name)

1.6 Calculate Mean EPA and Combine

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

qb_values <- left_join(qb_values, passer_epa, by = "name") %>% select(-id)

1.7 Get Mean Values (for testing)

mean(qb_values$grades_pass)

[1] 74.33333

mean(qb_values$qb_rating)

[1] 97

mean(qb_values$twp_rate)

[1] 3.225926

mean(qb_values$btt_rate)

[1] 4.692593

mean(qb_values$mean_epa)

[1] 0.1962222

1.8 Create Rating Function

get_qb_ratings <- function(input_df) {
  input_df <- input_df %>% mutate(
    grades_pass = round(pmax(pmin((grades_pass-70) / 2, 10), 0), 2), # 70-90, mean 80
    qb_rating = round(pmax(pmin((qb_rating-90) / 3, 10), 0), 2), # 90-120, mean 105
    twp_rate = round(pmax(pmin(((100-twp_rate)-96) * 5, 10), 0), 2), # 4-2, mean 3
    btt_rate = round(pmax(pmin((btt_rate-3) * 3.33, 10), 0), 2), # 3-6, mean 4.5
    mean_epa = round(pmax(pmin((mean_epa) * 33.33, 10), 0), 2), # 0-.3, mean .15
  )
  
  return(input_df)
}

1.9 Create Final Dataset

qb_ratings <- get_qb_ratings(qb_values) %>% 
  mutate(total = rowSums(select(.,-name, -position, -team, -rank))) %>% 
  arrange(-total) #%>%
  #mutate(pos_rank_aft = row_number())

1.10 Display Ratings

datatable(qb_ratings)