028: NFL Draft Tiers

Load data + modify tier function

# load csv 
all_drafts <- read_csv("all_drafts.csv")

# find top teams since 1994
top_teams <- all_drafts %>% 
      group_by(team) %>% 
      count %>% arrange(-n) %>% 
      filter(n > 49) 

# find only acc teams 
acc_teams <- c("Miami (FL)", "North Carolina State", "Louisville", "Virginia", "Duke", "Virginia Tech", "North Carolina",
               "Notre Dame", "Florida State", "Boston College", "Clemson", "Georgia Tech",
               "Wake Forest", "Pittsburgh", "Syracuse")

acc_recent <- all_drafts %>% 
  filter(draft_year > 2014) %>%  
  filter(team %in% acc_teams) %>% 
  group_by(team) %>% 
  count %>% 
  arrange(-n) 

# modify tier function 
cfb_team_tiers <- function(data,
                           title = "CFB Team Tiers, 2021 as of Week 6",
                           subtitle = "created with the #cfbplotR Tiermaker",
                           caption = NULL,
                           tier_desc = c("1" = "Playoff",
                                         "2" = "Very Good",
                                         "3" = "Medium",
                                         "4" = "Bad",
                                         "5" = "What are they doing?",
                                         "6" = "",
                                         "7" = ""),
                           presort = FALSE,
                           alpha = 0.8,
                           width = 0.042,
                           no_line_below_tier = NULL,
                           devel = FALSE){

  rlang::check_installed("sjmisc", "to build the cfbplotR team tiers.")

  required_vars <- c("tier_no", "team")

  if (!all(required_vars %in% names(data))){
    cli::cli_abort("The data frame {.var data} has to include the variables {.var {required_vars}}!")
  }

  bg <- "floral white"
  lines <- "#e0e0e0"

  tiers <- sort(unique(data$tier_no))
  tierlines <- tiers[!tiers %in% no_line_below_tier] + 0.5
  tierlines <- c(min(tiers) - 0.5, tierlines)

  if (isTRUE(presort)){
    data <- data %>%
      dplyr::group_by(.data$tier_no) %>%
      dplyr::arrange(.data$team) %>%
      dplyr::mutate(tier_rank = 1:dplyr::n()) %>%
      dplyr::ungroup()
  }

  if (!"tier_rank" %in% names(data)){
    data <- data %>%
      dplyr::group_by(.data$tier_no) %>%
      dplyr::mutate(tier_rank = 1:dplyr::n()) %>%
      dplyr::ungroup()
  }

  data$team <- cfbplotR::clean_school_names(as.character(data$team))

  p <- ggplot2::ggplot(data, ggplot2::aes(y = .data$tier_no, x = .data$tier_rank)) +
    ggplot2::geom_hline(yintercept = tierlines, color = lines)

  if(isFALSE(devel)) p <- p + cfbplotR::geom_cfb_logos(ggplot2::aes(team = .data$team), width = width, alpha = alpha)
  if(isTRUE(devel))p <- p + ggplot2::geom_text(ggplot2::aes(label = .data$team), color = "#333333")

  p <- p +
    ggplot2::scale_y_continuous(
      expand = ggplot2::expansion(add = 0.1),
      limits = rev(c(min(tiers) - 0.5, max(tiers) + 0.5)),
      breaks = rev(tiers),
      labels = function(x) sjmisc::word_wrap(tier_desc[x], 15),
      trans = "reverse"
    ) +
    ggplot2::labs(title = title, subtitle = subtitle, caption = caption) +
    ggplot2::theme_minimal(base_size = 11.5) +
    ggplot2::theme(
      plot.title = ggplot2::element_text(color = "#333333", face = "bold"),
      plot.subtitle = ggplot2::element_text(color = "#8e8e93"),
      plot.caption = ggplot2::element_text(color = "#8e8e93", hjust = 1),
      plot.title.position = "plot",
      axis.text.x = ggplot2::element_blank(),
      axis.text.y = ggplot2::element_text(color = "#333333", face = "bold", size = ggplot2::rel(1.1)),
      axis.title = ggplot2::element_blank(),
      panel.grid = ggplot2::element_blank(),
      plot.background = ggplot2::element_rect(fill = bg, color = bg),
      panel.background = ggplot2::element_rect(fill = bg, color = bg)
    ) +
    NULL

  p
}

Build the tier charts

dft_pos <- top_teams %>%
  mutate(team = case_match(team,
        "Miami (FL)" ~ "Miami",
        "Louisiana State" ~ "LSU",
        "North Carolina State" ~ "NC State",
        "Texas Christian" ~ "TCU",
        team ~ team)) %>% 
  mutate(tier_no = case_when(
          n > 150 ~ 1,
          n < 149 & n > 99 ~ 2, 
          n < 100 & n > 74 ~ 3,
          n < 75 & n > 50 ~ 4
          )) %>% 
  cfb_team_tiers(title = "NFL Draft Picks by College Team Since 1994",
                 subtitle = "1994 NFL draft was the first draft with only seven rounds.",
                 caption = "@dadgumboxscores | April 26, 2023 | drafthistory.com",
                 tier_desc = c("1" = "Elite Tier (150+ picks)",
                               "2" = "Good Tier (100+)",
                               "3" = "Solid Tier (75+)",
                               "4" = "Mid Tier (50+)"),
                 )
  
# save it
ggsave(
  "dft_pos_94.png",
  dft_pos,
  w = 6,
  h = 6,
  dpi = 600,
  type = 'cairo'
)

dft_pos 

# now do acc teams since 2014
acc_pos <- acc_recent %>%
  mutate(team = case_match(team,
                           "Miami (FL)" ~ "Miami",
                           "North Carolina State" ~ "NC State",
                           team ~ team)) %>% 
  mutate(tier_no = case_when(
    n > 30 ~ 1,
    n < 30 & n > 19 ~ 2, 
    n < 20 & n > 9 ~ 3,
    n < 10 & n > 0 ~ 4
  )) %>% 
  cfb_team_tiers(title = "NFL Draft Picks by ACC Team Since 2015",
                 subtitle = "2014 was first season with all 14 teams, including Notre Dame because why not.",
                 caption = "@dadgumboxscores | April 26, 2023 | drafthistory.com",
                 width = 0.075,
                 tier_desc = c("1" = "Elite Tier (30+ picks)",
                               "2" = "Good Tier (20+)",
                               "3" = "Solid Tier (10+)",
                               "4" = "#goacc Tier (Less than 10)"),
  )

# save it
ggsave(
  "acc_pos_14.png",
  acc_pos,
  w = 6,
  h = 6,
  dpi = 600,
  type = 'cairo'
)

acc_pos