013: ACC Refs

Load data and manipulate

# load data scraped from kp to find official ids
# kp_data <- read_csv("data.csv")

# find ids
# ids <- kp_data$ref_id

# loop function 
# add_fun <- function(id) {
#   kp_referee(referee = id, year = 2023)
# }

# save refs - this is terribly inefficient 
# refs <- lapply(ids, add_fun)

# make dataframe and filter down to acc
# acc_refs <- as.data.frame(do.call(rbind, refs)) %>%
#           filter(conference == "ACC")

acc_refs <- read_csv("acc_refs_jan_26.csv")

full <- acc_refs %>%
            add_row(referee_name ="Jenna Reneau", thrill_score = 12.5) %>%
            add_row(referee_name ="Courtney Smith", thrill_score = 50.4) %>%
            group_by(referee_name) %>%
            summarize(avg_t = mean(thrill_score),
            count = n()) %>%
            arrange(count)

# get full rankings
ranks <- kp_officials(year = most_recent_mbb_season()) %>%
         rename(referee_name = official_name)

# combine with previous table 
acc_rks <- merge(ranks, full, by = "referee_name") %>%
           select(referee_name, rk, ref_rating, gms, avg_t, count) %>%
           arrange(rk)

Set the themes for the plot

# theme
  theme_me <- function () {
    theme_minimal(base_size = 10, base_family = "RobotoCondensed-Regular") %+replace%
      theme (
        plot.title = element_text(
          hjust = 0.5,
          size = 24,
          face = "bold"
        ),
        plot.subtitle = element_text(
          hjust = 0.5,
          size = 10,
          lineheight = 0.25,
          vjust = -0.5
        ),
        plot.caption = element_text(
          hjust = 1,
          size = 6,
          lineheight = 0.35,
          margin = margin(t = 20)
        ),
        panel.grid.minor = element_blank(),
        plot.background = element_rect(fill = "floral white", color = "floral white")
      )
  }

Make the bar plot

ref_plot <- full %>%
  mutate(referee_name = factor(referee_name, levels = .$referee_name)) %>%
  mutate(color = case_when(count > 9 ~ "#e9a3c9",
                           count > 4 ~ "#a1d76a",
                           ## all others should be gray
                           TRUE ~ "#acacac")) %>%
  ggplot(aes(count, referee_name, fill = color)) +
  geom_col() +
  scale_x_continuous(breaks = seq(0, 14, 1), limits = c(0, 14)) +
  labs(
    x = "Games",
    y = "",
    title = "<span style='color:#333333;'>Who are the *referees* on the court for ACC games? </span>",
    caption = "dadgumboxscores | January 26, 2023 | data via kenpom"
  ) +
  theme_me() +
  scale_fill_identity(guide = "none") +
  theme(plot.title = element_textbox_simple()) +
  annotate(
    "text",
    x = 12.8,
    y = 35,
    label = "10 or  \n more games",
    family = "Chalkboard Bold",
    size = 4,
    fontface = "bold",
    color = "#e9a3c9",
  ) +
  annotate(
    "text",
    x = 9,
    y = 29,
    label = "5 or  \nmore games",
    family = "Chalkboard Bold",
    size = 4,
    fontface = "bold",
    color = "#a1d76a",
  ) +
  annotate(
    "text",
    x = 5,
    y = 13,
    label = "Fewer than \n5 games",
    family = "Chalkboard Bold",
    size = 4,
    color = "#acacac",
    fontface = "bold",
  )

# save it
ggsave(
  "ref.png",
  ref_plot,
  w = 6.5,
  h = 8,
  dpi = 300,
  type = 'cairo'
)


ref_plot

Make the ref tables

table <- acc_rks %>%
  gt() %>%
  cols_label(
    # rename columns
    referee_name = "Ref",
    rk = "Rank",
    ref_rating = "Rating",
    gms = "G",
    avg_t = "Rating",
    count = "G"
  ) %>%
  tab_spanner(label = "ACC only",
              columns = c(avg_t, count)) %>%
  tab_spanner(label = "All Games",
              columns = c(rk, ref_rating)) %>%
  fmt_number(columns = avg_t,
             decimals = 2,
             use_seps = FALSE) %>%
  gt_theme_dot_matrix() %>%
  tab_header(title = md("Ref ratings from kenpom with #goacc refs")) %>%
  tab_source_note(source_note = "@dadgumboxscores | January 19, 2023 | data via kenpom") %>%
  tab_options (
    source_notes.font.size = px(10),
    row.striping.background_color = '#ffffed',
    table.font.size = px(10),
    column_labels.text_transform = 'capitalize'
  )

gtsave_extra(table,
             "ref_table.png",
             vwidth = 650,
             vheight = 650)

table

Ref ratings from kenpom with #goacc refs
Ref	All Games		G	ACC only
	Rank	Rating		Rating	G
Keith Kimble	1	54.79	62	61.67	3
Doug Sirmons	2	52.58	62	42.68	6
Kipp Kissinger	4	49.57	61	42.66	5
Roger Ayers	5	49.57	60	47.52	13
Paul Szelc	6	48.36	48	50.30	1
Bert Smith	7	47.59	58	47.89	14
Ron Groover	8	46.80	60	47.27	12
Terry Oglesby	10	46.20	54	66.70	1
Lee Cassell	12	44.94	54	51.31	10
Mike Roberts	16	42.61	47	58.93	3
John Gaffney	18	41.55	57	49.89	7
Doug Shows	19	41.03	47	53.04	5
Jeffrey Anderson	20	40.86	47	42.83	7
Brian O'Connell	22	40.46	53	55.00	7
Brent Hampton	26	38.95	47	37.02	5
Pat Driscoll	27	38.26	49	41.92	6
Tony Chiazza	28	37.74	51	63.60	1
Clarence Armstrong	31	36.11	53	43.09	12
Jeff Clark	32	35.66	49	54.43	3
Earl Walton	37	33.89	51	39.70	1
Matt Potter	38	33.67	55	41.96	7
Jeb Hartness	42	33.09	50	35.20	3
Lamar Simpson	44	31.89	49	30.63	3
Jamie Luckie	45	31.88	42	47.95	6
Tommy Morrissey	46	31.80	46	51.34	12
Bill Covington Jr.	48	30.94	43	47.77	10
Greg Evans	58	28.73	43	62.40	1
A.J. Desai	61	27.99	46	49.13	6
Ted Valentine	66	27.52	43	47.86	14
Tim Clougherty	71	26.59	40	43.77	7
Tony Henderson	74	26.41	37	52.40	7
Chance Moore	80	24.64	40	31.90	1
Jerry Heater	91	22.06	37	39.05	2
Justin Porterfield	94	21.26	34	41.38	4
Mark Schnur	97	20.87	34	44.90	2
Jeff Pon	103	19.31	28	49.65	2
Trey Styons	118	17.81	28	43.50	1
Raymond Styons	122	16.72	30	38.08	4
Jemel Spearman	129	15.35	28	39.60	3
@dadgumboxscores | January 19, 2023 | data via kenpom