006: Hoops points distribution

basketball
Published

December 13, 2022

Load data and make data

Code 
# data was manually created from kenpom
season <- tibble::tribble(
  ~Season,~Games,~Type,~Values,
  "2021-22",39,"Threes",32.4,
  "2021-22",39,"Twos",49.2,
  "2021-22",39,"Free Throws",18.4,
  "2022-23",11,"Threes",23.6,
  "2022-23",11,"Twos",51.9,
  "2022-23",11,"Free Throws",24.5
)

# load csv from sportsreference
assists <- read_csv('assists_turnovers.csv')

Manipulate data and make tables

Code 
# season table
season_tb <- season %>%
  group_by(Season) %>%
  summarize(list_data = list(Values)) %>%
  arrange(Season)

join_season <- season %>%
  group_by(Season) %>%
  summarize(Games = Games[1])

final_season <- merge(season_tb, join_season, by = "Season") %>%
  select(Season, Games, list_data)

ff <- final_season %>%
  gt() %>%
  gt_plt_bar_stack(
    column = list_data,
    width = 50,
    labels = c("Threes", "Twos", "Free Throws"),
    palette = c("#d38956", "#56a0d3", "#d356a0"),
    fmt_fn = scales::label_number(accuracy = 0.1)
  ) %>%
  gt_theme_nytimes() %>%
  # set title + caption + subtitle
  tab_header(title = "Points distribution under Hubert Davis",
             subtitle = "Percentage of points scored from three-pointers, two-pointers, and free throws.") %>%
  tab_source_note(source_note = "@dadgumboxscores | December 14, 2022")  %>%
  # fix font of source note
  tab_options (source_notes.font.size = px(10),
               table.font.size = px(12)) 

ff
Points distribution under Hubert Davis
Percentage of points scored from three-pointers, two-pointers, and free throws.
Season Games Threes||Twos||Free Throws
2021-22 39 32.449.218.4
2022-23 11 23.651.924.5
@dadgumboxscores | December 14, 2022
Code 
# assists data
to_a <- assists %>%
  slice(1:11) %>%
  mutate(Opp = if_else(Opp == "College of Charleston", "Charleston", Opp)) %>%
  mutate("AST:TO" = (AST - TOV),
         logo = Opp) %>%
  adorn_totals() %>%
  mutate(logo = if_else(logo == "-", 'North Carolina', logo)) %>%
  select(G, Opp, logo, `W/L`, FG, FGA, AST, TOV, `AST:TO`) %>%
  gt() %>%
  gt_fmt_cfb_logo(columns = "logo") %>%
  cols_label(Opp = "Opponent",
             logo = "",
             G = "",
             FG = "FGM") %>%
  fmt(
    columns = c(`AST:TO`),
    fns = function(x) {
      ifelse(x > 0, paste0("+", x), x)
    }
  ) %>%
  gt_highlight_rows(
    rows = c(2, 4, 5, 10, 11),
    fill = "#d0e4f3",
    bold_target_only = TRUE,
    target_col = c(`AST:TO`),
  ) %>%
  gt_theme_nytimes() %>%
  # set title + caption + subtitle
  tab_header(title = "Made Field Goals and Assist to Turnover Ratio") %>%
  tab_source_note(source_note = "@dadgumboxscores | December 14, 2022")  %>%
  # fix font of source note
  tab_options (source_notes.font.size = px(10),
               table.font.size = px(12))

to_a
Made Field Goals and Assist to Turnover Ratio
Opponent W/L FGM FGA AST TOV AST:TO
1 UNC Wilmington W 22 48 4 8 -4
2 Charleston W 35 58 16 8 +8
3 Gardner-Webb W 23 60 8 10 -2
4 James Madison W 27 61 14 12 +2
5 Portland W 31 58 17 13 +4
6 Iowa State L 22 50 12 14 -2
7 Alabama L (4 OT) 38 91 15 18 -3
8 Indiana L 20 59 5 9 -4
9 Virginia Tech L 24 51 6 10 -4
10 Georgia Tech W 27 57 15 11 +4
11 The Citadel W 32 65 24 12 +12
Total - - 301 658 136 125 +11
@dadgumboxscores | December 14, 2022