024: NET changes

Load data

 # function to grab NET data
net_ranks <- function(url) {
  
  net_page <- read_html(url)
  
  net_rk <- net_page %>%
    html_nodes("table") %>%
    .[1] %>%
    html_table(fill = TRUE)
  
  net_table <- as.data.frame(net_rk)  

  net_table <- net_table %>%   
    mutate(prev_day_result = net_table[,3]) %>% 
    mutate(date = gsub("[^0-9.-]", "", colnames(net_table[3]))) %>% 
    mutate(date = str_sub(date, end = -2)) %>%
    mutate(record = str_split(WL, "-", simplify = T),
           wins = record[,1],
           losses = record[,2],
           conf_record = str_split(Conf..Record, "-", simplify = T),
           wins_conf = conf_record[,1],
           losses_conf = conf_record[,2],
           non_conf_record = str_split(Non.Conference.Record, "-", simplify = T),
           wins_non_conf = non_conf_record[,1],
           losses_non_conf = non_conf_record[,2],
           first_q = str_split(Quadrant.1, "-", simplify = T),
           q1_win = first_q[,1],
           q1_loss = first_q[,2],
           second_q = str_split(Quadrant.2, "-", simplify = T),
           q2_win = second_q[,1],
           q2_loss = second_q[,2],
           third_q = str_split(Quadrant.3, "-", simplify = T),
           q3_win = third_q[,1],
           q3_loss = third_q[,2],
           fourth_q = str_split(Quadrant.4, "-", simplify = T),
           q4_win = fourth_q[,1],
           q4_loss = fourth_q[,2],
    ) %>% 
    rename(team = Team, conf = Conference, net = NET, prev_net = Prev.NET, 
           sos = NET.SOS, non_conf_sos = NET.NonConf.SOS) %>% 
    select(team, conf, net, prev_net, prev_day_result, sos, non_conf_sos, wins, 
           losses, wins_non_conf, losses_non_conf, q1_win, q1_loss, 
           q2_win, q2_loss, q3_win, q3_loss, q4_win, q4_loss, date) %>% 
    mutate_at(vars(-team, -conf, -date, -prev_day_resu), as.numeric)

}

# function to loop through and grab all data
# net_nums <- function(id) {
# net_ranks(url = id)
# }

# ids <- read_csv("ids.csv) 
# ids <- ids$urls 

# function to get ACC games 
# all_net_rks <- lapply(ids, net_nums)

# load csv of scraped ids

# full_net <- as.data.frame(do.call(rbind, all_net_rks)) 

# load just the acc teams 
lun_acc <- read_csv("acc_teams.csv")

Create GT Table

table <- lun_acc %>%
  select(team, opponent, prev_day_result, net, prev_net, diff) %>%
  gt() %>%
  cols_label(
    # rename columns
    team = "Team",
    opponent = "Opponent",
    prev_day_result = "Result",
    net = "After",
    prev_net = "Before",
    diff = "+/-"
  ) %>%
  data_color(columns = c(diff),
             colors = scales::col_numeric(
               c(
                 "#ca5800",
                 "#cfe8f3",
                 "#73bfe2",
                 "#0a4c6a",
                 "#fff2cf",
                 "#fdd870",
                 "#fdbf11"
               ),
               domain = NULL
             )) %>%
  tab_spanner(label = "NET Change",
              columns = c(net, prev_net, diff)) %>%
  gt_theme_538() %>%
  tab_header(title = "ACC teams that went down in NET ranking after a win") %>%
  tab_source_note(source_note = "@dadgumboxscores | March 30, 2023 | data via NCAA") %>%
  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,
             "lun_acc.png",
             vwidth = 650,
             vheight = 650)

table

ACC teams that went down in NET ranking after a win
Team	Opponent	Result	NET Change
			After	Before	+/-
NC State	Louisville	W, 76-64	56	42	-14
Wake Forest	Georgia Tech	W, 71-70	73	67	-6
Syracuse	Louisville	W, 70-69	143	137	-6
NC State	Notre Dame	W, 85-82	41	36	-5
Clemson	Louisville	W, 83-70	59	54	-5
Wake Forest	Notre Dame	W, 66-58	84	80	-4
NC State	Georgia Tech	W, 72-64	42	38	-4
North Carolina	Boston College	W, 72-64	37	33	-4
Pittsburgh	Georgia Tech	W, 76-68	53	50	-3
Duke	Louisville	W, 79-62	29	26	-3
Virginia	Louisville	W, 61-58	16	13	-3
Clemson	Florida State	W, 82-81	59	56	-3
Duke	Miami	W, 68-66	29	26	-3
Virginia	Syracuse	W, 73-66	21	18	-3
Virginia Tech	Notre Dame	W, 67-64	70	68	-2
Miami	Louisville	W, 93-85	32	30	-2
Duke	North Carolina	W, 63-57	25	23	-2
Duke	North Carolina	W, 62-57	24	23	-1
Virginia	Clemson	W, 64-57	29	28	-1
Miami	Wake Forest	W, 96-87	31	30	-1
Duke	Notre Dame	W, 68-64	33	32	-1
Virginia Tech	Notre Dame	W, 93-87	56	55	-1
Florida St.	Louisville	W, 81-78	209	208	-1
Notre Dame	Louisville	W, 76-62	184	183	-1
@dadgumboxscores | March 30, 2023 | data via NCAA