data-raw/mbb_team_info.R

In JackLich10/gamezoneR: A Scraping Interface for STATS LLC GameZone College Basketball Data

### Make team information dictionary

# Gather ESPN names, ids, colors, logos
espn_ids <- hoopR::espn_mbb_teams() %>%
  janitor::clean_names() %>%
  dplyr::transmute(espn_id = as.numeric(team_id),
                   espn_name = team,
                   espn_pbp = nickname,
                   espn = short_name,
                   dplyr::across(c(color, alternate_color),
                                 ~ ifelse(. == "NA", NA, paste0("#", .))),
                   team_logo_espn = logo,
                   alt_team_logo_espn = logo_dark)
# tidyr::pivot_longer(cols = c(espn_name, espn_pbp, espn),
#                     names_to = "name_type",
#                     values_to = "team_name") %>%
# dplyr::distinct(espn_id, team_name, .keep_all = TRUE) %>%
# dplyr::select(team_name, dplyr::everything(), -name_type)

sched <- hoopR::load_mbb_schedule(season = 2018:2023)

team_info <- bind_rows(
  distinct(sched, espn_id = home_id, espn = home_location, espn_pbp = home_short_display_name, color = home_color, alt_color = home_alternate_color, logo = home_logo),
  distinct(sched, espn_id = away_id, espn = away_location, espn_pbp = away_short_display_name, color = away_color, alt_color = away_alternate_color, logo = away_logo)
) %>%
  collapse::fsubset(espn_id > 0L) %>%
  distinct(espn_id, .keep_all = TRUE) %>%
  mutate(espn_id = as.integer(espn_id),
         team_logo_espn = logo,
         dplyr::across(c(color, alt_color), ~ ifelse(. == "NA", NA, paste0("#", .))))

espn_to_gamezone <- readr::read_csv("~/Desktop/RStudio/college_basketball/mens/data/espn_to_gamezone.csv")

espn_to_gamezone <- dplyr::inner_join(team_info, espn_to_gamezone, by = "espn_id")

# Dictionary of names from many sites
name_dictionary <- ncaahoopR::dict %>%
  janitor::clean_names() %>%
  cbbdbR::join_cbb_team_info(join_name = "ncaa") %>%
  dplyr::rename(conference = conference.x) %>%
  dplyr::select(-c(game_zone_id, espn, espn_pbp, conference.y, team_name, organization,
                   dplyr::contains("team_logo_espn"), dplyr::contains("color_espn"))) %>%
  dplyr::mutate(espn_id = as.integer(espn_id)) %>%
  dplyr::full_join(espn_to_gamezone, by = c("espn_id")) %>%
  subset(!is.na(gamezone_id)) %>%
  dplyr::left_join(gamezoneR::mbb_team_info[,c("game_zone", "game_zone_id")], by = c("gamezone_id" = "game_zone_id")) %>%
  mutate(gamezone = coalesce(game_zone, team_name)) %>%
  select(-team_name, -game_zone, -logo) %>%
  distinct(espn_id, .keep_all = TRUE)

espn_ids %>%
  filter(stringr::str_detect(espn_name, "Centenary"))

# Join together
dictionary <- name_dictionary %>%
  dplyr::mutate(
    gamezone_id = dplyr::case_when(
      espn == "St. Thomas - Minnesota" ~ 1730,
      TRUE ~ gamezone_id
    ),
    espn_id = dplyr::case_when(
      espn == "Bethune-Cookman" ~ 2065,
      espn == "Birmingham-Southern" ~ 3,
      espn == "Centenary" ~ 2113,
      espn == "LIU" ~ 112358,
      espn == "St. Thomas - Minnesota" ~ 2900,
      espn == "MD-E Shore" ~ 2379,
      TRUE ~ espn_id),
    espn_pbp = dplyr::case_when(
      espn_pbp == "CS Fullerton" ~ "CSU Fullerton",
      espn_pbp == "Mcneese St" ~ "McNeese",
      espn_pbp == "Ul Monroe" ~ "UL Monroe",
      espn_pbp == "Mt. St. Mary'S" ~ "Mt. St. Mary's",
      espn_pbp == "Georgia St" ~ "Georgia State",
      espn_pbp == "Arkansas St" ~ "Arkansas State",
      espn_pbp == "Massachusetts" ~ "UMass",
      espn_pbp == "UT San Antonio" ~ "UTSA",
      espn_pbp == "Arkansas-Little Rock" ~ "Little Rock",
      espn_pbp == "Connecticut" ~ "UConn",
      espn_pbp == "Presbyterian College" ~ "Presbyterian",
      TRUE ~ espn_pbp))

%>%
  dplyr::left_join(espn_ids %>%
                     dplyr::select(-c(espn_name, espn_pbp, espn)),
                   by = c("espn_id")) %>%
  dplyr::left_join(gamezone_ids,
                   by = c("gamezone_id"))

# Most common names across sources
most_common_names <- dictionary %>%
  dplyr::filter(!is.na(espn_id)) %>%
  tidyr::pivot_longer(cols = c(ncaa:sref_name, gamezone),
                      names_to = "name_type",
                      values_to = "team_name",
                      values_drop_na = TRUE) %>%
  dplyr::group_by(espn_id, gamezone_id) %>%
  dplyr::summarise(team_name = collapse::fmode(team_name, na.rm = TRUE),
                   .groups = "drop")

# Make final dictionary
mbb_team_info <- dictionary %>%
  dplyr::left_join(most_common_names, by = c("espn_id", "gamezone_id")) %>%
  dplyr::select(espn_id, game_zone_id = gamezone_id, team_name, game_zone = gamezone, espn, espn_pbp,
                ncaa:sref_name, conference,
                color_espn = color, alt_color_espn = alt_color,
                team_logo_espn,
                dplyr::everything()) %>%
  dplyr::arrange(game_zone_id)

mbb_team_info %>% colnames()

# Use the data
usethis::use_data(mbb_team_info, overwrite = TRUE)