readme.md

NCAA Women's Basketball Tournament

The data this week comes from FiveThirtyEight. The original raw data is on their [GitHub]. More details about the NCAA Women's Basketball Tournament which expended to 64 teams in 1994. There are some additional data points at that Wikipedia link if you're curious!

Note that for their dataviz, they converted seed to a 100 point scale based off the average wins/seed. Note that FiveThirtyEight used Simple Rating System scores from Sports-Reference, but I've simplified it into simply the average per seed.

A quick table of this as seen below:

To measure this, we awarded “seed points” in proportion to a given seed number’s expected wins in the tournament, calibrated to a 100-point scale where the No. 1 seed gets 100 points, No. 2 gets 70 points, and so forth.

This aligns to (based off the averages):

Seed	Points
1st	100
2nd	72.7
3rd	54.5
4th	48.5
5th	33.3
6th	33.3
7th	27.3
8th	21.2
9th	18.2
10th	18.2
11th	18.2
12th	15.2
13th	9.09
14th	6.06
15th	3.03
16th	0

Their modeled fit:

You could see the quick plot of these points, but again note that this will vary a bit from the FiveThirtyEight table as they included SRS score in their equation.

tibble(
  seed = c(1:16),
  exp_wins = c(3.3, 2.4, 1.8, 1.6, 1.1, 1.1, 0.9, 0.7, 0.6, 0.6, 0.6, 0.5, 0.3, 0.2, 0.1, 0)
  
) %>% 
  mutate(
    points = exp_wins/3.3 * 100
  ) %>% 
  ggplot(aes(x = seed, y = points)) +
  geom_point() +
  geom_smooth() +
  geom_hline(yintercept = 0, color = "black") +
  scale_y_continuous(breaks = seq(0, 100, by = 20)) +
  coord_cartesian(ylim = c(0, 100)) +
  scale_x_continuous(breaks = seq(1, 16, by = 3)) +
  theme_minimal() +
  labs(
    x = "Tournament Seed", y = "Seed Points",
    title = "How much is that seed worth?"
  )

Thus, to get the points for each team/tournament season you can multiply the teams initial seed (1 - 16) by the assigned points as defined in the above table of 100 - 0.

Get the data here

# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2020-10-06')
tuesdata <- tidytuesdayR::tt_load(2020, week = 41)

tournament <- tuesdata$tournament

# Or read in the data manually

tournament <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-10-06/tournament.csv')

Data Dictionary

Seed Point Value as defined in a separate FiveThirtyEight Article

Seed	Points
1st	100
2nd	72.7
3rd	54.5
4th	48.5
5th	33.3
6th	33.3
7th	27.3
8th	21.2
9th	18.2
10th	18.2
11th	18.2
12th	15.2
13th	9.09
14th	6.06
15th	3.03
16th	0

tournament.csv

To get the points for each team/tournament season you can multiply the teams initial seed (1 - 16) by the assigned points as defined in the above table of 100 - 0.

variable	class	description
year	double	Tournament year
school	character	School name
seed	double	Seed rank
conference	character	Conference name
conf_w	double	Conference wins
conf_l	double	Conference losses
conf_percent	double	Conference win/loss percent
conf_place	character	Conference placement (ie, 1st, 2nd, etc)
reg_w	double	Regular season wins
reg_l	double	Regular season losses
reg_percent	double	Regular season win/loss percent
how_qual	character	How qualified - Whether the school qualified with an automatic bid (by winning its conference or conference tournament) or an at-large bid
x1st_game_at_home	character	Whether the school played its first-round tournament games on its home court.
tourney_w	double	Tournament wins
tourney_l	double	Tournament games losses
tourney_finish	character	Tournament finish - The round of the final game for each team. OR=opening-round loss (1983 only); 1st=first-round loss; 2nd=second-round loss; RSF=loss in the Sweet 16; RF=loss in the Elite Eight; NSF=loss in the national semifinals; N2nd=national runner-up; Champ=national champions
full_w	double	Total sum of wins
full_l	double	Total sum of losses
full_percent	double	Total sum win/loss percent

Cleaning Script

library(tidyverse)

raw_df <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/ncaa-womens-basketball-tournament/ncaa-womens-basketball-tournament-history.csv")

clean_tourn <- raw_df %>% 
  janitor::clean_names() %>% 
  mutate(across(c(seed, conf_w:conf_percent, full_percent), parse_number))

clean_tourn %>% 
  write_csv("2020/2020-10-06/tournament.csv")