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NCAA Men's March Madness

March is NCAA basketball March Madness! This week's data is NCAA Men's March Madness data from Nishaan Amin's Kaggle dataset and analysis Bracketology: predicting March Madness.

March Madness is the NCAA Division I basketball tournament for women and men. It's a single-elimination tournament of 68 teams that compete in six rounds for the national championship. (The "March Madness" branding and logo was only extended to women in 2022.)

Each round of the tournament has a name:

  • First round: Round of 64
  • Second round: Round of 32
  • Third round: Sweet 16
  • Fourth round: Elite 8
  • Fifth round: Final 4
  • Sixth round: Finals

The data is from 2008 - 2024 for the men's teams. The year 2020 is not included because the tournament was canceled due to Covid. The first column of almost every dataset displays the year the data is from.

Nishaan Amin's dataset contains many different sets of data. For TidyTuesday we're using past team results and the predictions the public has for this year's tournament. How have teams done in past years? What teams are people predicting will do well this year? How does past performance correlate with expectations for this year?

The Data

# Option 1: tidytuesdayR package 
## install.packages("tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2024-03-26')
## OR
tuesdata <- tidytuesdayR::tt_load(2024, week = 13)

team_results <- tuesdata$`team-results`
public_picks <- tuesdata$`public-picks`


# Option 2: Read directly from GitHub

team_results <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-03-26/team-results.csv')
public_picks <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-03-26/public-picks.csv')

How to Participate

  • Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
  • Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
  • Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.

Data Dictionary

team-results.csv

variable class description
TEAMID integer Unique identifier for the team.
TEAM character Division I college basketball team name.
PAKE double Performance against Komputer Expectations.
PAKERANK integer Rank of PAKE from all teams.
PASE double Performance Against Seed Expectations.
PASERANK integer Rank of PASE from all teams.
GAMES integer The total amount of tournament games the team has played.
W integer The total amount of tournament game wins.
L integer The total amount of tournament game losses.
WINPERCENT double The winning percent of the team.
R64 integer Amount of times the team made it to the Round of 64.
R32 integer Amount of times the team made it to the Round of 32.
S16 integer Amount of times the team made it to the Sweet 16.
E8 integer Amount of times the team made it to the Elite 8.
F4 integer Amount of times the team made it to the Final 4.
F2 integer Amount of times the team made it to the Finals.
CHAMP integer Amount of times the team was a Champion.
TOP2 integer Amount of times the team was awarded a 1 or 2 seed.
F4PERCENT character Likelihood of a team getting to at least 1 Final Four.
CHAMPPERCENT character Likelihood of a team winning at least 1 Championship (per efficiency rating).

public-picks.csv

variable class description
YEAR integer Ending year of the team's season
TEAMNO integer Unique identifier for the team and the year they played in
TEAM character Division I college basketball team name.
R64 character The percent of people who picked the team to win the game in the Round of 64.
R32 character The percent of people who picked the team to win the game in the Round of 32.
S16 character The percent of people who picked the team to win the game in the Sweet 16.
E8 character The percent of people who picked the team to win the game in the Elite 8.
F4 character The percent of people who picked the team to win the game in the Final 4.
FINALS character The percent of people who picked the team to win the game in the Finals.

Cleaning Script

No cleaning. The files from March Madness Data are Team Results.csv and Public Picks.csv.