Brackets
Brackets
touRnamentofchampions is a collection of data sets detailing events across all seasons of Tournament of Champions. It includes Chef information, randomizer information, challenge results, and judge names.
Not yet on CRAN. So please use: devtools::install.github(“celevitz/touRnamentofchampions”). If it’s not appearing to be updated, restart your R sessions, install it again, and call it into your library.
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Data were collected manually while watching each season of Tournament of Champions.
Huge thanks to https://github.com/doehm for all his support!
Across datasets, key joining variables include:
chefseasoncoastregionroundepisodechallengeSee the sections below for additional information on datasets.
Seeds
The unique identifiers of this dataset are chef-season.
chef: Chef name (full name)season: Season numberseed: Seed within their section of the bracket: values of 1 through 8. Chefs that played in the qualifiers but didn’t make the final bracket will have seeds of 8.2, 8.3, or 8.4.coast: Are they East or West Coast?region: The region depends on how many chefs start the competition. If there are 16 chefs, then the region is left blank. If there are 32 chefs, then the regions are A or B.seeds
#> # A tibble: 146 × 5
#> chef season seed coast region
#> <chr> <dbl> <dbl> <chr> <chr>
#> 1 Alex Guarnaschelli 1 1 East <NA>
#> 2 Marc Murphy 1 2 East <NA>
#> 3 Rocco DiSpirito 1 3 East <NA>
#> 4 Amanda Freitag 1 4 East <NA>
#> 5 Elizabeth Falkner 1 5 East <NA>
#> 6 Maneet Chauhan 1 6 East <NA>
#> 7 Christian Petroni 1 7 East <NA>
#> 8 Darnell Ferguson 1 8 East <NA>
#> 9 Antonia Lofaso 1 1 West <NA>
#> 10 Michael Voltaggio 1 2 West <NA>
#> # ℹ 136 more rows Chefs
The unique identifier of this dataset is chef.
chef: Chef name (full name)nickname: Guy Fieri’s nickname for the chefhandedness: Whether the chef is right-handed, left-handed, or ambidextrousgender: male, female, non-binarychefs
#> # A tibble: 71 × 4
#> chef nickname handedness gender
#> <chr> <chr> <chr> <chr>
#> 1 Aaron May <NA> <NA> male
#> 2 Aarthi Sampath <NA> <NA> female
#> 3 Adam Sobel Mr. Delicious <NA> male
#> 4 Adriana Urbina <NA> <NA> female
#> 5 Alex Guarnaschelli N/A Right-handed female
#> 6 Amanda Freitag Chef AF Right-handed female
#> 7 Antonia Lofaso Warrior Princess Right-handed female
#> 8 Beau MacMillan Beau Mac Right-handed male
#> 9 Bobby Marcotte <NA> <NA> male
#> 10 Brian Malarkey Shenanigans Left-handed male
#> # ℹ 61 more rows Randomizer
The unique identifiers of this dataset are season-episode-round-challenge. The reason that episode is a unique identifier is because in Season 2, Jet and Antonia tied in all scores and so had a rematch in the Quarter-finals (episodes 6 and 7).
season: Season numberepisode: Episode numberround: Stage of the tournament: Qualifier semi-final, Qualifier final, Round of 32, Round of 16, Quarterfinals, Semifinals, Finalchallenge: Variable to help distinguish rounds within the same Coast & Roundcoast: Are they East or West Coast?region: The region depends on how many chefs start the competition. If there are 16 chefs, then the region is left blank. If there are 32 chefs, then the regions are A or B.randomizer1: First wheel of randomizerrandomizer2: Second wheel of randomizerrandomizer3: Third wheel of randomizerrandomizer4: Fourth wheel of randomizertime: Length of challenge. Unit is minutesrandomizer5: Fifth wheel of randomizerrandomizer
#> # A tibble: 138 × 12
#> season episode round challenge coast region randomizer1 randomizer2
#> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 1 Round of 16 Alex/Darne… East <NA> Pork tende… Peas
#> 2 1 1 Round of 16 Antonia/Ma… West <NA> Cod Avocado
#> 3 1 1 Round of 16 Eric/Jet West <NA> Top sirloin Mushrooms
#> 4 1 2 Round of 16 Amanda/Eli… East <NA> Pork blade… Squash
#> 5 1 2 Round of 16 Beau/Richa… West <NA> Ground lamb Broccoli
#> 6 1 2 Round of 16 Christian/… East <NA> Shrimp Carrots
#> 7 1 3 Quarter-final Antonia/Be… West <NA> Quail Bok choy
#> 8 1 3 Round of 16 Brooke/Mic… West <NA> Chicken br… Radish
#> 9 1 3 Round of 16 Maneet/Roc… East <NA> Chicken th… Kale
#> 10 1 4 Quarter-final Amanda/Dar… East <NA> Rack of la… Nopales
#> # ℹ 128 more rows
#> # ℹ 4 more variables: randomizer3 <chr>, randomizer4 <chr>, time <dbl>,
#> # randomizer5 <chr> Randomizer (long form)
A dataset containing information about each challenge: protein, vegetables, equipment, style, time. However, it’s in “long form” so each challenge shows up multiple times. It categorizes the randomizer ingredients into categories and subcategories. If a value from one randomizer wheel better fits into another category, it has been reclassified (e.g., “Habanero” from the “wildcard” randomizer has been reclassified as “produce.”)
The unique identifiers of this dataset are season-episode-round-challenge-randomizer.
season: Season numberepisode: Episode numberround: Stage of the tournament: Qualifier semi-final, Qualifier final, Round of 32, Round of 16, Quarterfinals, Semifinals, Finalchallenge: Variable to help distinguish challenges within the same Coast & Roundcoast: East or Westregion: The region depends on how many chefs start the competition. If there are 16 chefs, then the region is left blank. If there are 32 chefs, then the regions are A or B.time: Length of challenge. Unit is minutesrandomizer: What wheel was spun (1, 2, 3, 4, or 5)value: What was the value/item on the randomizer wheel?category: Categorical variable: protein,produce,equipment,style,wildcardsubcategory: Subcategories for protein (Beef, Fish, Game, Other, Pork, Poultry, Shellfish) and style (Region/country, Style, Theme)randomizerlongform
#> # A tibble: 615 × 11
#> season episode round challenge coast region time randomizer value category
#> <dbl> <dbl> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 1 1 Round … Alex/Dar… East <NA> 35 randomize… Pork… protein
#> 2 1 1 Round … Alex/Dar… East <NA> 35 randomize… Peas produce
#> 3 1 1 Round … Alex/Dar… East <NA> 35 randomize… Waff… equipme…
#> 4 1 1 Round … Alex/Dar… East <NA> 35 randomize… Glaz… style
#> 5 1 1 Round … Antonia/… West <NA> 30 randomize… Cod protein
#> 6 1 1 Round … Antonia/… West <NA> 30 randomize… Avoc… produce
#> 7 1 1 Round … Antonia/… West <NA> 30 randomize… Micr… equipme…
#> 8 1 1 Round … Antonia/… West <NA> 30 randomize… Sweet style
#> 9 1 1 Round … Eric/Jet West <NA> 35 randomize… Top … protein
#> 10 1 1 Round … Eric/Jet West <NA> 35 randomize… Mush… produce
#> # ℹ 605 more rows
#> # ℹ 1 more variable: subcategory <chr> Results
The unique identifiers of this dataset are season-episode-round-challenge-chef.
season: Season numberepisode: Episode numberround: Stage of the tournament: Qualifier semi-final, Qualifier final, Round of 32, Round of 16, Quarterfinals, Semifinals, Finalchallenge: Variable to help distinguish rounds within the same Coast & Roundcoast: Are they East or West Coast?region: The region depends on how many chefs start the competition. If there are 16 chefs, then the region is left blank. If there are 32 chefs, then the regions are A or B.chef: Name of chefcommentator: Who presented their food to the judges: Simon Majumdar or Justin Warner?order: When did their food get presented to the judges: Presented first or Presented secondscore_taste: Score that chef received for the taste of their dish: values of 0- 50score_randomizer: Score that chef received for how well they used the Randomizer: values of 0- 30score_presentation: Score that chef received for the presentation of their dish: values of 0- 20total: Total score that chef received: between 0 and 100winner: Winner, loser, or tiex: Numeric X value to help when creating the brackety: Numeric Y value to help when creating the bracketresults
#> # A tibble: 284 × 16
#> season episode round challenge coast region chef commentator order
#> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 1 Round of 16 Alex/Darnell East <NA> Darn… Justin War… Pres…
#> 2 1 1 Round of 16 Alex/Darnell East <NA> Alex… Simon Maju… Pres…
#> 3 1 1 Round of 16 Antonia/Marc… West <NA> Marc… Simon Maju… Pres…
#> 4 1 1 Round of 16 Antonia/Marc… West <NA> Anto… Justin War… Pres…
#> 5 1 1 Round of 16 Eric/Jet West <NA> Jet … Justin War… Pres…
#> 6 1 1 Round of 16 Eric/Jet West <NA> Eric… Simon Maju… Pres…
#> 7 1 2 Round of 16 Amanda/Eliza… East <NA> Eliz… Justin War… Pres…
#> 8 1 2 Round of 16 Amanda/Eliza… East <NA> Aman… Simon Maju… Pres…
#> 9 1 2 Round of 16 Beau/Richard West <NA> Rich… Justin War… Pres…
#> 10 1 2 Round of 16 Beau/Richard West <NA> Beau… Simon Maju… Pres…
#> # ℹ 274 more rows
#> # ℹ 7 more variables: score_taste <dbl>, score_randomizer <dbl>,
#> # score_presentation <dbl>, total <dbl>, winner <chr>, x <dbl>, y <dbl> Judges
The unique identifier is season-episode-round, because occasionally a judge will only judge for one round within an episode.
season: Season numberepisode: Episode numberjudge: Name of guest judgegender: Gender of guest judgeround: Stage of the tournament: Qualifier semi-final, Qualifier final, Round of 32, Round of 16, Quarterfinals, Semifinals, Finaljudges
#> # A tibble: 165 × 5
#> season episode judge gender round
#> <dbl> <dbl> <chr> <chr> <chr>
#> 1 1 1 Curtis Stone male Round of 16
#> 2 1 1 Marcus Samuelsson male Round of 16
#> 3 1 1 Nancy Silverton female Round of 16
#> 4 1 2 Marcus Samuelsson male Round of 16
#> 5 1 2 Ming Tsai male Round of 16
#> 6 1 2 Nancy Silverton female Round of 16
#> 7 1 3 Marcus Samuelsson male Round of 16
#> 8 1 3 Marcus Samuelsson male Quarter-final
#> 9 1 3 Ming Tsai male Round of 16
#> 10 1 3 Ming Tsai male Quarter-final
#> # ℹ 155 more rowsSee the sections below for examples of how to use the data
Brackets
Gender distribution by season: Seasons 1 through 5
seeds %>% left_join(chefs) %>%
group_by(season,gender) %>%
summarise(n=n()) %>%
pivot_wider(names_from=gender,values_from=n)
#> Joining with `by = join_by(chef)`
#> `summarise()` has grouped output by 'season'. You can override using the
#> `.groups` argument.
#> # A tibble: 5 × 3
#> # Groups: season [5]
#> season female male
#> <dbl> <int> <int>
#> 1 1 6 10
#> 2 2 8 14
#> 3 3 13 19
#> 4 4 15 17
#> 5 5 20 24 Subcategories of proteins used in battles
randomizerlongform %>%
group_by(category,subcategory) %>%
filter(category %in% c("protein")) %>%
summarise(number_of_battles=n())
#> `summarise()` has grouped output by 'category'. You can override using the
#> `.groups` argument.
#> # A tibble: 7 × 3
#> # Groups: category [1]
#> category subcategory number_of_battles
#> <chr> <chr> <int>
#> 1 protein Beef 23
#> 2 protein Fish 31
#> 3 protein Game 27
#> 4 protein Other 4
#> 5 protein Pork 25
#> 6 protein Poultry 23
#> 7 protein Shellfish 15