touRnamentofchampions

1. Introduction to touRnamentofchampions

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.

2. Installation

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.

devtools::install_github("celevitz/touRnamentofchampions")
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3. News

Issues to work on:

Complete handedness and nicknames
Potentially add information on dish description (long-term goal)

4. References & Acknowledgements

Data were collected manually while watching each season of Tournament of Champions.

Huge thanks to https://github.com/doehm for all his support!

5. Datasets

Across datasets, key joining variables include:

chef
season
coast
region
round
episode
challenge

See the sections below for additional information on datasets.

Seeds

Seeds

The unique identifiers of this dataset are chef-season.

chef: Chef name (full name)
season: Season number
seed: 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

Chefs

The unique identifier of this dataset is chef.

chef: Chef name (full name)
nickname: Guy Fieri’s nickname for the chef
handedness: Whether the chef is right-handed, left-handed, or ambidextrous
gender: male, female, non-binary

chefs 
#> # 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

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 number
episode: Episode number
round: Stage of the tournament: Qualifier semi-final, Qualifier final, Round of 32, Round of 16, Quarterfinals, Semifinals, Final
challenge: Variable to help distinguish rounds within the same Coast & Round
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.
randomizer1: First wheel of randomizer
randomizer2: Second wheel of randomizer
randomizer3: Third wheel of randomizer
randomizer4: Fourth wheel of randomizer
time: Length of challenge. Unit is minutes
randomizer5: Fifth wheel of randomizer

randomizer 
#> # 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)

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 number
episode: Episode number
round: Stage of the tournament: Qualifier semi-final, Qualifier final, Round of 32, Round of 16, Quarterfinals, Semifinals, Final
challenge: Variable to help distinguish challenges within the same Coast & Round
coast: East or West
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.
time: Length of challenge. Unit is minutes
randomizer: 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,wildcard
subcategory: 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

Results

The unique identifiers of this dataset are season-episode-round-challenge-chef.

season: Season number
episode: Episode number
round: Stage of the tournament: Qualifier semi-final, Qualifier final, Round of 32, Round of 16, Quarterfinals, Semifinals, Final
challenge: Variable to help distinguish rounds within the same Coast & Round
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.
chef: Name of chef
commentator: 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 second
score_taste: Score that chef received for the taste of their dish: values of 0- 50
score_randomizer: Score that chef received for how well they used the Randomizer: values of 0- 30
score_presentation: Score that chef received for the presentation of their dish: values of 0- 20
total: Total score that chef received: between 0 and 100
winner: Winner, loser, or tie
x: Numeric X value to help when creating the bracket
y: Numeric Y value to help when creating the bracket

results 
#> # 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

Judges

The unique identifier is season-episode-round, because occasionally a judge will only judge for one round within an episode.

season: Season number
episode: Episode number
judge: Name of guest judge
gender: Gender of guest judge
round: Stage of the tournament: Qualifier semi-final, Qualifier final, Round of 32, Round of 16, Quarterfinals, Semifinals, Final

judges
#> # 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 rows

6. Examples

See the sections below for examples of how to use the data

Brackets

Brackets

Gender distribution by season: Seasons 1 through 5

Gender distribution by season: Season 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

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