74 seasons. 1403 people. 1 package!
survivoR is a collection of data sets detailing events across 74 seasons of Survivor US, Australia, South Africa, New Zealand and UK. It includes castaway information, vote history, immunity and reward challenge winners, jury votes, advantage details and a lot more.
For analysis and updates you can follow me on Bluesky @danoehm.bsky.social
For those that aren’t R users you can get the data on Google Sheets as wel, or download as an xlsx.
You can also access the data in JSON format to feed directly into applications
Install from CRAN (v2.3.5) or Git (v2.3.6).
If Git > CRAN I’d suggest install from Git. We are constantly improving the data sets so the github version is likely to be slightly improved.
install.packages("survivoR")devtools::install_github("doehm/survivoR")
castaway_scoresboot_order data setseason_name has been deprecated from all tables other
than season_summaryThe Survivor Dashboard is the survivoR package’s companion. It holds interactive tables and charts detailing the castaways, challenges, vote history, confessionals, ratings, and more.
Included in the package is a confessional timing app to record the length of confessionals while watching the episode.
To launch the app, first install the package and run,
library(survivoR)
launch_confessional_app()
To try it out online 👉 Confessional timing app
More info here.
There are 19 data sets included in the package:
advantage_movementadvantage_detailsboot_mappingcastaway_detailscastaway_scorescastawayschallenge_resultschallenge_descriptionchallenge_summaryconfessionalsjury_votesseason_summarytribe_colourstribe_mappingepisodesvote_historysurvivor_auctionauction_detailsscreen_timeseason_palettesjourneysSee the sections below for more details on the key data sets.
A table containing summary details of each season of Survivor, including the winner, runner ups and location.
season_summary
#> # A tibble: 74 × 26
#> version version_season season_name season location country tribe_setup n_cast
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <int>
#> 1 US US01 Survivor: … 1 Pulau T… Malays… "Two tribe… 16
#> 2 US US02 Survivor: … 2 Herbert… Austra… "Two tribe… 16
#> 3 US US03 Survivor: … 3 Shaba N… Kenya "Two tribe… 16
#> 4 US US04 Survivor: … 4 Nuku Hi… France "Two tribe… 16
#> 5 US US05 Survivor: … 5 Ko Taru… Thaila… "Two tribe… 16
#> 6 US US06 Survivor: … 6 Rio Neg… Brazil "Two tribe… 16
#> 7 US US07 Survivor: … 7 Pearl I… Panama "Two tribe… 16
#> 8 US US08 Survivor: … 8 Pearl I… Panama "Three tri… 18
#> 9 US US09 Survivor: … 9 Efate, … Vanuatu "Two tribe… 18
#> 10 US US10 Survivor: … 10 Koror, … Palau "A schooly… 20
#> 11 US US11 Survivor: … 11 Laguna … Guatem… "Two tribe… 18
#> 12 US US12 Survivor: … 12 Pearl I… Panama "Four trib… 16
#> 13 US US13 Survivor: … 13 Aitutak… Islands "Four trib… 20
#> 14 US US14 Survivor: … 14 Macuata… Fiji "Two tribe… 19
#> 15 US US15 Survivor: … 15 Zhelin,… China "Two tribe… 16
#> 16 US US16 Survivor: … 16 Koror, … Palau "Two tribe… 20
#> 17 US US17 Survivor: … 17 Wonga-W… Gabon "A schooly… 18
#> 18 US US18 Survivor: … 18 Jalapao… Brazil "Two tribe… 16
#> 19 US US19 Survivor: … 19 Upolu, … Samoa "Two tribe… 20
#> 20 US US20 Survivor: … 20 Upolu, … Samoa "Two tribe… 20
#> 21 US US21 Survivor: … 21 San Jua… Nicara… "Two tribe… 20
#> 22 US US22 Survivor: … 22 San Jua… Nicara… "Two tribe… 18
#> 23 US US23 Survivor: … 23 San Jua… Nicara… "Upolu, Sa… 18
#> 24 US US24 Survivor: … 24 San Jua… Nicara… "Two tribe… 18
#> 25 US US25 Survivor: … 25 Caramoa… Philip… "Three tri… 18
#> 26 US US26 Survivor: … 26 Caramoa… Philip… "Two tribe… 20
#> 27 US US27 Survivor: … 27 Palaui … Philip… "Two tribe… 20
#> 28 US US28 Survivor: … 28 Palaui … Philip… "Three tri… 18
#> 29 US US29 Survivor: … 29 San Jua… Nicara… "Nine pair… 18
#> 30 US US30 Survivor: … 30 San Jua… Nicara… "Three tri… 18
#> # ℹ 44 more rows
#> # ℹ 18 more variables: n_tribes <int>, n_finalists <int>, n_jury <int>,
#> # full_name <chr>, winner_id <chr>, winner <chr>, runner_ups <chr>,
#> # final_vote <chr>, timeslot <chr>, premiered <date>, ended <date>,
#> # filming_started <date>, filming_ended <date>, viewers_reunion <dbl>,
#> # viewers_premiere <dbl>, viewers_finale <dbl>, viewers_mean <dbl>,
#> # rank <dbl>This data set contains season and demographic information about each castaway. It is structured to view their results for each season. Castaways that have played in multiple seasons will feature more than once with the age and location representing that point in time. Castaways that re-entered the game will feature more than once in the same season as they technically have more than one boot order e.g. Natalie Anderson - Winners at War.
Each castaway has a unique castaway_id which links the
individual across all data sets and seasons. It also links to the
following ID’s found on the vote_history,
jury_votes and challenges data sets.
vote_idvoted_out_idfinalist_idcastaways |>
filter(season == 45)
#> Error in eval(expr, envir, enclos): object 'season' not foundA few castaways have changed their name from season to season or have
been referred to by a different name during the season e.g. Amber
Mariano; in season 8 Survivor All-Stars there was Rob C and Rob M. That
information has been retained here in the castaways data
set.
castaway_details contains unique information for each
castaway. It takes the full name from their most current season and
their most verbose short name which is handy for labelling.
It also includes gender, date of birth, occupation, race, ethnicity and other data. If no source was found to determine a castaways race and ethnicity, the data is kept as missing rather than making an assumption.
african_american, asian_american,
latin_american, native_american,
race, ethnicity, and bipoc data
is complete only for the US. bipoc is TRUE
when any of the *_american fields are TRUE.
These fields have been recorded as per the (Survivor wiki)[https://survivor.fandom.com/wiki/Main_Page]. Other
versions have been left blank as the data is not complete and the term
‘people of colour’ is typically only used in the US.
I have deprecated the old field poc in order to be more
inclusive and to make using the race/ethnicity fields simpler.
I have included a collar field is experimental and
derived from a language model. I suggest caution with it’s use as many
occupations may not fit neatly into a classification.
castaway_details
#> # A tibble: 1,160 × 22
#> castaway_id full_name full_name_detailed castaway last_name date_of_birth
#> <chr> <chr> <chr> <chr> <chr> <date>
#> 1 US0001 Sonja Christ… Sonja Christopher Sonja Christop… 1937-01-28
#> 2 US0002 B.B. Andersen B.B. Andersen B.B. Andersen 1936-01-18
#> 3 US0003 Stacey Still… Stacey Stillman Stacey Stillman 1972-08-11
#> 4 US0004 Ramona Gray Ramona Gray Ramona Gray 1971-01-20
#> 5 US0005 Dirk Been Dirk Been Dirk Been 1976-06-15
#> 6 US0006 Joel Klug Joel Klug Joel Klug 1972-04-13
#> 7 US0007 Gretchen Cor… Gretchen Cordy Gretchen Cordy 1962-02-07
#> 8 US0008 Greg Buis Greg Buis Greg Buis 1975-12-31
#> 9 US0009 Jenna Lewis Jenna Lewis Jenna L. Lewis 1977-07-16
#> 10 US0010 Gervase Pete… Gervase Peterson Gervase Peterson 1969-11-02
#> 11 US0011 Colleen Hask… Colleen Haskell Colleen Haskell 1976-12-06
#> 12 US0012 Sean Kenniff Sean Kenniff Sean Kenniff 1969-11-27
#> 13 US0013 Susan Hawk Susan Hawk Sue Hawk 1961-08-17
#> 14 US0014 Rudy Boesch Rudy Boesch Rudy Boesch 1928-01-20
#> 15 US0015 Kelly Wigles… Kelly Wiglesworth Kelly Wigleswo… 1977-06-24
#> 16 US0016 Richard Hatch Richard Hatch Richard Hatch 1961-04-08
#> 17 US0017 Debb Eaton Debb Eaton Debb Eaton 1955-06-11
#> 18 US0018 Kel Gleason Kel Gleason Kel Gleason 1968-01-05
#> 19 US0019 Maralyn Hers… Maralyn Hershey Maralyn Hershey 1949-01-24
#> 20 US0020 Mitchell Ols… Mitchell Olson Mitchell Olson 1977-03-17
#> 21 US0021 Kimmi Kappen… Kimmi Kappenberg Kimmi Kappenbe… 1972-11-11
#> 22 US0022 Michael Skup… Michael Skupin Michael Skupin 1962-01-29
#> 23 US0023 Jeff Varner Jeff Varner Jeff Varner 1966-04-16
#> 24 US0024 Alicia Calaw… Alicia Calaway Alicia Calaway 1968-05-01
#> 25 US0025 Jerri Manthey Jerri Manthey Jerri Manthey 1970-09-05
#> 26 US0026 Nick Brown Nick Brown Nick Brown 1977-04-02
#> 27 US0027 Amber Mariano Amber Mariano Amber Mariano 1978-08-11
#> 28 US0028 Rodger Bingh… Rodger Bingham Rodger Bingham 1947-07-05
#> 29 US0029 Elisabeth Fi… Elisabeth Filarski Elisabe… Filarski 1977-05-28
#> 30 US0030 Keith Famie Keith Famie Keith Famie 1960-02-11
#> # ℹ 1,130 more rows
#> # ℹ 16 more variables: date_of_death <date>, gender <chr>, african <lgl>,
#> # asian <lgl>, latin_american <lgl>, native_american <lgl>, bipoc <lgl>,
#> # lgbt <lgl>, personality_type <chr>, occupation <chr>, collar <chr>,
#> # three_words <chr>, hobbies <chr>, pet_peeves <chr>, race <chr>,
#> # ethnicity <chr>I have created a measure for challenge success, vote history or tribal council success and advantage success. For more details please see follow the links:
castaway_scores
#> # A tibble: 875 × 52
#> version version_season season castaway castaway_id score_overall score_result
#> <chr> <chr> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 US US01 1 Sonja US0001 0.0504 0
#> 2 US US01 1 B.B. US0002 0.0878 0.0714
#> 3 US US01 1 Stacey US0003 0.130 0.143
#> 4 US US01 1 Ramona US0004 0.215 0.214
#> 5 US US01 1 Dirk US0005 0.282 0.286
#> 6 US US01 1 Joel US0006 0.342 0.357
#> 7 US US01 1 Gretchen US0007 0.526 0.429
#> 8 US US01 1 Greg US0008 0.519 0.5
#> 9 US US01 1 Jenna US0009 0.472 0.571
#> 10 US US01 1 Gervase US0010 0.535 0.643
#> 11 US US01 1 Colleen US0011 0.553 0.714
#> 12 US US01 1 Sean US0012 0.494 0.786
#> 13 US US01 1 Sue US0013 0.507 0.857
#> 14 US US01 1 Rudy US0014 0.520 0.929
#> 15 US US01 1 Kelly US0015 0.788 1
#> 16 US US01 1 Richard US0016 0.654 1
#> 17 US US02 2 Debb US0017 0.0504 0
#> 18 US US02 2 Kel US0018 0.0862 0.0714
#> 19 US US02 2 Maralyn US0019 0.227 0.143
#> 20 US US02 2 Mitchell US0020 0.268 0.214
#> 21 US US02 2 Kimmi US0021 0.283 0.286
#> 22 US US02 2 Michael US0022 0.442 0.357
#> 23 US US02 2 Jeff US0023 0.489 0.429
#> 24 US US02 2 Alicia US0024 0.485 0.5
#> 25 US US02 2 Jerri US0025 0.525 0.571
#> 26 US US02 2 Nick US0026 0.537 0.643
#> 27 US US02 2 Amber US0027 0.443 0.714
#> 28 US US02 2 Rodger US0028 0.472 0.786
#> 29 US US02 2 Elisabe… US0029 0.485 0.857
#> 30 US US02 2 Keith US0030 0.554 0.929
#> # ℹ 845 more rows
#> # ℹ 45 more variables: score_jury <dbl>, score_vote <dbl>, score_adv <dbl>,
#> # score_inf <dbl>, r_score_chal_all <dbl>, r_score_chal_immunity <dbl>,
#> # r_score_chal_reward <dbl>, r_score_chal_tribal <dbl>,
#> # r_score_chal_tribal_immunity <dbl>, r_score_chal_tribal_reward <dbl>,
#> # r_score_chal_individual <dbl>, r_score_chal_individual_immunity <dbl>,
#> # r_score_chal_individual_reward <dbl>, r_score_chal_team <dbl>, …This data frame contains a complete history of votes cast across all seasons of Survivor. This allows you to see who who voted for who at which Tribal Council. It also includes details on who had individual immunity as well as who had their votes nullified by a hidden immunity idol. This details the key events for the season.
There is some information on split votes to help calculate if a player engaged in a split vote but ultimately hit their target. There are events which influence the vote e.g. Extra votes, safety without power, etc. These are recorded here as well.
vh <- vote_history |>
filter(
season == 45,
episode == 9
)
#> Error in eval(expr, envir, enclos): object 'episode' not found
vh
#> Error in eval(expr, envir, enclos): object 'vh' not foundvh |>
count(vote)
#> Error in eval(expr, envir, enclos): object 'vh' not foundNote: From v1.1 the challenge_results dataset has been
improved but could break existing code. The old table is maintained at
challenge_results_dep
There are 3 tables challenge_results,
challenge_description, and
challenge_summary.
A tidy data frame of immunity and reward challenge results. The winners and losers of the challenges are found recorded here.
challenge_results |>
filter(season == 45) |>
group_by(castaway) |>
summarise(
won = sum(result == "Won"),
lost = sum(result == "Lost"),
total_challenges = n(),
chosen_for_reward = sum(chosen_for_reward)
)
#> Error in eval(expr, envir, enclos): object 'season' not foundThe challenge_id is the primary key for the
challenge_description data set. The
challange_id will change as the data or descriptions
change.
Note: This data frame is going through a massive revamp. Stay tuned.
This data set contains the name, description, and descriptive features for each challenge where it is known. Challenges can go by different names so have included the unique name and the recurring challenge name. These are taken directly from the Survivor Wiki. Sometimes there can be variations made on the challenge but go but the same name, or the challenge is integrated with a longer obstacle. In these cases the challenge may share the same recurring challenge name but have a different challenge name. Even if they share the same names the description could be different.
The features of each challenge have been determined largely through string searches of key words that describe the challenge. It may not be 100% accurate due to the different and inconsistent descriptions but in most part they will provide a good basis for analysis.
If any descriptive features need altering please let me know in the issues.
challenge_description
#> # A tibble: 1,864 × 45
#> version version_season season episode challenge_id challenge_number
#> <fct> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 US US01 1 1 1 1
#> 2 US US01 1 2 2 1
#> 3 US US01 1 2 3 2
#> 4 US US01 1 3 4 1
#> 5 US US01 1 3 5 2
#> 6 US US01 1 4 6 1
#> 7 US US01 1 4 7 2
#> 8 US US01 1 5 8 1
#> 9 US US01 1 5 9 2
#> 10 US US01 1 6 10 1
#> 11 US US01 1 6 11 2
#> 12 US US01 1 7 12 1
#> 13 US US01 1 8 13 1
#> 14 US US01 1 8 14 2
#> 15 US US01 1 9 15 1
#> 16 US US01 1 9 16 2
#> 17 US US01 1 10 17 1
#> 18 US US01 1 10 18 2
#> 19 US US01 1 11 19 1
#> 20 US US01 1 11 20 2
#> 21 US US01 1 11 21 3
#> 22 US US01 1 12 22 1
#> 23 US US01 1 12 23 2
#> 24 US US01 1 13 24 1
#> 25 US US01 1 13 25 2
#> 26 US US02 2 1 1 1
#> 27 US US02 2 2 2 1
#> 28 US US02 2 2 3 2
#> 29 US US02 2 3 4 1
#> 30 US US02 2 3 5 2
#> # ℹ 1,834 more rows
#> # ℹ 39 more variables: challenge_type <chr>, name <chr>, recurring_name <chr>,
#> # description <chr>, reward <chr>, additional_stipulation <chr>,
#> # balance <lgl>, balance_ball <lgl>, balance_beam <lgl>, endurance <lgl>,
#> # fire <lgl>, food <lgl>, knowledge <lgl>, memory <lgl>, mud <lgl>,
#> # obstacle_blindfolded <lgl>, obstacle_cargo_net <lgl>,
#> # obstacle_chopping <lgl>, obstacle_combination_lock <lgl>, …
challenge_description |>
summarise_if(is_logical, ~sum(.x, na.rm = TRUE)) |>
glimpse()
#> Rows: 1
#> Columns: 33
#> $ balance <int> 356
#> $ balance_ball <int> 46
#> $ balance_beam <int> 154
#> $ endurance <int> 449
#> $ fire <int> 68
#> $ food <int> 24
#> $ knowledge <int> 77
#> $ memory <int> 29
#> $ mud <int> 49
#> $ obstacle_blindfolded <int> 52
#> $ obstacle_cargo_net <int> 149
#> $ obstacle_chopping <int> 32
#> $ obstacle_combination_lock <int> 22
#> $ obstacle_digging <int> 96
#> $ obstacle_knots <int> 40
#> $ obstacle_padlocks <int> 74
#> $ precision <int> 301
#> $ precision_catch <int> 64
#> $ precision_roll_ball <int> 13
#> $ precision_slingshot <int> 54
#> $ precision_throw_balls <int> 79
#> $ precision_throw_coconuts <int> 23
#> $ precision_throw_rings <int> 20
#> $ precision_throw_sandbags <int> 63
#> $ puzzle <int> 408
#> $ puzzle_slide <int> 17
#> $ puzzle_word <int> 29
#> $ race <int> 1331
#> $ strength <int> 131
#> $ turn_based <int> 237
#> $ water <int> 357
#> $ water_paddling <int> 149
#> $ water_swim <int> 262See the help manual for more detailed descriptions of the features.
The challenge_summary table is solving an annoying
problem with challenge_results and the way some challenges
are constructed. You may want to count how many individual challenges
someone has won, or tribal immunities, etc. To do so you’ll have to use
the challenge_type, outcome_type, and
results fields. There are some challenges which are
combined e.g. Team / Individual challenges which makes this
not a straight process to summarise the table.
Hence why challenge_summary exisits. The
category column consists of the following categories:
There is obviously overlap with the categories but this structure makes it simple to summarise the table how you desire e.g.
challenge_summary |>
group_by(category, version_season, castaway) |>
summarise(
n_challenges = n(),
n_won = sum(won)
)
#> `summarise()` has grouped output by 'category', 'version_season'. You can
#> override using the `.groups` argument.
#> # A tibble: 11,667 × 5
#> # Groups: category, version_season [756]
#> category version_season castaway n_challenges n_won
#> <chr> <chr> <chr> <int> <dbl>
#> 1 All AU01 Andrew 17 7
#> 2 All AU01 Barry 9 5
#> 3 All AU01 Bianca 3 2
#> 4 All AU01 Brooke 29 20
#> 5 All AU01 Conner 22 8
#> 6 All AU01 Craig 18 7
#> 7 All AU01 Des 2 0
#> 8 All AU01 El 35 16
#> 9 All AU01 Evan 5 1
#> 10 All AU01 Flick 34 18
#> 11 All AU01 Jennah-Louise 27 18
#> 12 All AU01 Kat 15 5
#> 13 All AU01 Kate 23 7
#> 14 All AU01 Kristie 35 6
#> 15 All AU01 Kylie 25 19
#> 16 All AU01 Lee 35 17
#> 17 All AU01 Matt 33 18
#> 18 All AU01 Nick 24 17
#> 19 All AU01 Peter 6 5
#> 20 All AU01 Phoebe 21 5
#> 21 All AU01 Rohan 14 5
#> 22 All AU01 Sam 32 18
#> 23 All AU01 Sue 26 7
#> 24 All AU01 Tegan 11 7
#> 25 All AU02 AK 21 12
#> 26 All AU02 Adam 5 3
#> 27 All AU02 Aimee 10 5
#> 28 All AU02 Anneliese 28 13
#> 29 All AU02 Ben 22 11
#> 30 All AU02 Henry 29 15
#> # ℹ 11,637 more rowsHow to add the challenge scores to challenge summary.
challenge_summary |>
group_by(category, version_season, castaway_id, castaway) |>
summarise(
n_challenges = n_distinct(challenge_id),
n_won = sum(won),
.groups = "drop"
) |>
left_join(
castaway_scores |>
select(version_season, castaway_id, starts_with("score_chal")) |>
pivot_longer(c(-version_season, -castaway_id), names_to = "category", values_to = "score") |>
mutate(
category = str_remove(category, "score_chal_"),
category = str_replace_all(category, "_", " "),
category = str_to_title(category)
) |>
select(category, version_season, castaway_id, score),
join_by(category, version_season, castaway_id)
)
#> Error in `pivot_longer()`:
#> ! `cols` must select at least one column.See the R docs for more details on the fields. Join to
challenge_results with version_season and
challenge_id.
History of jury votes. It is more verbose than it needs to be, however having a 0-1 column indicating if a vote was placed or not makes it easier to summarise castaways that received no votes.
jury_votes |>
filter(season == 45)
#> Error in eval(expr, envir, enclos): object 'season' not foundjury_votes |>
filter(season == 45) |>
group_by(finalist) |>
summarise(votes = sum(vote))
#> Error in eval(expr, envir, enclos): object 'season' not foundThis dataset lists the hidden idols and advantages in the game for
all seasons. It details where it was found, if there was a clue to the
advantage, location and other advantage conditions. This maps to the
advantage_movement table.
advantage_details |>
filter(season == 45)
#> Error in eval(expr, envir, enclos): object 'season' not foundThe advantage_movement table tracks who found the
advantage, who they may have handed it to and who the played it for.
Each step is called an event. The sequence_id tracks the
logical step of the advantage. For example in season 41, JD found an
Extra Vote advantage. JD gave it to Shan in good faith who then voted
him out keeping the Extra Vote. Shan gave it to Ricard in good faith who
eventually gave it back before Shan played it for Naseer. That movement
is recorded in this table.
advantage_movement |>
filter(advantage_id == "USEV4102")
#> Error in eval(expr, envir, enclos): object 'advantage_id' not foundA dataset containing the number of confessionals for each castaway by season and episode. There are multiple contributors to this data. Where there are multiple sets of counts for a season the average is taken and added to the package. The aim is to establish consistency in confessional counts in the absence of official sources. Given the subjective nature of the counts and the potential for clerical error no single source is more valid than another. So it is reasonable to average across all sources.
Confessional time exists for a few seasons. This is the total cumulative time for each castaway in seconds. This is a much more accurate indicator of the ‘edit’.
confessionals |>
filter(season == 45) |>
group_by(castaway) |>
summarise(
count = sum(confessional_count),
time = sum(confessional_time)
)
#> Error in eval(expr, envir, enclos): object 'season' not foundThe confessional index is available on this data set. The index is a standardised measure of the number of confessionals the player has received compared to the others. It is stratified by tribe so it measures how many confessionals each player gets proportional to even share within tribe e.g. an index of 1.5 means that player as received 50% more than others in their tribe.
The tribe grouping is important since the tribe that attends tribal council typical get more screen time, which is fair enough. I don’t think we should expect even share across everyone in the pre-merge stage of the game.
The index is cumulative with episode, so the players final index is the index in their final episode.
confessionals |>
filter(season == 45) |>
group_by(castaway) |>
slice_max(episode) |>
arrange(desc(index_time)) |>
select(castaway, episode, confessional_count, confessional_time, index_count, index_time)
#> Error in eval(expr, envir, enclos): object 'season' not foundThis dataset contains the estimated screen time for each castaway during an episode. Please note that this is still in the early days of development. There is likely to be misclassification and other sources of error. The model will be refined over time.
An individuals’ screen time is calculated, at a high-level, via the following process:
Frames are sampled from episodes on a 1 second time interval
MTCNN detects the human faces within each frame
VGGFace2 converts each detected face into a 512d vector space
A training set of labelled images (1 for each contestant + 3 for Jeff Probst) is processed in the same way to determine where they sit in the vector space. TODO: This could be made more accurate by increasing the number of training images per contestant.
The Euclidean distance is calculated for the faces detected in the frame to each of the contestants in the season (+Jeff). If the minimum distance is greater than 1.2 the face is labelled as “unknown”. TODO: Review how robust this distance cutoff truly is - currently based on manual review of Season 42.
A multi-class SVM is trained on the training set to label faces. For any face not identified as “unknown”, the vector embedding is run into this model and a label is generated.
All labelled faces are aggregated together, with an assumption of 1-5 full second of screen time each time a face is seen and factoring in time between detection capping at a max of 5 seconds.
screen_time |>
filter(version_season == "US45") |>
group_by(castaway_id) |>
summarise(total_mins = sum(screen_time)/60) |>
left_join(
castaway_details |>
select(castaway_id, castaway = short_name),
by = "castaway_id"
) |>
arrange(desc(total_mins))
#> Error in eval(expr, envir, enclos): object 'version_season' not foundCurrently it only includes data for season 42. More seasons will be added as they are completed.
A mapping table to detail who is still alive at each stage of the game. It is useful for easy filtering to say the final players.
# filter to season 45 and when there are 6 people left
# 18 people in the season, therefore 12 boots
still_alive <- function(.version, .season, .n_boots) {
survivoR::boot_mapping |>
filter(
version == .version,
season == .season,
final_n == 6,
game_status %in% c("In the game", "Returned")
)
}
still_alive("US", 45, 6)
#> Error in still_alive("US", 45, 6): object 'season' not foundEpisodes is an episode level table. It contains the episode information such as episode title, air date, length, IMDb rating and the viewer information for every episode across all seasons.
episodes |>
filter(season == 45)
#> Error in eval(expr, envir, enclos): object 'season' not foundThere are 2 data sets, survivor_acution and
auction_details. survivor_auction simply shows
who attended the auction and auction_details holds the
details of the auction e.g. who bought what and at what price.
auction_details |>
filter(season == 45)
#> Error in eval(expr, envir, enclos): object 'season' not foundDetails on Journeys in the New Era including the advantage they won and if they lost their vote.
journeys |>
filter(season == 45)
#> Error in eval(expr, envir, enclos): object 'season' not foundGiven the variable nature of the game of Survivor and changing of the rules, there are bound to be edges cases where the data is not quite right. Before logging an issue please install the git version to see if it has already been corrected. If not, please log an issue and I will correct the datasets.
New features will be added, such as details on exiled castaways across the seasons. If you have a request for specific data let me know in the issues and I’ll see what I can do.
Carly Levitz has developed a fantastic dashboard showcasing the data and allowing you to drill down into seasons, castaways, voting history and challenges.
This looks at the number of immunity idols won and votes received for each winner.
A big thank you to:
castaways data frame.Data was sourced from Wikipedia and the Survivor Wiki. Other data, such as the tribe colours, was manually recorded and entered by myself and contributors.