70 seasons. 1319 people. 1 package!
survivoR is a collection of data sets detailing events across 70 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 or Threads @_survivordb
Install from CRAN (v2.3.4) or Git (v2.3.4).
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")
- Adding a new key
sog_id(stage of game ID) toboot_mapping,challenge_results, andvote_history. This makes it easier to join those tables and reference a particular stage of the game. The stage of the game is determined by a change in players/tribe setup e.g. whenever someone is voted out, medically evacuated, switches tribes, or simply starting a new episode thesog_idincrease by 1. This is now available but still being developed and running a bunch of tests, so please let me know if there are inconsistencies. n_bootsis now onboot_mapping.
- Adding complete seasons
- US46
- New data set added
- episode_summary - the summary of the episode from Wikipedia
- challenge_summary - a summarised version of challenge_results for easy analysis
- New fields added
- team on challenge_results - identifying the team which the castaways were on during the challenge
Any corrections needed, please let me know.
The Sanctuary is the survivoR package’s companion. It holds interactive tables and charts detailing the castaways, challenges, vote history, confessionals, ratings, and more. Confessional counts from myself, Carly Levitz, Sam, Grace.
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_detailscastawayschallenge_resultschallenge_descriptionchallenge_summaryconfessionalsjury_votesseason_summarysurvivor_auctiontribe_colourstribe_mappingepisodesvote_historyauction_detailsscreen_timeseason_palettes
See the sections below for more details on the key data sets.
Season summary
A table containing summary details of each season of Survivor, including the winner, runner ups and location.
season_summary
#> # A tibble: 70 × 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 tribes… 16
#> 2 US US02 Survivor: … 2 Herbert… Austra… Two tribes… 16
#> 3 US US03 Survivor: … 3 Shaba N… Kenya Two tribes… 16
#> 4 US US04 Survivor: … 4 Nuku Hi… France Two tribes… 16
#> 5 US US05 Survivor: … 5 Ko Taru… Thaila… Two tribes… 16
#> 6 US US06 Survivor: … 6 Rio Neg… Brazil Two tribes… 16
#> 7 US US07 Survivor: … 7 Pearl I… Panama Two tribes… 16
#> 8 US US08 Survivor: … 8 Pearl I… Panama Three trib… 18
#> 9 US US09 Survivor: … 9 Efate, … Vanuatu Two tribes… 18
#> 10 US US10 Survivor: … 10 Koror, … Palau A schoolya… 20
#> # ℹ 60 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>Castaways
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_id
castaways |>
filter(season == 45)
#> # A tibble: 18 × 27
#> version version_season season_name season full_name castaway_id castaway
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 US US45 Survivor: 45 45 Hannah Rose US0669 Hannah
#> 2 US US45 Survivor: 45 45 Brandon Donl… US0665 Brandon
#> 3 US US45 Survivor: 45 45 Sabiyah Brod… US0677 Sabiyah
#> 4 US US45 Survivor: 45 45 Sean Edwards US0678 Sean
#> 5 US US45 Survivor: 45 45 Brando Meyer US0664 Brando
#> 6 US US45 Survivor: 45 45 J. Maya US0670 J. Maya
#> 7 US US45 Survivor: 45 45 Sifu Alsup US0679 Sifu
#> 8 US US45 Survivor: 45 45 Kaleb Gebrew… US0673 Kaleb
#> 9 US US45 Survivor: 45 45 Kellie Nalba… US0675 Kellie
#> 10 US US45 Survivor: 45 45 Kendra McQua… US0676 Kendra
#> 11 US US45 Survivor: 45 45 Bruce Perrea… US0657 Bruce
#> 12 US US45 Survivor: 45 45 Emily Flippen US0668 Emily
#> 13 US US45 Survivor: 45 45 Drew Basile US0667 Drew
#> 14 US US45 Survivor: 45 45 Julie Alley US0672 Julie
#> 15 US US45 Survivor: 45 45 Katurah Topps US0674 Katurah
#> 16 US US45 Survivor: 45 45 Jake O'Kane US0671 Jake
#> 17 US US45 Survivor: 45 45 Austin Li Co… US0663 Austin
#> 18 US US45 Survivor: 45 45 Dee Valladar… US0666 Dee
#> # ℹ 20 more variables: age <dbl>, city <chr>, state <chr>, episode <dbl>,
#> # day <dbl>, order <dbl>, result <chr>, jury_status <chr>,
#> # original_tribe <chr>, jury <lgl>, finalist <lgl>, winner <lgl>,
#> # result_number <dbl>, acknowledge <lgl>, ack_look <lgl>, ack_speak <lgl>,
#> # ack_gesture <lgl>, ack_smile <lgl>, ack_quote <chr>, ack_score <dbl>A 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.
castaway_details
#> # A tibble: 1,118 × 20
#> castaway_id full_name full_name_detailed castaway date_of_birth date_of_death
#> <chr> <chr> <chr> <chr> <date> <date>
#> 1 US0001 Sonja Ch… Sonja Christopher Sonja 1937-01-28 2024-04-26
#> 2 US0002 B.B. And… B.B. Andersen B.B. 1936-01-18 2013-10-29
#> 3 US0003 Stacey S… Stacey Stillman Stacey 1972-08-11 NA
#> 4 US0004 Ramona G… Ramona Gray Ramona 1971-01-20 NA
#> 5 US0005 Dirk Been Dirk Been Dirk 1976-06-15 NA
#> 6 US0006 Joel Klug Joel Klug Joel 1972-04-13 NA
#> 7 US0007 Gretchen… Gretchen Cordy Gretchen 1962-02-07 NA
#> 8 US0008 Greg Buis Greg Buis Greg 1975-12-31 NA
#> 9 US0009 Jenna Le… Jenna Lewis Jenna L. 1977-07-16 NA
#> 10 US0010 Gervase … Gervase Peterson Gervase 1969-11-02 NA
#> # ℹ 1,108 more rows
#> # ℹ 14 more variables: gender <chr>, african <lgl>, asian <lgl>,
#> # latin_american <lgl>, native_american <lgl>, bipoc <lgl>, lgbt <lgl>,
#> # personality_type <chr>, occupation <chr>, three_words <chr>, hobbies <chr>,
#> # pet_peeves <chr>, race <chr>, ethnicity <chr>Vote history
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
)
vh
#> # A tibble: 9 × 24
#> version version_season season_name season episode day tribe_status tribe
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 US US45 Survivor: 45 45 9 17 Merged Dakuwaqa
#> 2 US US45 Survivor: 45 45 9 17 Merged Dakuwaqa
#> 3 US US45 Survivor: 45 45 9 17 Merged Dakuwaqa
#> 4 US US45 Survivor: 45 45 9 17 Merged Dakuwaqa
#> 5 US US45 Survivor: 45 45 9 17 Merged Dakuwaqa
#> 6 US US45 Survivor: 45 45 9 17 Merged Dakuwaqa
#> 7 US US45 Survivor: 45 45 9 17 Merged Dakuwaqa
#> 8 US US45 Survivor: 45 45 9 17 Merged Dakuwaqa
#> 9 US US45 Survivor: 45 45 9 17 Merged Dakuwaqa
#> # ℹ 16 more variables: castaway <chr>, immunity <chr>, vote <chr>,
#> # vote_event <chr>, vote_event_outcome <chr>, split_vote <chr>,
#> # nullified <lgl>, tie <lgl>, voted_out <chr>, order <dbl>, vote_order <dbl>,
#> # castaway_id <chr>, vote_id <chr>, voted_out_id <chr>, sog_id <dbl>,
#> # challenge_id <dbl>vh |>
count(vote)
#> # A tibble: 3 × 2
#> vote n
#> <chr> <int>
#> 1 Jake 1
#> 2 Kendra 6
#> 3 <NA> 2Challenges
Note: 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)
)
#> # A tibble: 18 × 5
#> castaway won lost total_challenges chosen_for_reward
#> <chr> <int> <int> <int> <int>
#> 1 Austin 10 7 18 1
#> 2 Brando 4 3 7 0
#> 3 Brandon 0 3 3 0
#> 4 Bruce 8 5 13 0
#> 5 Dee 9 9 18 2
#> 6 Drew 8 8 16 0
#> 7 Emily 3 11 14 0
#> 8 Hannah 0 2 2 0
#> 9 J. Maya 6 2 8 0
#> 10 Jake 5 12 18 2
#> 11 Julie 7 8 17 1
#> 12 Kaleb 3 5 9 0
#> 13 Katurah 6 11 18 2
#> 14 Kellie 5 4 10 0
#> 15 Kendra 5 5 11 0
#> 16 Sabiyah 1 4 5 0
#> 17 Sean 1 5 6 0
#> 18 Sifu 7 2 9 0The 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: 1,801 × 46
#> version version_season season_name season episode challenge_id
#> <fct> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 US US01 Survivor: Borneo 1 1 1
#> 2 US US01 Survivor: Borneo 1 2 2
#> 3 US US01 Survivor: Borneo 1 2 3
#> 4 US US01 Survivor: Borneo 1 3 4
#> 5 US US01 Survivor: Borneo 1 3 5
#> 6 US US01 Survivor: Borneo 1 4 6
#> 7 US US01 Survivor: Borneo 1 4 7
#> 8 US US01 Survivor: Borneo 1 5 8
#> 9 US US01 Survivor: Borneo 1 5 9
#> 10 US US01 Survivor: Borneo 1 6 10
#> # ℹ 1,791 more rows
#> # ℹ 40 more variables: challenge_number <dbl>, 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>, …
challenge_description |>
summarise_if(is_logical, ~sum(.x, na.rm = TRUE)) |>
glimpse()
#> Rows: 1
#> Columns: 33
#> $ balance <int> 343
#> $ balance_ball <int> 44
#> $ balance_beam <int> 148
#> $ endurance <int> 430
#> $ fire <int> 67
#> $ food <int> 24
#> $ knowledge <int> 77
#> $ memory <int> 28
#> $ mud <int> 46
#> $ obstacle_blindfolded <int> 51
#> $ obstacle_cargo_net <int> 146
#> $ obstacle_chopping <int> 32
#> $ obstacle_combination_lock <int> 22
#> $ obstacle_digging <int> 92
#> $ obstacle_knots <int> 40
#> $ obstacle_padlocks <int> 74
#> $ precision <int> 288
#> $ precision_catch <int> 63
#> $ precision_roll_ball <int> 13
#> $ precision_slingshot <int> 53
#> $ precision_throw_balls <int> 74
#> $ precision_throw_coconuts <int> 23
#> $ precision_throw_rings <int> 19
#> $ precision_throw_sandbags <int> 55
#> $ puzzle <int> 400
#> $ puzzle_slide <int> 16
#> $ puzzle_word <int> 29
#> $ race <int> 1292
#> $ strength <int> 126
#> $ turn_based <int> 228
#> $ water <int> 350
#> $ water_paddling <int> 148
#> $ water_swim <int> 255See 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:
- All: All challenge types
- Reward
- Immunity
- Tribal
- Tribal Reward
- Tribal Immunity
- Individual
- Individual Reward
- Individual Immunity
- Team
- Team Reward
- Team Immunity
- Duel
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: 7,663 × 5
#> # Groups: category, version_season [514]
#> category version_season castaway n_challenges n_won
#> <chr> <chr> <chr> <int> <dbl>
#> 1 All US01 B.B. 3 2
#> 2 All US01 Colleen 21 8
#> 3 All US01 Dirk 9 4
#> 4 All US01 Gervase 18 8
#> 5 All US01 Greg 14 8
#> 6 All US01 Gretchen 12 6
#> 7 All US01 Jenna 16 6
#> 8 All US01 Joel 11 6
#> 9 All US01 Kelly 25 10
#> 10 All US01 Ramona 7 3
#> # ℹ 7,653 more rowsSee the R docs for more details on the fields. Join to
challenge_results with version_season and challenge_id.
Jury votes
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)
#> # A tibble: 24 × 9
#> version version_season season_name season castaway finalist vote castaway_id
#> <chr> <chr> <chr> <dbl> <chr> <chr> <dbl> <chr>
#> 1 US US45 Survivor: … 45 Bruce Austin 1 US0657
#> 2 US US45 Survivor: … 45 Drew Austin 1 US0667
#> 3 US US45 Survivor: … 45 Emily Austin 0 US0668
#> 4 US US45 Survivor: … 45 Julie Austin 0 US0672
#> 5 US US45 Survivor: … 45 Kaleb Austin 0 US0673
#> 6 US US45 Survivor: … 45 Katurah Austin 0 US0674
#> 7 US US45 Survivor: … 45 Kellie Austin 0 US0675
#> 8 US US45 Survivor: … 45 Kendra Austin 1 US0676
#> 9 US US45 Survivor: … 45 Bruce Dee 0 US0657
#> 10 US US45 Survivor: … 45 Drew Dee 0 US0667
#> # ℹ 14 more rows
#> # ℹ 1 more variable: finalist_id <chr>jury_votes |>
filter(season == 45) |>
group_by(finalist) |>
summarise(votes = sum(vote))
#> # A tibble: 3 × 2
#> finalist votes
#> <chr> <dbl>
#> 1 Austin 3
#> 2 Dee 5
#> 3 Jake 0Advantages
This 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)
#> # A tibble: 10 × 9
#> version version_season season_name season advantage_id advantage_type
#> <chr> <chr> <chr> <dbl> <dbl> <chr>
#> 1 US US45 Survivor: 45 45 1 Hidden Immunity Idol
#> 2 US US45 Survivor: 45 45 2 Hidden Immunity Idol
#> 3 US US45 Survivor: 45 45 3 Safety without Power
#> 4 US US45 Survivor: 45 45 4 Goodwill Advantage
#> 5 US US45 Survivor: 45 45 5 Amulet
#> 6 US US45 Survivor: 45 45 6 Amulet
#> 7 US US45 Survivor: 45 45 7 Amulet
#> 8 US US45 Survivor: 45 45 8 Hidden Immunity Idol
#> 9 US US45 Survivor: 45 45 9 Hidden Immunity Idol
#> 10 US US45 Survivor: 45 45 10 Challenge Advantage
#> # ℹ 3 more variables: clue_details <chr>, location_found <chr>,
#> # conditions <chr>The 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")
#> # A tibble: 0 × 15
#> # ℹ 15 variables: version <chr>, version_season <chr>, season_name <chr>,
#> # season <dbl>, castaway <chr>, castaway_id <chr>, advantage_id <dbl>,
#> # sequence_id <dbl>, day <dbl>, episode <dbl>, event <chr>, played_for <chr>,
#> # played_for_id <chr>, success <chr>, votes_nullified <dbl>Confessionals
A 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)
)
#> # A tibble: 18 × 3
#> castaway count time
#> <chr> <dbl> <dbl>
#> 1 Austin 72 1436
#> 2 Brando 10 147
#> 3 Brandon 12 214
#> 4 Bruce 38 735
#> 5 Dee 67 1102
#> 6 Drew 64 1171
#> 7 Emily 62 1332
#> 8 Hannah 4 44
#> 9 J. Maya 11 210
#> 10 Jake 60 1290
#> 11 Julie 46 814
#> 12 Kaleb 45 692
#> 13 Katurah 66 1169
#> 14 Kellie 29 515
#> 15 Kendra 37 506
#> 16 Sabiyah 22 342
#> 17 Sean 16 325
#> 18 Sifu 11 236The 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)
#> # A tibble: 18 × 6
#> # Groups: castaway [18]
#> castaway episode confessional_count confessional_time index_count index_time
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Emily 11 8 203 1.09 1.31
#> 2 Kaleb 7 7 96 1.43 1.22
#> 3 Sabiyah 3 6 112 1.32 1.20
#> 4 Brandon 2 6 115 1.13 1.20
#> 5 Austin 13 14 214 1.09 1.17
#> 6 Kellie 8 6 81 1.11 1.16
#> 7 Bruce 10 5 104 1.01 1.12
#> 8 Drew 12 9 158 1.15 1.12
#> 9 Jake 13 14 250 0.946 1.10
#> 10 Katurah 13 8 203 1.04 1.00
#> 11 Dee 13 11 173 1.04 0.896
#> 12 Kendra 9 6 83 1.11 0.895
#> 13 Sean 4 9 211 0.783 0.884
#> 14 Julie 13 5 64 0.714 0.665
#> 15 Hannah 1 4 44 0.828 0.597
#> 16 Brando 5 5 71 0.648 0.579
#> 17 J. Maya 6 2 47 0.593 0.574
#> 18 Sifu 7 1 33 0.486 0.535Screen time
This 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 `select()`:
#> ! Can't subset columns that don't exist.
#> ✖ Column `short_name` doesn't exist.Currently it only includes data for season 42. More seasons will be added as they are completed.
Boot mapping
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)
#> # A tibble: 6 × 14
#> version version_season season_name season episode order n_boots final_n sog_id
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 US US45 Survivor: … 45 12 12 12 6 13
#> 2 US US45 Survivor: … 45 12 12 12 6 13
#> 3 US US45 Survivor: … 45 12 12 12 6 13
#> 4 US US45 Survivor: … 45 12 12 12 6 13
#> 5 US US45 Survivor: … 45 12 12 12 6 13
#> 6 US US45 Survivor: … 45 12 12 12 6 13
#> # ℹ 5 more variables: castaway_id <chr>, castaway <chr>, tribe <chr>,
#> # tribe_status <chr>, game_status <chr>Episodes
Episodes 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)
#> # A tibble: 13 × 15
#> version version_season season_name season episode_number_overall episode
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 US US45 Survivor: 45 45 610 1
#> 2 US US45 Survivor: 45 45 611 2
#> 3 US US45 Survivor: 45 45 612 3
#> 4 US US45 Survivor: 45 45 613 4
#> 5 US US45 Survivor: 45 45 614 5
#> 6 US US45 Survivor: 45 45 615 6
#> 7 US US45 Survivor: 45 45 616 7
#> 8 US US45 Survivor: 45 45 617 8
#> 9 US US45 Survivor: 45 45 618 9
#> 10 US US45 Survivor: 45 45 619 10
#> 11 US US45 Survivor: 45 45 620 11
#> 12 US US45 Survivor: 45 45 621 12
#> 13 US US45 Survivor: 45 45 622 13
#> # ℹ 9 more variables: episode_title <chr>, episode_label <chr>,
#> # episode_date <date>, episode_length <dbl>, viewers <dbl>,
#> # imdb_rating <dbl>, n_ratings <dbl>, episode_summary_wiki <chr>,
#> # episode_summary <chr>Survivor Auction
There 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)
#> # A tibble: 11 × 19
#> version version_season season_name season item item_description category
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 US US45 Survivor: 45 45 1 Salty Pretzels And… Food an…
#> 2 US US45 Survivor: 45 45 2 French Fries, Ketc… Food an…
#> 3 US US45 Survivor: 45 45 3 Cheese Platter, De… Food an…
#> 4 US US45 Survivor: 45 45 4 Chocolate Milkshake Food an…
#> 5 US US45 Survivor: 45 45 5 Two Giant Fish Eyes Bad item
#> 6 US US45 Survivor: 45 45 5 Two Giant Fish Eyes Bad item
#> 7 US US45 Survivor: 45 45 6 Bowl Of Lollies An… Food an…
#> 8 US US45 Survivor: 45 45 7 Slice Of Pepperoni… Food an…
#> 9 US US45 Survivor: 45 45 8 Toothbrush And Too… Comfort
#> 10 US US45 Survivor: 45 45 9 Chocolate Cake Food an…
#> 11 US US45 Survivor: 45 45 10 Pbandj Sandwich, C… Food an…
#> # ℹ 12 more variables: castaway <chr>, castaway_id <chr>, cost <dbl>,
#> # covered <lgl>, money_remaining <dbl>, auction_num <dbl>,
#> # participated <chr>, notes <chr>, alternative_offered <lgl>,
#> # alternative_accepted <lgl>, other_item <chr>, other_item_category <chr>Given 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:
- Carly Levitz for ongoing data collection and curation
- Sam for contributing to the confessional counts
- Dario Mavec for developing the face detection model for estimating total screen time
- Matt
Stiles
for collecting and contributing the acknowledgment features on the
castawaysdata frame. - Camilla Bendetti for collating the personality type data for each castaway.
- Uygar Sozer for adding the filming start and end dates for each season.
- Holt Skinner for creating the castaway ID to map people across seasons and manage name changes.
- Kosta Psaltis for the original race data.
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.